Design Hybrid Electric Vehicle Using Intelligent Battery Management System

Abstract

With the increasing adoption of Hybrid Electric Vehicles (HEVs), the need for a sophisticated and intelligent Battery Management System (BMS) has become crucial for enhancing battery performance, safety, and longevity. This study introduces an innovative Intelligent Battery Management System (IBMS) that improves battery efficiency, monitoring, and control within HEVs. The system leverages real-time monitoring, advanced data-driven algorithms, and predictive analytics to precisely determine the State of Charge (SoC) and State of Health (SoH) of the battery. These capabilities optimize energy utilization and extend battery lifespan. The IBMS utilizes machine learning techniques and adaptive control strategies to reduce battery degradation and enhance overall performance. Additionally, it incorporates advanced fault diagnosis and thermal management functions to ensure safety and reliability. The effectiveness of this system has been validated through comprehensive simulations and experimental evaluations, demonstrating notable improvements over traditional BMS. The proposed IBMS represents a significant step forward in advancing next-generation HEVs by promoting efficient and intelligent energy management. We have converted normal Petrol Bike to Hybrid Bike by using Hub motor on backside wheel of bike. The Intelligent BMS is playing important role in monitoring all the parameters of the LiFePO4 Battery.