Journal
ENERGY
Volume 251, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2022.123774
Keywords
Hybrid energy storage system; Model predictive control; Vehicle-following; Energy management; Economy analysis; V2V; V2I communication
Categories
Funding
- National Natural Science Foundation of China [61672537]
- Post-graduate Scientific Research Innovation Project of Hunan Province [CX20200202]
- Fundamental Research Funds for the Central Universities of Central South University [2020zzts125]
- China Scholarship Council [202006370153]
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This paper proposes a hierarchical model predictive control framework for electric vehicles to optimize power demand and energy management, improving energy efficiency and driving safety.
For electric vehicles with hybrid energy storage system, driving economy depends not only on novel energy management strategies but also on load power demand. In order to optimize the power demand and energy management simultaneously, this paper proposes a hierarchical model predictive control framework for electric vehicles with a Li-ion battery/supercapacitor hybrid energy storage system under vehicle-following scenarios. In the vehicle-following level, based on vehicle-to-vehicle and vehicle-to infrastructure communications, the following vehicle can acquire the real-time velocity and position of the preceding vehicle, optimize the motor electricity consumption, and ensure driving safety through velocity planning. Such cost-effective power demand is further allocated in the energy management level, in order to minimize battery degradation and power losses. Urban, suburban, and highway driving conditions are tested to evaluate the effectiveness and robustness of the proposed method. Determination of prediction horizon and detailed comparison with existing methods are investigated. Simulation results show that compared with optimizing energy management alone under a classical car-following model, the proposed method can reduce the total operation cost by 4.69-14.55% and yield results closer to offline dynamic programming, which provides the globally optimal results.(c) 2022 Published by Elsevier Ltd.
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