Real-Time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming

• Published in: IEEE transactions on vehicular technology Vol. 70; no. 5; pp. 4113 - 4128
• Main Authors: Ghandriz, Toheed, Jacobson, Bengt, Murgovski, Nikolce, Nilsson, Peter, Laine, Leo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-05-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Batteries; Energy consumption; Energy management; Engines; Fuels; Gears; Heavy vehicles; hybrid electric heavy vehicle; Nonlinear programming; Optimal control; Power management; Power sources; Predictive control; Predictive energy management strategy; Quadratic programming; Real time; Roads; sequential linear programming; State of charge; Trajectory optimization; Vehicles; Wheels
• ISSN: 0018-9545; 1939-9359; 1939-9359
• DOI: 10.1109/TVT.2021.3069414

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.