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A Novel Fast-Charging Framework Based on Model Predictive Control
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DOI：10.1002/est2.70293.png)
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Abstract
En 中文
Charging strategy optimization for lithium-ion batteries is crucial to improve the efficiency of new energy devices. In this study, three conventional methods are compared: constant-current constant-voltage (CCCV) charging is the most efficient but slowest, pulse charging (PC) is the fastest but least efficient, and multistage constant-current (MSCC) is a compromise between speed and efficiency. To this end, we propose a dynamic optimal charging strategy based on model predictive control (MPC) that balances rapid-charging speed with battery safety. By integrating a low-order electrochemical-thermal-aging coupled model with real-time state estimation provided by an extended Kalman filter (EKF), a rolling-horizon framework is established to track both state-of-charge (SOC) and temperature reference trajectories. Experiments show that EKF has stronger initial error robustness (maximum deviation < 2%) than unscented Kalman filter (UKF) and unscented Kalman Bucy filter (UKBF), which provides reliable feedback for MPC. The new strategy achieves an optimal balance between charging efficiency and safety by dynamically adjusting the charging profile and significantly improves the charging speed under closed-loop control compared to the CCCV method, while controlling the temperature rise within 5 degrees C.
Keywords:
battery state observer
charging strategy
lithium-ion batteries
model predictive control
state of charge
Journal
E
IF:
4
Papers:
985
Citations:
2.2K