A State of Health Estimation Method for Lithium-Ion Batteries Based on Improved Particle Filter Considering Capacity Regeneration

Haipeng Pan, Chengte Chen and Minming Gu
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Haipeng Pan: School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China
Chengte Chen: School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China
Minming Gu: School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China

Energies, 2021, vol. 14, issue 16, 1-12

Abstract: Accurately estimating the state of health (SOH) of a lithium-ion battery is significant for electronic devices. To solve the nonlinear degradation problem of lithium-ion batteries (LIB) caused by capacity regeneration, this paper proposes a new LIB degradation model and improved particle filter algorithm for LIB SOH estimation. Firstly, the degradation process of LIB is divided into the normal degradation stage and the capacity regeneration stage. A multi-stage prediction model (MPM) based on the calendar time of the LIB is proposed. Furthermore, the genetic algorithm is embedded into the standard particle filter to increase the diversity of particles and improve prediction accuracy. Finally, the method is verified with the LIB dataset provided by the NASA Ames Prognostics Center of Excellence. The experimental results show that the method proposed in this paper can effectively improve the accuracy of capacity prediction.

Keywords: lithium-ion battery; capacity regeneration; capacity estimation; calendar time; improved particle filter (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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