Real-time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming
Preprint, 2020

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 decides power split between different vehicle power sources and brakes. Based on the model predictive control (MPC) and sequential programming, the optimal trajectories of the vehicle velocity and battery state of charge are found for upcoming horizons of 5-20 km length. Then, acceleration and brake pedal positions together with the battery usage are regulated to follow the requested speed and state of charge that is verified using a vehicle plant model. The main contribution of this paper is developing a sequential linear program for predictive energy management that is faster compared to the sequential quadratic programming with a factor of 5 in tested solvers, giving very close trajectories to the best attained trajectories found by non-linear programming. The performance of the method is also compared to two different sequential quadratic programs.

predictive energy management strategy

sequential linear programming

optimal control

hybrid electric heavy vehicle

Author

Toheed Ghandriz

Chalmers, Mechanics and Maritime Sciences, Vehicle Engineering and Autonomous Systems

Bengt J H Jacobson

Chalmers, Mechanics and Maritime Sciences, Vehicle Engineering and Autonomous Systems

Nikolce Murgovski

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Peter Nilsson

Chalmers, Mechanics and Maritime Sciences, Vehicle Engineering and Autonomous Systems

Leo Laine

Chalmers, Mechanics and Maritime Sciences

Optimal Distributed Propulsion

VINNOVA, 2015-10-01 -- 2019-12-31.

Swedish Energy Agency, 2015-10-01 -- 2019-12-31.

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Mechanical Engineering

Vehicle Engineering

Control Engineering

Other Electrical Engineering, Electronic Engineering, Information Engineering

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Created

9/1/2020 1