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Optimal Power Management and Control of Solar Powered Electric Vehicle

December 13, 2021 @ 11:00 am - 12:00 pm

Abstract: In this project, to support the Electric Vehicle (EV) energy requirement during the running condition, a solar panel has been installed on the vehicle’s roof. Here, vehicle rooftop solar panel means, the upper body of the vehicle is made of solar cells. That’s why in the daytime, vehicle during running condition or parked in open space, its solar panel continuously generates power, which supports the overall energy utilization. However, here maximum energy extraction from the panel is the biggest challenge. Because most of the time, vehicles run in the city or society, where the shadow of tall buildings, towers, trees, poles etc. falls on the rooftop solar panel of the EV. Due to this shadow, the power characteristic of solar PV deteriorates and consists of multiple peaks. Moreover, due to the curved structure of the vehicle’s upper body, the solar irradiance falls on the installed solar cells with different angles, which again deteriorates solar PV power characteristic and develops multiple peaks. The multiple peaks in the power-voltage characteristics of the solar panel are known as partial shading condition. However, this partial shading issue is completely different from the static solar panel. Because solar-powered EV runs at high speed, so the shadow also changes with high speed. Therefore, during the running condition, the pattern of PV characteristics change with high speed. In this high dynamic change partial shaded condition, the maximum power extraction is the biggest challenge. To solve this issue and extract maximum power from vehicle’s rooftop solar panel in running condition, a novel “Human Psychology Optimization (HPO)” algorithm has developed. This HPO algorithm is based on an ambitious person’s psychological and mental states, which updates behavior before taking any physical action, on four psychological factors, such as excitement, self-motivation, inspiration, and lesson. Moreover, one-third of poor performer searching agents are replaced with the new chromosomes in each iteration, generated through heuristic mutation and crossover process between one third-best performer searching agents. These controlled factors help in quick searching and rapid conversance on a global solution. Moreover, the power envelope concept has used for severe and accurate dynamic change condition detection and classification, which helps in ‘search area expansion’ during dynamic change conditions. Therefore, during EV running condition, the proposed HPO is able to extract the optimal maximum power from partial shaded vehicle’s rooftop solar panel with high-speed dynamic change condition. Moreover, after every second, the search solution is stored in the cloud with the location coordinate. Therefore, when another vehicle enters in that zone, the previous vehicle’s solution is used as an initial solution during searching to detect the optimal solution. Moreover, after one second, this vehicle updates the previous solution, which another vehicle can use when enter in this area. This vehicle-to-vehicle solution updating, and communication ramp-ups the overall searching behavior. The proposed all solutions have validated on MATLAB based simulation platform as well as on a hardware prototype. Moreover, obtained hardware experimental results have also been validated on European EN50530 Standard, which proves the commercial level potential of the solution. Speaker(s): Dr Nishant Kumar, Virtual: https://events.vtools.ieee.org/m/289454

Details

Date:
December 13, 2021
Time:
11:00 am - 12:00 pm
Event Category:
Website:
https://events.vtools.ieee.org/m/289454

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