Adaptive Remaining Capacity Estimator of Lithium-Ion Battery Using Genetic Algorithm-Tuned Random Forest Regressor Under Dynamic Thermal and Operational Environments

Uzair Khan, Mohd Tariq and Arif I. Sarwat ()
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Uzair Khan: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Mohd Tariq: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Arif I. Sarwat: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA

Energies, 2024, vol. 17, issue 22, 1-18

Abstract: The increasing interests and recent advancements in artificial intelligence and machine learning have significantly accelerated the development of novel techniques for the state estimation of batteries in electrified vehicles’ battery management systems (BMSs). Determining the remaining capacity among the several BMS states is crucial for ensuring the safe and stable functioning of an electric vehicle. This paper proposes an adaptive estimator for the remaining capacity of lithium-ion batteries, leveraging a Genetic Algorithm (GA)-tuned random forest (RF) regressor. The estimator is designed to function effectively under varying thermal conditions. The optimization of critical parameters, namely, the number of estimators (n-estimators) and the minimum number of samples per leaf (min-samples-leaf), is a focal point of this study to enhance model accuracy and robustness. The model effectively captures the battery’s dynamic behavior and inherent non-linearity. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) achieved during testing demonstrate promising accuracy and superior prediction. The results demonstrated significant improvements in state of charge (SOC) estimation accuracy. The proposed GA-optimized RF model achieved an MAE of 0.0026 at 25 °C and 0.0102 at −20 °C, showing a 41.37% to 50% reduction in the MAE compared to traditional random forest models without GA optimization. The RMSE was also reduced by 18.57% to 31.01% across the tested temperature range. These improvements highlight the model’s ability to accurately estimate the SOC in varying thermal conditions.

Keywords: state of charge; estimation battery management system; SOC; machine learning; genetic algorithm; energy storage; EV; Li-ion batteries (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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