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作 者:付智城 孙丙香[1] 贾一鸣 龚敏明[1] 马仕昌 庞俊峰 FU Zhicheng;SUN Bingxiang;JIAYiming;GONG Minming;MA Shichang;PANG Junfeng(National Active Distribution Network Technology Research Center,Beijing Jiaotong University,Beijing 100044,China)
机构地区:[1]北京交通大学国家能源主动配电网技术研发中心,北京100044
出 处:《电力系统保护与控制》2024年第22期34-46,共13页Power System Protection and Control
基 金:国家自然科学基金面上项目资助(52177206)。
摘 要:针对车用锂离子电池在不同驾驶习惯下的电池衰退差异性的问题,提出了一种与容量衰退相关的驾驶习惯差异性特征提取方法。不同分段加速过程及其占比是不同驾驶习惯下影响电池衰退的关键因素,并设计了一种差异性特征提取方法。首先,基于最小时间为0.1 s的CLTC-P及实车测试数据,从平均车速和分段加速过程占比量化驾驶习惯,分析不同驾驶习惯的差异性。然后,利用主成分分析法提取电流关键影响因素,借助BI-KMEANS聚类方法分析脉冲电流,结合不同驾驶习惯下电流的差异性提取相关特征。最后,通过实验探究差异性特征与容量衰退的关联性。实验结果表明,不同分段加速过程带来的不同极化次数和极化强度是不同驾驶习惯下影响电池衰退差异的主要原因。A method for extracting the driving habit-specific features related to capacity degradation is proposed to address the issue of battery degradation variability in automotive lithium-ion batteries when the driving habits are different.The key factors affecting battery degradation under different driving habits are the different segmented acceleration processes and their proportions.A differential feature extraction method is designed.First,based on CLTC-P with a minimum time of 0.1 s and real vehicle test data,the driving habits are quantified by average speed and the proportion of the acceleration process,and the differences in the driving habits are analyzed.Then,the principal component analysis method is used to extract key influencing factors of the current,and the BI-KMEANS clustering method is employed to analyze pulse currents,combining the current differences under different driving habits to extract relevant features.Finally,the experiments are designed based on the differential features to explore their association with capacity degradation.The result shows that the different polarization times and intensities brought by different segmented acceleration processes are the main reasons for the variability in battery degradation when there are different driving habits.This work is supported by the General Program of National Natural Science Foundation of China(No.52177206).
关 键 词:电动汽车 驾驶习惯 锂离子电池 电流特征 容量衰退
分 类 号:U469.72[机械工程—车辆工程] TM912[交通运输工程—载运工具运用工程]
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