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.
Highlights
I NCREASING concerns about the environment and global warming together with regulations and consumer expectations have motivated the development of new solutions for reducing emissions from road transport
This paper proposes using a sequential linear program (SLP) rather than the sequential quadratic program (SQP) presented in [16], [17], [21] to find optimal trajectories of the vehicle velocity and state of charge (SOC) together with continuous and discrete inputs
Different variants of approximating the Hessian exist in the literature, e.g., the BFGS method, [31]. These types of computations can still be performed in real time onboard the vehicle with a long horizon using the real-time iteration of the SLP/SQP rather than the sequential iterations, as explained by [32]–[34]
Summary
I NCREASING concerns about the environment and global warming together with regulations and consumer expectations have motivated the development of new solutions for reducing emissions from road transport. According to [1], heavy-duty vehicles are responsible for 25% of CO2 emissions from road transport in Europe. Among the different solutions for reducing emissions, energy management control strategies based on road topographic data have been shown to be effective in reducing the fuel consumption of conventional heavy vehicles [2]–[4]. The challenge is to efficiently solve optimal control problems that. Manuscript received March 29, 2020; revised November 12, 2020 and February 1, 2021; accepted March 7, 2021.
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