Direct Predictive Power Control Strategy to Improve Charging and Power Quality of Autonomous Robotic Electric Vehicle Charging Systems

Rasool, Haaris; Jaman, Shahid; Chakraborty, Sajib; Geury, Thomas; Baghdadi, Mohamed El; Hegazy, Omar

doi:10.1109/speedam53979.2022.9842257

Cited by 1 publication

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References 23 publications

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“…Albeit, the work does not provide a simulation of discharging mode and its effects on power compensation. Smart charging with the V2G and G2V using model predictive control (MPC) [16], and direct model reference adaptive control (DMRAC) algorithm has developed [17]. Although, the sensitivity of the controller in both methods is slow which increase the time response to overcome the changes during the operation.…”

Section: Introductionmentioning

confidence: 99%

Improve power quality of charging station unit using African vulture optimization algorithm

et al. 2023

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In recent years, there is growth in acceptance to consume fewer fossil fuels globally and the manufacturing of electric vehicles (EVs) has become more popular. However, the increase in the number of systems connected to the grid that contain EVs with a huge power capacity leads to unstable working in the power system. To assess the stability of the electric charging station several control approaches in AC part and DC parts during charging mode and discharging modes are tested. African vulture optimization algorithm (AVOA) has been utilized to tune the system controllers (proportional integral derivative (PID)/tilt integral derivative (TID) controllers). The superiority of AVOA is confirmed by comparing the performance with the genetic algorithm (GA). Two objective functions have been used i.e. integral time absolute error (ITAE) and integral square time error (ISTE). AVOA-tuned TID controllers using ISTE were found to be the best to contain the frequency deviations. The results have shown of the AC part and DC part is within an acceptable limit recommended by IEEE standard. Further, maximum peak overshoot, undershoot, and settle time obtained by AVOA-tuned PID and TID controllers are found the best. Finally, the improvement of the performance index obtained by AVOA over its counterpart GA is confirmed.

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Section: Introductionmentioning

confidence: 99%

Improve power quality of charging station unit using African vulture optimization algorithm

et al. 2023

View full text Add to dashboard Cite

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Direct Predictive Power Control Strategy to Improve Charging and Power Quality of Autonomous Robotic Electric Vehicle Charging Systems

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Cited by 1 publication

References 23 publications

Improve power quality of charging station unit using African vulture optimization algorithm

Improve power quality of charging station unit using African vulture optimization algorithm

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