锂离子电池在不同区间下的衰退影响因素分析及任意区间的老化趋势预测

Analysis of Influencing Factors of Degradation under Different Interval Stress and Prediction of Aging Trend in Any Interval for Lithium-Ion Battery

准确估计锂离子电池的健康状态(SOH)对于控制策略制定和运行维护至关重要。考虑到充放电区间和电压相变过程对电池老化的影响,该文针对2.75A×h 18650型号三元电池设计了11个荷电状态(SOC)区间的循环寿命测试与性能测试。根据实验结果,分别分析循环区间荷电状态(SOC)宽度、恒压充电时间、平均SOC和充电相变过程对电池老化快慢的作用机制。结合电池老化机理和实验结果,提取量化SOC区间对老化影响程度大小的特征参数。建立预测健康状态的循环神经网络(LSTM RNN)模型,用于学习电池老化对于循环次数及特征参数的长期依赖关系。分别采用误差最大值、平均绝对误差、方均根误差和方差对模型的准确性和可靠性进行分析。结果表明,该文提出的区间循环寿命模型能实现任意区间的老化趋势预测,节省测试时间和测试成本。

The accurate estimation of the state of health(SOH)of lithium-ion batteries is very important for the development of controlling strategies and operating maintenance.Considering the influence of charge-discharge interval and voltage phase transition process on battery aging,in this paper,11 cycle life and performance tests in different state of charge(SOC)intervals were designed for 2.75Ah 18650 energy Lithium-ion battery.According to the experimental results,the mechanism of SOC width,constant voltage charging process,average SOC and charging phase transition process on battery aging were analyzed.Based on the aging mechanism and experimental results of batteries,the characteristic parameters which quantify the influence of partial SOC intervals on aging were extracted.The SOH prediction model based on recurrent neural network with long-short term memory network(LSTM RNN)was established to study the long-term dependence of battery aging on cycle numbers and characteristic parameters.The accuracy and reliability of the model were analyzed by the maximum error,the average absolute error,the root mean square error and the variance.The results show that the cycle life model proposed in this paper can predict the capacity degradation trend of any SOC interval and save testing time and cost.