A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning

TL;DR

This paper introduces a fuzzy reinforcement learning strategy for fuel cell hybrid electric vehicles that reduces fuel consumption, extends fuel cell lifetime, and adapts to changing conditions without requiring system modeling.

Contribution

It proposes a model-free fuzzy Q-learning approach that minimizes fuel cell startups and handles continuous state-action spaces, improving energy management in hybrid vehicles.

Findings

Reduces fuel consumption effectively.

Extends fuel cell lifetime by minimizing startups.

Adapts well to initial, model, and driving condition changes.

Abstract

Modeling difficulty, time-varying model, and uncertain external inputs are the main challenges for energy management of fuel cell hybrid electric vehicles. In the paper, a fuzzy reinforcement learning-based energy management strategy for fuel cell hybrid electric vehicles is proposed to reduce fuel consumption, maintain the batteries' long-term operation, and extend the lifetime of the fuel cells system. Fuzzy Q-learning is a model-free reinforcement learning that can learn itself by interacting with the environment, so there is no need for modeling the fuel cells system. In addition, frequent startup of the fuel cells will reduce the remaining useful life of the fuel cells system. The proposed method suppresses frequent fuel cells startup by considering the penalty for the times of fuel cell startups in the reward of reinforcement learning. Moreover, applying fuzzy logic to approximate the value function in Q-Learning can solve continuous state and action space problems. Finally, a python-based training and testing platform verify the effectiveness and self-learning improvement of the proposed method under conditions of initial state change, model change and driving condition change.