基于CGOA-MAM-TCN算法的车用锂电池荷电状态估计

Estimation for SOC of Vehicle Lithium-Ion Battery Based on CGOA-MAM-TCN Algorithm

针对数据驱动的锂电池荷电状态估计方法仍然存在对大量标定数据的依赖、同时应对动态变化和复杂运行状况时表现不佳等问题,提出了改进蝗虫算法优化结合时域卷积网络和多头注意力机制的锂电池荷电状态估计方法。首先利用时域卷积网络对锂电池荷电时间序列数据中的长期依赖关系进行建模,同时采用多头注意力机制学习数据特征长期依赖关系,利用每个注意力头去计算序列中不同张量的依赖关系,辅助时域卷积神经网络增强对依赖关系的捕获,降低其对大量标定数据的依赖;另外为使模型发挥出最佳性能,改进了混沌蝗虫算法优化模型的超参数。试验结果表明:在不同温度条件下,相较于其他方法,优化模型在锂电池荷电状态估计任务中表现出更好的准确率和稳定性。

Data-driven estimation method for the state of charge(SOC)of lithium batteries still relies on a large amount of calibration data and shows poor performance in dealing with dynamic changes and complex operating conditions.Therefore,an improved locust algorithm optimization combined with time-domain convolutional networks and multi-head attention mechanisms was proposed to estimate the SOC of lithium battery.A time-domain convolutional network was first used to model the long-term dependency relationships in the time series data of lithium battery charging.Meanwhile,multi-head attention was used to learn the long-term dependency relationships of data features,and each attention head was used to calculate the dependency relationships of different tensors in the sequence to assist the time-domain convolutional neural network in enhancing the capture of dependency relationships and reducing its dependence on a large amount of calibration data.In addition,the chaotic locust algorithm was improved to optimize the hyperparameters of model to maximize the performance of model.The experimental results show that,compared with other methods,the optimized model can exhibit better accuracy and stability in the task of estimating the SOC of lithium battery under different temperature conditions.