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

Abstract

Abstract:

Accurate estimation of lithium-ion (Li-ion) batteries' state of charge (SOC) is significant 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 between battery charging and discharging, the parallel network of diode resistors is adopted to replace the internal resistance of the traditional PNGV model. On this basis, a resistor-capacitor (RC) parallel network is employed to characterize the dynamic and static characteristics of the battery. Using a ternary Li-ion battery as the research object, online parameter identification of the improved model is performed using the forgetting factor recursive least square method. The main charging and discharging experiments were proposed to simulate and analyze the working characteristics of lithium batteries. 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%. In the main discharge process, the average error of SOC estimation is 0.807%, with a maximum estimation error of 3.38%. It is demonstrated that both the improved 2RC-PNGV model and the joint estimation algorithm can estimate SOC.

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

CLC Number:

TM 911

Yuyang LIU, Shunli WANG, Yanxin XIE, Weikang JI, Yixing ZHANG. Research on Li-ion battery modeling and SOC estimation based on online parameter identification and improved 2RC-PNGV model[J]. Energy Storage Science and Technology, 2021, 10(6): 2312-2317.

Figures/Tables 4

References 12

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