Model Based Dynamic Optimization Strategies for Lithium-Ion Batteries

Accurate estimation of battery internal states i.e. state of charge (SOC), state of health (SOH) and battery power is of great significance for safe and optimal performance of proactive Battery Management Systems (BMS) (1).

Mathematical models for lithium-ion batteries (2, 3) vary with complexity, computational costs and reliability of predictions. Incorporating more physicochemical phenomena in a model can improve its prediction accuracy but they cannot be directly employed for online control due to their high computational requirements. So, it is important to have reformulated battery models for online control and accurate predictions of internal states. Efforts have been made in past (4), to reformulate battery models to significantly reduce its computational expenses, allowing for real time control and simulation. 

In this work, we explored the use of an experimentally validated reformulated pseudo two dimensional model to derive the optimal charging profiles at different temperatures. The two distinct dynamic optimization strategies i.e. simultaneous optimization (5) and control vector parameterization (CVP) (6, 7, 8) to estimate optimal charging current profile for effective use of lithium-ion batteries are evaluated.   

 Acknowledgements

The work presented herein was funded in part by the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000275.

References

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