A Comprehensive Approach to Battery Management System Incorporating Dynamic Power Limiting with Thermal Management Algorithm

Abstract

This study critically presents the model of a Battery Management System (BMS) for monitoring and controlling battery parameters in various applications. The presented model allows for comprehensive monitoring and analysis of critical battery metrics such as State of Charge (SOC), charging, discharging, and voltage. By developing and testing the model prior to hardware implementation, significant advantages including time and cost savings, as well as error identification, are realized. The BMS serves as a crucial component in these applications, ensuring efficient battery utilization, optimizing performance, and enhancing system safety. Through the developed model, advanced algorithms and control strategies can be tested and fine-tuned to achieve optimal battery management. This iterative process helps overcome challenges and limitations associated with battery systems, leading to improved energy efficiency, extended battery life, and enhanced overall system performance. However, future research and development efforts are warranted to further enhance the simulation model's accuracy and responsiveness. Integration of real-time data acquisition and advanced control algorithms can facilitate more precise and adaptive battery management. Additionally, addressing limitations such as temperature effects, aging, and environmental variations will contribute to the continued advancement of BMS technology. In conclusion, the design and model of the BMS presented in this study critically contribute to the ongoing progress in battery technology. By enabling comprehensive monitoring and control, the BMS facilitates the widespread adoption of battery-powered systems, supporting energy efficiency and sustainability in various industries and sectors.