Modeling of Lithium-ion Battery Considering Temperature and Aging Uncertainties

Battery;Modeling;Temperature;State of health;State of charge;Estimation;Automotive Systems Engineering;CECS Automotive Systems Engineering

Gong, XianzhiXi, Zhimin ;

University of Michigan

关键词: Battery;    Modeling;    Temperature;    State of health;    State of charge;    Estimation;    Automotive Systems Engineering;    CECS Automotive Systems Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/134041/Gong%20Dissertation%20Final.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

This dissertation provides a systematic methodology for analyzing and solving thetemperature and aging uncertainties in Li-ion battery modeling and states estimation inthe electric vehicle applications. This topic is motivated by the needs of enhancing theperformance and adaptability of battery management systems. In particular, temperatureand aging are the most crucial factors that influence battery performance, modeling, andcontrol.First, the basic theoretical knowledge of Li-ion battery modeling and State of Charge(SoC) estimation are introduced. The thesis presents an equivalent circuit battery modelbased SoC estimation using Adaptive Extended Kalman Filter (AEKF) algorithm to solvethe initial SoC problem and provide good estimation result.Second, the thesis focuses on the understanding of the temperature-dependentperformance of Li-ion battery. The temperature influence is investigated throughElectrochemical Impedance Spectroscopy (EIS) tests to enhance the theoretical basisunderstanding and to derive model compensation functions for better model adaptabilityat different temperatures.Third, the battery aging mechanisms are revisited first and then a series of agingtests are conducted to understand the degradation path of Lithium-ion battery. Moreover,the incremental capacity analysis (ICA) based State of Health (SoH) estimation methodxivare applied to track battery aging level and develop the bias correction modeling methodfor aged battery.In the final phase, the study of parallel-connected battery packs is presented. Theinconsistency problem due to different battery aging levels and its influence toparallel-connected packs are discussed. Based on simulation and experimental test results,it shows that the current difference in parallel connected cells is increased significantly atlow SoC, despite the battery aging levels and the number of cells in parallel.In total, this dissertation utilizes physics-based battery modeling and statesestimation method to optimize battery management under temperature and aginguncertainties in electric vehicle applications. The unique contributions include developinganalytical compensation functions to improve equivalent circuit battery modeladaptability under temperature uncertainty and developing ICA based SoH estimation andbattery modeling method to overcome aging uncertainty.