Improved 2rc-Pngv Modeling and Adaptive Sage-Husa H∞ Filtering for Battery Power State Estimation Based on Multi-Parameter Constraints

Abstract

With the transformation of the world energy pattern, lithium-ion batteries have become an important part of the new energy storage field. The key parameters to accurately estimate the battery state are the state of charge and the state of power. To enhance the precise assessment of the lithium-ion battery's state of charge and power On the basis of considering the dynamic and static characteristics of the battery, this paper first establishes an improved 2RC-PNGV battery equivalent circuit model, introduces an innovative method for enhancing the dynamics of particle swarm optimization., designs an adaptive H∞ filtering algorithm based on Sage-Husa and a multi-parameter constraint-based State of Power (SOP) estimation method for lithium-ion batteries considering temperature. Among them, the real-time dynamic particle swarm optimization algorithm adjusts the forgetting factor during each iteration, while the adaptive H∞ filtering algorithm based on Sage-Husa aims to enhance State of Charge (SOC) estimation accuracy by adapting the noise covariance matrix. The SOP estimation method for lithium-ion batteries based on multi-parameter constraints can track instantaneous and different duration SOPs. The improved forgetting factor least square method demonstrates superior accuracy compared to the traditional approach, with an error of less than 0.02V in voltage simulation testing. Additionally, the adaptive H∞ filtering algorithm based on Sage-Husa achieves higher estimation precision, ensuring that SOC estimation errors remain below 2% across three complex operational scenarios. The maximum estimation error of the SOP estimation method for lithium-ion batteries based on multi-parameter constraints is less than 84.00W. Ultimately, various tests under different working conditions have demonstrated the enhanced algorithm's exceptional precision. This finding establishes a theoretical foundation for ensuring the safe and effective functioning of the battery.

Keywords: lithium-ion battery, Estimation strategy of SOC, Power state estimation strategy, Dynamic particle swarm optimization algorithm, H-infinity filtering, State joint estimation