Battery states online estimation based on exponential decay particle swarm optimization and proportional-integral observer with a hybrid battery model

Xiaolong Yang, Yongji Chen, Bin Li and Dong Luo

Energy, 2020, vol. 191, issue C

Abstract: The rational design of the battery management system requires a high-fidelity battery state estimation method. However, the nonlinear varieties of battery parameters, external interference and the dynamic behaviors of battery capacity will cause big problems for battery state estimation. A new state estimation method for lithium-ion batteries is proposed. A hybrid battery model is firstly established to better reflect the dynamic behaviors of battery capacity and voltage. Then the model parameters are identified online using the exponential decay particle swarm optimization (EDPSO). Finally, a proportional-integral observer (PIO) is designed for battery state-of-charge (SOC) estimation. In addition, the battery maximum available capacity (Cmax) is online estimated using the accumulated charge and variation of battery open-circuit voltage (OCV), which helps to update SOC estimation at different aging cycles. A verifying experiment is carried out based on the urban dynamometer driving schedule (UDDS) cycles. The results indicate that the proposed method has good performance and high accuracy. The online estimated parameters consist well with experimental data, the error of the terminal voltage is less than 0.02 V. The error of the estimated SOC can be controlled within 1%. Moreover, the estimated capacity could converge in 12 min with an error less than 2%.

Keywords: Lithium-ion battery; Hybrid battery model; State of charge estimation; Maximum available capacity estimation; Exponential decay particle swarm optimization; Proportional-integral observer (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:191:y:2020:i:c:s0360544219322042

DOI: 10.1016/j.energy.2019.116509

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