An Actor-Critic-Identifier Control Design for Increasing Energy Efficiency of Automated Electric Vehicles

TL;DR

This paper introduces a neural-network-based actor-critic reinforcement learning control system for electric vehicles that learns power consumption mapping online, leading to a 12.84% increase in energy recovery and improved efficiency.

Contribution

It presents a novel actor-critic-identifier architecture that eliminates the need for explicit power-input models, enhancing EV energy efficiency through online learning and control.

Findings

Increases energy recovery by 12.84% compared to traditional controllers.

Maintains accurate speed tracking while optimizing energy efficiency.

Validated performance through simulation with Lyapunov stability analysis.

Abstract

Electric vehicles (EVs) are increasingly deployed, yet range limitations remain a key barrier. Improving energy efficiency via advanced control is therefore essential, and emerging vehicle automation offers a promising avenue. However, many existing strategies rely on indirect surrogates because linking power consumption to control inputs is difficult. We propose a neural-network (NN) identifier that learns this mapping online and couples it with an actor-critic reinforcement learning (RL) framework to generate optimal control commands. The resulting actor-critic-identifier architecture removes dependence on explicit models relating total power, recovered energy, and inputs, while maintaining accurate speed tracking and maximizing efficiency. Update laws are derived using Lyapunov stability analysis, and performance is validated in simulation. Compared to a traditional controller, the method increases total energy recovery by 12.84%, indicating strong potential for improving EV energy efficiency.