Data-driven predictive prognostic model for power batteries based on machine learning

Under the pressure of energy and environmental protection, new energy vehicles have become the future di-rection of automotive development. However, the safety performance of the power battery has always been the most critical indicator in the new energy vehicle industry. The battery will be aged in the continuous charging and discharging cycle, and the aging will cause safety hazards when it reaches a limit. A model that can predict the battery life can be obtained using Machine Learning. To obtain models that can predict power battery life relatively accurately, this paper revolves around the chaos sparrow search optimization algorithm, Random Forest, XGBoost, LightGBM, CatBoost, and NN, the importance assessment of the features, the hyperparameter search process, and the comparison of the differences and performance between the different algorithms are discussed. CatBoost has the highest prediction accuracy, with the amount of predicted data with a relative error of less than 10% being 88.44%. (a total of 10,275 data in the test set). And finally comes up with a general approach to predicting power battery life using Machine Learning.

Automated summary

Under the pressure of energy and environmental protection, new energy vehicles are becoming the future direction of automotive development. The safety performance of the power battery has always been the most critical indicator in the new energy vehicle industry. A model that can predict the battery life can be obtained using Machine Learning. This paper discusses the importance assessment of features, the hyperparameter search process, and the comparison of different algorithms, and concludes that CatBoost has the highest prediction accuracy.