A Review on Towards Long Lifetime Battery: AI Based Manufacturing and Management
M. Lakshmi1, Dr. G. Indhira kishore2, P. Dharani 3. N.Roshini 4, M. Rambabu5
1,3,4,5,B .Tech Student, Department of EEE, GMR Institute of Technology, Rajam-532127, Andhra Pradesh, India
2 Assistant Professor, Department of EEE, GMR Institute of Technology, Rajam-532127, Andhra Pradesh, India
Email: 22341A0277@gmrit.edu.in1
Abstract— A Battery Management System (BMS) is crucial for maintaining the health and efficiency of batterypowered devices. Integrating Artificial Intelligence (AI) into BMS offers advanced capabilities for optimizing battery performance. AI algorithms analyze real-time data from the battery, such as voltage, temperature, and charge cycles, to predict battery life, enhance safety, and improve energy efficiency.AI can detect patterns and anomalies that traditional systems might miss, allowing for proactive adjustments to charging and discharging processes. This helps prevent overcharging, overheating, and deep discharges, which can damage batteries and reduce their lifespan. Additionally, AIdriven BMS can learn from historical data to refine its predictions and adapt to changing conditions. It also supports better energy management in electric vehicles and renewable energy systems, where efficient battery use is critical. By continuously monitoring and optimizing battery performance, AI enhances the overall reliability and longevity of battery systems, making technology more sustainable and efficient. Thus, the use of AI in BMS represents a significant advancement in battery technology, contributing to improved performance and safety in various applications. This study explores battery management systems by optimizing performance, extending lifespan, and improving efficiency and ensure safety through predictive analysis and real-time monitoring. Keywords: Battery Management system, artificial intelligence, state of charge, state of health, battery life extension, fault detection.
