) 高新船舶与深海开发装备协同创新中心, 上海 200240
收稿日期:2019-08-05出版日期:2021-07-28发布日期:2021-07-30通讯作者:陈自强E-mail:chenziqiang@sjtu.edu.cn作者简介:姜 余(1994-),男,黑龙江省七台河市人,硕士生,研究方向为动力电池热管理基金资助:国家自然科学基金项目(51677119)Average Temperature Estimation for Lithium-Ion Batteries at Variable Environment Temperature
JIANG Yu, CHEN Ziqiang() Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
Received:2019-08-05Online:2021-07-28Published:2021-07-30Contact:CHEN Ziqiang E-mail:chenziqiang@sjtu.edu.cn摘要/Abstract
摘要： 锂离子电池的温度变化会极大地影响其使用性能,对锂离子电池进行温度信息采集对热管理系统的开发具有重要意义.为了解决传统温度传感器布置方式所导致的局部温度无法代替整体平均温度的问题,需要在可变环境温度下对10 A·h三元镍钴锰酸锂离子电池开展平均温度估计研究.首先,对锂离子电池进行建模并搭建实验平台,获取建模所需的参数.其次,提出了基于递推最小二乘与拓展卡尔曼滤波算法相结合的锂离子电池平均温度估计方法.最后,为了验证所提出方法的有效性,使用改进混合动力脉冲能力特性(IHPPC)测试工况获取电池在可变环境温度下的实验数据.结果表明:所提方法能够实时估计锂离子电池的平均温度.温度估计模型算法收敛后最大误差为1.8 ℃,平均误差仅为1 ℃.
关键词: 锂离子电池, 时变环境温度, 改进混合动力脉冲能力特性(IHPPC), 平均温度估计
Abstract: The performance of lithium-ion batteries is greatly affected by the temperature change. Therefore, it is of great significance to get the temperature information to develop the thermal management system. In order to solve the problem that the local temperature caused by the traditional temperature sensor arrangement cannot replace the overall average temperature, it is necessary to conduct studies of average temperature estimation for 10 A·h ternary nicke clobalt manganese lithium-ion batteries at variable environment temperature. First, a lithium-ion battery is modeled and the experimental platform is built to obtain the parameters which are necessary for modeling. Then, an average temperature estimation method for lithium-ion batteries based on recursive least squares and the extended Kalman filter algorithm is proposed. Finally, to verify the effectiveness of the proposed method, the experimental data of the battery at variable environment temperature is obtained under improved hybrid pulse power characteristic (IHPPC). The results show that the proposed method can estimate the average temperature of lithium-ion batteries in real time. The maximum error of the temperature estimation model after the algorithm convergence is 1.8 ℃, and the average error is only 1 ℃.
Key words: lithium-ion battery, time-varying environment temperature, improved hybrid pulse power characteristic (IHPPC), average temperature estimation
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