Real-time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming
Journal article, 2021

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 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 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 that is verified using a 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 gives trajectories that are very close to the best trajectories found by nonlinear programming. The performance of the method is also compared to two different sequential quadratic programs.

optimal control

sequential linear programming

predictive energy management strategy

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

Volvo Group

Leo Laine

Volvo Group

IEEE Transactions on Vehicular Technology

0018-9545 (ISSN)

Vol. 70 5 4113-4128

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Subject Categories

Mechanical Engineering

Vehicle Engineering

Control Engineering

Other Electrical Engineering, Electronic Engineering, Information Engineering

Driving Forces

Sustainable development

Innovation and entrepreneurship

Areas of Advance

Transport

Energy

Roots

Basic sciences

Infrastructure

ReVeRe (Research Vehicle Resource)

Learning and teaching

Pedagogical work

DOI

10.1109/TVT.2021.3069414

More information

Latest update

6/16/2021