Improved Quantile Convolutional and Recurrent Neural Networks for Electric Vehicle Battery Temperature Prediction

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 225 629 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.