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SOC Prediction and Health Management System of UPS Based on EKF-Markov
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Jundong FU, Haojie CHEN, Xiang SUN, Shenshen LIU
Journal of Power Supply | 2024, 22(6) : 225 - 233
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Journal of Power Supply | 2024, 22(6): 225-233
• Battery and Energy Storage •
SOC Prediction and Health Management System of UPS Based on EKF-Markov
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Jundong FU, Haojie CHEN, Xiang SUN, Shenshen LIU
Affiliations
  • School of Electrical and Automation Engineering East China Jiaotong University Nanchang 330013 China
Published: 2024-11-30 doi: 10.13234/j.issn.2095-2805.2024.6.225
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According to the demand of enterprises which produce UPS, a condition based maintenance(CBM) management system of UPS based on extended Kalman filter(EKF)-Markov is designed. Under the permission of users, the status data of online position and real-time operation of the equipment is visualized by using the geographic information system. Compared with the traditional post-maintenance scheme, the weighted method is used in data preprocessing to model the CBM of the data-driven collected information and reduce differences caused by different types of data. The EKF is used to eliminate the influence of noise on the sampling results, and the average error of state-of-charge(SOC)predicted using the algorithm is 0.434 3%. Combined with the Markov decision process, the UPS battery state is analyzed, the health management and CBM strategy in charge-change mode is implemented, and the maintenance time is reduced by 57.12% on average. Results show that compared with the traditional maintenance, the state prediction and health management system can improve the maintenance efficiency and accelerate the transformation from traditional planned maintenance to CBM mode.

Uninterruptible power supply(UPS)  /  condition based maintenance(CBM)  /  state-of-charge(SOC) prediction  /  extended Kalman filter(EKF)  /  Markov decision  /  state prediction and health management
Jundong FU, Haojie CHEN, Xiang SUN, Shenshen LIU. SOC Prediction and Health Management System of UPS Based on EKF-Markov[J]. Journal of Power Supply, 2024 , 22 (6) : 225 -233 . DOI: 10.13234/j.issn.2095-2805.2024.6.225
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Year 2024 volume 22 Issue 6
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Article Info
doi: 10.13234/j.issn.2095-2805.2024.6.225
  • Receive Date：2021-12-20
  • Online Date：2025-07-19
  • Published：2024-11-30
Article Data
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  • Received：2021-12-20
  • Revised：2022-03-06
  • Accepted：2022-03-09
Affiliations
    School of Electrical and Automation Engineering East China Jiaotong University Nanchang 330013 China
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