Abstract
The integration of photovoltaic systems (PVS) in electric vehicles (EV) increases the vehicle’s autonomy by providing an additional energy source other than the battery. However, current solar cell technology generates around 200 W for a 1.4 m2 panel (to be installed on the roof of the EV) at stable irradiance conditions. This limitation in production and the sudden changes in irradiance produced by shadows of clouds, buildings, and other structures make developing a fast and efficient maximum power point tracking (MPPT) technique in this area necessary. This article proposes an artificial neural network (ANN)-based MPPT, called DS-ANN, that uses manufacturer datasheet parameters as inputs to the network to address this problem. The Bayesian backpropagation-regularization performs the training, ensuring that the MPPT technique operates satisfactorily on different PVS without retraining. We simulated the response of 20 commercial modules against actual irradiance data to validate the proposed method. The results show that our method achieves an average tracking efficiency of 99.66%, improving by 1.21% over an enhanced P&O method.
Highlights
Solar energy is a promising and freely available energy source for countering carbon dioxide emissions (CO2) produced by burning fossil fuels
We present an approach based on electric vehicles (EV), the same fundamental concepts apply to all photovoltaic systems (PVS), so our algorithm is not limited to EVs, but it is suitable for any maximum power point tracking (MPPT) application
We present a case study to analyze the performance of the proposed DS-artificial neural network (ANN) MPPT technique, in which the original P&O and an improved P&O [91] MPPT techniques are used as the basis for comparison
Summary
Solar energy is a promising and freely available energy source for countering carbon dioxide emissions (CO2) produced by burning fossil fuels. PVS has become a popular form of electrical generation due to its advantages, such as the absence of fuel cost, infrequent maintenance, and noiseless operation. This growth is supported by extensive research, with more than 48,000 studies published on solar energy since 1900. Half of those publications appeared between 2015 and 2020. The broad integration of photovoltaic (PV) generation in the residential, commercial, and industrial sectors drastically decreased their CO2 emissions [5]
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