Data-driven modeling and supervisory control system optimization for plug-in hybrid electric vehicles

TL;DR

This paper develops a data-driven, reinforcement learning-based energy management system for PHEVs, improving fuel economy and disturbance resistance, validated through simulations and hardware-in-the-loop tests.

Contribution

It introduces a practical control framework combining RL and ECMS for PHEVs, addressing reliability issues and enhancing real-vehicle applicability.

Findings

Demonstrates improved fuel economy over existing strategies.

Shows enhanced disturbance resistance in energy management.

Validates effectiveness through simulation and hardware tests.

Abstract

Learning-based intelligent energy management systems for plug-in hybrid electric vehicles (PHEVs) are crucial for achieving efficient energy utilization. However, their application faces system reliability challenges in the real world, which prevents widespread acceptance by original equipment manufacturers (OEMs). This paper begins by establishing a PHEV model based on physical and data-driven models, focusing on the high-fidelity training environment. It then proposes a real-vehicle application-oriented control framework, combining horizon-extended reinforcement learning (RL)-based energy management with the equivalent consumption minimization strategy (ECMS) to enhance practical applicability, and improves the flawed method of equivalent factor evaluation based on instantaneous driving cycle and powertrain states found in existing research. Finally, comprehensive simulation and hardware-in-the-loop validation are carried out which demonstrates the advantages of the proposed control framework in fuel economy over adaptive-ECMS and rule-based strategies. Compared to conventional RL architectures that directly control powertrain components, the proposed control method not only achieves similar optimality but also significantly enhances the disturbance resistance of the energy management system, providing an effective control framework for RL-based energy management strategies aimed at real-vehicle applications by OEMs.