Crested Porcupine Optimizer for Enhanced Power Management in Plug-In Hybrid Electric Vehicles

Power Management (PM) in Plug-in Hybrid Electric Vehicles (PHEV) is to enhance the distribution of energy between battery and the internal combustion engine with the intention of improving efficiency and reducing fuel consumption. Another major challenge is the reduction of Greenhouse Gas (GHG) emissions; inefficient power distribution may result in higher fuel usage and subsequently higher environmental impacts. This paper proposes a Crested Porcupine Optimizer (CPO) to improve PM in PHEVs. The CPO is used to reduce Green House Gas (GHG) emission in PHEVs. By optimizing control parameters, CPO effectively improves energy allocation, reducing fuel consumption and emissions while maintaining optimal performance. By dynamically adjusting power distribution, the proposed method enhances overall efficiency, leading to better fuel economy and lower environmental impact in hybrid vehicle operation. By then the proposed CPO method is implemented in MATLAB platform and evaluated their performance with various existing methods such as Particle Swarm Optimization (PSO), Aquila Optimizer Algorithm with Artificial Neural Network (AOA-ANN), Waterwheel Plant Algorithm with Dual Stream Spectrum De-convolution Neural Network (WWPA-DSSDNN), Adaptive Firework Algorithm (AFWA), and Multi Island Genetic Algorithm (MIGA). The proposed CPO method outperforms all the existing methods with minimizing GHG emission to 1.79 104 g, demonstrating their potential effectiveness in reducing emissions compared to traditional approaches.