How to improve the limitations of SOP estimation methods for lithium-ion batteries?

With the expanding adoption of lithium-ion batteries in electric vehicles (EVs), accurately estimating state of power (SOP) online is critical for real-time battery management to ensure safe and efficient EV operation. However, existing online SOP estimation methods have limitations in terms of model accuracy, computational efficiency, and robustness to varying conditions. So I have some questions: 1. What are the limitations of existing online state of power (SOP) estimation methods for lithium-ion batteries? 2. How can these methods be improved, particularly in terms of the model structure, parameter identification techniques, and SOP estimation algorithms employed?

Limitations of SOP estimation methods include reliance on oversimplified models that cannot capture complex battery dynamics, use of inaccurate or inefficient parameter identification techniques, and SOP algorithms that lack adaptability to varying conditions. This leads to errors and unreliability in real-world EV usage.

Improvements should focus on developing enhanced physics-based models, advanced parameterization through real-time operating data, and adaptive techniques to estimate SOP. This could improve accuracy across dynamic loads, enhance computational speed, and strengthen resilience to changes over long-term usage. Specific opportunities exist in electrochemical modeling, recursive parameter identification, state observation techniques, and testing validated improvements under virtual or real automotive drive cycles. Advancing the fundamentals of the modeling, parameterization, and estimation process holds promise for next-generation EV battery management capabilities.


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