Multi-objective Optimized Smart Charge Controller for Electric Vehicle Applications

Zunaib Ali, Ghanim Putrus, Mousa Marzband*, Hamid Reza Gholinejad, Komal Saleem, Bidyadhar Subudhi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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The continuous deployment of distributed energy sources and increase in the adoption of Electric Vehicles (EVs) require smart charging algorithms. Existing EV chargers offer limited flexibility and controllability, and do not fully consider factors (such as, EV user waiting time and 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 charging cost. In this paper, a Neuro-Fuzzy-PSO based novel and advanced smart charge controller is proposed which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (PV) output and battery state of health. A rule based Fuzzy controller becomes complex as the number of inputs to the controller increases. Also, it becomes difficult to achieve an optimum operation due to conflicting nature of control requirements. To optimize the controller response, Particle Swarm Optimization (PSO) technique is proposed to provide a global optimum solution based on a pre-defined cost function and to address the implementation complexity PSO is combined with neural network. The proposed Neuro-Fuzzy-PSO control algorithm meets EV user requirements, work within technical constraints and is simple to implement in real-time (and requires less processing time). Simulation using MATLAB and experimental results using dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller.
Original languageEnglish
Number of pages14
JournalIEEE Transactions on Industry Applications
Early online date5 Apr 2022
Publication statusE-pub ahead of print - 5 Apr 2022


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