Abstract
Detection of battery power has always been the core of the battery management system of electric vehicles, and the fast and accurate estimation of charged state can guarantee the safe operation of electric vehicles. The key to improving accurate state-of-charge estimation is an appropriate model establishment coupled with a suitable estimation algorithm. This research seeks to adopt and accomplish a lithium-ion battery state-of-charge estimation based on the Gaussian function to build up the open-circuit voltage algorithm. A reduced-order extended Kalman filtering algorithm is proposed with hybrid pulse power characterization parameter identification to estimate the battery characterization state-of-charge. The model’s parameters in different state-of-charge points are calculated through the lithium-ion battery’s charge and discharge process; the 2RC modeling correction method and Reduced-order extended Kalman filter method are used separately based on the High-order equivalent 2RC modeling. The Experimental results show that the above method can achieve state-of-charge estimation more accurately and conveniently, providing a certain reference value for the rational management and distribution of power lithium-ion batteries. The maximum error of state-of-charge estimation based on the established high-order equivalent 2RC model using the Reduced-order extended Kalman filtering algorithm is less than 1.85%. The REKF algorithm achieved a maximum voltage error of 0.0409V and an average error of 0.0299V and therefore can satisfy the accuracy of the battery management system application needs. Keywords: Lithium-ion battery; state-of-charge; high-order equivalent 2RC modeling; open-circuit voltage; parameter identification; reduced-order extended Kalman filtering algorithm DOI: 10.7176/JETP/11-3-03 Publication date: June 30 th 2021
Highlights
As the technology in today’s society becomes increasingly mobile and as information networks grow, the importance of lithium-ion batteries (LIBs) increases considerably with its use in most electronic equipment and many other applications because of a range of advantages, such as a high operating voltage, small self-discharge, and no memory effects
To improve the performance of LIBs, many researchers have studied the high efficiency of SOC estimation of the LIB and there are several methods www.iiste.org proposed to estimate the SOC
The OCV curve at 40, 30, 20, 10, and 0 degrees describe a more accurate OCV-SOC curve, and the results show that the improved OCV method can elevate SOC estimation effects
Summary
As the technology in today’s society becomes increasingly mobile and as information networks grow, the importance of lithium-ion batteries (LIBs) increases considerably with its use in most electronic equipment and many other applications because of a range of advantages, such as a high operating voltage, small self-discharge, and no memory effects. The high-order 2RC equivalent model is utilized in this paper because it provides an accurate approximation for the lithium-ion battery dynamics model structure The Kalman filter algorithm estimates the essence of the lithium-ion battery SOC by using the ampere-time integral method to calculate the SOC and uses the measured voltage value to correct the SOC value as shown in Equation (3) and (4). The voltage and current curves of the whole HPPC experimental pulse charge and discharge process correspond to the High-order 2RC equivalent model showing the polarization effect of the battery. The open-circuit voltage method is mainly used for offline estimation, as well as the high-precision SOC estimation methods such as the neural network method, fuzzy reasoning method, and the Kalman filtering method
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