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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

1. 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. 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

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.