Enhanced Second-Order RC Equivalent Circuit Model with Hybrid Offline–Online Parameter Identification for Accurate SoC Estimation in Electric Vehicles under Varying Temperature Conditions

Hao Zhou, Qiaoling He, Yichuan Li, Yangjun Wang, Dongsheng Wang and Yongliang Xie ()
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Hao Zhou: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Qiaoling He: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Yichuan Li: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Yangjun Wang: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Dongsheng Wang: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Yongliang Xie: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Energies, 2024, vol. 17, issue 17, 1-19

Abstract: Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery’s surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency.

Keywords: equivalent circuit model; battery management system; parameter identification; State-of-Charge (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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