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.