Life prediction of battery based on random forest optimized by genetic algorithm

When the random forest algorithm is used for battery life prediction, the prediction result is unstable, and it is difficult to ensure the accuracy of the model. In view of the above problems, this study proposes to use genetic algorithms to optimize the random forest prediction model. While ensuring the prediction accuracy, the depth and number of decision trees in the random forest are optimized, and the optimal combination of decision tree depth and number is used Life prediction. Using the lithium-ion battery data published by NASA to conduct simulation experiments and evaluate the prediction performance of the model, and then compare with the prediction results of the random forest prediction model and lasso prediction model.