Control Strategies for Fast-Charging Protocols to Minimize Battery Degradation
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I2P104Keywords:
Battery Degradation, Fast Charging, Lithium-Ion Batteries, Battery Management System (BMS), Model Predictive Control (MPC), Reinforcement Learning, Pulse Charging, Electric Vehicles (EV), State of Charge (SOC), State of Health (SOH)Abstract
The development of electric vehicle (EV) technology and the rampant use of portable electronic devices have created a pressing demand for effective and reliable battery charging systems very much. Although lithium-ion batteries, with their long life cycle and high energy density, are very much in favor, they suffer from degradation due to high-rate charging, reducing their functional lifetime and undermining safety. Fast-charging protocols are needed to limit the charging time, but they induce thermal and chemical stress, resulting in irreversible capacity loss, high internal resistance, and mechanical stress. This research considers control strategies of fast-charging protocols that will limit battery degradation. We present a number of charging methods, such as constant current-constant voltage (CC-CV), multi-stage charging, pulse charging, model predictive control (MPC), and reinforcement learning-based methods. The presented methods are contrasted in charging time, heat rise, SOH effect, and cycle life.
The ageing behavior was appraised by simulation models and experiment data in light of electrochemical, thermal, and ageing model. It placed specific emphasis in striking a trade-off between faster charging rate and battery lifetime. We endorse a hybrid adaptive control strategy blending real-time monitoring, data-driven optimization, and predictive analytics for dynamically optimizing charging profiles based on battery condition and environmental conditions. Case studies obtain up to 25% rate of degradation reduction with charging times comparable to those of conventional strategies. The system utilizes thermal management, current modification, and SOC dependent control in optimizing the charging process. This paper supports the creation of smart charging infrastructure and battery management systems (BMS) that enable affordable energy storage, lower maintenance costs, and higher user satisfaction. Future research directions involve the integration of battery digital twins, cloud-based predictive health diagnostics, and V2G (vehicle-to-grid) charging applications. In general, the paper offers a comprehensive study of fast-charging control strategies and a roadmap for more resilient and efficient energy storage systems
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