Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle
  with Reinforcement Learning
Xinyang Wu Elisabeth Wedernikow Christof Nitsche Marco F. Huber
In recent years, the development of Artificial Intelligence (AI) has shown
tremendous potential in diverse areas. Among them, reinforcement learning (RL)
has proven to be an effective solution for learning intelligent control
strategies. As an inevitable trend for mitigating climate change, hybrid
electric vehicles (HEVs) rely on efficient energy management strategies (EMS)
to minimize energy consumption. Many researchers have employed RL to learn
optimal EMS for specific vehicle models. However, most of these models tend to
be complex and proprietary, making them unsuitable for broad applicability.
This paper presents a novel framework, in which we implement and integrate
RL-based EMS with the open-source vehicle simulation tool called FASTSim. The
learned RL-based EMSs are evaluated on various vehicle models using different
test drive cycles and prove to be effective in improving energy efficiency.