Improved 2RC-PNGV Modeling and Adaptive Sage-Husa H Infinity Filtering for Battery Power State Estimation Based on Multi-Parameter Constraints

With the transformation of the global energy landscape, lithium-ion batteries have become an important component in the field of new energy storage. Accurate assessment of battery status plays a crucial role in efficiently utilizing electrical energy and extending the battery's service life. The key parameters of battery status include charging state (SOC) and power state (SOP). This paper constructs an improved 2RC-PNGV battery equivalent circuit model and introduces an innovative method to enhance the dynamics of particle swarm optimization. At the same time, an adaptive H infinity (<i>∞</i>) filtering algorithm based on Sage-Husa and a temperature-constrained SOP estimation method for lithium-ion batteries is designed. Among them, the real-time dynamic particle swarm optimization algorithm adjusts the forgetting factor in each iteration; the adaptive H<i>∞</i> filtering algorithm based on Sage-Husa improves the accuracy of SOC estimation by adapting the noise covariance matrix. Moreover, the multi-parameter constrained state estimation method for lithium-ion batteries can effectively track the changes in state quantities with different durations and instantaneous values. The improved forgetting factor least squares method has an error of fewer than 0.02 volts in the voltage simulation test, with high accuracy. The adaptive H<i>∞</i> filtering algorithm based on Sage-Husa achieves higher estimation accuracy in three complex operating scenarios, ensuring that the state quantity estimation error remains below 2%. The maximum estimation error of the multi-parameter constrained state quantity estimation method is less than 84.00 watts. These research results provide a solid theoretical foundation for ensuring the safety and efficient operation of batteries.