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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Electrochemical Energy Storage
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1494541
This article is part of the Research Topic Advances in Battery Technologies View all articles

State of Charge Estimation Method for Lithium-Ion Battery Pack Using BP-MDM-TEM

Provisionally accepted
Haiyan Yao Haiyan Yao 1*Yuefei Xu Yuefei Xu 2*Qiang Guo Qiang Guo 1*Yufeng Miu Yufeng Miu 1*Xufeng Zhang Xufeng Zhang 1*Jiadong Wei Jiadong Wei 1*Yunning Zhang Yunning Zhang 3*Chunshi Wang Chunshi Wang 3
  • 1 Yuhang Qunli Complete Electrical Manufacturing Branch, Hangzhou Electric Power Equipment Manufacturing Co.Ltd, Hangzhou 311199, China, hangzhou, China
  • 2 Hangzhou Electric Power Equipment Manufacturing Co.Ltd, Hangzhou 310018, China, hangzhou, China
  • 3 Hangzhou Dianzi University, Hangzhou, China

The final, formatted version of the article will be published soon.

    Addressing the fluctuating core temperatures during series battery pack operation that cause inconsistency in pack battery characteristics, the paper introduces a Battery-Pack Mean Difference-Method With Thermal-Electrical Coupled Model (BP-MDM-TEM). Thermal-electric coupling model describes the mean characteristics of battery pack, while the difference model quantifies variations among individual cells. A dual-time-scale method is employed to estimate State of Charge (SoC) of battery pack, reducing the computational load of the difference model.If the unique battery characteristics exceed system limits, a significant battery method is used to estimate SoC of pack battery. Building upon the BP-MDM-TEM framework,the paper employs the Improved Sage-Husa Adaptive Extended Kalman Filter (ISH-AEKF) to estimate SoC, mitigating the impact of process and measurement noise on system estimation. A hardware test platform is established in this paper, with a lithium-ion battery pack as the research subject, where charging and discharging experiments are conducted to validate the accuracy of the SoC estimation method.

    Keywords: Mean Difference Model, Thermal-electric Coupling Model, EKF, ISH-AEKF, Dual-time-scale

    Received: 11 Sep 2024; Accepted: 18 Nov 2024.

    Copyright: © 2024 Yao, Xu, Guo, Miu, Zhang, Wei, Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Haiyan Yao, Yuhang Qunli Complete Electrical Manufacturing Branch, Hangzhou Electric Power Equipment Manufacturing Co.Ltd, Hangzhou 311199, China, hangzhou, China
    Yuefei Xu, Hangzhou Electric Power Equipment Manufacturing Co.Ltd, Hangzhou 310018, China, hangzhou, China
    Qiang Guo, Yuhang Qunli Complete Electrical Manufacturing Branch, Hangzhou Electric Power Equipment Manufacturing Co.Ltd, Hangzhou 311199, China, hangzhou, China
    Yufeng Miu, Yuhang Qunli Complete Electrical Manufacturing Branch, Hangzhou Electric Power Equipment Manufacturing Co.Ltd, Hangzhou 311199, China, hangzhou, China
    Xufeng Zhang, Yuhang Qunli Complete Electrical Manufacturing Branch, Hangzhou Electric Power Equipment Manufacturing Co.Ltd, Hangzhou 311199, China, hangzhou, China
    Jiadong Wei, Yuhang Qunli Complete Electrical Manufacturing Branch, Hangzhou Electric Power Equipment Manufacturing Co.Ltd, Hangzhou 311199, China, hangzhou, China
    Yunning Zhang, Hangzhou Dianzi University, Hangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.