Predicting the Remaining Useful Life of Lithium-Ion Batteries for Vehicle Using Machine Learning Algorithms
Keywords:
Energy efficiency, battery management system, electrified vehicle, lithium-ion batteries, machine learning, remaining useful life, orange data mining software, state of charge, state of healthAbstract
The objective of this research to predicting the remaining useful life (RUL) of Lithium-Ion Batteries (LIB’s) system by machine learning model for optimization of electrified vehicle battery storage system. Predicting RUL accurately can help extend the lifespan of batteries, reduce maintenance costs, and enhance overall system performance and safety Consequently, management system, battery state estimation, and estimation of the RUL. Various techniques are then used to predicted the model parameters, such as Linear Regression (LR), Support vector machines (SVM), Gradient Boosting (GB), AdaBoost (AB), Tree, and Random Forest (RF). The results showed that the best model is AdaBoost with a value of Mean Squared Error (MSE) is 9.816; Root Mean Squared Error (RMSE) with a value is 3.133; Mean Absolute Error (MAE) with a value 1.557; Mean Absolute Percentage Error (MAPE) with a value 0.007; and R-Squared (R2) with a value 1.000. Second model is Tree regression with a value of MSE is 32.734; RMSE with a value is 5.721; MAE with a value 2.914; MAPE with a value 0.011; and R-Squared (R2) with a value 1.000. According to a result, the suggested future research activity of battery efficiency AdaBoost models are useful for managing batteries. In this paper, quantitative evaluation is presented using two datasets with different batteries under different conditions. Quantitative evaluation of predictive models, particularly for estimating the RUL of lithium-ion batteries. This structured approach ensures a comprehensive and transparent presentation of the quantitative evaluation, allowing readers to understand the effectiveness of different predictive models under varying conditions. In addition, it is important to highlight that effective management and accurate prediction of battery usage can significantly contribute to improving the efficiency and sustainability of energy storage systems. This promotes the adoption of clean energy, reduces emissions, and encourages innovation and collaboration.
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