An AI-Driven MPPT Algorithm for Enhanced Performance of Solar-Powered Electric Vehicle Charging Stations
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Abstract
The fast-tracked implementation of electric vehicles (EVs) has prompted the development of effective, secure, and sustainable charging systems. Solar powered EV charging stations assure that renewable energy is utilized while minimizing the need of traditional grid energy in the process. However, because of the nature of solar radiation and temperature changes, the PV operating point varies continuously, which makes the need for maximum power point tracking (MPPT) in order to gain the highest available amount of power crucial. Although traditional MPPT methods such as perturb and observe (P&O) and incremental conductance (INC) can be characterized as straightforward and inexpensive, they are likely to cause oscillations while remaining close to the maximum power point and experience lower efficiency in the process of adapting to the environmental variations. Artificial intelligence-based MPPT approaches, including artificial neural networks, fuzzy logic, adaptive learning, and hybrid methods can provide better intelligence in the process of adapting since they can learn the non-linear behavior of the PV system. The fast-tracked implementation of electric vehicles (EVs) has prompted the development of effective, secure, and sustainable charging systems. Solar powered EV charging stations assure that renewable energy is utilized while minimizing the need of traditional grid energy in the process. However, because of the nature of solar radiation and temperature changes, the PV operating point varies continuously, which makes the need for maximum power point tracking (MPPT) in order to gain the highest available amount of power crucial. Although traditional MPPT methods such as perturb and observe (P&O) and incremental conductance (INC) can be characterized as straightforward and inexpensive, they are likely to cause oscillations while remaining close to the maximum power point and experience lower efficiency in the process of adapting to the environmental variations. Artificial intelligence-based MPPT approaches, including artificial neural networks, fuzzy logic, adaptive learning, and hybrid methods can provide better intelligence in the process of adapting since they can learn the non-linear behavior of the PV system.
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References
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