Abstract

State of Charge (SOC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of lithium-ion batteries. SOC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and distinct nonlinear behavior. This paper compares two of the basic methods and algorithms for SOC estimation of lithium-ion batteries (LIBs) focusing on the description of the two techniques in a test experiment and the elaboration of their differences for use in battery management systems (BMS) applications. A Reduced-order unscented Kalman filter method is used for estimation and tracking to realizer real-time high-precision estimation of lithium-ion battery state of charge. Experimental tests are carried out with a lithium-ion battery cell for model and state estimation validations. Many researchers have proposed different methods of estimating SOC that raised the challenge of establishing a relationship between the accuracy and robustness of the method. The experimental results of the OCV-SOC estimation method, Hybrid Pulse Power Characterization (HPPC) test, and Beijing Bus Dynamic Stress Test (BBDST) working condition method are analyzed. The error of SOC estimation based on the established Thevenin RC modeling using the Reduced-order-unscented Kalman filter is less than 0.3%. The result from the use of the reduced-order unscented Kalman filtering algorithm proves that it has high accuracy in the state of charge estimation of the lithium-ion batteries. Keywords : lithium-ion battery, open-circuit voltage, state of charge estimation, battery management system, HPPC test, reduced-order unscented Kalman filter DOI: 10.7176/JETP/11-6-05 Publication date: December 30 th 2021

Highlights

  • The lithium-ion battery (LIB) is perhaps the most used battery among the new energy sources and is more likely to replace lead-acid batteries

  • A battery based on the Thevenin RC modeling is used, taking into account its advantages of low error, long-term testing, and accounting polarization effects, and transient analysis for power battery charging and discharging

  • The Open-circuit voltage (OCV)-state of charge (SOC) and the Hybrid Pulse Power Characterization (HPPC) method of estimating SOC was analyzed through experiments

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Summary

Introduction

The lithium-ion battery (LIB) is perhaps the most used battery among the new energy sources and is more likely to replace lead-acid batteries. A lithium-ion battery and Nickel Metal Hydride battery (Ni-MH) is characterized by high capacity, long life, and high-power density, the present to be the main energy storage system of the electric vehicle going with the declining costs [32]. The economy is another very important aspect that will determine whether they can survive in the market competition. The estimation of SOC generally requires modeling of the battery cell and packs, the www.iiste.org structure of the battery model, and parameter identification should be based on characteristics of the chargedischarge test under different temperatures, which need sufficient test data of batteries.

Thevenin RC modeling
Reduced-order UKF Estimation
Implementation of the test platform
Conclusions
Findings
38. Chapter 7 - Lithium-Secondary Cell
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