Exploring the Model Design Space for Battery Health Management
battery health management bhm is a core enabling technology for the success and widespread adoption of the emerging electric vehicles of today although battery chemistries have been studied in detail in literature an accurate run time battery life prediction algorithm has eluded us current reliability based techniques are insufficient to manage the use of such batteries when they are an active power source with frequently varying loads in uncertain environments the amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state of charge soc but also other factors like battery health and the discharge or load profile imposed this paper presents a particle filter pf based bhm framework with plug and play modules for battery models and uncertainty management the batteries are modeled at three different levels of granularity with associated uncertainty distributions encoding the basic electrochemical processes of a lithium polymer battery the effects of different choices in the model design space are explored in the context of prediction performance in an electric unmanned aerial vehicle uav application with emulated flight profiles