Elman neural network-based temperature prediction and optimization for lithium-ion batteries

Reliable and precise temperature prediction is one of the most crucial challenges for improving battery performance and preventing thermal runaway. This paper uses a highly adaptive Elman neural network (Elman-NN) to construct a temperature prediction model for lithium-ion batteries in a metal foam aluminum thermal management system. Numerical modeling methods obtain experimental data sets for model training and testing. The input parameters of the neural network prediction model are ambient temperature, battery discharge rate, cooling air flow rate, and state of charge; the output parameters are the maximum, minimum, and average battery temperature. However, due to the limitations of the gradient descent algorithm, the training process of the Elman neural network tends to fall into local optimum solutions. To further improve the prediction accuracy, the Elman-NN structure was optimized using the PSO algorithm, and the model performance was tested and validated. Compared with the original Elman-NN, the hybrid PSO-Elman-NN has smaller MSE and MAE values, with a maximum reduction of 43% and 25%, respectively. For the three test conditions, the maximum predicted temperature difference does not exceed 1.5 K, and the temperature difference decreases further as the discharge time increases. Moreover, the hybrid model's prediction accuracy is significantly improved, with the coefficients of determination (R-2) increasing by 1.736%, 0.706%, and 1.851%, respectively. The PSO-Elman-NN performed well in terms of compatibility and accuracy of the battery temperature prediction.

Automated summary

This paper proposes a highly adaptive Elman neural network (Elman-NN) for accurate temperature prediction of lithium-ion batteries in a metal foam aluminum thermal management system. The Elman-NN is optimized using the PSO algorithm to improve prediction accuracy. Compared to the original Elman-NN, the hybrid PSO-Elman-NN model achieves significant reduction in MSE and MAE values and demonstrates improved prediction accuracy. The proposed model shows excellent compatibility and accuracy in battery temperature prediction.