Innovative strategies for assessing battery health in electric vehicles

Abstract

Energy generation, which promotes a nation's economic stability and advancement, is one of the most significant facets of modern society. Recent years have seen significant advancements in energy conversion and storage technology, particularly in mobile gadgets and electric vehicles. Lithium-ion batteries are utilized in energy storage and electric vehicles because of their low self-discharge rates, long cycle life, and high energy density. Therefore, precise evaluations of battery conditions are necessary for safe operation. This study proposes a hybrid model based on the Bidirectional Gated Recurrent Unit (Bi-GRU) with the Giant Trevally Optimizer (GTO) for state of health (SOH) prediction, which will help in improving the predictive accuracy. In the estimation of SOH, some key features of charge-discharge cycle characteristics are used based on the NASA lithium-ion battery dataset. The proposed GTO-Bi-GRU model outperforms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models by incorporating the bidirectional learning abilities of Bi-GRU, which captures the complex trend in battery degradation more effectively. Meanwhile, GTO performs the hyperparameter tuning optimally, outperforming classical optimization techniques such as particle swarm optimization (PSO), genetic algorithm (GA), and Cuckoo search algorithm (CS). This comparative study demonstrates that GTO-Bi-GRU achieves the highest prediction accuracy among all with coefficients of determination values of 0.9969, 0.9917, 0.9948, and 0.9882 on B5, B6, B7, and B18 battery cells. These results depict that GTO-Bi-GRU outperforms PSO-Bi-GRU, GA-Bi-GRU, and CS-Bi-GRU by a great margin, hence establishing it as a very effective model for SOH estimation. The results prove that GTO-Bi-GRU is robust enough and scalable for battery health monitoring applications in electric vehicles.