Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model
| Autor: | Dairong Hu, Liu Yuan, Lin Su, Guangxu Zhou, Yunhai Zhu |
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| Rok vydání: | 2021 |
| Předmět: |
Technology
Control and Optimization fractional order model Renewable Energy Sustainability and the Environment Energy Engineering and Power Technology Kalman filter Battery pack Lithium-ion battery Fractional calculus State of charge SOC estimation Robustness (computer science) Colors of noise Control theory Filter (video) Electrical and Electronic Engineering lithium ion battery Engineering (miscellaneous) Energy (miscellaneous) Mathematics |
| Zdroj: | Energies, Vol 14, Iss 6307, p 6307 (2021) Energies Volume 14 Issue 19 |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en14196307 |
| Popis: | Accurate estimation of the state of charge (SOC) of lithium batteries is paramount to ensuring consistent battery pack operation. To improve SOC estimation accuracy and suppress colored noise in the system, a fractional order model based on an unscented Kalman filter and an H-infinity filter (FOUHIF) estimation algorithm was proposed. Firstly, the discrete state equation of a lithium battery was derived, as per the theory of fractional calculus. Then, the HPPC experiment and the PSO algorithm were used to identify the internal parameters of the second order RC and fractional order models, respectively. As discovered during working tests, the parameters identified via the fractional order model proved to be more accurate. Furthermore, the feasibility of using the FOUHIF algorithm was evaluated under the conditions of NEDC and UDDS, with obvious colored noise. Compared with the fractional order unscented Kalman filter (FOUKF) and integer order unscented Kalman filter (UKF) algorithms, the FOUHIF algorithm showed significant improvement in both the accuracy and robustness of the estimation, with maximum errors of 1.86% and 1.61% under the two working conditions, and a terminal voltage prediction error of no more than 5.29 mV. |
| Databáze: | OpenAIRE |
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