Review of SoC Estimation Methods for Electric Vehicle Li-ion Batteries

Lithium-ion battery has been widely used in electric vehicle industry because of its long cycle life, high energy density, low self-discharge rate and low environmental pollution. The Battery Management System (BMS) in electric vehicles can maintain and monitor the battery status to ensure the security and reliability of the battery. The battery's State of Charge (SoC) represents the remaining power in the battery and is one of the important parameters of the BMS. Real-time and accurate SoC estimation can extend battery life and ensure driving safety. However, Lithium-ion battery is indeed a highly complex nonlinear time-varying system, many unknown factors, such as battery life, ambient temperature, battery self-discharge and so on, will affect the estimation accuracy, which greatly increases the difficulty of estimation. To meet the requirements of accurate, rapid and real-time SoC estimation for Lithium-ion batteries under different conditions, further research and improvement of SoC estimation algorithm are needed. In recent years, some review literature on the Lithium-ion battery SoC estimation has been published. However, the existing literature has not summarized the estimation methods comprehensively and lack of process description. In this paper, the working principle of Lithium-ion battery is firstly introduced, as well as the factors affecting the SoC estimation of battery. Secondly, summary and analysis of the battery SoC estimation methods are conducted by the latest research achievement. According to the different characteristics of various algorithms, the battery SoC estimation can be divided into five categories: look-up table method, ampere hour integration method, model-based method, data-driven method and hybrid method. The main characteristics of various estimation methods are explained and the advantages and disadvantages of models or algorithms are comprehensively compared and discussed. Finally, the future development direction of SoC estimation methods for Lithium-ion batteries in electric vehicles is prospected.