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Research on Modeling and SOC Algorithm of LIFePO4 Battery

Author: ChenYongJun
Tutor: SunJinZuo
School: Harbin Institute of Technology
Course: Instrument Science and Technology
Keywords: Battery model SOC estimation Extended Kalman filter Lithium iron phosphate battery
CLC: TM912
Type: Master's thesis
Year: 2011
Downloads: 390
Quote: 2
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Abstract


In recent years, with the energy crisis, environmental issues have become increasingly prominent, lithium iron phosphate gradually to receive widespread attention, has been widely used in the automotive, backup power and other areas of its own security, pollution-free, and many performance advantages. Establish accurate model of the battery characteristics for the battery management system and the engineering development has a very important meaning, and as the power battery of the electric vehicle for the in-depth study of the battery, the accurate estimation of the state of charge (SOC) of battery to improve the life and vehicle performance is important. In order to establish an accurate model of the battery, the papers were a large number of charge and discharge test to study the performance characteristics of lithium iron phosphate HPPC composite pulse charge-discharge tests under different temperatures, different discharge rate, a different state of charge , obtain the charge-discharge both parameters. Combined with the existing equivalent circuit model for the first-order and second-order model improvements. By contrast verify the three conditions and the response of the actual working conditions, established two models have higher accuracy, the average error in the first-order correction model of the complex conditions 0.025V second-order correction model The average error of 0.0103V, later extended Kalman filter algorithm to estimate SOC provides accurate models. As one of the driving force of the development of the core technology of electric vehicles battery state of charge (SOC) estimation, the key to the industrialization of electric vehicles practical. This article first paper, we establish a model based on the extended Kalman filter algorithm to estimate the remaining capacity of the battery. Kalman filter algorithm were compared in four different conditions, first-order model Kalman filter algorithm is slightly higher than the second-order model algorithm precision accuracy in constant conditions, the second-order model algorithm in variable conditions higher accuracy. By the convergence of the second order model verification found in the complex operating conditions, in the initial error is 20%, the battery after 9 minutes of run, the estimation accuracy of the algorithm to converge to about 5%. Initial error of 50%, the algorithm after the 40-minute adjustment, the estimation accuracy of the algorithm to converge to within 10%. The dynamic performance of the the established correction model to better reflect battery, can basically meet the actual demand of simulation and precision; battery state of charge estimate for high accuracy, good dynamic adaptability, and has a faster convergence speed.

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CLC: > Industrial Technology > Electrotechnical > Independent power supply technology (direct power) > Battery
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