A novel health indicator for online health estimation of lithium-ion batteries using partial incremental capacity and dynamic voltage warping

Li, Lianfeng; Li, Yang; Cui, Weiwei; Chen, Zhiwei; Wang, Dexin; Zhang, Bo; Hong, Dongpao

doi:10.1016/j.jpowsour.2022.231961

Cited by 14 publications

(3 citation statements)

References 46 publications

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“…Since noise always exists in current and voltage measurements, a filter is usually needed to smooth the IC curve [52,67,68]. Usually, a low-pass filter [69], Savitzky-Golay filter [70,71] and Kalman filter [72] have been used to process the IC curve for reducing the noise sensitivity. A two dimensional Luenberger-Gaussian-moving-average filter is designed in [68] to enhance the IC curve extraction.…”

Section: B Analysis-based Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

et al. 2022

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With the widespread use of Lithium-ion (Li-ion) batteries in Electric Vehicles (EVs), Hybrid EVs and Renewable Energy Systems (RESs), much attention has been given to Battery Management System (BMSs). By monitoring the terminal voltage, current and temperature, BMS can evaluate the status of the Li-ion batteries and manage the operation of cells in a battery pack, which is fundamental for the high efficiency operation of EVs and smart grids. Battery capacity estimation is one of the key functions in the BMS, and battery capacity indicates the maximum storage capability of a battery which is essential for the battery State-of-Charge (SOC) estimation and lifespan management. This paper mainly focusses on a review of capacity estimation methods for BMS in EVs and RES and provides practical and feasible advice for capacity estimation with onboard BMSs. In this work, the mechanisms of Li-ion batteries capacity degradation are analyzed first, and then the recent processes for capacity estimation in BMSs are reviewed, including the direct measurement method, analysis-based method, SOC-based method and data-driven method. After a comprehensive review and comparison, the future prospective of onboard capacity estimation is also discussed. This paper aims to help design and choose a suitable capacity estimation method for BMS application, which can benefit the lifespan management of Li-ion batteries in EVs and RESs.

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Section: B Analysis-based Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

et al. 2022

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“…Lyu et al (2021) established a model-data-fusion method for battery SOH estimation and RUL prognostics by combining a data-driven battery degradation model and a particle filter, and battery aging tests verified the proposed model-data-fusion method. Li et al (2022a) built a novel health indicator for online health estimation of lithium-ion batteries by combining an interpolation method and incremental capacity curve filtering. Ma et al (2021) built a new framework for capacity regeneration point detection and RUL prognostics through the particle filter and autoregressive model.…”

Section: Frontiers In Energy Researchmentioning

confidence: 99%

Remaining useful life prognostics of lithium-ion batteries based on a coordinate reconfiguration of degradation trajectory and multiple linear regression

Chen

Li²,

Cui³

et al. 2023

Front. Energy Res.

Self Cite

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Lithium battery has been widely applied as new energy to cope with pressures in both form environment and energy. The remaining useful life (RUL) prognostics of lithium-ion batteries have become more critical. Convenient battery life prediction allows early detection of performance deficiencies to help maintain the battery system promptly. This paper proposes a RUL prognostics model of lithium-ion batteries based on a coordinate reconfiguration of degradation trajectory and multiple linear regression. First, a new sampling rule is used to reconfigure the coordinates of degradation data of new batteries and truncated similar batteries. Then, the relationship between similar and new lithium-ion batteries is established by using the reconfiguration data. Moreover, a new RUL prognostics model based on a coordinate reconfiguration of degradation trajectory and multiple linear regression is established by considering the influence of time-varying factors, which can improve prediction accuracy with small sample data and significantly reduce product development time and cost.

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“…The equalization circuit enables a consistent control of individual cells by transferring energy. The function of the state estimation is to capture the internal states of the battery, including state of charge (SOC) and state of health (SOH), thus, providing data support for controlling, operating, and maintaining the battery energy storage system [9,10]. The protection and warning function combines external measurement and the internal state to realize the regulation of and protection against abnormal working conditions during the battery's operation, as well as warnings of safety-related failures [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Fast Impedance Spectrum Construction for Lithium-Ion Batteries Using a Multi-Density Clustering Algorithm

Zhu,

Peng,

Meng

et al. 2024

Batteries

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Effectively extracting a lithium-ion battery’s impedance is of great importance for various onboard applications, which requires consideration of both the time consumption and accuracy of the measurement process. Although the pseudorandom binary sequence (PRBS) excitation signal can inject the superposition frequencies with high time efficiency and an easily implementable device, processing the data of the battery’s impedance measurement is still a challenge at present. This study proposes a fast impedance spectrum construction method for lithium-ion batteries, where a multi-density clustering algorithm was designed to effectively extract the useful impedance after PRBS injection. According to the distribution properties of the measurement points by PRBS, a density-based spatial clustering of applications with noise (DBSCAN) was used for processing the data of the lithium-ion battery’s impedance. The two key parameters of the DBSCAN were adjusted by a delicate workflow according to the frequency range. The validation of the proposed method was proved on a 3 Ah lithium-ion battery under nine different test conditions, considering both the SOC and temperature variations.

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A novel health indicator for online health estimation of lithium-ion batteries using partial incremental capacity and dynamic voltage warping

Cited by 14 publications

References 46 publications

A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

Remaining useful life prognostics of lithium-ion batteries based on a coordinate reconfiguration of degradation trajectory and multiple linear regression

Fast Impedance Spectrum Construction for Lithium-Ion Batteries Using a Multi-Density Clustering Algorithm

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