一种精确估算电动汽车动力电池SOC&SOH的循环门控模型

Cycle gating model for accurate estimation of SOC&SOH of power battery in electric vehicles

针对现有电动汽车动力电池SOC和SOH估计方法存在运算效率低、实时性差以及估算准确率低的问题,提出一种精确估算电动汽车动力电池SOC&SOH的循环门控模型。首先,改进门控循环单元GRU中更新门和重置门的计算方式并将候选隐藏状态激活函数替换为ThLU函数,缩短训练时间,有效缓解梯度消失。其次,优化序列数据输入方式,引入环形GRU计算模式,提升模型运算效率和估计精度。最后,基于卷积神经网络CNN和改进门控循环单元IGRU,利用传感器采集到的电压V、电流I、温度T数据,实现全周期SOH和SOC同步估算,并将SOH估计值计入SOC估算,消除老化因素对SOC估算造成的不利影响。利用牛津大学电池数据集进行实验验证,结果表明,相比传统估计模型,循环门控模型SOC估计精确度有效提升,预测误差基本保持在0.5%以内。

Aiming at the problems of low computing efficiency,poor real-time performance and low estimation accuracy of the existing SOH and SOC estimation methods for electric vehicle power battery,a recurrent gated neural network model is proposed to accurately estimate the SOC&SOH of electric vehicle power battery.Firstly,the calculation methods of update gate and reset gate in the Gated Recurrent Unit are improved and the candidate hidden state activation function is replaced by the ThLU function to shorten the training time and effectively alleviate the gradient vanishing.Secondly,the sequence data input method is optimized,and the loop GRU calculation mode is introduced to improve the model computing efficiency and estimation accuracy.Lastly,the model is based on the convolutional neural network and the improved gate recurrent unit,the full-cycle SOH and SOC are simultaneously estimated using the voltage,current,and temperature data collected by the sensors,and the SOH estimation is included in the SOC estimation to eliminate the adverse effects of the aging factor on the SOC estimation.Experimental validation using the Oxford University battery dataset shows that compared with the traditional estimation model,the SOC estimation accuracy of the model proposed in this paper is effectively improved,and the prediction error basically stays within 0.5%.