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<p>The energy consumption estimation and remaining driving range estimation are important aspects of electric vehicles for intelligent transportation systems, battery management systems, and for reducing range anxiety. Typical deterministic models, however, do not account for the uncertainty associated with dynamic driving behaviour, traffic variations, environmental conditions, and battery characteristics, and therefore have limited prediction ability. This study implements a comparative analysis of several uncertainty-aware machine learning models to estimate energy consumption and driving range of EVs using real-world driving data. The developed methods include SVR Residual Bootstrap, RF Variance Estimation, SVR Conformal Prediction, SVR Adaptive Conformal Prediction, LightGBM Quantile Regression, LightGBM Conformal Quantile Regression (CQR), and LightGBM Adaptive Conformal Quantile Regression (ACQR). The models are evaluated using deterministic metrics including RMSE, MAE, R<sup>2</sup>, and uncertainty-specific metrics including PICP and MPIW. The experimental results showed that accuracy of predictions, uncertainty coverage reliability, and interval sharpness were not equally good for the frameworks evaluated, resulting in significant trade-offs. SVR Residual Bootstrap had the best prediction performance in a deterministic prediction with RMSE = 2.358, MAE = 1.742, and R<sup>2</sup> = 0.893. RF Variance and LightGBM CQR had higher uncertainty coverage but with wider intervals, while LightGBM ACQR had better balance between uncertainty coverage reliability and interval efficiency. Among all implemented approaches, the results of the SVR Conformal approach showed consistent and stable uncertainty estimation with reliability of the coverage (PICP = 0.95) and moderate interval width (MPIW = 15), making it suitable for real-world EV applications. The results underscore the significance of incorporating uncertainty quantification in EV prediction systems to ensure reliable and dependable sustainable transportation applications.</p>

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Machine learning-based uncertainty quantification for energy consumption and driving range estimation in electric cargo vehicles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月17日

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  • Mrugank Gandhi,
  • Archana Y. Chaudhari

摘要

The energy consumption estimation and remaining driving range estimation are important aspects of electric vehicles for intelligent transportation systems, battery management systems, and for reducing range anxiety. Typical deterministic models, however, do not account for the uncertainty associated with dynamic driving behaviour, traffic variations, environmental conditions, and battery characteristics, and therefore have limited prediction ability. This study implements a comparative analysis of several uncertainty-aware machine learning models to estimate energy consumption and driving range of EVs using real-world driving data. The developed methods include SVR Residual Bootstrap, RF Variance Estimation, SVR Conformal Prediction, SVR Adaptive Conformal Prediction, LightGBM Quantile Regression, LightGBM Conformal Quantile Regression (CQR), and LightGBM Adaptive Conformal Quantile Regression (ACQR). The models are evaluated using deterministic metrics including RMSE, MAE, R2, and uncertainty-specific metrics including PICP and MPIW. The experimental results showed that accuracy of predictions, uncertainty coverage reliability, and interval sharpness were not equally good for the frameworks evaluated, resulting in significant trade-offs. SVR Residual Bootstrap had the best prediction performance in a deterministic prediction with RMSE = 2.358, MAE = 1.742, and R2 = 0.893. RF Variance and LightGBM CQR had higher uncertainty coverage but with wider intervals, while LightGBM ACQR had better balance between uncertainty coverage reliability and interval efficiency. Among all implemented approaches, the results of the SVR Conformal approach showed consistent and stable uncertainty estimation with reliability of the coverage (PICP = 0.95) and moderate interval width (MPIW = 15), making it suitable for real-world EV applications. The results underscore the significance of incorporating uncertainty quantification in EV prediction systems to ensure reliable and dependable sustainable transportation applications.