Power management optimisation for hybrid electric systems using reinforcement learning and adaptive dynamic programming

Sanusi, I., Mills, A., Konstantopoulos, G. and DODD, Tony (2019) Power management optimisation for hybrid electric systems using reinforcement learning and adaptive dynamic programming. In: 2019 American Control Conference (ACC). 2019 American Control Conference (ACC), 10-12 Jul 2019. IEEE, Philadelphia, PA, USA, pp. 2608-2613. ISBN 9781538679012

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Abstract or description

This paper presents an online learning scheme based on reinforcement learning and adaptive dynamic programming for the power management of hybrid electric systems. Current methods for power management are conservative and unable to fully account for variations in the system due to changes in the health and operational conditions. These conservative schemes result in less efficient use of available power sources, increasing the overall system costs and heightening the risk of failure due to the variations. The proposed scheme is able to compensate for modelling uncertainties and the gradual system variations by adapting its performance function using the observed system measurements as reinforcement signals. The reinforcement signals are nonlinear and consequently neural networks are employed in the implementation of the scheme. Simulation results for the power management of an autonomous hybrid system show improved system performance using the proposed scheme as compared with a conventional offline dynamic programming approach.

Item Type:	Book Chapter, Section or Conference Proceeding
Additional Information:	© 2019 AACC. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Faculty:	School of Creative Arts and Engineering > Engineering
Depositing User:	Library STORE team
Date Deposited:	15 Jul 2020 14:34
Last Modified:	24 Feb 2023 13:58
URI:	https://eprints.staffs.ac.uk/id/eprint/6227

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