基于渐消记忆递推最小二乘法的电动汽车电池荷电状态在线估计

doi:10.16183/j.cnki.jsjtu.2020.172

上海交通大学学报 ›› 2020, Vol. 54 ›› Issue (12): 1340-1346.doi: 10.16183/j.cnki.jsjtu.2020.172

• • 上一篇

基于渐消记忆递推最小二乘法的电动汽车电池荷电状态在线估计

陈玉珊¹, 秦琳琳¹(), 吴刚¹, 毛俊鑫²

^1.中国科学技术大学　信息科学技术学院，合肥　230026
^2.天津恒天新能源汽车研究院有限公司　汽车电子与电源系统研究所，天津　300451

收稿日期:2019-09-29 出版日期:2020-12-01 发布日期:2020-12-31
通讯作者: 秦琳琳 E-mail:qll@ustc.edu.cn
作者简介:陈玉珊(1995-)，女，福建省厦门市人，硕士生，从事电池参数估计研究．

Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares

CHEN Yushan¹, QIN Linlin¹(), WU Gang¹, MAO Junxin²

^1.School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China
^2.Automotive Electronics and Power System Research Institute, Tianjin Hengtian New Energy Automobile Research Institute Co. , Ltd. , Tianjin 300451, China

Received:2019-09-29 Online:2020-12-01 Published:2020-12-31
Contact: QIN Linlin E-mail:qll@ustc.edu.cn

摘要/Abstract

摘要：

电动汽车中，先进的电池管理系统可以为电池的安全高效使用提供保障．荷电状态(SOC)无法直接测量得到，电池管理系统的主要任务是准确、可靠地估计电池的SOC.为了估计电池的SOC，选择一阶电阻电容(RC)等效电路模型描述电池的外特性，模型参数中包含开路电压(OCV)，通过渐消记忆递推最小二乘法(FFRLS)辨识模型参数，再用SOC-OCV模型实时计算．使用马里兰大学高级生命周期工程研究中心(CALCE)电池组提出的镍钴锰酸锂(LNMC)/石墨电池在动态应力测试(DST)和北京动态应力测试(BJDST)工况下的数据检验算法，结果表明，SOC估计误差在DST工况下不超过 3.419 0%，在BJDST工况下不超过 4.233 5%，实现了SOC的在线估计．

关键词: 递推最小二乘法, 荷电状态, 在线估计, 电动汽车

Abstract:

An advanced battery management system ensures the safe and efficient use of batteries in electric vehicles. As the state of charge (SOC) cannot be measured directly, it is important for the battery management system to accurately and reliably estimate the SOC of batteries. In order to estimate SOC, a first-order resistor-capacitance (RC) equivalent circuit model is used to describe the external characteristic of batteries. The model parameters are identified by forgetting factor recursive least-squares (FFRLS). Open circuit voltage (OCV) is one of the model parameters, and then SOC can be estimated by the SOC-OCV model. The CALCE battery research group in the University of Maryland has proposed some data, which include the data of LNMC/graphite battery working under dynamic stress test (DST) and Beijing dynamic stress test (BJDST) conditions. These data are used to verify the proposed algorithm. The results show that the estimation error does not exceed 3.419 0% in DST and 4.233 5% in BJDST, which indicates that the proposed method can realize online SOC estimation.

Key words: recursive least squares, state of charge (SOC), online estimation, electric vehicle

中图分类号:

N945.12

陈玉珊, 秦琳琳, 吴刚, 毛俊鑫. 基于渐消记忆递推最小二乘法的电动汽车电池荷电状态在线估计[J]. 上海交通大学学报, 2020, 54(12): 1340-1346.

CHEN Yushan, QIN Linlin, WU Gang, MAO Junxin. Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares[J]. Journal of Shanghai Jiao Tong University, 2020, 54(12): 1340-1346.

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基于渐消记忆递推最小二乘法的电动汽车电池荷电状态在线估计

Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares

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