A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis

Zhang, Shuzhi; Xu, Guo; Dou, Xiaoxin; Zhang, Xiongwen

doi:10.1016/j.jpowsour.2020.228740

Cited by 149 publications

(38 citation statements)

References 43 publications

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“…Pouch commercial LiNiMnCoO 2 /graphite lithium‐ion battery with nominal capacity as 27 Ah was examined, whose specifications can be found in Ref. [29].…”

Section: Methodsmentioning

confidence: 99%

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An application‐oriented multistate estimation framework of lithium‐ion battery used in electric vehicles

2021

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Summary Considering prediction accuracy and adaptability to unpredictable operating conditions simultaneously, this paper presents an application‐oriented multistate estimation framework of lithium‐ion battery used in electric vehicles. Under static and dynamic operating conditions, three commonly used online model parameters identification algorithms, including extended Kalman filter (EKF), particle swarm optimization, and recursive least square, are compared first, whose comparison results show that EKF's comprehensive performance is optimal. Taking identified open‐circuit voltage as observation information, two first‐order EKFs are established to online estimate state‐of‐charge (SOC) and state‐of‐energy (SOE). To maintain high accuracy and reliability under unpredicted operating conditions, fixed accumulation charge and fixed accumulation energy are innovatively seen as triggers, successfully realizing periodically capacity (state‐of‐health) and maximum available energy prediction with estimated SOC and SOE. Finally, with identified model parameters and estimated battery states, peak discharge/charge power can be further calculated in real time. Notably, parameters tuning for multistate estimation is also discussed in this work. Furthermore, the feasibility and prediction accuracy of the proposed multistate estimation framework is verified with sophisticated driving simulation under different temperatures. The validation results indicate that the presented framework can provide precise and reliable multistate estimation with relatively low computation cost. Highlights An application‐oriented multistate estimation framework is proposed Different online model parameters identification methods are compared Parameters tuning for multistate estimation is discussed Fixed accumulation charge is innovatively taken as capacity updating trigger The feasibility of the proposed framework is verified under various temperatures

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“…Pouch commercial LiNiMnCoO 2 /graphite lithium‐ion battery with nominal capacity as 27 Ah was examined, whose specifications can be found in Ref. [29].…”

Section: Methodsmentioning

confidence: 99%

“…Based on the interaction relationship between capacity and SOC, capacity calculation formula can be easily derived from coulomb counting method, as presented in Ref. [29]. Following by this thought, many researchers have proposed dual/joint framework to co‐estimate these two states 30‐34 .…”

Section: Introductionmentioning

confidence: 99%

An application‐oriented multistate estimation framework of lithium‐ion battery used in electric vehicles

2021

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“…This method is referred to as the pdf-method and is a simplified variant of ICA where the need to fit a curve to the charge/discharge data are eliminated. Similarly as with ICA, the probability density function will exhibit clear peaks around voltage plateaus, that is, voltages that occur more fre-quently during a charge or discharge cycle, and the idea is that the state of the battery can be inferred by these peaks.A fusion of Coulomb counting and differential voltage analysis is proposed in (S. Zhang, Guo, Dou, & Zhang, 2020) as a model-free approach to obtain SOH estimation from constant current discharge data.…”

Section: Incremental Capacity Analysis (Ica) and Differential Voltage Analysis (Dva)mentioning

confidence: 99%

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

Vanem¹,

Alnes

Lam³

2021

PHM_CONF

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Battery systems are becoming an increasingly attractive alternative for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses data-driven diagnostics for state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. Thus, this paper presents a comprehensive review of different data-driven approaches to state of health modelling, and aims at giving an overview of current state of the art. Furthermore, the various methods for data-driven diagnostics are categorized in a few overall approaches with quite different properties and requirements with respect to data for training and from the operational phase. More than 300 papers have been reviewed, most of which are referred to in this paper. Moreover, some reflections and discussions on what types of approaches can be suitable for modelling and independent verification of state of health for maritime battery systems are presented.

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“…Unfortunately, original IC/DV curve is noisy, which must be fitted or smoothed primarily to extract some useful information. To address this issue, SOC‐based DVA was proposed in our previous work, 23 where two special points that can reflect SOC levels were identified from original DV curve to compute battery capacity successfully through CCM. Even so, it is still worth noting that ICA/DVA based method can only provide accurate SOH estimation under constant current charging/discharging mode, whose prediction accuracy may be lowered when operating current is variable.…”

Section: Introductionmentioning

confidence: 99%

A variable multi‐time‐scale based dual estimation framework for state‐of‐energy and maximum available energy of lithium‐ion battery

Zhang

Peng

Zhang

2021

Intl J of Energy Research

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Summary To lower the computation burden and enhance co‐estimation reliability under unpredicted operating conditions, this paper presents a novel variable multi‐time‐scale based dual estimation framework for state‐of‐energy (SOE) and maximum available energy. Through forgetting factor recursive least squares (FFRLS) based model parameters identification method, the first‐order RC model is online built firstly to simulate battery dynamics. Subsequently, identified model parameters are inputted into an adaptive extended Kalman filter to predict SOE. Meanwhile, with battery data and two estimated SOE, inaccurate maximum available energy can be further updated by FFRLS when energy accumulation reaches pre‐defined threshold. Especially, to determine the optimal macro time‐scale considering co‐estimation performance comprehensively, a multi‐objective decision analysis method by fusion of analytic hierarchy process and the entropy weight is innovatively proposed. The dual estimation accuracy and robustness ability of the proposed framework are verified with experimental data of Federal Urban Driving Schedule tests conducted under various temperatures, whose results show that the presented method has satisfactory co‐estimation accuracy and robustness ability. Furthermore, the comparison with other algorithms not only indicates the necessity of maximum available energy updating on SOE prediction but also the superiority of the presented framework on dual estimation accuracy and computational cost.

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A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis

Cited by 149 publications

References 43 publications

An application‐oriented multistate estimation framework of lithium‐ion battery used in electric vehicles

An application‐oriented multistate estimation framework of lithium‐ion battery used in electric vehicles

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

A variable multi‐time‐scale based dual estimation framework for state‐of‐energy and maximum available energy of lithium‐ion battery

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