**Lili Ren 1, Zhihui Yang 1,2, Lei Huang 1 , Yingjie He 1, Haiying Wang 1,2,\* and Liyuan Zhang 3,4,\***

Received: 4 March 2020; Accepted: 17 March 2020; Published: 23 March 2020

**Abstract:** Here, a nanofiber-exfoliated bacteria cellulose aerogel with improved water affinity and high mass transfer was synthesized. Consequently, poly Schiff base can be uniformly coated within the body of bacteria cellulose aerogel without the traditional dispersion treatment. The composite aerogel has adequate mechanical and thermal stability and high mass transfer efficiency. Such an aerogel can serve as a superior adsorbent for flow through adsorption of pollution. Typically, the adsorption capacity towards Cr(VI), Cu(II), Re(VII), Conga red, and Orange G reaches as high as 321.5, 256.4, 153.8, 333.3, and 370.3 mg g−1, respectively. Moreover, the adsorption by this composite aerogel is very fast, such that, for example, at just 2 s, the adsorption is almost finished with Cr(VI) adsorption. Moreover, the composite aerogel exhibits a good adsorption-desorption capability. This research will hopefully shed light on the preparation of bacteria cellulose-derived macroscopic materials powerful in not only environmental areas, but also other related applications.

**Keywords:** bacteria cellulose; poly Schiff base; chromium; adsorption


**Abstract**

An association between the measles virus (MV) and classic Hodgkin lymphoma (CHL) was reported by our center in Beer-Sheva, starting in 2003. Our basic evidence on the relationship had been established by immunohistochemistry, using several commercial and specific experimental anti-measles antibodies in about 50% of our patients. It relied also on the reverse transcriptase-polymerase chain reaction (RT-PCR) and *in situ* hybridization (ISH) of MV RNA, and overall, was essentially descriptive. The first and only response to our thesis was advanced in 2007 in doublet articles, in a single issue of a Cancer Journal. Our conclusions were hereby dismissed. Since reservations relating the above refutation seemed self-evident, the subsequent publication of several of our articles was launched. They disclosed evidence of an association of the MV with further categories of cancers, as well as its absence in some distinct tumors. A mechanism by which the MV may regulate apoptosis in the tumor cells of CHL was also proposed. A suspected role for the MV in oncomodulation is advocated. We deplore, however, the absence of further interest in our hypothesis among world experts.


**6. Conclusions**

The analysis was based on data from RaceCapture Pro2 as shown in **Figure 14.**

Max Challenge Series.

120 Green Electronics

The maximum speed on the test section was 52.64 mph, that is, 84.75 km/h (**Figure 9**). The maximum measured acceleration measured on a straight run was 13,787 s from 0 to 100 km/h. Acceleration was extrapolated from an increase in speed from 40.41 km/h to 84.75 km/h in 6.12 s, on a straight line allowing maximum acceleration. In **Figure 15** the extrapolated acceleration of the eKarts with comparison to three categories of the Rotax

Comparing the maximum speed and acceleration of the eKart with ICE go-kart of the Rotax Max Challenge categories shown in **Table 6**; not considering the movement resistance and air resistance, it can be assumed that, by increasing the gear ratio by 22% in eKart, eKart would

have similar parameters to the MiniMax category of the Rotax Max Challenge series.

**Figure 15.** Comparison of extrapolated accelerations of eKart and the category Rotax Max.

**Figure 14.** Data from eKart road tests on Tor Łódź © circuit.

With existing Li-ion battery technology, it is possible to construct eKarts for children and junior categories. With the current technology, it is not possible to create eKarts for senior categories in line with the current regulations for go-karts as the mass of the battery exceeds the weight of the entire drive train system of the go-kart. In order to provide a solution for senior categories, it would be necessary to change the regulations, for example, race time and vehicle weight, or wait for emerging battery technology to provide an energy density of at least 350 Wh/kg, which should take place within 3 years.

In this chapter, author proves that current technology is enabled to create eKart which is competitive and more efficient to ICE go-kart MiniMax category from Rotax Max Challenge series. eKart also has better functional parameters; for example, noise generated by eKart is about six times lower than that for ICE go-kart MiniMax category from Rotax Max Challenge series. Due to electronic differential, which can actively manage torque and power on each of the rear wheels, called torque vectoring, eKart with 10 kW has parameters comparable with 12 kW ICE go-kart. That means that positive influence on natural environment is not only caused by applied electric motor but also due to algorithm in so-called green electronic differential. Due to electronic differential, which can actively manage torque and power on each of the rear wheels, called torque vectoring, eKart with 10 kW has parameters comparable with 12 kW ICE go-kart. That means that positive influence on natural environment is not only caused by applied electric motor but also due to algorithm in so-called green electronic differential.

[11] Van Noorden R. A better battery—Chemists are reinventing rechargeable cells to drive

Vehicle Dynamics and Green Electronic Differential for eKart

http://dx.doi.org/10.5772/intechopen.72892

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[12] Xu B, Oudalov A, Ulbig A, Andersson G, Kirschen DS. Modeling of lithium-ion battery degradation for cell life assessment. IEEE Transactions on Smart Grid. June 2016

[13] Lawder MT, Northrop PWC, Subramanian VR. Model-based SEI layer growth and capacity fade analysis for EV and PHEV batteries and drive cycles. Journal of the

[14] Technical drawing No. 1 chassis frame and chassis main parts, http://www.cikfia.com/

[15] Milliken WF, Milliken DL. Race Car Vehicle Dynamics: Problems, Answers and Experi-

[17] Golebiowski W, Kubiak P. Analysis of vehicle dynamics parameters for electric gokart (eKart) design. Mechanics and Mechanical Engineering International Journal.

[18] Golebiowski W, Szosland A, Kubiak P. Concept of the construction of electric go-kart eKart. The Archives of Automotive Engineering. issn 1234/754X; accepted for publication

[19] Surmacz A. Regulamin Krajowy Pucharu Rotax Max Challenge Poland 2015 ROK, http://polskikarting.pl/wp-content/uploads/2015/04/regulamin\_krajowy\_pucharu\_

ments. Society of Automotive Engineers Inc; 2003. http://www.sae.org/

Lodz University of Technology. 2017;**21**(3):763-777. ISSN 1428-1511

down costs and boost capacity. Nature. 6 March 2014;**507**

Electrochemical Society. 2014;(14)

regulations/technical.html, CIK-FIA

rotax\_2015\_-\_wersja-ostateczna.pdf

[16] http://kosmickart.com/telai\_mercurys\_eng.php


**Acknowledgements**

This material is based upon work supported by the National Science Foundation under grants No. 0851449 and 1345156 (CREST Program). The authors would like to thank Ms. Barbara Sanchez along with the UPRM Biology Department for the use of their facilities. Professor Matias Cafaro provided insightful comments for the completion of this research.


**Author details**

Wladimir Schubert1 , Beáta Gavurová2 \*, Viliam Kováč2 and Martin Užík1

\*Address all correspondence to<EMAIL_ADDRESS>
1 Berlin School of Economics and Law, Berlin, Germany

2 Department of Banking and Investment, Faculty of Economics, Technical University of Košice, Košice, Slovak Republic


**1. Introduction**

Energy storage systems (ESSs) are among the most critical components concerning the full adoption of renewable energy sources and electric transportation [1]. In such applications, energy management systems (EMSs) are required for coordinating the operations of systems with multiple energy generation resources, as often is the case with microgrids and hybrid ESSs, which employ multiple complementary energy storage subsystems. For the energy management of ESSs, the most commonly desired objectives are: improved energy efficiency, extended lifetimes for storage elements and compliance with constraints of internal energy storage modules, e.g., no over-charging and no over-discharging [2]. In the case of rechargeable batteries, those goals typically require accurate monitoring; state of charge (SoC) and state of health (SoH) estimations; and charging control, which can be accomplished by using dedicated battery management systems (BMS). The methods employed for these operations range from experiment-based ones, such as incremental capacity [3], to data-driven ones, including machine learning [4]. State estimation and control algorithms in a BMS may require battery models capable of approximating the battery's response under given operating conditions [5].

Equivalent circuit models (ECMs) are preferred over electrochemical or empirical models, as they approximate the dynamic behaviour of a battery with relatively high accuracy [6], while offering simplified descriptions of the complex physical and chemical processes occurring within batteries, by representing them with a set of lumped elements, including resistors, capacitors and inductors. ECMs have been widely used as parts of battery state estimation and charging control strategies. The adoption of ECM-based optimal state estimation, by directly solving the model-constrained optimisation problem [7] or using techniques such as the Kalman filter and all its variants [8], has enabled the implementation of online SoC and SoH estimations in EMS frameworks. The implementation of

**Citation:** Ospina Agudelo, B.; Zamboni, W.; Monmasson, E. A Comparison of Time-Domain Implementation Methods for Fractional-Order Battery Impedance Models. *Energies* **2021**, *14*, 4415. https://doi.org/10.3390/en14154415

Academic Editor: Hugo Morais

Received: 1 May 2021 Accepted: 18 July 2021 Published: 22 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

online optimal charging strategies, known for their high computational burden, has also been enabled by algorithms, including ECMs, by profiting from the accuracy–complexity trade off offered by such models [9].

The parameters of an ECM can be fitted from voltage and current data obtained during specific operating conditions [6]. However, these circuit components are often insufficient for modelling the dynamics of electrochemical processes such as charge and mass transfers and double layer capacitance in a battery, due to the spatial distribution of those processes [10]. This lack of physical significance may compromise the identification of relationships between health estimation and ECM parameters. Said drawback may be overcome by substituting capacitors in the ECM with constant phase elements (CPEs), defined in the frequency domain and analytically derived from the electrochemical principles of the diffusion processes. Such elements are often used to fit electrochemical impedance spectroscopy (EIS) data [11]. The reduced number of parameters in ECMs using CPEs instead of capacitors is mainly attractive for SoH estimation approaches based on the analysis of variations in the parameters associated with specific electrochemical processes [12].

The direct time-domain implementation of such models is particularly challenging, because they represent dynamic systems with non-integer-order derivative operations, hence the name fractional order models (FOM) [10]. The time-domain implementations of FOMs' electrical responses have been employed in applications such as online SoC estimation using fractional-order (FO) Kalman filters [13] and a time-domain characterisation of battery diffusion dynamics [14]. Three main implementation approaches have been identified: the multiple RC (mRC) circuit [15], the high-order integer transfer function [16] and the Grünwald–Letnikov (GL) fractional derivative [17] approximations. However, the literature is rather obscure about the motivation for the selection of one approach over the others. This work fills this gap by presenting a comparative study of the three approaches. The comparisons are made in terms of:

We used a simulation environment with a set of FOMs representing the middlefrequency range impedance of Li-ion batteries, normally represented by the so-called ZARC elements. Our study identifies which of the analysed approaches offers the best compromise between accuracy and computational burden for applications such as real-time simulations. The second goal was to understand for which of the three cases the timedomain identification of the FOM leads to a correct frequency-domain response, keeping the impedance model meaningful. This analysis may serve as a guide for the selection of implementation approaches for FOMs in EMS applications.

The paper is organised as follows. Section 2 introduces the battery impedance models based on constant phase elements, Section 3 presents, in detail, the three time-domain implementation approaches studied in this work. Then, the results of the comparative analysis in terms of accuracy and complexity are presented in Sections 4 and 5, respectively. Section 6 presents a time-domain identification study performed for the three implementation approaches. Finally, the conclusions are drawn in Section 7.

### **2. Battery Impedance Models**

EIS analyses the impedance of battery cells in a specific range of frequencies, drawing conclusions about internal electrochemical processes with different time constants. A typical EIS test is performed by injecting a sinusoidal voltage or current signal; acquiring the cell response; and computing the impedance through the amplitude ratio and the phase difference between voltage and current signals. That experiment is repeated for several

frequencies in a selected frequency range. Then, by calculating the impedance, a Nyquist plot, often called impedance plot, can be generated. If this is performed for multiple battery operating conditions (temperature, SoC and dc operating point), it is possible to visualise and quantify their influences over the frequency response. For a Li-ion battery cell, a typical impedance plot is shown in Figure 1 [18].

**Figure 1.** Qualitative impedance spectrum of a Li-ion cell and an ECM approximating it.

In the qualitative impedance plot shown in Figure 1, it is possible to identify four sections that can be associated with particular electrochemical processes [18]. In the first part, an inductive behaviour can be seen at high frequencies, related to the inductive reactances of metallic elements in the cell and wires. The presence of an ohmic resistance is revealed by the intersection with the real axis at a nonzero value. This corresponds to the sum of the current collectors, active material, electrolyte and separator resistances. The semi-circle-like section represents the double layer capacitance and charge transfer processes at the electrodes. Finally, at low frequencies, the main effect corresponds to the diffusion processes of the active material of the electrodes, which manifests as a section with a constant slope in the impedance plot. It is worth noting that measured spectra often show variations with respect to the qualitative curve presented. For example, the number of semi-circles can be increased to two or more, or the inductive part can exhibit a slope with an increasing real part. Moreover, the semi-circle section may tend to present a depression at its mid-point (non-constant radius) [19].

For a relatively accurate and meaningful reproduction of the impedance spectrum of a battery cell, the ECMs presented in Figure 1 can be considered [20]. The behaviour of the cell at high frequencies is represented by an ideal inductor *L* and the resistor *Rs* represents the ohmic resistance of the cell elements. The semi-circle section can be represented using a ZARC element, which corresponds to a parallel connection between a resistor and a CPE. The impedance of a CPE, *ZCPE*, is presented in:

$$Z\_{\rm CPE}(\omega) = \frac{1}{Q(j\omega)^{\Phi'}} \tag{1}$$

where *Q* corresponds to a generalised capacitance, *φ* to the depression factor, *ω* to the angular frequency and *j* to the imaginary unity [21].

The low-frequency response is represented by a Warburg impedance, which models a semi-infinite linear diffusion process. The Warburg impedance, namely, *ZW*, is characterised by a 45◦ phase, and therefore can be also represented by a CPE:

$$Z\_W(\omega) = \frac{A\_w}{\sqrt{\omega}}(1 - j) = \frac{\sqrt{2}A\_w}{\sqrt{j\omega}} = \frac{1}{Q\_w(j\omega)^{\Phi\_w}}\tag{2}$$

where *Aw* is the diffusion coefficient [21], and the equivalent generalised capacitance and depression factor for the Warburg element are given by *Qw* = 1/( √2*Aw*) and *φw* = 0.5, respectively.

CPEs are used instead of capacitors because when they are connected in parallel with a resistor forming a ZARC element, *φ* represents the depression often observed in the semi-circles in the impedance plot of a Li-ion cell. The factor *φ* can take values between zero and one; when *φ* = 0, the ZARC element represents only an ohmic resistor, and if *φ* = 1, the response of an RC element is obtained. The ZARC impedance is represented by:

$$Z\_{ZARC}(\omega) = \frac{R\_{p1}}{1 + R\_{p1}Q\_1(j\omega)^{\Phi\_1}},\tag{3}$$

where *Rp*1, *Q*1 and *φ*1 characterise the resistance and CPE parameters of the ZARC element. The overall impedance of the circuit presented in Figure 1 is then defined by:

$$Z\_{\rm bat}(\omega) = j\omega L + \mathbb{R}\_s + Z\_{\rm ZARC}(\omega) + Z\_{\mathcal{W}}(\omega). \tag{4}$$

For the sake of simplicity, the discussion that follows focuses on the computation of the voltage *v*(*t*) of the ZARC element for a given current signal *i*(*t*). The presented results can easily be extended to the Warburg element using similar principles, and from there to the whole battery's impedance.

### **3. Time-Domain Implementation of the ZARC Element Response**

In the framework of battery modelling, three main approaches for approximating the time-domain responses of elements with FO transfer functions have been proposed.

Flowcharts for the three implementation approaches are given in Figure 2. In the three approaches, the time response of the FOM is approximated by a set of discrete equations, corresponding to discrete state-space representations for the mRC and OU approaches and to a difference equation for the GL approach. These discrete implementations were evaluated in our simulation environment in terms of accuracy and complexity, as presented in the following sections. The main differences between the approaches lie in the parametrisation

processes. For the GL approach, the coefficients in the difference equation are computed directly from the impedance parameters using predefined mathematical relationships, which are introduced in Section 3.3. As illustrated in Figure 2, the other two approaches require an initial approximation of the FO transfer function with an integer-order system, for which a discrete state-space representation can be obtained. The main difference between the mRC and OU approaches is the structure of the integer-order transfer function and the method used for the approximation of the FO transfer function, which are introduced in Sections 3.1 and 3.2, respectively.

In this section we will focus on defining difference equations for the computation of the ZARC voltage *v*(*t*) for each one of the mentioned approaches. The resulting expressions have the form given by:

$$
\upsilon[k] = \mathbb{C}\mathfrak{x}[k] + \mathrm{Di}[k].\tag{5}
$$

Equation (5) allows one to compute the ZARC element voltage in the discrete instant *k* as a function of the input value *i* and the vector *x*. The vector *x* may be the states' vector or a set of previous values of *v*, depending on the implementation approach. The values of vector *C* and scalar *D* are functions of the parameters of each approximation, as is discussed in the following subsections.

**Figure 2.** A summary of the FOM implementation approaches considered.

### *3.1. Approximation 1: Multiple RC Approach*

For the approximation of the ZARC element impedance, series connections of multiple parallel RC circuits have been considered in the literature [15]. The values of the components for this kind of approximations are normally fitted from time measurements directly, by minimising the differences between experimental data and the model voltage output. However, for the case in which the initial point is an impedance model, the estimation process requires fitting the impedance spectra. The idea is to approximate the transfer function of the ZARC element with a set of RC parallel elements:

$$Z\_{ZARC}(\mathbf{s}) = \frac{R\_{p1}}{1 + R\_{p1}Q\_1\mathbf{s}^{\Phi\_1}} \approx \sum\_{h=1}^{n\_{R\mathcal{C}}} \frac{R\_h}{1 + R\_h\mathbf{C}\_h\mathbf{s}} = Z\_{R\mathcal{C}}(\mathbf{s}).\tag{6}$$

In (6), a transfer function representation is used for the impedance, using *s* as the Laplace complex variable. The approximation of the ZARC impedance *ZRC*(*s*) employs a set of *nRC* RC branches. The parameters *Rh* and *Ch* represent the resistance and capacitance of the *h*-th RC parallel branch in the approximation of the ZARC impedance presented in (6), accounting for 2*nRC* parameters to fit.

The values for the resistance and capacitance in (6) can be computed by minimising the difference between the ZARC impedance, given in (3), and the mRC approximation. One method for solving this minimisation problem is presented with detail in Appendix A. This method is employed from this point on during the parametrisation stage of the mRC-based implementations.

Once the values for *Rh* and *Ch* have been fitted, the continuous time response of the mRC circuit can be written in state-space representation:

$$
\dot{\mathbf{x}}\_{RC}(t) = A\_{RC}\mathbf{x}\_{RC}(t) + B\_{RC}i(t) \tag{7}
$$

$$
\boldsymbol{w}\_{\rm{RC}}(t) = \mathbb{C}\_{\rm{RC}} \mathbf{x}\_{\rm{RC}}(t) + D\_{\rm{RC}} i(t), \tag{8}
$$

where the states' vector *xRC* contains the RC elements voltages, *x*˙*RC* represents the states' derivatives and *vRC* is the approximation of the ZARC voltage using the mRC approach. Additionally, the matrices *ARC*, *BRC*, *CRC* and *DRC* are *nRC* × *nRC*, *nRC* × 1, 1 × *nRC* and 1 × 1 matrices, respectively, given by:

$$A\_{RC} = \text{diag}\left(-\frac{1}{R\_1 C\_1}, -\frac{1}{R\_2 C\_2}, \dots, -\frac{1}{R\_{nRC} C\_{nRC}}\right) \tag{9}$$

$$B\_{R\mathbb{C}} = \begin{bmatrix} \frac{1}{\mathbb{C}\_1} & \frac{1}{\mathbb{C}\_2} & \cdots & \frac{1}{\mathbb{C}\_{R\mathbb{C}}} \end{bmatrix}^\top \tag{10}$$

$$\mathcal{C}\_{\rm RC} = \begin{bmatrix} 1 & 1 & \cdots & 1 \end{bmatrix} \tag{11}$$

$$D\_{R\Gamma} = 0.\tag{12}$$

For the discretisation of this set of equations, the derivatives are approximated using the backward Euler approximation for stability reasons [28]. The obtained set of difference equations can be rewritten and used for estimating the value of the ZARC element voltage in a discrete instant *k*, *vRC*[*k*], as a function of the current *i*[*k*] and the RC elements voltages *xRC*[*k*]:

$$\mathbf{x}\_{\rm R\mathcal{C}}[k] = \left(I\_{\rm R\mathcal{C}} - T A\_{\rm R\mathcal{C}}\right)^{-1} \mathbf{x}\_{\rm R\mathcal{C}}[k-1] + T \left(I\_{\rm R\mathcal{C}} - T A\_{\rm R\mathcal{C}}\right)^{-1} B\_{\rm R\mathcal{C}} i[k] \tag{13}$$

$$
\omega\_{RC}[k] = \mathcal{C}\_{RC} \mathbf{x}\_{RC}[k] + D\_{RC} i[k]\_{\prime} \tag{14}
$$

in which *InRC* is the *nRC* × *nRC* identity matrix, and *T* is the sampling period. Equations (13) and (14) can be used for implementing the response of the RC approximation given a current signal and a set of initial values for the RC branch voltages.

### *3.2. Approximation 2: Oustaloup Approach*

This approach relies on approximating the transfer function of the CPE in the ZARC element using a transfer function with integer order *nOU* (the same number of zeros and poles) in a given frequency range. One of the most popular methods for the approximation of the CPE transfer function with a rational transfer function of odd order *nOU* was presented by Oustaloup et al. [16]. The approximation is valid in the frequency range *ω* ∈ [*ωl*, *ωh*], where *ωl* and *ωh* are the lower and higher frequency limits, respectively. Following this method, the transfer function of the CPE can be rewritten as:

$$Z\_{\rm CPE}(\mathbf{s}) = \frac{1}{Q\_1 s^{\theta\_1}} = K\_{\rm CPE} \left(\frac{\mathbf{s}}{\omega\_{\rm c}}\right)^{-\phi\_1} \approx K\_{\rm CPE} \left(\frac{\omega\_{\rm l}}{\omega\_{\rm c}}\right)^{-\phi\_1} \prod\_{h=-N}^{N} \frac{1 + \frac{s}{\omega\_{\rm c(CPE)h}}}{1 + \frac{s}{\omega\_{\rm p(CPE)h}}} = Z\_{\rm OII(\rm CPE)}(\mathbf{s}),$$

where *ZOU*(*CPE*)(*s*) is the integer-order approximation of the CPE impedance using the OU approach; *ωc* = √*ωlωh* is the central frequency between the bounds of the range of interest; and *KCPE* and *N* are given by *KCPE* = 1 *Q*1*ωφ*1 *c* and *N* = *nOU*−1 2 . In order to estimate the *nOU* zeros *ωz*(*CPE*)*h* and *nOU* poles *ωp*(*CPE*)*h* in (15), Oustaloup et al. proposed [16]:

$$
\omega\_{z(CPE)h} = \omega\_l \left(\frac{\omega\_h}{\omega\_l}\right)^{\frac{h + \frac{n\_{\text{OII}} + \phi\_1}{2}}} \qquad \qquad \omega\_{p(CPE)h} = \omega\_l \left(\frac{\omega\_h}{\omega\_l}\right)^{\frac{h + \frac{n\_{\text{OII}} - \phi\_1}{2}}} \cdot \tag{15}
$$

The transfer function can be used to approximate the frequency response of the CPE. Then, the approximation *ZOU*(*s*) of the whole ZARC impedance using the OU approach is:

$$Z\_{ZARC}(\mathbf{s}) = \frac{R\_{p1}}{1 + R\_{p1}Q\_1\mathbf{s}^{\Phi\_1}} \approx \frac{R\_{p1}}{1 + \frac{R\_{p1}}{Z\_{OL(CPE)}\{\mathbf{s}\}}} = Z\_{OL}(\mathbf{s}).\tag{16}$$

In order to obtain a time representation of the response of this approximation, the transfer function *ZOU*(*s*) needs to be rewritten in zero-pole-gain form, as expressed in:

$$Z\_{\rm OII}(\mathbf{s}) = K\_{ZAR\mathbb{C}} \prod\_{h=1}^{n\_{\rm OI}} \frac{\left(\mathbf{s} - \omega\_{zh}\right)}{\left(\mathbf{s} - \omega\_{ph}\right)} \tag{17}$$

where *KZARC*, *ωzh* and *ωph* represent the gain, zeros and poles of the *ZOU*(*s*) transfer function, respectively. A time-domain state-space representation for the OU approximation with the structure presented in (17) was introduced in [28]:

$$
\dot{\mathbf{x}}\_{\rm OUT}(t) = A\_{\rm OUT} \mathbf{x}\_{\rm OUT}(t) + B\_{\rm OUT} i(t) \tag{18}
$$

$$w\_{OL}(t) = \mathcal{C}\_{OL}\mathbf{x}\_{OL}(t) + D\_{OL}i(t),\tag{19}$$

where *xOU* and *x*˙*OU* represent the system states and their derivatives, and *vOU* is the approximation of the ZARC element voltage. The matrices *AOU nOU* × *nOU*, *BOU nOU* × 1, *COU* 1 × *nOU* and *DOU* 1 × 1, are given by:

$$\begin{aligned} A\_{\text{OU}} &= \begin{bmatrix} \omega\_{p1} & 0 & 0 & \cdots & 0 \\ (\omega\_{p2} - \omega\_{z2}) & \omega\_{p2} & 0 & \cdots & 0 \\ (\omega\_{p3} - \omega\_{z3}) & (\omega\_{p3} - \omega\_{z3}) & \omega\_{p3} & \cdots & 0 \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ (\omega\_{p\text{n}\_{\text{OU}}} - \omega\_{z\text{n}\_{\text{OU}}}) & (\omega\_{p\text{n}\_{\text{OU}}} - \omega\_{z\text{n}\_{\text{OU}}}) & \cdots & (\omega\_{p\text{n}\_{\text{OU}}} - \omega\_{z\text{n}\_{\text{OU}}}) & \omega\_{p\text{n}\_{\text{OU}}} \end{bmatrix} \\ B\_{\text{OU}} &= K\_{ZARC} \begin{bmatrix} (\omega\_{p1} - \omega\_{z1}) & (\omega\_{p2} - \omega\_{z2}) & \cdots & (\omega\_{p\text{n}\_{\text{OU}}} - \omega\_{z\text{n}\_{\text{OU}}}) \end{bmatrix}^{\top} \\ C\_{\text{OU}} &= \begin{bmatrix} 1 & 1 & \cdots & 1 \end{bmatrix} \\ D\_{\text{OU}} &= K\_{ZARC} \end{aligned} \tag{21}$$

Again, the backward Euler approximation was used to discretise the obtained statespace system. This discrete representation is obtained by replacing *ARC*, *BRC*, *CRC* and *DRC* with *AOU*, *BOU*, *COU* and *DOU* in (13) and (14).

### *3.3. Approximation 3: Grünwald–Letnikov Approach*

The ZARC element response can be approximated by adopting a FO derivative definition in the time domain. The FO differential equations representing the ZARC voltage can be obtained from transfer function (3) by replacing *ZZARC*(*s*) = *V*(*s*)/*I*(*s*) and rewriting the equation as:

$$s^{\phi\_1}V(s) = \frac{I(s)}{Q\_1} - \frac{V(s)}{R\_{p1}Q\_1} \, \tag{24}$$

where *V*(*s*) and *I*(*s*) represent the Laplace transform of the ZARC voltage and current, respectively. Then, by applying inverse Laplace transform, the FO differential equation is rewritten as:

$$
\partial^{\phi\_1} v(t) = \frac{i(t)}{Q\_1} - \frac{v(t)}{R\_{p1}Q\_1}.\tag{25}
$$

D*φ*1 represents the derivative of FO *φ*1. Among the multiple definitions of the FO derivative, the GL one is of particular interest, as it allows one to directly obtain difference equations for the approximations of the time response of a FO system [17]. The definition of the considered equation is:

$$\partial^{a}f(t) = \lim\_{T \to \infty} \frac{1}{T^{a}} \sum\_{h=0}^{\lfloor \frac{t}{T} \rfloor} (-1)^{h} \binom{a}{h} f(t - hT), \tag{26}$$

where the derivative of FO *α* of the causal function *f*(*t*) is computed between 0 and *t*. In (26), *T* is the sampling period, *t T* represents the integer part of *t*/*T* and ( *α h*) represents the Newtonian binomial coefficients generalised to real numbers, computed as:

$$
\binom{a}{h} = \frac{a(a-1)(a-2)\cdots(a-h+1)}{h!} = \frac{\Gamma(a+1)}{\Gamma(h+1)\Gamma(a-h+1)},\tag{27}
$$

where Γ(·) stands for the gamma function, which works as a generalisation of the factorial operator for real numbers.

It is worth noting that, according to the GL definition in (26), the derivative of order *α* of the function at time *t* depends on all the values of that function in [0, *t*], which is due to the non-local property of fractional derivatives [29].

By fixing the value of *T* to an appropriately low value for the application, and adopting the discrete variable *k* instead of the continuous time *t*, it is possible to obtain the first order discrete approximation of the FO derivative:

$$\partial^{\mathfrak{A}}f[k] = \frac{1}{T^{\mathfrak{a}}} \sum\_{h=0}^{k} (-1)^{h} \binom{\mathfrak{a}}{h} f[k-h]. \tag{28}$$

This approximation may be used to obtain a difference equation for the numerical evaluation of function *f* [*k*]. The discrete version also requires all the data points of *f* [*k*] since *k* = 0 for the computation of the derivative approximation, which may imply large memory requirements for simulations using this approach. This drawback may be addressed by applying the short-memory principle reported in reference [17], taking into account the behaviour of the signal in only the recent past, in the interval [*k* − *L*, *k*], where *L* is the memory length. Applying this principle, we rewrite:

$$\partial^{\mathfrak{a}}f[k] = \frac{1}{T^{\mathfrak{a}}} \sum\_{h=0}^{L} (-1)^{h} \binom{\mathfrak{a}}{h} f[k-h]. \tag{29}$$

This short-memory approximation allows one to implement numerical difference equations in cases in which the required memory is a critical constraint. Obviously, this introduces some inaccuracy, mostly manifested in the form of static error [30].

By replacing (29) in (25), we obtain the difference equation:

$$\upsilon\_{GL}[k] = T^{\Phi\_1} \frac{R\_{p1}}{R\_{p1}Q\_1 + T^{\Phi\_1}} \mathbf{i}[k] - \frac{R\_{p1}Q\_1}{R\_{p1}Q\_1 + T^{\Phi\_1}} \sum\_{h=1}^{L} (-1)^h \binom{\Phi\_1}{h} \upsilon\_{GL}[k-h],\tag{30}$$

which allows one to compute the approximation of the ZARC element voltage using the GL approach *vGL*. For the sake of comparison with the other implementation approaches, (30) can be written in matrix form:

$$
\sigma\_{GL}[k] = \mathbb{C}\_{GL} \mathbf{x}\_{GL}[k] + D\_{GL} i[k], \tag{31}
$$

where *xGL*[*k*] is a *L* × 1 vector with the *L* previous values of the ZARC voltage:

$$\mathbf{x}\_{GL} = \begin{bmatrix} \upsilon\_{GL}[k-1] & \upsilon\_{GL}[k-2] & \cdots & \upsilon\_{GL}[k-L] \end{bmatrix}^\top. \tag{32}$$

The matrices *CGL* and *DGL*, having size *L* × 1 and 1 × 1, respectively, are given by:

$$\mathcal{L}\_{GL} = -\frac{R\_{p1}Q\_1}{R\_{p1}Q\_1 + T\Phi\_1} \begin{bmatrix} -\binom{\phi\_1}{1} & \binom{\phi\_1}{2} & \cdots & (-1)^h \binom{\phi\_1}{h} & \cdots & (-1)^L \binom{\phi\_1}{L} \end{bmatrix} \tag{33}$$

$$D\_{\rm GL} = T^{\Phi\_1} \frac{R\_{p1}}{R\_{p1}Q\_1 + T^{\Phi\_1}}.\tag{34}$$

It is worth noting that for the matrix *CGL*, the coefficients (−1)*h*( *φ*1 *h* ) for the previous samples can be precomputed for the implementation of the ZARC voltage.

### **4. Accuracy Comparison**

Ideally, the data required for the validation of approximations of the responses of FO battery impedance models must consider EIS and pulsed current tests both performed under similar SoC, SoH and temperature conditions. Due to the lack of availability of such data in the known datasets, and in order to perform accuracy comparisons between the analysed approximations, a reference model based on the analytical solution of FO differential equations was utilised for the generation of the reference data.

### *4.1. Reference Data Generation from the Analytical Solution of Fractional Differential Equations*

Assuming that the current signal can be written as a set of steps:

$$\dot{a}(t) = \sum\_{h=1}^{N\_u} \mathcal{U}\_h \mu(t - t\_{uh}),\tag{35}$$

where *u*(*t*) corresponds to the unit step function. Each one of the *Nu* current steps in *i*(*t*) is characterised by an amplitude *Uh* and application time *tuh*.

The ZARC element voltage can be expressed as [17]:

$$w(t) = \sum\_{h=1}^{N\_u} \frac{\mathcal{U}\_h}{Q\_1} (t - t\_{\text{uh}})^{\phi\_1} E\_{\phi\_1, \phi\_1 + 1} \left( -\frac{1}{R\_{p1} Q\_1} (t - t\_{\text{uh}})^{\phi\_1} \right) u(t - t\_{\text{uh}}), \tag{36}$$

where the function *Eα*,*β* is the two-parameter Mittag–Leffler function, defined by a series expansion presented in Appendix B, which also includes a derivation of (36).

Equation (36) was implemented in Matlab® for the generation of the reference data. For the Mittag–Leffler function, the implementation introduced in [31] was employed. It is worth mentioning that such an analytical voltage representation is not suitable for online implementation, due to the limitations imposed by the assumed input signal and to the iterative nature of the Mittag–Leffler function computation, which makes the evaluation of a single data point highly demanding from a computational point of view.

### *4.2. Analysis of the Voltage Approximation Signals*

The evaluation of the accuracy of the considered approximations requires one to perform an analysis of the differences between the responses of the reference model and the approximation of interest, for given ZARC element parameters and while using the same input current signal, as illustrated in Figure 3.

**Figure 3.** A schematic diagram representing the accuracy tests performed.

The test proposed in Figure 3 requires one to define an input current signal, a set of ZARC elements parameters and the order or memory length of the approximations. Tests such as this one were performed for six sets of ZARC element parameters, for currents generated with different sampling and dynamic characteristics and with variations of *nRC*, *nOU* and *L*. In order to generate the input currents and select the ZARC elements to be employed, first, we focused on the typical middle-frequency range of the dynamic response of Li-ion batteries, namely, the range between 0.01 and 200 Hz. This dynamic range is normally associated with the response of the double layer capacitance and the charge transfer resistance [18].

The considered input signals contained two stages: one aimed at evaluating the transient responses of the different approximations, and the second stage was for testing the steady state error. An input signal sample is presented in Figure 4. In this signal, the first stage has a total duration of 200 s, for which the amplitude and duration of each current pulse were selected randomly in the ranges [−1, 1] A and [0.5, 10] s, respectively. The second stage contains one single 0.5 A step with a duration of 500 s, with fixed 150 s rests before and after the step.

**Figure 4.** An example of the current profiles used during the accuracy tests.

The six sets of ZARC parameters we used are presented in Table 1. The ZARC parameters were selected for obtaining characteristic frequencies *ω*0 = (1/(*Rp*1*Q*1))1/*φ*1 covering the frequency range of interest, with *ω*0 = 2*π f*0, while keeping the parameters' values inside typical ranges—namely, *Rp*1 ∈ [0.1, 100] mΩ, *Q*1 ∈ [1, 1000] F s*φ*1−1 and *φ*1 ∈ [0.5, 0.9] [32,33].

**Table 1.** Values of the parameters for the ZARC elements considered during the accuracy tests.

The evaluation of accuracy was performed for:

Figures 5 and 6 present some examples of the voltage computed by mRC, OU and GL approximations for ZARC 4 employing *T* = 0.01 s. The plots in Figure 5a present the reference voltage and the voltages obtained for three mRC-based approximations during the first 20 s of the random stage of the accuracy test. In general, all the implementations were able to approximate the dynamics of the reference signal, with only appreciable differences for the approximation being of the lowest order. The error signals, presented in Figure 7a for this set of implementations, show spikes always under a few hundred μV during all the current steps, which reduce in magnitude as the order increases. Similar results can be observed for the OU-based implementations, as shown in Figure 5b and the first 200 s of the error signal in Figure 7b. The low-order OU approximations resulted in higher error magnitudes than the mRC ones. In the case of the GL approximations, as seen in Figure 5c, the lower *L* values caused higher offset errors. This is further illustrated by the error signal in Figure 7c during the first 200 s on which the spikes, at least for the lower memory lengths, seem to be wider than those in the mRC and OU approximations. An increase in *L* led to decreases in the magnitudes of the error signal, showing that adding terms to the sum in (30) leads to a better approximation of the analytical response, as expected.

**Figure 5.** Examples of ZARC 4 voltages during the dynamic stage of the accuracy test. (**a**) mRC; (**b**) OU; (**c**) GL.

**Figure 6.** Examples of ZARC 4 voltages during the static stage of the accuracy test. (**a**) mRC; (**b**) OU; (**c**) GL.

**Figure 7.** Examples of ZARC 4 voltage errors during the accuracy test. (**a**) mRC; (**b**) OU; (**c**) GL.

As is shown in the static state results presented in Figure 6a for three mRC-based implementations, increasing the order of the approximation reduced the errors during this stage. A higher value of *nRC* allowed a better approximation of the distribution of time constants represented by the FO element, leading to an extension of the validity of the approximation over a wider frequency range. Similar considerations apply for the OU approximations, presented in Figure 6b, except for the worse performance at low orders, under nine, with respect to the mRC case. The offset error for the GL approximations is more evident during the static stage of the test, as shown in Figures 6c and 7c. The static error obtained for GL implementations with *L* under a few thousand for ZARC 4 is considerably higher than the errors obtained for the other approximations. This highlights the main drawback of the GL approximations using the short memory principle: by reducing the number of previous samples that are used for the computation, some level of inaccuracy appears, particularly in static state. For the sake of completeness, it is worth mentioning that Podlubny [17] proposed a relationship for estimating a suitable memory length for the approximation of the FO derivative presented in (28), given an expected error level.

### *4.3. Effects of the Approximation Order and Memory Length on The Accuracy*

In order to analyse the effects of the parameters in each implementation approach, namely, *nRC*, *nOU* and *L*, on the approximation accuracy, the mean relative errors during the dynamic and the static stages were used as indicators. For ZARCs 1, 3 and 6, the mean relative errors during the dynamic and the static stages are presented in Figures 8a and 9a, respectively. The very similar results for the other ZARC elements are not reported for the sake of brevity.

**Figure 8.** Average relative errors during the dynamic stage of the test. (**a**) mRC; (**b**) OU; (**c**) GL.

**Figure 9.** Average relative errors during the static stage of the test. (**a**) mRC; (**b**) OU; (**c**) GL.

The error during the static stage of the tests for the RC approximations remained under 1%, even for the approximations with fewer RC branches. This result was expected, as in the fitting procedure adopted in this work (Appendix A), the sum of the resistances in the mRC approximation was set to match the value of *Rp*1, leading to similar voltage drops in the response after the initial transitory. On the other hand, even if the values of the relative error during the dynamic stage tended to decrease with the number of employed RC elements, considerable improvements were only obtained up to *nRC* = 9. After that point, further improvements could be achieved by decreasing the value of *T*. The observed behaviour at the highest orders may be associated with the variability introduced by the fitting procedure required for the computation of the parameters of the mRC case from the ZARC parameters. Nevertheless, for cases over *nRC* = 5, the mean relative errors were always under 5%.

In the case of the OU-based approximations, the mean relative errors during the dynamic and the static stages are presented in Figures 8b and 9b, respectively. In general, for the OU approximations during the dynamic stage, orders *nOU* higher than nine are required for reaching average relative errors under 5%. It is worth noting that, compared with the mRC approach, similar average relative errors were achievable in general, but with approximations of higher order. Again, the average relative errors for the static tests were almost always below 1%. For both stages of the accuracy tests, a monotonic reduction in the errors could be observed with increases in the approximation order, highlighting the advantage of computing the integer order approximation of the FO transfer function with a set of predefined equations instead of performing a fitting. This behaviour can be useful when trying to select the approximation order by analysing the accuracy–complexity trade off.

Then, for a set of GL-based approximations, with *L* values between 5 and 10,000 samples (which correspond to time windows between 0.05 s and 100 s), a similar accuracy analysis was performed. Figures 8c and 9c show the results obtained for the dynamic and static average relative errors obtained for this set of approximations. Regarding the results in the dynamic stage, only the higher memory lengths, over 500 samples, allowed us to reach values in the same order of magnitude as the ones obtained for the other approaches. In the case of the static stage, the average relative error is always about

one order for magnitude higher, and it is evident that higher memory lengths or sampling times need to be considered for reducing the static error to a similar range. The errors were higher for the ZARC elements with slower dynamics, showing that slower systems require longer memory lengths to reach an acceptable accuracy level.

### **5. Computational Burden Comparison**

A battery ECM can be used in an EMS as a part of the BMS state estimation structure or for battery simulation purposes during validation of energy management algorithms, particularly in real-time simulation scenarios. In both cases, considering that normally middle to low-end processing devices are often favoured due to budgetary restrictions, care needs to be taken in regard to the computational requirements of the battery model implementation. Here, we analyse those requirements in a general sense, by addressing the sizes of the matrices and the number of operations for each FOM implementation approach as indicators of required memory and computational complexity, respectively, in an eventual deployment.

For the three approaches, the time implementation relies on a set of matrix additions and multiplications. For mRC and OU-based approaches, the implementations consist of sets of equations in the form of (37) and (5) for the state and output equations, respectively. The discrete state equation established for the mRC approach can be generalised as:

$$\mathbf{x}[k] = A\_d \mathbf{x}[k-1] + B\_d i[k],\tag{37}$$

where *Ad* and *Bd*, namely, the discrete state and input matrices, are given by:

$$A\_d = \left(I\_{n\_{RC}} - TA\_{RC}\right)^{-1} \tag{38}$$

$$B\_d = T\left(I\_{\text{fl}\_{\text{RC}}} - TA\_{\text{RC}}\right)^{-1}B\_{\text{RC}}.\tag{39}$$

Conversely, the GL-based implementation relies only on a difference equation with the structure of (5), but in which *x*[*k*] does not represent the system states vector but a vector with *L* previous values of the ZARC voltage.

Table 2 presents the sizes of the matrices and vectors used in the three approaches. Even if the mRC and OU approaches seem to be equivalents in terms of memory requirements, the simpler structure of the mRC can be exploited for the reduction of its memory requirements. Additionally, even if the number of arrays required for a GL implementation is lower and its size dependency given *L* seems simpler than those for the matrices in the other approaches, it is worth keeping in mind that in general for a given accuracy level *L* will take values in the range of hundreds or thousands, while *nRC* or *nOU* will be under 20.

**Table 2.** Sizes of the matrices in the state and output equations in the analysed implementations.

Table 3 summarises the numbers of additions and multiplications required by each implementation approach. The specific structures of the matrices can be also exploited in the mRC and OU-based implementations, to refine the results presented in Table 3 by skipping the multiplications by zero and expressing the multiplication of column vectors by row vectors full of ones as an addition. The new operations count with this considerations is presented in Table 4.

**Table 3.** Numbers of additions and multiplications required for the analysed implementations.

**Table 4.** Numbers of additions and multiplications with simplifications.

Similarly to what was concluded for the array dimensions discussion, the expressions in Table 4 show that for the same order, a mRC implementation will require fewer operations than an OU one. It is worth mentioning that for the GL approach, even if there is not dependence on the square of *L* in the expressions for the number of operations required, the value of *L* needs to be considerably higher than the order for the other approaches to reach a given accuracy level.

The number of multiplications required for the evaluation of each type of implementation was used for assessing the computational burden in each case. Figure 10 plots the accuracy against the computational burden in terms of the number of multiplications for ZARC 4. The errors in static and dynamic stages are shown in Figure 10a,b, respectively.

**Figure 10.** Average relative errors vs. numbers of multiplications for mRC, OU and GL. (**a**) Dynamic stage; (**b**) static stage.

The curves for *T* = 0.01 s in Figure 10a show clearly how for a fixed mean relative error level in the dynamic stage of the accuracy tests, the number of required multiplications is always lower for the mRC approach, followed by the OU one. It is worth mentioning that the three approaches converge to values in the same order of magnitude for the mean relative error when increasing the complexity of the implementation; this may be an effect of the local truncation error due to the discretisation process. The asymptotic values of the analysed errors are comparable for a fixed sampling time. This can be ascribed to the fact that both the backward Euler and the GL derivative approximations are firstorder approximations, leading to an *O*(*T*) local truncation error, using big O notation [17]. To show the dependency on the sampling time of the identified error asymptotic values, the accuracy test was repeated using the current signal in Figure 4, but downsampled using *T* = 0.1 s. Those results are also summarised in Figure 10a, showing how the limit in the mean relative error is reached at a higher value, confirming the relationship between the sampling time and the maximum achievable accuracy.

The results presented in Figure 10b show that an increase in complexity has a more pronounced effect on the accuracy under static conditions, which tracks back to the requirement of higher orders or memory lengths for covering a wider time-constant range in the response approximation. In this case the previously observed oscillatory behaviour for higher orders in the mRC implementations is more evident for the two considered values of *T*. The effect of the local truncation error due to the discretisation process is also observed in this case, even if for lower values of the mean relative error.

From the results of the accuracy against complexity analysis, it can be clearly observed that the best accuracy–complexity compromise is offered by the mRC approach, allowing one to reach low error levels with a small computational burden. It is worth mentioning that this is the case provided good fitting of the ZARC element in the frequency domain can be performed. This is true for applications in which the model is used for battery simulation, but it is not the case when identification of impedance models from time measurements is required. In such instances, a good relationship between the parameters fitted from time-domain experiments with the frequency response is required. Thus, it is worth checking the suitability of the implementations for the time-domain identification of impedance parameters.

### **6. Analysis of the Suitability of Time-Domain Identification of the Battery Impedance**

One of the main reasons for adopting battery FOMs is the capability of accurately approximating the voltage response while requiring a low number of parameters, which is of interest for tasks such as state estimation and battery characterisation. For this reason, in order to illustrate the applicability of the considered FOM implementation approaches in the framework of battery model identification using time-domain measurements, a set of time-domain FOM fitting tests were performed.

For the fitting tests, the reference voltage data *v*[*k*] correspond to the random stage of the accuracy tests as the reference voltage signals presented in Figure 5. For all the ZARC elements and the implementations considered in the accuracy analysis, the associated parameters were fitted by minimising the mean square error between the reference voltage *v*[*k*] and *vx*[*k*], which corresponded to *vRC*[*k*], *vOU*[*k*] or *vGL*[*k*] depending on the evaluated approximation. For all cases, the minimisation problem was solved in Matlab® using the default particle swarm optimisation (PSO) algorithm, implemented by the Matlab® function "particleswarm". The default PSO algorithm employs a number of particles automatically selected as the minimum between 10 times the number of parameters to be fitted and 100 particles; a function tolerance of 10−6; and a maximum iteration number of 200 times the number of parameters to find [34].

The set of identified parameters changes depending on the approach evaluated. In the case of the GL-based implementations, the three parameters of the ZARC element, namely, *Rp*1, *Q*1 and *φ*1, can directly be identified due to the nature of this implementation, where the time response of the FO element is directly approximated, as presented in Section 3.3. This can be observed in the schematic of the identification procedure presented in Figure 11c, where the parametrisation process takes the identified values of the ZARC parameters as inputs for generating the vectors required for the time-domain implementation. Similarly, for the OU approach, the direct identification of the ZARC element parameters from time measurements is possible due to the direct relationship between *Rp*1, *Q*1, *φ*1 and the poles and zeros of the implemented integer-order transfer function, as introduced in Section 3.2. Figure 11b shows the implementation based on the OU approach, which was not modified for the fitting tests; only the source of the ZARC element parameters' changes, now being generated by the minimisation algorithm.

For the mRC approach, an initial frequency-domain fitting of the FO element impedance is required. For identification using time-domain measurements, this step cannot be performed. The resistance and capacitance values needed to be fit directly, as represented in the flowchart in Figure 11a.

On the one hand, for the OU and the GL approaches, the number of parameters to be identified was always 3, independently of the order *nOU* or memory length *L*. On the other hand, the number of parameters to identify with the mRC approach increased with the order, being equal to 2*nRC*. This highlights the main drawback of the mRC approach: when fitting the time response of the FO element, overfitting issues may arise due to the high number of parameters.

For all the fitting tests, the mean of the relative error between the reference voltage and the response of the fitted approximation was computed as an indicator of the goodness of the time-domain fit. Additionally, the indicator of how close the obtained impedance is to the expected one in the range from 0.01 to 20 Hz is the following:

$$\delta\_{\rm Z}(\omega) = \frac{\left| Z\_{\rm ZARC}(\omega) - Z\_{app}(\omega) \right|}{\left| Z\_{\rm ZARC}(\omega) \right|}. \tag{40}$$

Here, *δZ* corresponds to the relative distance between the impedance of the fitted approximation, called *Zapp*, and the one of the original ZARC element, *ZZARC*.

The results obtained for ZARC 3 are summarised in Figure 12 as a plot of the relative error in time against the one in frequency. Even if relative errors in time under 5% were obtained for multiple instances of each approach, similar results in frequency were only reached for OU and GL approximations. In the case of mRC-based approximations, a good fitting in time is not necessarily translated to a low distance between the approximation impedance and the original one. It is worth mentioning that the minimisation algorithm was not optimised, but despite this, good accordance between the original and approximated impedance was reached for the OU and the GL approximations and not for the mRC case. Similar results were reached for all the other ZARC elements, illustrating how for the mRC approach, the lack of a direct relationship between the approximation parameters—namely, the resistance and capacitance values—and the ZARC impedance parameters reduces the possibility of identifying an approximation in time domain that also has a frequency response close to the real one.

An on-board-oriented implementation of these identification methods should address additional issues. One of them is the noise affecting the current and voltage measurements. Such noise contributes to causing a bias in the identified parameters, which should be compensated for with advanced fitting algorithms, as shown, for instance, in reference [35]. The noise compensation is worthy to be a matter of future study.

**Figure 12.** Fitting ZARC 3: average relative voltage error vs. average relative impedance distance.

### **7. Conclusions**

In this work, the three main approaches adopted in the literature for the implementation of the time-domain response of battery FOMs were introduced and compared in terms of accuracy, computational requirements and suitability for the time-domain identification of battery impedance. The study was performed in a simulation framework with six different ZARC elements, which are normally used for the approximation of battery impedance in the middle-frequency range. The comparison was performed in a simulation environment where the reference solution was an analytical expression for the response of a ZARC element under a multiple-step current. The proposed expression, obtained using FO calculus theory, was used for generating the reference data required for the accuracy analysis of the considered implementations. Even if the discussion focused on ZARC elements, the results can be extended to the Warburg element and to the total battery impedance.

The primary results of the study can be summarised as follows.

The selection of the FOM implementation method depends on the application requirements. On the one hand, if the interest is only in the battery response simulation, the mRC approach offers the best accuracy–complexity compromise, which is desirable for real-time simulations oriented towards the validation of energy management algorithms. On the other hand, if the application requires accurate identification of the impedance parameters from time-domain measurements, the OU approach offers the best compromise among identifying the impedance model parameters, the complexity and the accuracy requirements.

**Author Contributions:** Conceptualization, B.O.A. and W.Z.; methodology, B.O.A. and W.Z.; software, B.O.A.; validation, B.O.A.; formal analysis, B.O.A.; investigation, B.O.A. and W.Z.; resources, W.Z.; data curation, B.O.A.; writing—original draft preparation, B.O.A. and W.Z.; writing—review and editing, B.O.A., W.Z. and E.M.; visualization, B.O.A.; supervision, B.O.A. and W.Z.; project administration, W.Z. and E.M.; funding acquisition, W.Z. and E.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported in part by funds from the the project "Holistic approach to EneRgy-efficient smart nanOGRIDS—HEROGRIDS" (PRIN 2017 2017WA5ZT3) within the Italian MUR 2017 PRIN programme; by the "Ex-WISCH e D-CODE" and FARB funds of the University of Salerno; and by funds of a public grant overseen by the French National Research Agency (ANR) as part of the "Investissements d'Avenir" program (reference: ANR-16-IDEX-0008).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **Abbreviations**

The following abbreviations are used in this manuscript:

### **Appendix A. Fitting of the Multiple RC Approaches' Parameters**

The values of *Rh* and *Ch*, from the mRC approximation of the ZARC impedance introduced in Equation (6), can be computed by solving the minimisation problem:

$$\min\_{\mathcal{Q}\_{\mathcal{KC}}} \left\{ \left| Z\_{ZAR\mathcal{C}}(j\omega) - Z\_{\mathcal{RC}}(j\omega) \right|^2 \right\},\tag{A1}$$

where the array of parameters is given by Θ*RC* = [*R*1, ··· , *RM*+1, *C*1, ··· , *CM*+1]. This optimisation problem has been previously solved in the literature. The most widely accepted solution is the one proposed by [15], which presents the approximations of ZARC elements for given values of *φ*1 using 1, 3 and 5 RC branches. That approximation relies on a number of optimisation parameters precomputed in [15] only for a few discrete values of *φ*1. Here, we present a generalisation of this method for an odd number of RC branches *nRC* = *M* + 1 and any positive value of *φ*1 < 1.

For the procedure, we take as starting point the parameters of the ZARC element, namely, *Rp*1, *Q*1 and *φ*1. We want to approximate the ZARC frequency response with *M* + 1 RC branches, with the values of the resistances and capacitors sorted as follows: *RRC* = [*R*1, *R*2, ··· , *RM*/2+1, ··· , *RM*, *RM*+1] and *CRC* = [*C*1, *C*2, ··· , *CM*/2+1, ··· , *CM*, *CM*+1]. The generalisation can be obtained by defining the values of the resistance "in the middle", defined by the index *M*/2 + 1, according to:

$$R\_{M/2+1} = \frac{K\_1 R\_{p1} \sin\frac{\phi\_1 \pi}{2}}{1 + \cos\frac{\phi\_1 \pi}{2}}.\tag{A2}$$

For all the remaining resistances, except those with indexes 1 and *M* + 1, and by taking advantage of the the symmetry of the ZARC element impedance spectra, the values are computed as fractions of the difference between *Rp*1 and *RM*/2+1, as expressed in:

$$R\_h = R\_{M+2-h} = \frac{K\_h \left(R\_{p1} - R\_{M/2+1}\right)}{2}, \quad 2 \le h \le \frac{M}{2}.\tag{A3}$$

Finally the most "external" resistances are computed as:

$$R\_1 = R\_{M+1} = \frac{R\_{p1} - \sum\_{h=2}^{M} R\_h}{2}.\tag{A4}$$

Then, the values of the capacitors can be estimated using the resistance and the characteristic frequency *ω*0 = (1/(*Rp*1*Q*1))1/*φ*1 values as follows:

$$\mathbb{C}\_{M/2+1} = \frac{1}{\omega \eta R\_{M/2+1}}\tag{A5}$$

$$\mathcal{C}\_{h} = \frac{1}{K\_{M/2+h}\omega\_{0}\mathcal{R}\_{h}}, \quad 1 \le h \le \frac{M}{2} \tag{A6}$$

$$\mathcal{C}\_{h} = \frac{1}{\frac{\omega\_{0}}{\overline{\mathcal{K}\_{3M/2+2-h}}} R\_{h}^{\prime}} \quad \frac{M}{2} + 1 \le h \le M + 1. \tag{A7}$$

While using this method for rewriting the optimisation problem presented in (A1), we can replace the parameters vector for Θ*RC* = [*K*1,..., *KM*], reducing the number of unknown parameters from 2*M* + 2 (the number of resistors plus the number of capacitors) to *M*. The solution of the optimisation problem presented in (A1), and the set of Equations (A2)–(A7), were implemented in Matlab®, allowing us to obtain the parameters of the RC circuit that approximate the frequency response of an ideal ZARC element.

### **Appendix B. ZARC Element—Analytical Voltage Expression**

For a ZARC element, the voltage *v*(*t*) = L −1{*V*(*s*)} can be computed as [17]:

$$v(t) = \mathcal{L}^{\ell-1} \left\{ \frac{\frac{1}{Q\_1}}{s^{\Phi\_1} + \frac{1}{R\_{p1}Q\_1}} I(s) \right\} = \frac{1}{Q\_1} \left[ t^{\Phi\_1 - 1} E\_{\Phi\_1 \Phi\_1} \left( -\frac{1}{R\_{p1}Q\_1} t^{\Phi\_1} \right) \right] \* i(t)$$

$$= \frac{1}{Q\_1} \int\_0^t (t - \tau)^{\Phi\_1 - 1} E\_{\Phi\_1 \Phi\_1} \left( -\frac{1}{R\_{p1}Q\_1} (t - \tau)^{\Phi\_1} \right) i(\tau) d\tau,\tag{A8}$$

where L −1{*X*(*s*)} is the inverse Laplace transform of *X*(*s*), *Eα*,*β* is the two-parameter Mittag–Leffler function, the symbol ∗ stands for convolution and *τ* is the integration variable. The two-parameter Mittag–Leffler function is defined by the following series expansion:

$$E\_{a,\beta}(t) = \sum\_{h=0}^{\infty} \frac{t^h}{\Gamma(ah+\beta)},\tag{A9}$$

and it can be seen as a generalisation of the exponential function *et* , which can be considered as a particular case of the *Eα*,*β*(*t*) function with *α* = *β* = 1 [17].

Equation (A8) can be used for computing the voltage response of the ZARC element from the current signal. The main limitation of this solution is that it requires an expression for the current in order to be evaluated. Additionally, this analytical expression is very demanding computationally, as the computation for a given time value requires all the previous values. Simplifications of this analytical expression can be achieved by considering a step current signal *i*(*t*) = *Uu*(*t* − *tu*), where *U* and *tu* are the step amplitude and time, and *u*(*t*) represents the unit step function. By taking (A8), and using *I*(*s*) for the Laplace transform of the current step, it is possible to obtain an expression that does not require the evaluation of an integral for the computation of the ZARC voltage:

$$v(t) = \mathcal{L}^{-1}\{V(s)\} = \mathcal{L}^{-1}\left\{\frac{\frac{1}{Q\_1}}{s^{\Phi\_1} + \frac{1}{R\_{p1}Q\_1}}\frac{\mathcal{U}}{s}s^{-t\_u s}\right\}
$$

$$= \frac{\mathcal{U}}{Q\_1}(t - t\_u)^{\Phi\_1} E\_{\Phi\_1\Phi\_1 + 1}\left(-\frac{1}{R\_{p1}Q\_1}(t - t\_u)^{\Phi\_1}\right)u(t - t\_u). \tag{A10}$$

Using the superposition principle, it is possible to extend these results for current signals composed by combinations of sets of *Nu* step signals, meaning that they can be written as:

$$\dot{u}(t) = \sum\_{h=1}^{N\_w} \mathcal{U}\_h u(t - t\_{uh})\_\prime \tag{A11}$$

which is true for inputs such as square or pseudo-random binary sequence signals. If such an input current signal is considered, the resulting voltage signal for the ZARC element is given by:

$$v(t) = \sum\_{h=1}^{N\_{\rm u}} \frac{lI\_h}{Q\_1} (t - t\_{\rm uh})^{\Phi\_1} E\_{\Phi\_1, \Phi\_1 + 1} \left( -\frac{1}{R\_{p1} Q\_1} (t - t\_{\rm uh})^{\Phi\_1} \right) u(t - t\_{\rm uh}). \tag{A12}$$

### **References**


*Article* **Maternal Knowledge, Attitude and Practices toward Free Sugar and the Associations with Free Sugar Intake in Children**

**Walaa Abdullah Mumena**

Clinical Nutrition Department, College of Applied Medical Sciences, Taibah University, P.O. Box 344, Madinah 42353, Saudi Arabia<EMAIL_ADDRESS>
**Abstract:** Research addressing factors related to free sugar (FS) consumption among children in Saudi Arabia is lacking. We aimed to evaluate maternal knowledge, attitude, and practices toward FS and the associations with children's intake of FS. This cross-sectional study included 424 Saudi children aged 6–12 years and their mothers. Data related to maternal knowledge, attitude, and practices were collected using an online survey. Data concerning children's habitual intake of FS were collected through phone interviews using a validated food frequency questionnaire. Limited knowledge on FS was observed among mothers of children [median 7.00 [interquartile range 6.00–8.00] out of 11.0. Maternal knowledge was not correlated with maternal attitude or practices toward FS. Maternal knowledge towards FS did not predict children's intake of FS, whereas maternal attitude and practices toward limiting the consumption of FS predicted lower intake of FS among Saudi children, particularly the FS consumed from solid food sources (B: −5.73 [95% confidence interval (CI): −9.79 to −1.66]) and (B: −6.85 [95% CI: −11.9 to −1.80]), respectively. Despite the limited knowledge pertaining to FS among mothers in Saudi Arabia, they were making efforts to limit their children's consumption of FS.

**Keywords:** maternal; knowledge; attitude; practices; children; free sugar intake; Saudi Arabia

#### **1. Introduction**

Excessive intake of free sugar (FS) among children has been reported in several settings [1–3]. High intake of FS has been of concern due to its negative impact on health [4,5]. Existing evidence suggests that excessive intake of sugary food replaces the consumption of important foods in the diet, such as fruits and vegetables, leading to lower intake of crucial nutrients and lower quality of diet [6–8]. Insufficient nutrient intake among children for a prolonged period of time may result in growth impairment in addition to many other long-term health consequences [9].

The theory of knowledge, attitude, and practice is important to explain health-related behaviors. The theory consists of three components: acquiring knowledge, generating a belief, and forming a behavior [10]. However, the palatability and convenience of sugary foods could also influence practices related to the consumption of FS foods [11–14]. In addition, the cost per calorie for energy-dense nutrient-poor foods is generally less, which may influence food-related decisions among individuals and result in excessive intake of FS [15,16].

National interventions have been implemented to reduce the consumption of FS among Saudis, e.g., sugary drink tax policy and food labeling regulations to limit FS consumption among the population of Saudi Arabia [17,18]. However, the problem of high consumption of FS remains despite efforts that have been made [3].

Limited knowledge of the FS recommendation is one important factor explaining the high consumption of FS [19,20]. For example, a female parent, a female child, and a higher education level were found to be linked to high recognition level of FS [19]. Understanding the link between maternal knowledge, attitude, and practices toward FS is crucial to design effective interventions aiming to reduce the consumption of FS among Saudi children.

**Citation:** Mumena, W.A. Maternal Knowledge, Attitude and Practices toward Free Sugar and the Associations with Free Sugar Intake in Children. *Nutrients* **2021**, *13*, 4403. https://doi.org/10.3390/nu13124403

Academic Editors: Silvia Scaglioni, Alessandra Mazzocchi and Valentina De Cosmi

Received: 16 October 2021 Accepted: 6 December 2021 Published: 9 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

During childhood, parents play a significant role in shaping the dietary behaviors of children. The impact of maternal feeding practices on children's dietary intake has been previously established [21,22]. Maternal adherence to a healthy lifestyle, including a high-quality diet, and the utilization of healthy feeding practices have been shown to be effective in promoting positive dietary behaviors in children [23,24]. However, research addressing factors related to the consumption of FS among children in Saudi Arabia is lacking. Hence, the present study aimed to evaluate maternal knowledge, attitude, and practices related to FS and the associations with children's intake of FS.

#### **2. Materials and Methods**

In this cross-sectional study, a minimum of 365 healthy children aged between 6–12 years and their mothers were needed based on an effect size (expected correlation coefficient between maternal knowledge and children intake of FS) of 0.20, 99.9 confidence level (two-sided test), and 90% power [25]. Exclusion criteria include children with allergies, chronic diseases, non-Saudi, sibling of a child who is already part of this study, and children who reside outside of Saudi Arabia within the past three months. Initially, we recruited 539 children and their mothers online through different social media channels (Instagram, Telegram, Twitter, WhatsApp, and Facebook). An invitation for participation was accompanied by a short online survey that collected demographic data [age of mother and child, child's sex, maternal education level, and family income per month] and assessed maternal knowledge, attitude, and practices toward FS. A phone interview was scheduled with each mother within one week of collecting the initial data. The online survey and the phone interview were all administered in Arabic. The ethical approval for this study was granted from the ethical review board of the College of Applied Medical Sciences, Taibah University (certificate no. 2020/55/202/CLN). Informed consent was obtained online from all mothers involved in the study before collecting children's data.

#### *2.1. Assessment of Children's Intake of Free Sugar*

Data concerning children's intake of FS were collected from mothers during the phone interviews. FS intake among children was assessed using a previously validated food frequency questionnaire (FFQ) that was specifically designed to assess the habitual intake of FS among children in Saudi Arabia [26]. The FFQ included 12 food groups and 40 food items.  A template table presenting the response options (frequency of consumption) along with a guide to estimate portion sizes of food consumed was sent to mothers during the phone interview via WhatsApp/text message to improve the accuracy of responses to the FFQ. The questionnaire provided estimates concerning quantities of FS intake per day coming from liquid food sources, solid food sources, and total FS intake.

*2.2. Assessment of Maternal Knowledge Related to Free Sugar*

Maternal knowledge related to FS was assessed using 11 items as follows:

(1) Do you think eating too much FS is bad for your child's health?

Response options were "Yes" and "No". Mothers who responded "Yes" were awarded a score of one.

	- (2.1.) Sugar added to coffee and tea;
	- (2.2.) Sugar added to food during processing or cooking;
	- (2.3.) Sugar used to prepare sweets;
	- (2.4.) Sugar exist in fruits and milk.
	- (3.1.) Diet Pepsi;
	- (3.2.) Cookies;

Mothers were instructed to select multiple responses as applicable for items included in sections two and three. Mothers who selected responses 2.1, 2.2, 2.3, 3.2, 3.4, and 3.6 were awarded a score of one for each question. A zero score per question was awarded to mothers who selected items 2.4, 3.1, 3.3, and 3.5, and a score of one was awarded for each of these items if the items were not selected. The total knowledge score was calculated (total score ranged between zero and 11), wherein a score of 11 indicated the highest level of knowledge on added sugar.

#### *2.3. Assessment of Maternal Attitude to Limit Children's Intake of Free Sugar*

Maternal attitude towards limiting children's FS intake was assessed using three items as follows:

Responses to all three items were "Yes" or "No". Mothers who responded "Yes" were awarded a score of one per item, whereas a score of zero was awarded if mothers responded as "No" to any of the items. The total attitude score was later calculated (total scores ranged between zero and three).

#### *2.4. Assessment of Maternal Practices to Limit Children's Intake of Free Sugar*

Maternal practices related to limiting children's intake of FS were assessed using positive behaviors, and consisted of three items as follows:

(1) How often do you read the nutrition fact label of your child's favorite products to determine the amount of free sugar intake?

Responses to this item were "Always" (awarded two scores); "Sometimes" (awarded one score); "Never" (recorded as a zero score).

(2) Are you discussing with your child the importance of replacing foods that are high in free sugar with healthy food options?

Responses to this item were "Yes" (awarded one score) and "No" (recorded as a zero score).

(3) Mother successfully limits/controls her child's free sugar intake (assessed using the FFQ).

A score of one was awarded if the mother successfully limits/controls her child's intake of FS to meet the recommendation of the World Health Organization (WHO) and the Ministry of Health in Saudi Arabia (MOH) (<25 g of FS per day) [27,28], whereas a score of zero was recorded if the mother did not limit/control her child's intake of FS to meet the WHO/MOH recommendation (≥25 g of FS per day).

Scoring of maternal practices toward limiting children's consumption of FS ranged between zero and three.

#### *2.5. Statistical Analyses*

Data for continuous variables included in this study are presented as median [interquartile range (IQR)] and mean ± standard deviation (SD), while data for categorical variables are presented as frequency and percentage (%). The Shapiro–Wilk test was used to assess the normality of all continuous variables. Data of maternal knowledge, attitude, and practices toward FS were skewed; thus, non-parametric tests were used to analyze data presented in this study. Spearman's correlation test was used to assess the correlations between maternal knowledge, attitude, and practices toward FS. The Mann–Whitney and

Kruskal Wallis tests were used to explore differences in mean scores of maternal knowledge, attitude, and practices toward FS within the categorical variables. Pairwise comparisons were performed to further investigate the significant associations found in the Kruskal Wallis test. Multiple linear regression analyses were performed to investigate if maternal knowledge, attitude, and practices toward FS predict children's intake of FS after adjusting for children's age and sex. Tests used in this study were two-tailed with a significance level of 95%. We corrected for multiple testing using the Bonferroni adjustment method when the significance of the pairwise comparisons were assessed. The SPSS (version 20, SPSS, Inc., Chicago, IL, USA) was used for data analysis.

#### **3. Results**

#### *3.1. Characteristics of the Study Sample*

Children with allergies and chronic diseases were excluded (*n* = 36, 6.68%). A total of 79 mothers (14.7%) were excluded due to missing dietary data (no phone interview was conducted) for one of the following reasons: (1) mother did not provide her contact information; (2) mother did not respond to text messages and reminders to schedule the phone interview to collect data concerning child's FS intake. The final analysis of this study included data of 424 children and their mothers. Detailed data of the characteristics of children and their mothers are presented in Table 1.

**Table 1.** Characteristics of children and their mothers (*n* = 424).

**Table 1.** *Cont.*

1 SR: Saudi Riyal (\$1 = SR 3.75).

#### *3.2. Maternal Knowledge, Attitude, and Practices toward Free Sugar*

Items included to assess maternal knowledge, attitude, and practices toward FS are provided in Table 2. The median of knowledge was 7.00 (6.00–8.00) out of 11.0. The median score of maternal attitude to limit children's intake of FS was 2.00 (1.00–3.00) out of a total score of 3.00. The median score of maternal practices to limit children's intake of FS was 2.00 (2.00–2.00) out of a total score of 4.00.

#### **Table 2.** *Cont.*

1 Response awarded a score of one.

Spearman's correlation test showed a negligible correlation between maternal knowledge and attitude toward FS (rs = 0.20; *p* < 0.001) and between maternal knowledge and practices toward FS (rs = 0.18; *p* < 0.001). A low positive correlation was observed between maternal attitude and practices toward limiting FS intake (rs = 0.35; *p* < 0.001).

#### *3.3. Maternal Knowledge, Attitude, and Practices toward Free Sugar and the Associations with Sociodemographic Characteristics*

Detailed data concerning the associations between maternal knowledge, attitude, and practices related to FS and children's characteristics are presented in Table 3. The mean maternal knowledge score towards FS was significantly different across the different regions in Saudi Arabia (*p* = 0.015). After using the Bonferroni adjustment method to correct for multiple testing, the pairwise comparisons showed a significantly higher mean of knowledge score among mothers in the Eastern region as compared to mothers in the Western region (7.83 ± 1.66 vs. 7.07 ± 1.55, respectively, *p* = 0.018). The mean maternal knowledge score toward FS was similar for all other sociodemographic variables. The mean maternal attitude score related to limiting FS intake was similar for all sociodemographic variables. The mean maternal practices score related to limiting FS intake was significantly different across regions in Saudi Arabia (*p* = 0.005). After using the Bonferroni adjustment method to correct for multiple testing, the pairwise comparisons showed significantly higher mean scores of practices among mothers in the Eastern region as compared to mothers in the Western region (2.17 ± 0.64 vs. 1.83 ± 0.73, respectively, *p* = 0.005). In

addition, unemployed mothers had a higher mean maternal practices score toward limiting the consumption of FS compared to employed mothers (2.02 ± 0.64 vs. 1.77 ± 0.72, respectively, *p* < 0.001). The mean score of maternal practices related to limiting FS intake was similar for all other characteristics of children included in this study.

**Table 3.** Associations between maternal knowledge, attitude, and practices related to free sugar and children's characteristics (*n* = 424).

**Table 3.** *Cont.*

1 The numbers presented are mean ± SD and median [IQR]. 2 SR: Saudi Riyal [\$1 = SR 3.75]. 3 *p*-value was considered statistically significant at the 95% confidence level.

#### *3.4. Maternal Knowledge, Attitude, and Practices Related to Free Sugar and the Associations with Children's Intake of Free Sugar*

Multiple linear regression analysis was performed to investigate the association between maternal knowledge, attitude, and practices related to FS and children's intake of FS adjusting for children's age and sex (Table 4). Maternal knowledge toward FS was not found to be associated with children's intake of FS (from liquid food sources, solid food sources, and total FS from all food sources). Positive maternal attitude related to limiting FS intake predicted lower intake of FS from solid food sources in children (B: −5.73, SE: 2.07 [95% confidence interval (CI): −9.79 to −1.66], R-square = 0.02) and total FS from all food sources (B: −7.60, SE: 3.01 [95% CI: −13.5 to −1.68], R-square = 0.03), but no association with children's intake of FS from liquid food sources was observed. Positive maternal practices related to limiting FS intake predicted lower intake of FS from solid food sources in children (B: −6.85, SE: 2.57 [95% CI: −11.9 to −1.80], R-square = 0.02) and total FS from all food sources (B: −7.92, SE: 3.74 [95% CI: −15.3 to −0.56], R-square = 0.02). Maternal practices related to FS were not associated with children's intake of FS coming from liquid food sources.

**Table 4.** Multiple linear regression analysis of associations between maternal knowledge, attitude, and practices related to free sugar and children's intake of free sugar 1.

1 All models were adjusted for children's age and sex. 2 *p*-value was considered statistically significant at the 95% confidence level.

#### **4. Discussion**

Limited knowledge about FS was observed among the mothers included in this study. Maternal knowledge was not correlated with maternal attitude or practices related to limiting the intake of FS. Maternal knowledge towards FS did not predict children's intake of FS, whereas maternal attitude and practices to limit the consumption of FS predicted lower intake of FS among Saudi children, specifically FS coming from solid food sources.

The limited knowledge related to FS reported among mothers in our study has also been reported in other settings. A study conducted in China showed limited maternal knowledge related to FS [19]. In Ireland, a study also shows limited knowledge related to FS among adults where 65% of participants did not know the WHO recommendation for FS [20]. In fact, knowledge regarding FS might be influenced by many factors. The current food labeling practices in the Saudi market are inadequate [29], but notable progress has been observed [30,31]. For example, the Saudi government has implemented a policy for establishing front-of-pack nutrition labeling requirements and the FS nutritional labeling requirement [18,32,33]. Clear information provided in nutrition labels can help in increasing knowledge regarding FS content in prepackaged foods. However, improving knowledge of how to read nutrition labels is needed, as limited interpretation skills of nutrition labels have been reported previously in Saudi Arabia [34]. It is noteworthy that obtaining information from creditable sources is very important, especially with the wide use of social media applications, where inaccurate or even incorrect information is posted. Previous research in Ghana conducted among young adults reported that nutrition-related information is mostly obtained from online resources, whereas the least used source was healthcare professionals, e.g., nutritionists [35]. Thus, social media has been used effectively by national and international health organizations to deliver health-related messages to the public to raise awareness and improve knowledge.

In this study, the univariate analysis revealed that unemployed mothers have better practices related to limiting children's intake of FS. This finding could be explained by the greater time availability for stay-at-home mothers. It has been noted that time constraints are the most common reasons for negative nutrition-related practices, e.g., not reading food labels [34]. Therefore, unemployed mothers who have more time on their hands can be more inclined to better nutrition-related practices, including reading food labels, discussing with children the importance of replacing less healthy foods with healthful food options, and limiting/controlling their children's intake of FS.

Despite their limited knowledge of FS, mothers were making efforts to limit children's intake of FS. Yet, the majority of children exceeded the recommendation of the WHO/MOH. The excessive consumption of FS could be due to the affordability, palatability, and convenience of sugary foods [13,14,16]. In fact, it is evident that the sweet taste of food is preferred, particularly among children [11,12]. Our data also showed that maternal attitude and practices related to limiting FS only predicted children's intake of FS from solid food sources and total FS, but not FS coming from liquid food sources. The lack of association between maternal attitude and practices to limit children's intake of FS from liquid food sources could be due to efforts that have been made by the Saudi government to limit the intake of sugary drinks [17]. In fact, the sugary drink tax policy implemented in 2017 might result in limiting the consumption of FS from liquid food sources. Liquid food sources of FS were found to be the greatest contributor to total energy intake among many populations [1,36]. However, data regarding top food sources of FS among children in Saudi Arabia are lacking. Previous research showed high consumption of sweet snacks and candies among children and university students [37,38].

Maternal attitude and practices toward FS can influence children's intake of FS despite their level of knowledge. Efforts made by mothers to limit children's intake of FS can help in eliminating many food sources that contain high quantities of FS and replace them with healthy food options. Previous nutrition education interventions conducted among preschoolers and their parents were found to be effective in reducing children's consumption of FS and increasing the density of important nutrients, including protein, fiber, potassium, iron, and zinc [39]. However, replacing sugary foods with healthy food options, e.g., fruits and vegetables, might be more costly [15,16].

This is the first study in the region to explore maternal knowledge, attitude, and practices related to FS in relation to children's intake of FS. The FS intake was assessed using a validated FFQ that can estimate the habitual intake of FS among Saudi children. The study is limited by the convenience sampling method used. Thus, the findings of this study might be generalizable only to mothers who commonly use social media channels.

#### **5. Conclusions**

Despite the limited knowledge observed pertaining to FS among mothers in Saudi Arabia, these mothers were making efforts to limit their children's consumption of FS. Future research should be directed towards exploring other potential predictors of FS intake among Saudi children. In addition, longitudinal associations between knowledge, attitude, and practices related to FS intake should be further explored among individuals from different age groups in Saudi Arabia. Nutrition education interventions to improve maternal knowledge on FS and its food sources are urgently needed.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The ethical approval for this study was granted from the ethical review board of the College of Applied Medical Sciences, Taibah University (certificate no. 2020/55/202/CLN).

**Informed Consent Statement:** Informed consent was obtained from all mothers involved in the study.

**Data Availability Statement:** The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

**Acknowledgments:** Thanks are due to all members of the Dietary Intake of Saudis project for their contribution to data collection. Special thanks to Fatima Abdulhakeem and Najwan Jannadi from the Virtual Dietary Assessment Lab for their support during dietary data entry. Additional thanks to all mothers and children who participated in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

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1 Centre for Innovation, Canadian Blood Services, University of British Columbia, Vancouver, BC, Canada

2 Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada

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4 Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada

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