Predicting the state of health of VRLA batteries in UPS using data-driven method

Shang, Yitong; Zheng, Weike; Yan, Xiaoyun; Nguyễn, Đình Hòa; Jian, Linni

doi:10.1016/j.egyr.2023.04.264

Cited by 3 publications

(1 citation statement)

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“…When the power grid is in emergency conditions, these batteries are usually used to power critical loads. Lead-acid batteries, pioneers in the field of energy storage, are the most common type of rechargeable batteries globally [2] and represent the only closed-loop operational battery technology. Despite their relatively lower energy density, they exhibit robust performance, simple control, and lower costs, making them more environmentally friendly compared to other battery types.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Method-Based Estimation of Battery State-of-Health with Dynamical Response Characteristics of Load Surges

Fan,

Lin,

Huang

2024

Energies

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Battery state-of-health (SOH) estimation is an effective approach to evaluate battery reliability and reduce maintenance costs for battery-based backup power supply systems. This paper proposes a novel SOH estimation method for batteries, which only uses the response characteristics of load surges and is, therefore, non-destructive to the estimated battery and its system. The discrete wavelet transform (DWT) method based on multi-resolution analysis (MRA) is used for wavelet energy features extraction, and the fuzzy cerebellar model neural network (FCMNN) is introduced to design the battery SOH estimator. The response voltage signals to load surges are used in the training and detection process of the FCMNN. Compared to conventional methods, the proposed method only exploits characteristics of online response signals to the inrush currents rather than injecting interference signals into the battery. The effectiveness of the proposed method is validated by detailed simulation analysis and experiments.

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Section: Introductionmentioning

confidence: 99%