The battery thermal management of electric vehicles can be improved using neural networks predicting quantile sequences of the battery temperature.This work extends a method for the development of Quantile Convolution...The battery thermal management of electric vehicles can be improved using neural networks predicting quantile sequences of the battery temperature.This work extends a method for the development of Quantile Convolutional and Quantile Recurrent Neural Networks(namely Q*NN).Fleet data of 225629 drives are clustered and balanced,simulation data from 971 simulations are augmented before they are combined for training and testing.The Q*NN hyperparameters are optimized using an efficient Bayesian optimization,before the Q*NN models are compared with regression and quantile regression models for four horizons.The analysis of point-forecast and quantile-related metrics shows the superior performance of the novel Q*NN models.The median predictions of the best performing model achieve an average RMSE of 0.66°C and R^(2) of 0.84.The predicted 0.99 quantile covers 98.87%of the true values in the test data.In conclusion,this work proposes an extended development and comparison of Q*NN models for accurate battery temperature prediction.展开更多
基金support by the KIT-Publication Fund of the Karlsruhe Institute of Technology.
文摘The battery thermal management of electric vehicles can be improved using neural networks predicting quantile sequences of the battery temperature.This work extends a method for the development of Quantile Convolutional and Quantile Recurrent Neural Networks(namely Q*NN).Fleet data of 225629 drives are clustered and balanced,simulation data from 971 simulations are augmented before they are combined for training and testing.The Q*NN hyperparameters are optimized using an efficient Bayesian optimization,before the Q*NN models are compared with regression and quantile regression models for four horizons.The analysis of point-forecast and quantile-related metrics shows the superior performance of the novel Q*NN models.The median predictions of the best performing model achieve an average RMSE of 0.66°C and R^(2) of 0.84.The predicted 0.99 quantile covers 98.87%of the true values in the test data.In conclusion,this work proposes an extended development and comparison of Q*NN models for accurate battery temperature prediction.
