Calibration of electric vehicle energy consumption models: A context-Integrated transfer bayesian optimization approach

Résumé fourni par la source

Precise modeling of electric vehicle (EV) energy consumption is fundamental to the efficient design and management of modern transportation systems. While physics-based models offer superior interpretability, they often struggle with limited adaptability to dynamic driving conditions and heterogeneous vehicle platforms. To bridge this gap, achieving real-time and accurate calibration of physical model parameters becomes essential. This paper proposes a novel two-stage Bayesian optimization framework that integrates Contextual Bayesian Optimization (CBO) and Transfer Bayesian Optimization (TBO). In the first stage, the CBO module learns a context-aware mapping between operating conditions and physical parameters within a source domain. In the second stage, the TBO module leverages the learned prior knowledge to achieve rapid adaptation to a target vehicle domain with minimal data requirements. We evaluate the proposed framework using real-world datasets from BMW i3 and Tesla Model 3. Experimental results demonstrate that the proposed framework achieves a per-second WMAPE of 17.27% in cross-condition scenarios. For cross-vehicle transfer, the primary out-of-sample evaluation on the held-out 70% of the Tesla trips yields a WMAPE of 24.82% and a total energy error of 12.65%. The CBO results further demonstrate rapid convergence under a limited online evaluation budget. This research provides a scalable and sample-efficient solution for high-fidelity energy modeling across diverse driving conditions and vehicle platforms.

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