Hybrid method based energy management of electric vehicles using battery-super capacitor energy storage

This paper presents a hybrid technique for managing the Energy Management of a hybrid Energy Storage System (HESS), like Battery, Supercapacitor (SC), and integrated charging in Electric Vehicle (EV). The proposed hybrid method combines the Namib Beetle Optimization (NBO) and Quantum Neural Networks (QNN) technique and is commonly known as the NBO-QNN approach. The proposed energy management technique reduces EV power use and maximizes battery life. QNN forecasts and combines power supply and charge levels to fulfill load needs. EV energy management uses NBO to regulate the output voltage, generate references, and regulate current continuously. Higher energy density battery and power density SC meet vehicle needs. An uncontrolled rectifier with a DC-to-DC buck converter balances charging and ensures energy transmission. The Proposed technique is implemented using the MATLAB platform, and its performance is compared to existing methods. HESS performance is evaluated by comparing it to existing systems. The research shows that the proposed strategy reduces the primary and secondary source stress, enhances performance of charging unit, and extends the life of battery. Furthermore, the NBO-QNN technique outperforms other existing methods, such as the Cooperation Search Algorithm (CSA), Latent Semantic Analysis (LSA), and Grasshopper Optimization Algorithm (GOA). The proposed method displays the best output in all existing Cooperation Search Algorithm (CSA), Latent Semantic Analysis (LSA), and Grasshopper Optimization Algorithm (GOA) methods. The result concludes that the NBOQNN approach based on THD value is less than existing methods.