Improved State of Charge Estimation for High Power Lithium Ion Batteries Considering Current Dependence of Internal Resistance

Cunxue Wu, Rujian Fu, Zhongming Xu and Yang Chen
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Cunxue Wu: College of Automotive Engineering, Chongqing University, Chongqing 40044, China
Rujian Fu: A123 Systems, LLC., Livonia, MI 48377, USA
Zhongming Xu: College of Automotive Engineering, Chongqing University, Chongqing 40044, China
Yang Chen: A123 Systems, LLC., Livonia, MI 48377, USA

Energies, 2017, vol. 10, issue 10, 1-17

Abstract: For high power Li-ion batteries, an important approach to improve the accuracy of modeling and algorithm development is to consider the current dependence of internal resistance, especially for large current applications in mild/median hybrid electric vehicles (MHEV). For the first time, the work has experimentally captured the decrease of internal resistance at an increasing current of up to the C-rate of 25 and developed an equivalent circuit model (ECM) with current dependent parameters. The model is integrated to extended Kalman filter (EKF) to improve SOC estimation, which is validated by experimental data collected in dynamic stress testing (DST). Results show that EKF with current dependent parameters is capable of estimating SOC with a higher accuracy when it is compared to EKF without current dependent parameters.

Keywords: Li-ion battery modeling; current dependence; state of charge estimation; extended Kalman filter; battery management system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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