基于的在线开放电路电压识别,用于精确估计离子电池的状态
Zhengyang Li1, Cheng Chen1, Ruixin Yang1
1Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
iScience
|October 7, 2025
概括
本研究介绍了一种在线方法,使用运行数据识别电动汽车 (EV) 离子电池的开放电路电压 (OCV) -充电状态 (SOC) 曲线. 该方法通过解决动态电压响应中的不确定性来确保准确的SOC估计.
科学领域:
- * 电化学能量储存系统
- * 电池管理系统 电池管理系统
- * 离子电池的特性描述
背景情况:
- *开放电路电压 (OCV) -充电状态 (SOC) 曲线对于精确的电池管理至关重要,但会受到电池退化和不一致的影响.
- * 传统的基于实验室的OCV-SOC曲线识别耗时,引发了人们对使用电动汽车 (EV) 操作数据的在线方法的兴趣.
- *在线识别OCV-SOC曲线面临挑战,因为在许多电动汽车的操作条件下,动态电压激发不足,导致不确定性.
研究的目的:
- * 开发一种准确的在线方法,使用电动汽车操作数据识别离子电池的OCV-SOC曲线.
- *为了解决因有限的动态电压响应而导致的在线OCV识别的不确定性.
- *通过增强OCV-SOC曲线构造来提高电荷状态 (SOC) 估计的准确性.
主要方法:
- * 利用测量信号的香农来评估已识别的OCV的准确性.
- *使用终端电压误差,从确定参数计算,作为OCV准确性的指标.
- * 在构建的OCV-SOC段内插入了安培小时计数的起始点,使用已识别的OCV来确保SOC的准确性.
主要成果:
- * 在线构建的OCV-SOC曲线显示最大偏差低于22mV.
- *当应用于SOC估计时,拟议方法的错误率低于2.2%.
- * 香农和电压误差的整合有效量化了所识别的OCV的准确性.
结论:
- * 在线开发的OCV-SOC曲线识别方法对于电动汽车中的离子电池是准确和可靠的.
- *该方法成功地减轻了与EV运行期间有限的动态电压数据相关的不确定性.
- *精确的OCV-SOC曲线识别直接转化为改善电动汽车电池管理系统的SOC估计精度.
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