Abstract
Optimal performance of a proactive Battery Management System (BMS) needs real time knowledge of internal states of the battery such as Sate-of-Charge (SOC), State-of-Health (SOH) and battery power [1]. The effect of inaccurate state estimations may cause severe damage to the battery. The physics based battery models that could predict the internal states are inherently computationally challenging. This is difficult for implementation in dedicated microprocessors or microcontrollers with limited on-chip memory and low CPU clock. Efforts have been made in the past to come up with different low complexity and reformulated models which are capable of being implemented in microcontrollers for BMS operation [2-4].In this work, we propose reformulated physics-based electrochemical models which are highly computationally efficient to deploy in low cost microcontrollers (like 32-bit AVR or Beagle Bone) for accurate state estimation of batteries. This talk will particularly focus on numerical simulation techniques that enable real-time simulation and optimization in microcontroller environment. Acknowledgements The authors acknowledge financial support provided by the US Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E), under award number DE-AR0000275.
Published Version
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