Design and Development of Hybrid Electric Vehicle Using Battery Pack and Analysis Using Machine Learning
Keywords:
hybrid electric vehicles (HEVs), battery pack, vehicle's powertrain system, regression analysis, clustering, and neural networksAbstract
The increasing environmental concerns and the need for sustainable energy solutions have accelerated the development of hybrid electric vehicles (HEVs). This study focuses on the design and development of a hybrid electric vehicle powered by a battery pack and its performance analysis using machine learning techniques. The HEV combines the efficiency of electric power with the range and convenience of traditional internal combustion engines. The design phase involves selecting an appropriate battery pack, optimizing its placement within the vehicle, and integrating it with the vehicle's powertrain system. Advanced simulation tools are employed to model the vehicle dynamics and evaluate different design configurations. Emphasis is placed on maximizing energy efficiency, reducing emissions, and ensuring optimal performance under various driving conditions. Machine learning plays a crucial role in analyzing the vehicle's performance. Data collected from various sensors during test drives is used to train machine learning models that predict energy consumption, identify driving patterns, and optimize the control strategy. Techniques such as regression analysis, clustering, and neural networks are employed to derive insights from the data.
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