Advanced Battery Management Technologies for Electric Vehicles

2Battery Modeling

2.1 Background

Battery modeling plays an important role in estimating battery states which include state of charge (SOC), state of health (SOH), state of energy (SOE), and state of power (SOP). These states can hardly be measured directly and have to be estimated based on battery models. The batteries in electric vehicles (EVs) are operating in a complex and highly dynamic environment, which means they possess highly time‐varying characteristics. Electrochemical models (EMs), black box models, and equivalent circuit models (ECMs) have been used to describe such characteristics. EMs apply partial differential equations (PDEs) to describe the electrochemical reaction process inside batteries [1]. These models generally have high accuracy but their computation burden is too heavy to integrate into on‐board battery management systems (BMSs) for EVs. Black box models such as neural network (NN) models require a large amount of experimental data to train the models. Since the parameters of the trained NN models do not have physical meanings, the obtained NN models can only be used in similar working conditions that the experimental data are collected from [2]. This limits the applications of the NN models into on‐board BMSs for EVs. ECMs use electrical circuits to represent battery terminal behaviors [3, 4]. They are compatible with the circuits of BMSs for EVs and can be easily embedded into BMSs. This chapter first provides a brief introduction of EMs and black ...