Enhancing electric vehicle performance through buck‐boost converters with renewable energy integration using hybrid approach

The electrification of vehicles has emerged as a pivotal technique for addressing environmental concerns and reducing reliance on conventional fuel sources. Conversely, the best way to incorporate renewable energy into electric vehicles (EVs) is still a challenging task, particularly in enhancing the performance of EVs through efficient energy management. The transition to EVs has gained momentum as part of global efforts to mitigate environmental impacts and reduce dependence on fossil fuels. This paper proposes a hybrid method for enhancing EV performance through buck‐boost converters with renewable energy integration. The proposed technique is the joined execution of Flying Foxes Optimization (FFO) and Viscoelastic Constitutive Artificial Neural Networks (vCANNs) techniques. The proposed method's goal is to enhance the energy efficiency, minimize EV charging cost, and mitigating environmental impacts. The renewable energy sources: solar panels, fuel cells, and wind turbines, are integrated into the EV power system through buck‐boost converters. The buck‐boost converter's control signal is optimized through the FFO method. vCANNs are used to predict these control parameters. The proposed strategy is executed in MATLAB software and is compared with existing strategies. In comparison with other current approaches like particle swarm optimization, heap based optimizer, and wild horse optimize, the proposed method achieves a high efficiency of 99% and low cost of 0.05 €/KWh.