Predictive Energy Management for Battery Electric Vehicles with Hybrid Models

TL;DR

This paper introduces a hybrid modeling approach combining physics-based simulations and machine learning to accurately predict energy consumption in Battery Electric Vehicles, significantly reducing prediction errors.

Contribution

It presents a novel hybrid model that integrates physical simulations with machine learning to improve energy consumption predictions for BEVs.

Findings

Hybrid models reduce prediction error from 40% to 10%.

Machine learning techniques effectively account for external factors.

Hybrid approach outperforms pure physics-based models.

Abstract

This paper addresses the problem of predicting the energy consumption for the drivers of Battery electric vehicles (BEVs). Several external factors (e.g., weather) are shown to have huge impacts on the energy consumption of a vehicle besides the vehicle or powertrain dynamics. Thus, it is challenging to take all of those influencing variables into consideration. The proposed approach is based on a hybrid model which improves the prediction accuracy of energy consumption of BEVs. The novelty of this approach is to combine a physics-based simulation model, which captures the basic vehicle and powertrain dynamics, with a data-driven model. The latter accounts for other external influencing factors neglected by the physical simulation model, using machine learning techniques, such as generalized additive mixed models, random forests and boosting. The hybrid modeling method is evaluated with a real data set from TUM and the hybrid models were shown that decrease the average prediction error from 40% of the pure physics model to 10%.