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Enhanced Resilience in Battery Charging through Co-Simulation with Reinforcement Learning
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  • Mohammad Seyedi,
  • Kouhyar Sheida,
  • Savion Siner,
  • Farzad Ferdowsi
Mohammad Seyedi

Corresponding Author:[email protected]

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Kouhyar Sheida
Savion Siner
Farzad Ferdowsi


Controlling the charging and discharging procedures of Lithium-Ion Batteries is of paramount importance as violating safety constraints, such as current deviations, can lead to significant damage to the battery or circuit or interruption in service. Thus, it is crucial to employ a robust controller capable of handling uncertainties and unexpected scenarios. PI controllers have become prevalent in recent years for managing battery dynamics, but they exhibit limited robustness in unpredictable situations. In this paper, we propose a Reinforcement Learning (RL) driven control method as a substitute for the PI controller. The agent is trained using a co-simulation approach with simultaneous employment of Python and Matlab, ensuring an accurate estimation of the environment and, consequently, enhanced performance. A prototype of the proposed controller is developed using dSPACE rapid control prototyper. The performance is compared with the benchmark controller (PI) across different fault scenarios, considering three criteria: overshoot, undershoot, and stabilization time. The comparative analysis reveals that, in most scenarios, the RL agent outperforms the PI controller, exhibiting a remarkable 50% reduction in both overshoot and undershoot compared to the benchmark controller. This research contributes to advancing battery control systems by introducing an RL-based controller that proves to be a more robust alternative, delivering improved performance in the face of uncertainties and fault scenarios.
19 Feb 2024Submitted to TechRxiv
20 Feb 2024Published in TechRxiv