Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications

Journal article

Ali, Z., Putrus, G., Marzband, M., Gholinejad, H., Saleem, K. and Subudhi, B. (2023). Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications. IEEE Transactions on Industry Applications. 58 (5), pp. 5602-5615. https://doi.org/10.1109/TIA.2022.3164999
AuthorsAli, Z., Putrus, G., Marzband, M., Gholinejad, H., Saleem, K. and Subudhi, B.
Abstract

The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller.

KeywordsBatteries , Renewable energy sources , Tariffs , Voltage control , Real-time systems , Degradation , Costs
Year2023
JournalIEEE Transactions on Industry Applications
Journal citation58 (5), pp. 5602-5615
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN1939-9367
Digital Object Identifier (DOI)https://doi.org/10.1109/TIA.2022.3164999
Web address (URL)https://ieeexplore.ieee.org/document/9749948
Publication dates
Print05 Apr 2022
Publication process dates
Deposited01 Aug 2023
Accepted author manuscript
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File Access Level
Open
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Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

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