Energy Management in Hybrid Electric Vehicles: A Q-Learning Solution for Enhanced Drivability and Energy Efficiency

Alessia Musa (), Pier Giuseppe Anselma, Giovanni Belingardi and Daniela Anna Misul ()
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Alessia Musa: Department of Energy (DENERG), Politecnico di Torino, 10129 Torino, Italy
Pier Giuseppe Anselma: Center for Automotive Research and Sustainable Mobility (CARS), Politecnico di Torino, 10129 Torino, Italy
Giovanni Belingardi: Center for Automotive Research and Sustainable Mobility (CARS), Politecnico di Torino, 10129 Torino, Italy
Daniela Anna Misul: Department of Energy (DENERG), Politecnico di Torino, 10129 Torino, Italy

Energies, 2023, vol. 17, issue 1, 1-20

Abstract: This study presents a reinforcement-learning-based approach for energy management in hybrid electric vehicles (HEVs). Traditional energy management methods often fall short in simultaneously optimizing fuel economy, passenger comfort, and engine efficiency under diverse driving conditions. To address this, we employed a Q-learning-based algorithm to optimize the activation and torque variation of the internal combustion engine (ICE). In addition, the algorithm underwent a rigorous parameter optimization process, ensuring its robustness and efficiency in varying driving scenarios. Following this, we proposed a comparative analysis of the algorithm’s performance against a traditional offline control strategy, namely dynamic programming. The results in the testing phase performed over ARTEMIS driving cycles demonstrate that our approach not only maintains effective charge-sustaining operations but achieves an average 5% increase in fuel economy compared to the benchmark algorithm. Moreover, our method effectively manages ICE activations, maintaining them at less than two per minute.

Keywords: hybrid electric vehicles (HEVs); drivability; fuel economy; energy management; reinforcement learning (RL) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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