磷酸铁锂动力电池循环寿命预测方法

针对粒子滤波循环寿命预测算法对磷酸铁锂动力电池长期预测效果较差问题,通过神经网络对电池历史数据进行学习,将训练学习值作为观测值代入粒子滤波算法中,修正粒子状态值;针对磷酸铁锂电池动态方程中寿命没有直接与观测值建立联系的问题,推导了关于电池寿命与容量观测值的后验概率关系,得到蒙特卡洛方法下的后验概率密度关系,给出了电池寿命预测不确定性表达。实验结果表明以神经网络训练值,作为改进粒子滤波动态方程算法的观测值,方法有效,降低了预测误差。

Method for predicting cycle life of lithium iron phosphate power battery

Aiming at the problem that the particle filter cycle life prediction algorithm has a poor long term prediction effect on lithium iron phosphate battery, neural network is used to learn the historical data of the battery, and the training learning value is substituted as the observation value into the particle filter algorithm to modify the particle state value; for phosphoric acid In the dynamic equation of the lithium iron battery, there is no problem that the life is directly related to the observation value. The posterior probability relationship between the battery life and the capacity observation value is derived. The posterior probability density relationship under the Monte Carlo method is obtained, and the battery life is given. Forecast uncertainty expression. The experimental results show that the neural network training value is used as the observation value of the improved particle filter dynamic equation algorithm, and the method is effective and reduces the prediction error.