ENERGY MANAGEMENT STRATEGY FOR FUEL CELL HYBRID ELECTRIC VEHICLE

Abstract

The use of internal combustion engines is being increasingly scrutinized because of their high emission levels. The research and development of electric and hybrid vehicles has been prompted by the demand for cleaner energy technologies. Fuel cell vehicles are gaining attention because they are clean, sustainable, and have a high energy density. Thus, fuel cell hybrid vehicles have the potential to compete with vehicles powered by internal combustion engine in the future, yet there are challenges for fuel cell such as slow dynamics requiring that their operation together should be managed favourably. The main aim of the thesis is to tackle the issue of energy management in fuel cell vehicles. The power train model is the first thing developed for this purpose. Deep deterministic policy gradient (DDPG) is a model-free reinforcement learning algorithm used to achieve efficient energy management. The energy management strategy focuses on running the fuel cell in its high efficiency range while limiting the deviation of state of charge of the lithium-ion battery from a target value. It is found that the DDPG agent trained simply with step power inputs can achieve up to 2.7% less energy consumption compared to commonly used rule-based energy management strategies while maintaining the state of the charge of the battery within a certain interval. Our findings indicate that the DDPG algorithm has a promising potential for use in such applications.