【作者】 单毅；

【导师】 解晶莹；

【作者基本信息】 中国科学院研究生院（上海微系统与信息技术研究所） ， 材料物理与化学， 2008， 硕士

【摘要】 锂离子电池组在应用过程中,会因单体电池性能不一致引起组合后电池组寿命提前终结等问题。在电池组装配以前尽可能提高配组单体电池初始性能一致性和减小使用过程中差异放大成为锂离子电池应用中的重要问题。初始性能不一致主要依靠分选来解决;循环过程中的差异放大主要通过电源管理系统(BMS)对电池组进行状态管理和均衡管理来抑制。现有分选方法存在分选标准不够合理等问题;状态管理和均衡管理涉及到容量衰减预测,目前的容量衰减预测方法存在实用性不强等问题。本文对影响锂离子电池初始性能一致性和衰减速率的因素进行了分析,对分选方法和性能预测方法进行了研究。论文对电池性能差异的各种来源和影响因素进行了研究,重点研究了温度对电池性能衰减速率的影响。通过循环实验,分析了不同温度下电池容量衰减的速率,研究了充放电性能的变化;通过扫描电镜研究了不同温度下循环电极表面的形貌变化。结果表明温度对性能衰减速率有显著的影响。论文研究了单体锂离子电池的分选方法。单体电池性能的不一致会导致电池组在使用中的不均衡从而造成电池组过早失效。因此在组合成电池组以前通过一定的标准把单体电池分选是必要的。充放电曲线能够较全面地反映电池的特性,因此是合适的分选参数。通过统计分析软件Statistical Analysis System的计算,可以精确高效的对充放电曲线进行分类,结果表明通过分选后组合的电池组没有发生容量快速衰减和内阻快速增加,电池组的充放电曲线也很稳定,没有明显的波动。论文还研究了电池组使用过程中单体电池的性能预测。根据电池管理系统(BMS)记录的历史数据建立了锂离子电池容量衰减的时间序列模型,用该模型预测了电池容量的衰减情况。结果表明模型对不同倍率不同温度下循环的电池都能比较准确地预测,短期点预测的误差不超过3%。更多还原

【Abstract】 In lithium-ion battery pack applications, early failure even safety problems maybe occurred because of ununiformity of single battery characters and performances. Therefore how to classify single batteries in order to improve their uniformity and reduce the character changes of batteries in cycling have become a important issue in lithium battery application. But the existing classifying method is not inefficient and criterion of classifying is not in reason. The present prediction method is unpractical. From this point of view, the source of ununiformity, classifying method and predict model of battery capacity fade were presented in this paper.Various influence factors of uniformity were investigated, especially the temperature was studied in detail. Rate of capacity fade, charge and discharge performance, component and modality of electrode surface related to temperature were analyzed. The result shows that temperature is a critical factor to influence the uniformity of batteries.The uniformity of the single battery usually causes the imbalance in battery pack, then initial capacity loss and early battery pack failure was occurred. Therefore it is necessary to check out the uniformity of batteries and classify batteries according to the measurement result before batteries are assembled into battery pack. In common sense charge and discharge curve is the best reflection of battery character, the batteries with different character and performance have different charge and discharge curve. It’s a reasonable classifying parameter. With the help of software Statistical Analysis System(SAS), classifying is accurate and efficient. After classifying the performance of battery pack was improved. The initial capacity loss and early battery pack failure were restrained. The charge and discharge performance of battery pack is stable.According to the data collected by Battery Management System (BMS), using the time series method, the predict model of battery capacity fade was established. This model can predict capacity fade of battery cycled at different current and temperature. The predict error of short-term prediction of this method was less than 3%.更多还原