能量效率和工作温度对锂离子电池剩余寿命的影响  

Effect of energy efficiency and the working temperature about lithium-ion battery

作　　者：王帅[1] 王月 苏小红[1] 赵玲玲[1] 

机构地区：[1]哈尔滨工业大学计算机科学与技术学院,哈尔滨150001 [2]哈尔滨劳动技师学院电气技术系,哈尔滨150500

出　　处：《智能计算机与应用》2018年第1期162-168,171,共8页Intelligent Computer and Applications

摘　　要：利用锂离子电池已有物理性能,预测其剩余寿命是电池健康管理新兴的研究趋势。本文将表征锂离子电池性能的物理量,电流、电压、时间、温度和环境温度进行降维处理,得到2个特征物理量:电池由于工作产生的温度和电池能量效率。这样不但考虑了所有性能物理量对锂离子电池剩余寿命的影响,还考虑了各个物理量之间的关系。然后利用这2个特征物理量分别建立能量效率与工作温度对锂离子电池剩余寿命多步预测模型和能量效率与工作温度对锂离子电池剩余寿命整体预测模型。从实验得出,能量效率和工作温度对锂离子电池剩余寿命预测有密切的影响,并且利用这2个物理量建立锂离子电池剩余寿命预测的数据驱动模型更加合理。Accurate prediction of the remaining useful life of lithium-ion batteries is important for battery management systems.Traditional empirical data？driven approaches for remaining useful life prediction usually require multidimensional physicalcharacteristics including the current, voltage, usage duration, battery temperature, and ambient temperature. From a capacity fadinganalysis of lithium？ion batteries, it is found that the energy efficiency and battery working temperature are closely related to thecapacity degradation, which account for all performance metrics of lithium？ion batteries with regard to the RUL and the relationshipsbetween some performance metrics. Thus, the paper devises a non？iterative prediction model based on flexible support vectorregression and an iterative multi-step prediction model based on support vector regression using the energy efficiency and batteryworking temperature as input physical characteristics. The experimental results show that the proposed prognostic models have highprediction accuracy by using fewer dimensions for the input data than the traditional empirical models.

关 键 词：锂离子电池 数据驱动方法 特征提取 多步预测 剩余寿命预测 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置]