In recent years, the electric vehicle industry has developed rapidly with cuttingedge technologies such as 5G, Internet of Things, big data and artificial intelligence.“The new four modernizations of the automobile industry” with electrification, networking, intelligentization and sharing as its core is reshaping the pattern of the automobile industry, among which electrification is the best foundation for all these advanced intelligence technologies. The popularization of electric vehicles in the world has become an important development trend. The main energy source of electric vehicles is power batteries; available power cells include lead-acid batteries, nickel-cadmium batteries, lithiumion battery batteries and fuel cells, among which lithium-ion batteries are currently the mainstream power cells and the main object for this book. Battery management system (BMS) is an indispensable key component of new energy vehicle. Its core functions include data collection, state estimation, balance management, thermal management, communication and fault diagnosis. This book describes the electrochemical model, black box model, equivalent circuit model and other modeling methods, and corresponding parameter identification methods. In order to make better use of power battery, more and more requirements are put forward for BMS, especially in the aspect of high-precision battery state estimation of full climate, and full lifespan. This book elaborates the SOC estimation methods with model-based estimation methods and data-driven algorithms, combined with simulation cases. SOH estimation methods are introduced from the aspects of direct measurement, indirect analysis, data-driven and multi-scale joint estimation. In addition, the estimation methods of power, energy and safety state are discussed in detail combined with data-driven methods.

The available charge and discharge capacity of a battery pack is determined by the unit with the highest and lowest capacity respectively, and this kind of inconsistency will gradually increase with time accumulation, leading to accelerated aging. This may cause overcharge and overdischarge during cycling, resulting in the risk of thermal runaway safety. Therefore, the charge optimization control algorithm, which aims at the longest cycle life, needs to be implemented with passive equilibrium and the non-destructive active equilibrium. This can reduce the inconsistency, maximize