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

Abstract

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.

Transportation-Mission-Based Optimization of Heterogeneous Heavy-Vehicle Fleet Including Electrified Propulsion

“…This claim is true for combustion-powered vehicles with fixed hardware running on flat roads. For a vehicle moving on a road with variable grade, having the speed as an optimization variable is useful, as reported by [69,70]. However, speed optimization is not considered in this study owing to increased complexity and computational cost.…”

“…In plug-in hybrid vehicles the total power must be split between electric motors and ICE according to an energy management strategy [69]. A computational-efficient rule-based energy management strategy has been implemented in this paper, which was validated compared to a predictive energy management strategy in [49].…”

Supporting the Transition Towards Electromobility in the Construction and Mining Sector: Optimization Framework and Demonstration on an Electrical Hauler

“…These states may be continuous (such as vehicle velocity) or discrete (such as gear selection). The act of operating can either be discretized in the spatial or time domain, and there have been several studies in time (Luin et al, 2019) as well as spatial (Ghandriz et al, 2021;Ye et al, 2019) domains, respectively. Usually, a finer discretization achieves better results at the cost of computational time (Ye et al, 2019).…”

Battery Energy Management Techniques for an Electric Vehicle Traction System

“…It identifies the different power losses associated with the hybrid energy storage system and battery capacity due to the changing velocity profile, then formulates and solves a power split cost function through chaotic particle swarm optimization. The work of [33] formulates a power split function involving the battery SOC and the vehicle speed then uses sequential programming to solve the problem. The optimal position for the brake and acceleration pedals is regulated accordingly.…”