电动汽车锂离子电池荷电状态估算方法研究综述

张照娓; 郭天滋; 高明裕; 何志伟; 董哲康

doi:10.11999/JEIT200487

电动汽车锂离子电池荷电状态估算方法研究综述

doi: 10.11999/JEIT200487

张照娓^{1, 2},
郭天滋^{1, 2},
高明裕^{1, 2, ,},
何志伟^{1, 2},
董哲康^{1, 3}

1.
杭州电子科技大学电子信息学院杭州 310018
2.
浙江省装备电子研究重点实验室杭州 310018
3.
香港理工大学电机工程系香港 999077

基金项目: 国家自然科学基金(61671194)，浙江省属高校基本科研业务费项目(GK199900299012-010)，浙江省重点研发项目(2020C03098)，科技部重点研发计划(2020YFB1710600)

详细信息

作者简介:
张照娓：女，1995年生，博士生，研究方向为汽车电子技术

郭天滋：女，1996年生，博士生，研究方向为汽车电子技术

高明裕：男，1963年生，教授，博士生导师，研究方向为汽车电子技术和嵌入式系统应用

何志伟：男，1979年生，教授，博士生导师，研究方向为汽车电子技术和电池管理技术

董哲康：男，1989年生，副教授，硕士生导师，研究方向为深度学习、神经形态系统

通讯作者:
高明裕　mackgao@hdu.edu.cn

中图分类号: TM911
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出版历程
- 收稿日期: 2020-06-16
- 修回日期: 2020-10-31
- 网络出版日期: 2020-11-03
- 刊出日期: 2021-07-10

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

Zhaowei ZHANG^{1, 2},
Tianzi GUO^{1, 2},
Mingyu GAO^{1, 2
, ,},
Zhiwei HE^{1, 2},
Zhekang DONG^{1, 3}

1.
School of Electronics Information, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Zhejiang Provincial Key Laboratory of Equipment Electronics, Hangzhou 310018, China
3.
Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China

Funds: The National Natural Science Foundation of China (61671194), The Fundamental Research Funds for the Zhejiang Provincial Universities (GK199900299012-010), The Key R&D Program of Zhejiang Province (2020C03098), The Key R & D Plan of The Ministry of Science and Technology (2020YFB1710600)

摘要

摘要: 锂离子电池具有循环寿命长、能量密度高、自放电率低、环境污染小等优点，在电动汽车产业中得到广泛应用。电动汽车中的电池管理系统(BMS)可以维护和监测电池状态，确保电池的安全性和可靠性。电池荷电状态(SoC)表示电池中剩余的电量，是BMS的重要参数之一，实时精确的SoC估算可以延长电池寿命，保障行驶安全。然而锂离子电池是一个高度复杂的非线性时变系统，电池寿命、环境温度、电池自放电等许多未知因素均会对估算精度造成影响，使估算难度大大增加。为了满足不同条件下对锂离子电池SoC精确、快速、实时估算的要求，需要对SoC估计算法进行进一步研究与改进。近年来已有相关文献对锂离子电池SoC的估算方法进行了综述，然而已有相关综述对估算方法的总结不够全面且缺少流程表达。该文首先介绍了锂离子电池的工作原理，阐述了影响电池SoC估算的因素；其次，通过总结最新的研究成果对电池SoC估算方法进行了归纳分析，根据各类算法的不同特性将其分为查表法、安时积分法、基于模型的方法、数据驱动的方法以及混合方法五大类，说明了各类估算方法的主要特征并对模型或算法的优缺点进行综合的比较和讨论；最后，对电动汽车中锂离子电池SoC估算方法的未来发展方向做出展望。
- 电动汽车 /
- 锂离子电池 /
- 电池管理系统 /
- 荷电状态 /
- 估算方法
Abstract: 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.
- Electric vehicle /
- Lithium-ion battery /
- Battery Management System (BMS) /
- State of Charge (SoC) /
- Estimation methods

HTML全文

图 1 锂离子电池工作原理示意图

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图 2 锂离子电池SoC估算方法分类

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图 3 基于模型的锂离子电池SoC估计方法结构图

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图 4 基于数据的估算方法

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图 5 用于SoC估计的3层神经网络结构

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表 1 4种常用等效电路模型

等效电路模型	器件组成	模型特性方程	等效电路	参数辨识复杂程度	特点
R_int模型	U_v, R₀	U_t=U_v–IR₀		简单	模型简单；精度差；无法模拟电池动态特性
Thevenin模型	U_v,R₀, R₁,C₁	U₁=IR₁·[1–e^–t/τ₁] U_t=U_v–IR₀–U₁		复杂	考虑了电池极化的影响
2阶电阻电容并联等效模型	U_v,R₀,R₁, C₁,R₂,C₂	U₁=IR₁·[1–e^–t/τ₁] U₂=IR₂·[1–e^–t/τ₂] U_t=U_v–IR₀–U₁–U₂		非常复杂	考虑了电池极化的影响；模型精度高
PNGV模型	U_v, R₀, R₁, C₁, C₃	U₁=IR₁·[1–e^–t/τ₁] U₃=I/C₃ U_t=U_v–IR₀–U₁–U₃		非常复杂	考虑负载电流对OCV的影响

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表 2 各类SoC估计方法的主要优点和缺点

估算方法	优点	缺点
查表法	准确、可靠、原理简单	耗时长、能源浪费、不能实时估计
安时积分法	估算速度快、易实现	开环估计方法，存在累计误差
基于模型的估算方法	闭环估计，不需要精确的SoC初始值、估算误差小	建模困难、参数辨识难度大、计算量大
基于数据方法	不需要对电池建模、可以自学习网络参数、计算量小	对数据要求高、离线训练耗时长
混合方法	估算精度高、有很好的泛化性和鲁棒性	计算复杂、能耗大、估算速度慢

下载: 导出CSV

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