Lithium-ion battery SOH estimation based on improved particle filter

doi:10.19799/j.cnki.2095-4239.2020.0159

Abstract

Abstract:

With the increasing extensive use of lithium-ion batteries in electric vehicles and microgrid, much research has been performed to ensure a safe and reliable operation and reduce the maintenance costs of the lithium-ion battery management system (BMS). As one of the key functions of the BMS, the state of health (SOH) estimation is very important. A dynamic cuckoo search algorithm is proposed based on the analysis of the traditional cuckoo search optimization algorithm to improve the estimation accuracy. The relationship between search speed and accuracy is balanced by improving the step size and the discovery probability and by introducing the change trend of the function value into the step update equation. A dynamic cuckoo search algorithm is proposed to solve the particle degradation problem existing in the traditional particle filter itself. The particles are represented by cuckoo bird's nests. Moreover, the cuckoo group search simulation is used to guide the distribution of updated particles, while the improved dynamic cuckoo search is used to optimize the particle filter algorithm. The health index (HI) is extracted from the measurable parameters of the lithium-ion battery, and the mapping model between the HI index and the SOH is established and applied to the state space model observation. A battery SOH estimation method based on the improved particle filter algorithm is proposed. The experimental results show that this method is superior to the traditional particle filter algorithm and has good adaptability and accuracy in predicting the degradation process of lithium-ion batteries.

Key words: lithium-ion battery, particle filter algorithm, particle dilution, improved cuckoo search, SOH estimation, health indicator

CLC Number:

TM 912

Chao XU, Liwei LI, Yuxin YANG, Kai WANG. Lithium-ion battery SOH estimation based on improved particle filter[J]. Energy Storage Science and Technology, 2020, 9(6): 1954-1960.

Figures/Tables 6

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评价指标	ICS-PF/%	PF/%
RMSE	0.6452	0.8240
ME	2.3615	3.0948
MRE	2.5370	3.3232
AE	0.4935	0.6528

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