Research on Li-ion Battery Modeling and SOC Estimation Based on Online Parameter Identification and Improved 2RC-PNGV Model

doi:10.19799/j.cnki.2095-4239.2021.0220-1

Abstract

Abstract:

Accurate estimation of the state of charge (SOC) of li-ion batteries is of great significance for battery safety detection and energy efficient utilization. A new verification model is proposed, firstly, the battery Partnership for a New Generation of Vehicles (PNGV) model is improved, considering the difference of battery charging and discharging, the parallel network of diode resistor is adopted to replace the internal resistance of the traditional PNGV model. On this basis, a RC parallel network is employed to characterize the dynamic and static characteristics of the battery. Taking ternary li-ion battery as the research object, online parameter identification of the improved model is carried out by forgetting factor recursive least square method, and the main charging and discharging experiments were put forward to simulate and analyze the working characteristics of lithium battery , the FFRLS-EKF algorithm is used to estimate the SOC under the custom DST condition. The experimental results show that the improved 2RC-PNGV model can reflect the operating characteristics of the li-ion battery well. The average voltage error of the HPPC experiment is 0.17%, the model has higher accuracy. The average error of SOC estimation in the main charging process is 0.957%, and the maximum estimation error is 5.03%. The average error of SOC estimation in the main discharge process is 0.807%, and the maximum estimation error is 3.38%. It is shown that both the improved 2RC-PNGV model and the joint estimation algorithm can both be used for the actual estimation of SOC.

Key words: Li-ion battery, PNGV model, Forgetting factor recursive least square, Online parameter identification, EKF

Figures/Tables 4

References 12

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