Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm

• A specific driving cycle is constructed through a naturalistic data-driven method. • An energy management strategy based on the TD3 algorithm is proposed. • The health of the onboard lithium-ion battery system is taken into consideration. • Real velocity data and the constructed cycle are used as the training and testing datasets. • The superiority of the proposed strategy is validated compared with DDPG and DDQL. Energy management is critical to reduce energy consumption and extend the service life of hybrid power systems. This article proposes an energy management strategy based on deep reinforcement learning with awareness of battery health for an urban power-split hybrid electric bus. In this article, a specific driving cycle of the test bus route is constructed through a naturalistic data-driven method to evaluate the practical operating costs of the hybrid electric bus accurately. Furthermore, an energy management strategy based on twin delayed deep deterministic policy gradient algorithm considering battery health is innovatively designed to minimize the total operating cost with a tradeoff between fuel consumption and battery degradation. Finally, the superiority of the proposed strategy over other state-of-the-art deep reinforcement learning-based strategies including deep deterministic policy gradient and double deep Q-learning is validated. Simulation results show that the constructed driving cycle can effectively reflect the real traffic conditions of the test bus route, and the proposed strategy can reduce the total operating cost while extending the battery life efficiently. This article makes contribution to the reliable evaluation of the practical operating costs and the extension of the battery life for urban hybrid electric buses through deep reinforcement learning methods.