Robust state of charge estimation of lithium-ion battery via mixture kernel mean p-power error loss LSTM with heap-based-optimizer

Abstract

The state of charge (SOC) estimation of lithium-ion battery is an important function in the battery management system (BMS) of electric vehicles. The long short term memory (LSTM) model can be employed for SOC estimation, which is capable of estimating the future changing states of a nonlinear system. Since the BMS usually works under complicated operating conditions, i.e the real measurement data used for model training may be corrupted by non-Gaussian noise, and thus the performance of the original LSTM with the mean square error (MSE) loss may deteriorate. Therefore, a novel LSTM with mixture kernel mean p-power error (MKMPE) loss, called MKMPE-LSTM, is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework, which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises (or outliers) because of the MKMPE containing the p-order moments of the error distribution. In addition, a meta-heuristic algorithm, called heap-based-optimizer (HBO), is employed to optimize the hyper-parameters (mainly including learning rate, number of hidden layer neuron and value of p in MKMPE) of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance, and a novel hybrid model (HBO-MKMPE-LSTM) is established for SOC estimation under non-Gaussian noise cases. Finally, several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model, and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises, and the SOC estimation results in terms of mean square error (MSE), root MSE(RMSE), mean absolute relative error (MARE), and determination coefficient R2 are less than 0.05%, 3%, 3%,and above 99.8% at 25℃, respectively.