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Open Access

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

Karlsruhe Institute of Technology (KIT), Institute of Vehicle System Technology, Karlsruhe 76131, Germany, and also with Bayerische Motoren Werke (BMW) AG, Munich 80788, Germany
Technical University of Munich (TUM), Munich 80333, Germany
BMW AG, Munich 80788, Germany
KIT, Institute of Vehicle System Technology, Karlsruhe 76131, Germany
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Abstract

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 R2 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.

Keywords

deep learningbattery temperatureconvolutional and recurrent neural networkquantile forecasting

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Big Data Mining and Analytics
Volume 7 Issue 2,
June 2024
Pages 512-530
DOI: 10.26599/BDMA.2023.9020028
Cite this article:
Billert AM, Yu R, Erschen S, et al. Improved Quantile Convolutional and Recurrent Neural Networks for Electric Vehicle Battery Temperature Prediction. Big Data Mining and Analytics, 2024, 7(2): 512-530. https://doi.org/10.26599/BDMA.2023.9020028

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Received: 14 April 2023
Revised: 16 September 2023
Accepted: 09 October 2023
Published: 22 April 2024
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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