Neural Computing and Applications

, Volume 31, Issue 12, pp 8171–8183 | Cite as

Estimation algorithm research for lithium battery SOC in electric vehicles based on adaptive unscented Kalman filter

  • Bo Li
  • Shaoyi BeiEmail author
Machine Learning - Applications & Techniques in Cyber Intelligence


The state of charge (SOC) is a significant part of energy management for electric vehicle power battery, which has important influence on the safe operation of power battery and the judgment of driver’s operation. Because the battery SOC cannot be measured directly, many researchers use various estimation methods to obtain accurate SOC values. But the SOC is affected by the temperature, current, cycle life and other time-varying nonlinear factors, which make difficult to construct prediction model. The key problem of battery SOC estimation is the change rule of battery capacity. The Peukert equation is a good method for calculating the battery capacity. The traditional Peukert equation without considering the influence of temperature, but the differences of temperature lead to changes in the constants n and K of the Peukert equations. In this paper, the Peukert equation based on temperature, current change and cycle life is established to estimate the battery capacity. And the battery model state equation is established for estimation and measurement equations of charge and discharge parameters \( \left\{ {C_{\text{e}} ,R_{\text{e}} ,C_{\text{d}} ,R_{\text{d}} ,R_{0} } \right\} \) and \( V_{\text{OC}} \) by using the ampere-hour method and the second-order RC model. And the dynamic estimation of charge state of battery is realized by AUKF. The results show that the accuracy of the lithium battery SOC estimation algorithm based on the temperature, current and cycle life of the modified Peukert equation is about 8% higher than that of the traditional KF ampere-hour method.


State of charge Peukert equation AUKF Electric vehicle 



This project is supported by the National Natural Science Foundation of China (Grant No. 51705220), the Jiangsu Province Higher Education Natural Science Research Project (17KJD580001), the Jiangsu Provincial Higher Education Natural Science Research Major Project (17KJA580003), Foundation for Jiangsu Province ‘‘333 Project’’ Training Funded Project (BRA2015365).


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Vehicle and Traffic EngineeringJiangsu University of TechnologyChangzhouChina

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