基于Informer的电池荷电状态估算及其稀疏优化方法

State-of-Charge Estimation of Lithium-ion Battery Based on Informer and Its Sparse Optimization Method

准确估计电池荷电状态(State Of Charge,SOC)是延长电动汽车电池使用寿命,确保电动汽车行驶安全的重要基础.传统的深度学习估计方法存在并行化计算效率不高、训练时间长的问题.为此,利用基于自注意力机制的Informer模型来估计电池SOC.其降低了传统自注意力机制的时间复杂度、提高了硬件使用率、降低了训练时长,与其他深度学习方法相比估计更准确.然而Informer模型仍然存在体量大及参数冗余的问题,故提出稀疏优化方法 .利用基于彩票假设的幅值迭代剪枝方法对Informer进行稀疏化处理,突出主导注意力特征,实现了在降低参数冗余的同时提升模型估计精度.在室温下,提出的稀疏化Informer模型估计电池SOC的平均绝对误差和均方根误差分别达到0.285 8%和0.383 0%,相比于Informer模型在平均绝对误差指标上估计精度提升了25%.并验证了其具备估计不同类型锂电池SOC的泛化能力.与循环神经网络、卷积神经网络这类传统的深度学习模型相比,本模型进行电池SOC估计时训练速度更快,估计准确性和稳定性更高.

Accurate estimation of the state of charge(SOC) is an important basis for extending the battery life and ensuring the safety of electric vehicles. Traditional deep learning estimation methods suffer from inefficient parallelization and lengthy training time. To this end, the Informer model based on the self-attention mechanism is used to estimate the battery SOC, which reduces the time complexity of the traditional self-attention and training time, increases the hardware usage. It is more accurate than other deep learning methods. However, the Informer model still has the problems of large and redundant parameters, therefore a sparse optimization method is proposed. The iterative magnitude pruning method based on the lottery ticket hypothesis is used to sparse the Informer, highlighting the dominant attention feature and improving model estimation accuracy while reducing parameter redundancy. The proposed sparse-Informer’s root mean square error and mean absolute error are 0.383 0% and 0.285 8% at room temperature, respectively. The mean absolute error is decreased by 25%compared to Informer model. Additionally, the generalization ability to estimate the SOC across other lithium-ion battery types is confirmed. When performing battery SOC estimation, this model outperforms other established deep learning models in terms of training speed, estimation accuracy and stability.