A New Method for Estimating Lithium‑Ion Battery State‑of‑Energy Based on Multi‑timescale Filter

作     者：Guangming Zhao Wei Xu Yifan Wang Guangming Zhao;Wei Xu;Yifan Wang

作者机构：Road Traffic Safety Research Center of the Ministry of Public SecurityBeijing 100068China China Automotive Engineering Research Institute Co.LtdChongqing 401120China 

出 版 物：《Automotive Innovation》 (汽车创新工程（英文）)

年 卷 期：2023年第6卷第4期

页      面：611-621页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0802[工学-机械工程] 0823[工学-交通运输工程] 

基　　金：the financial support provided by the National Key R&D Program of China(Grant No.2020YFB1600605). 

主　　题：Forgetting factor State-of-energy Multi-timescale Lithium-ion battery 

摘      要：Accurate estimation of the state-of-energy(SOE)in lithium-ion batteries is critical for optimal energy management and energy optimization in electric vehicles.However,the conventional recursive least squares(RLS)algorithm struggle to track changes in battery model parameters under dynamic conditions.To address this,a multi-timescale estimator is proposed.A variable forgetting factor RLS approach is used to determine the model parameters at a macro timescale,and the H infinity filter is utilized to estimate the SOE at a micro timescale.The proposed algorithm is verified and analyzed and shown to have accurate and robust identification of battery model parameters.Finally,experiments under dynamic cycles demonstrate that the proposed algorithm has a high level of accuracy for SOE estimation.