联合精确估算电池的健康状态

以动力电池的电压、电流、温度和内阻作为输入，荷电状态作为输出，建立四输入一输出的神经网络仿真模型预测电池的荷电状态。再以荷电状态为基础，改进电池健康状态的估算方法，分别利用改进型容量法、改进型内阻法和电压法3种方法分别估算出电池的健康状态，并利用遗传神经网算法建立了3种方法联合在一起的电池健康状态估算模型。以4节12 V的串联锂离子电池组模块为研究对象分别进行了Simulink仿真和实验研究，通过采集动力电池充放电时的电压、电流、温度、内阻和放电量数据，测试了电池的荷电状态和健康状态。实验结果表明电池荷电状态的预测精度为1.6%，仿真模型运行和实验结果显示联合法估算健康状态的最大误差为1.5%，高于其他3种单独的方法。本文提出的健康状态预测方法，省略了传统神经网络算法估算健康状态寻找健康因子的复杂步骤，同时也避免现有电池的健康状态估计单一参量判定方法的局限性。

Combined accurate estimation of the health of battery

Taking the voltage, current, temperature and internal resistance of the power battery as input and the state of charge as output, a neural network simulation model with four inputs and one output was established to predict the state of charge of the battery. Then, based on the state of charge, the health state of the battery is estimated by the improved capacity method, the improved internal resistance method and the voltage method, respectively. The health state of the battery is estimated by the genetic neural network algorithm. By Simulink simulation and experimental study were carried out on four 12 V series lithium ion battery packs. The charge state and healthy state of the battery were tested by collecting voltage, current, temperature, internal resistance and discharge quantity data during charging and discharging. The experimental results show that the prediction accuracy of the battery state is 1.6%. The results of simulation and experiment show that the maximum error of the combined method is 1.5%, which is higher than the other three methods. In this paper, the health state prediction method is proposed, which omits the complex steps of finding health factors by traditional neural network algorithm, and avoids the limitation of single parameter judgment method for battery health state estimation.