Heat generation rate estimation of lithium-ion batteries for electric vehicles by BP-based optimized neural network

Abstract

Accurate estimation of heat generation rate (HGR) of lithium-ion batteries (LIBs) is a critical and essential task for their decent thermal management, thereby facilitating the safe driving of electric vehicles (EVs). In order to improve the accuracy of HGR estimation and reduce the structural complexity of network, a data-driven strategy is developed through integrating Bayesian optimization (BO), Adam optimization, and Principal Component Analysis (PCA) with Back Propagation (BP) neural network. The BO algorithm is utilized to optimize the hyperparameters of the BP neural network for prediction accuracy enhancement. The PCA is employed to extract the feature matrix thereby reducing the complexity of inputs. The Adam optimization algorithm is used to improve computational efficiency. The performance of the proposed strategy was validated based on a dataset derived from lab-scale experiments, as well as a publicly available dataset regarding practical driving conditions. The test results show that the proposed strategy can achieve accurate HGR estimation and result in a mean absolute error (MAE) of 0.0504 W, and a root mean square error (RMSE) of 0.0628 W, and a R2 of 0.9998. Compared to some other HGR estimation methods, the proposed strategy achieved a significant enhancement in accuracy indexes, indicating its superior accuracy and robustness.