A novel Gaussian model based battery state estimation approach: State-of-Energy

Hong Wen He*, Yong Zhi Zhang, Rui Xiong, Chun Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

112 Citations (Scopus)

Abstract

State-of-energy (SoE) is a very important index for battery management system (BMS) used in electric vehicles (EVs), it is indispensable for ensuring safety and reliable operation of batteries. For achieving battery SoE accurately, the main work can be summarized in three aspects. (1) In considering that different kinds of batteries show different open circuit voltage behaviors, the Gaussian model is employed to construct the battery model. What is more, the genetic algorithm is employed to locate the optimal parameter for the selecting battery model. (2) To determine an optimal tradeoff between battery model complexity and prediction precision, the Akaike information criterion (AIC) is used to determine the best hysteresis order of the combined battery model. Results from a comparative analysis show that the first-order hysteresis battery model is thought of being the best based on the AIC values. (3) The central difference Kalman filter (CDKF) is used to estimate the real-time SoE and an erroneous initial SoE is considered to evaluate the robustness of the SoE estimator. Lastly, two kinds of lithium-ion batteries are used to verify the proposed SoE estimation approach. The results show that the maximum SoE estimation error is within 1% for both LiFePO4 and LiMn2O4 battery datasets.

Original languageEnglish
Pages (from-to)41-48
Number of pages8
JournalApplied Energy
Volume151
DOIs
Publication statusPublished - 1 Aug 2015

Keywords

  • Akaike information criterion
  • Central difference Kalman filter
  • Electric vehicles
  • Gaussian model
  • Lithium-ion battery
  • State of energy

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