Energy Management in Plug-In Hybrid Electric Vehicles: Convex Optimization Algorithms for Model Predictive Control

This article details an investigation into the computational performance of algorithms used for solving a convex formulation of the optimization problem associated with model predictive control for energy management in hybrid electric vehicles with nonlinear losses. A projected interior-point method...

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Bibliographic Details
Published in:IEEE transactions on control systems technology Vol. 28; no. 6; pp. 2191 - 2203
Main Authors: East, Sebastian, Cannon, Mark
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithms
Alternating direction method of multipliers (ADMM)
Batteries
Computational geometry
Convex analysis
Convex functions
Convexity
Electric vehicles
Energy management
Engines
Heuristic algorithms
Hybrid electric vehicles
interior-point method
Mechanical power transmission
model predictive control (MPC)
Optimization
plug-in hybrid electric vehicles (PHEVs)
Predictive control
ISSN:1063-6536, 1558-0865
Online Access:Get full text
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Summary:This article details an investigation into the computational performance of algorithms used for solving a convex formulation of the optimization problem associated with model predictive control for energy management in hybrid electric vehicles with nonlinear losses. A projected interior-point method is proposed, where the size and complexity of the Newton step matrix inversion is reduced by applying inequality constraints on the control input as a projection, and its properties are demonstrated through simulation in comparison with an alternating direction method of multipliers (ADMM) algorithm and a general purpose convex optimization software CVX. It is found that the ADMM algorithm has favorable properties when a solution with modest accuracy is required, whereas the projected interior-point method is favorable when high accuracy is required, and that both are significantly faster than CVX.
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ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2019.2933793