Control Strategies for Fast-Charging Protocols to Minimize Battery Degradation

The rapid growth of portable electronics and electric vehicles has created a strong demand for fast and reliable battery charging methods. Lithium-ion batteries are widely used due to their high energy density and long cycle life; however, aggressive fast-charging conditions accelerate degradation and reduce safety and lifespan. This paper reviews advanced control strategies designed to minimize degradation during fast-charging operations. Conventional and emerging charging protocols—including constant-current/constant-voltage (CC–CV), multistage charging, pulse charging, model predictive control (MPC), and reinforcement learning–based adaptive charging—are analyzed and compared in terms of charging time, temperature rise, state of health (SOH), and cycle life. Experimental datasets are integrated with electrochemical–thermal–aging simulation models to quantify degradation mechanisms under different charging profiles. A hybrid adaptive control framework is proposed that dynamically adjusts charging current based on real-time battery states and environmental conditions using data-driven prediction and optimization. Case studies demonstrate that degradation rates can be reduced by up to 25% without increasing charging time. The proposed strategy combines SOC-dependent current modulation, thermal-aware control, and intelligent battery management integration to improve charging efficiency and battery longevity. The results support the development of user-friendly battery management systems and smart charging infrastructure for electric mobility. Future directions include battery digital twins, cloud-based predictive diagnostics, and vehicle-to-grid (V2G) charging integration. This study provides a comprehensive perspective on fast-charging control strategies for designing durable and high-performance energy storage systems.

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)

[1] Zhang, X., et al. (2018). Model predictive control for fast charging of lithium-ion batteries. Journal of Power Sources, 405, 106–116.

[2] Kim, D., et al. (2020). Reinforcement learning for battery charging control. IEEE Transactions on Industrial Electronics, 67(8), 6825–6835.

[3] Lee, J., et al. (2019). Pulse charging techniques for improving battery cycle life. Energy Conversion and Management, 196, 951–960.

[4] Plett, G. L. (2015). Battery Management Systems. Artech House.

[5] Barai, A., et al. (2018). A study on the impact of fast charging on battery degradation. Journal of Energy Storage, 18, 110–123.

[6] Kim, M., et al. (2024). Efficient computation of robust, safe, fast charging protocols for lithium-ion batteries. Journal of The Electrochemical Society, 171(9), 090517.

[7] Kim, M., & Junghwan, K. (2024). Advanced integrated fast-charging protocol for lithium-ion batteries by considering degradation. ACS Sustainable Chemistry & Engineering, 12(17), 6786–6796.

[8] Lee, D., et al. (2025). Development of a fast-charging strategy considering degradation in lithium-ion batteries. Case Studies in Thermal Engineering, 74, 107013.

[9] Zhang, Z., et al. (2025). Fast-charging optimization method for lithium-ion battery packs based on deep deterministic policy gradient algorithm. Batteries, 11(5), 199.

[10] Xie, W., et al. (2020). Challenges and opportunities toward fast-charging of lithium-ion batteries. Journal of Energy Storage.

[11] Leijon, J. (2025). Charging strategies and battery ageing for electric vehicles: A review. Energy Strategy Reviews, 57, 101641.

[12] Fuchs, L., et al. (2023). Optimal fast charging of lithium-ion batteries: Between model-based and data-driven methods. Journal of The Electrochemical Society, 170(12), 120508.

[13] Industrial & Engineering Chemistry Research. (2024). Balancing charging efficiency and thermal safety: Comparative analysis of multistage constant current charging protocols. Industrial & Engineering Chemistry Research, 63(22), 10054–10066.

[14] Xie, W., et al. (2020). Lithium-ion battery fast charging: A review. Journal of Power Sources.

[15] Hannan, M. A., et al. (2020). Review of fast charging strategies in lithium-ion battery electric vehicles. Renewable and Sustainable Energy Reviews.

[16] Shen, W., et al. (2012). Experimental comparison of charging algorithms for a lithium-ion battery. Conference Paper.

[17] Chowdhury, M. A., et al. (2024). Adaptive safe reinforcement learning-enabled optimization of battery fast-charging protocols. arXiv preprint.

[18] Yuan, M., & Zou, C. (2025). Lifelong reinforcement learning for health-aware fast charging of lithium-ion batteries. arXiv preprint.

[19] Lu, Z., et al. (2024). Integrated optimal fast charging and active thermal management of lithium-ion batteries in extreme ambient temperatures. arXiv preprint.

[20] Azimi, V., et al. (2022). Extending life of lithium-ion battery systems via optimal control-based active balancing strategy. arXiv preprint.

[21] Cheng, Y., et al. (2024). Data-driven quantification of battery degradation modes via critical features from charging. arXiv preprint.

[22] Barré, A., et al. (2013). Review on lithium-ion battery ageing mechanisms and estimations for automotive applications. Journal of Power Sources, 241, 680–689.

[23] Keil, P., & Jossen, A. (2016). Charging protocols for lithium-ion batteries and their impact on cycle life. Journal of Power Sources.

[24] Notten, P. H. L. (2005). Boostcharging Li-ion batteries: Charging without degradation? Journal of Power Sources, 147, 203–208.

[25] Xiong, R., et al. (2023). Model-based battery charging optimization framework for trade-offs between time and degradation. Automotive Innovation, 6, 204–219.