A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity

• Published in: Applied energy 2020-01, Vol.257, p.113900, Article 113900
• Main Authors: Hou, Jun, Song, Ziyou
• Format: Article
• Language: English
• Subjects: Energy management; Hybrid energy storage; Model predictive control; Real-time optimization; Vehicle-to-cloud connectivity
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2019.113900

•A novel energy management strategy is developed via vehicle to cloud connectivity.•Both electric cost and battery degradation cost are considered.•Dynamic programming provides the global optimization solution via cloud computing.•Model predictive control deals with uncertainties in the vehicle control unit.•Driving cycles including real bus driving cycles are used to validate the algorithm. In order to enhance energy efficiency and improve system performance, the road mobility system requires more preview information and advanced methods. This paper proposes a novel hierarchical optimal energy management strategy for electric buses with a battery/ultracapacitor hybrid energy storage system, to optimal split the power and reduce the battery life degradation. This method is based on vehicle-to-cloud connectivity. In the cloud platform, an optimal energy management strategy is developed using dynamic programming, where the battery degradation cost and the electric cost are taken into consideration. In the vehicle level, a model predictive control is developed to deal with the uncertainties, reduce the energy losses, and handle the system constraints. The cost function of the model predictive control includes the ultracapacitor state of charge planning and energy losses. In order to evaluate the effectiveness of the proposed method, a rule-based energy management strategy is developed as the baseline approach. The China bus driving cycle and other six real bus driving cycles recorded in China are used to validate the robustness of the proposed method. To be more realistic, the random uncertainties up to 20% are included in all driving cycles. Furthermore, the time delay and packet losses in communication are also considered. Simulation results show that the proposed method significantly outperforms the rule-based method, and the average improvement could be over 40% in the studied driving cycles.