Hybrid Electro-Thermal and FNN Framework for Joint SoC, SoH Estimation and Lifetime Prediction of Lithium-Ion Batteries in Electric Vehicles

Abstract

Improving the performance and lifespan of lithium-ion batteries is a key challenge for the development of electric vehicles. However, accurately estimating the state of charge (SoC), state of health (SoH), and life cycle remains complex due to the electrical, thermal, and aging phenomena associated with these energy storage systems. Against this backdrop, this study proposes a hybrid approach combining an electro-thermal model with a feedforward neural network (FNN) to improve the estimation of key lithium-ion battery performance indicators within a temperature range of 0 °C to 40 °C. The developed methodology was implemented in MATLAB/Simulink and applied to the analysis of the vehicle’s power profile, as well as the evolution of SoC, SoH, and battery life cycle. The results demonstrate an accuracy of 95.3% for state of charge (SoC) estimation, with a mean absolute error of 4.7%. For state of health (SoH) estimation, the accuracy is 95.8% accompanied by a mean absolute error of 4.2%. Lastly, for life cycle prediction, the accuracy is 92.5% with a mean absolute error of 7.5%. The performance results demonstrate the robustness of the proposed approach and its ability to replicate battery dynamic behavior under climatic conditions representative of the African context. This contribution opens up promising avenues for optimizing battery management systems and advancing the sustainable development of electric mobility.