期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 70, 期 5, 页码 4113-4128出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3069414
关键词
Gears; Batteries; Engines; Energy management; Roads; Wheels; Fuels; Predictive energy management strategy; hybrid electric heavy vehicle; optimal control; sequential linear programming
资金
- research program FFI
This paper proposes a real-time predictive energy management strategy for hybrid electric heavy vehicles, using a combination of model predictive control and sequential programming to optimize vehicle velocity and battery state of charge trajectories. By comparing the performance with two different sequential quadratic programs, it is found that the developed sequential linear program is faster and simpler in providing trajectories close to the best found by nonlinear programming.
With the objective of reducing fuel consumption, this paper presents real-time predictive energy management of hybrid electric heavy vehicles. We propose an optimal control strategy that determines the power split between different vehicle power sources and brakes. Based on model predictive control (MPC) and sequential programming, the optimal trajectories of the vehicle velocity and battery state of charge are found for upcoming horizons with a length of 5-20 km. Then, acceleration and brake pedal positions together with the battery usage are regulated to follow the requested speed and state of charge, which is verified using a high-fidelity vehicle plant model. The main contribution of this paper is the development of a sequential linear program for predictive energy management that is faster and simpler than sequential quadratic programming in tested solvers and provides trajectories that are very close to the best trajectories found by nonlinear programming. The performance of the method is also compared to that of two different sequential quadratic programs.
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