SOC Estimation Strategy and its Accuracy Analysis

Yuan Bin Yu; Zhou Cai; Kai Peng; Wen Qiang Lv

doi:10.4028/www.scientific.net/AMR.953-954.790

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The State of Charge Estimation of Lithium Battery in Electric Vehicle Based on Extended Kalman Filter
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 953-954SOC Estimation Strategy and its Accuracy Analysis

SOC Estimation Strategy and its Accuracy Analysis

Abstract:

Accurate battery state of charge is the prerequisite and precondition for optimal control of hybrid vehicles. This article will be based on the established dynamic model of battery, estimate the battery state of charge in real time. Firstly, analysis the application limitations of Kalman filtering algorithm estimates battery state of charge. Secondly, for some uncertain parameters contained in the model of battery system, paper proposes a parameter line identification extended Kalman filter algorithm to estimate the battery state of charge. Finally, experimental verification algorithm dynamic conditions in the battery state of charge estimation accuracy and effectiveness.

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Periodical:

Advanced Materials Research (Volumes 953-954)

Pages:

790-795

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.953-954.790

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Online since:

June 2014

Authors:

Yuan Bin Yu, Zhou Cai*, Kai Peng, Wen Qiang Lv

Keywords:

Experimental Verification, Kalman Filter, State of Charge (SOC)

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