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Study on SOC Estimation of Lithium Iron Phosphate Battery for EV

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Tutor: ChenQiGong
School: Anhui University of Engineering
Course: Detection Technology and Automation
Keywords: Charge state,Battery model,Parameter Identification,Kalman filter
CLC: TM912
Type: Master's thesis
Year:  2011
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Abstract:
Increasingly attracted world wide attention from its zero-emission electric vehicles with energy and environmental issues have become increasingly prominent. The battery management system is one of the key technologies for the development of electric vehicles, and accurately estimate battery SOC (State of Charge) the became good battery management system running on the premise and key. This article is dedicated to pure electric vehicles using lithium iron phosphate battery SOC estimated battery equivalent circuit model parameter identification based on. Article first briefly introduces the research background, the development of electric vehicles at home and abroad, the SOC definition cited SOC estimation method; overview of the paper focuses on the content. Then, on the history of the development of lithium iron phosphate, structure and working principle are briefly introduced, lithium iron phosphate batteries of the ideal electric vehicles with power; several battery equivalent circuit model and from the battery electrochemistry angle of view, improved Thevenin equivalent circuit model, as used herein, lithium iron phosphate equivalent circuit model. Then the laboratory measured the relationship of lithium iron phosphate battery EOC and SOC experimental methods and procedures; battery equivalent circuit model parameters calculated using the circuit analysis method, and battery equivalent circuit model pulse experiments parameters to verify the accuracy and validity of the model used, to a kind of emotional understanding parameters; online identification of the model parameters using the fading memory of the recursive least squares method; using experimental EOC and SOC function The mathematical model of the relationship as the Kalman filter; Kalman filter algorithm to calculate the initial value of the battery SOC simulation results with the experimental results shows that this method can effectively estimate the battery initial SOC value, and accuracy is relatively high, paving the way to improve the accuracy of online estimate battery SOC; Finally, parameter identification with the Kalman filter the Joint algorithm (first parameter identification, and the identification results are applied to the SOC estimation) to iron phosphate lithium battery SOC estimation this method with the Ah counting method, under constant parameters Kalman filtering method by comparing simulation results show that the algorithm can greatly improve the accuracy of estimation of SOC.
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