Particle Filter Based Robust State and Parameter Estimation for Estimating SOC and Discharge Current of Lithium Batteries
Abstract
The State of Charge (SOC) of lithium batteries can be estimated effectively by particle Filter (PF). However, it is difficult to accurately obtain the value of the parameter discharge current of the lithium battery in the actual process, which affects the accuracy of the PF for SOC estimation. In order to solve the problem, this paper proposes a particle filter based robust state and parameter estimation (PF-RSPE) method for estimating SOC and discharge current of lithium batteries. The method estimates SOC and the unknown parameter discharge current simultaneously. The unknown parameter is expanded to the states of the model. The kernel smoothing method with the fitness factor β is applied in the procedures of the simultaneous estimation, which improves the performance of the estimation. Finally, the proposed method is applied to the equivalent model of Thevenin battery for simulation verification. The results show that when the parameter discharge current is unknown, the PF-RSPE method can effectively estimate both SOC and discharge current.
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Particle filtering requires more computational effort than Kalman filtering and is especially suitable for strongly nonlinear systems.
“The above approaches of particle filtering require more computational effort as compared to the Kalman filtering; it is especially suitable for strongly nonlinear systems [63]”
From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [63]
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Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement
Rehman, A. (2024) · Power System Technology · doi:10.52783/pst.906 · Cited by 12 · see citing works
- Authors
- He, Y., Hong, Z., Zhang, Z., & Huang, S.
- Affiliations
- Wenzhou University (CN)
- Venue
- 2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)
- Publisher
- IEEE
- Published
- 2022-08-03
- OpenAlex
- W4293517998
- Cited by
- 2
- References
- 0
- Citations by year
- 2024: 2
- FWCI
- 0.51
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Data Driven Control and Learning Systems Conference (DDCLS) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- lithium batteries, state of charge estimation, particle filter, simultaneous state and parameter estimation, kernel smoothing, Thevenin equivalent circuit model, robust estimation
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