Predicting Battery Health for Electric Vehicles using Machine Learning Approach
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Keywords:
Lithium Li-Ion Batteries, State Of Health, State Of Charge, Battery Management SystemAbstract
Research utilized data-driven models to investigate SoH estimation methodologies for lithium
ion batteries, particularly focusing on their effectiveness in capturing degradation trends. The study
evaluated four different deep learning approaches-DNN, CNN, RNN, and LSTM-using various metrics,
including MAE, RMSE, R², and validation loss. Results reveal that the LSTM model outperforms the
others, achieving the lowest MAE (0.1293), RMSE (0.1680), and validation loss (0.0282), with an R² of
0.9790, making it the most reliable predictor of battery SoH. The study highlights a strong linear
correlation between SoH and parameters such as capacity and charge voltage, affirming their role as
critical indicators of battery health. Conversely, temperature exhibited negligible impact on SoH within
the narrow range studied, necessitating further research under diverse environmental conditions.
Anomalies in terminal current during charge-discharge cycles suggest potential operational irregularities
requiring deeper analysis. The study underscores the limitations of CNN in modeling temporal
dependencies, advocating for hybrid architectures like CNN-LSTM for enhanced predictive accuracy as
well as narrow temperature range of 25oc. Findings also demonstrate consistent SoC transitions across
cycles, emphasizing the stability of the battery's charge-discharge behavior and its implications for long
term durability.
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