Design of Hybrid Energy Storage and Management System in Hybrid Electric Vehicle Using Machine Learning Approach

The growing concern for reducing carbon emissions and the depletion Using fossil fuels has led to a considerable increase in the development of hybrid electric vehicles (HEVs) and their associated Controlling and storing energy systems. The blueprint of an efficient and effective System for storing and managing energy is crucial for the optimal performance of HEVs. In this context, this study proposes a novel strategies for designing mixed-source power storage and management system for HEVs using recurrent neural network (RNN)-based machine learning. The proposed approach involves the integration of RNN-based predictive models with the energy storage and management system of the HEV. The RNN-based predictive model is developed using historical driving data to predict the future driving pattern of the vehicle. The predicted driving pattern is then used to optimize the energy management system, which includes the control of the battery charging and discharging operations as well as the allocation of power between the battery and other power sources. Specifically, the suggested approach, simulations are carried out using a high-fidelity HEV model the actual world driving numbers. The end outcome of the simulations show that the proposed RNN-based energy storage and management system outperforms traditional rule-based control strategies in terms of energy efficiency, battery lifespan, and vehicle performance. In conclusion, the proposed approach of using RNN-based predictive models for designing hybrid energy storage and management systems in HEVs provides a promising avenue for the creation of more efficient and effective energy management systems for HEVs. This could lead to significant improvements in the fuel economy, reduced emissions, and extended battery lifespan of HEVs, thereby contributing to a greener and more sustainable future.