Battery open-circuit voltage estimation by a method of statistical analysis

Snihir, Iryna; Rey, William; Verbitskiy, Evgeny; Belfadhel-Ayeb, Afifa; Notten, Peter H. L.

doi:10.1016/j.jpowsour.2005.11.090

Cited by 142 publications

(40 citation statements)

References 4 publications

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“…Different procedures have been published where the OCV is approximated with data sampled during lapses shorter than a day, with varying accuracies; see, for instance, [11][12][13][14][15]. The problem is harder when the time window is shortened: if the battery has to be drained faster, the discharge current has to be raised accordingly and the memory effects of the battery are no longer negligible.…”

Section: State Of the Art In The State Of Health Monitoringmentioning

confidence: 99%

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

et al. 2017

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A model-based virtual sensor for assessing the health of rechargeable batteries for cyber-physical vehicle systems (CPVSs) is presented that can exploit coarse data streamed from on-vehicle sensors of current, voltage, and temperature. First-principle-based models are combined with knowledge acquired from data in a semiphysical arrangement. The dynamic behaviour of the battery is embodied in the parametric definition of a set of differential equations, and fuzzy knowledge bases are embedded as nonlinear blocks in these equations, providing a human understandable reading of the State of Health of the CPVS that can be easily integrated in the fleet through-life management.

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Section: State Of the Art In The State Of Health Monitoringmentioning

confidence: 99%

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

et al. 2017

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“…The degradation mechanism of individual LiB materials has previously been studied [2,3] as has the relationship between the main factors of degradation and the discharge curve line [4][5][6][7][8][9]. Other studies have developed models for LiB lifetime estimation under constant environmental conditions [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Developing Evaluation Module for Battery Lifetime

Zhang¹,

Tanaka²,

Gu³

2017

dtcse

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Abstract. Lithium-ion batteries (LiBs) have seen increasing use in automobiles and buildings over the past decade. Usable lifetimes of several years are sufficient for LiBs used in electronic devices, but infrastructure applications require lifetimes measured in decades. Users of LiBs are faced with uncertainty about LiB lifetimes, which hinders the spread of LiBs. Predicting LiB performance degradation and evaluating useful lifetimes by examining present performance is therefore important for planning. Our research group has developed an evaluation model for LiBs that comprises an empirically obtained degradation speed database and a method for recognizing LiB usage patterns.

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“…Since the OCV-SOC curves remain almost invariant at different aging levels [27,28], this proposed reliability criterion can be suitable for the battery at different aging levels. The offline identification U oc can be obtained by using the previous estimated SOC to look it up in Table 1 at the corresponding temperature.…”

Section: Parameters Reliability Criterionmentioning

confidence: 99%

“…(2) The OCV error between the offline and online value serves as an assessment criterion to judge the reliability of the online parameters. Making use of the property that the OCV-SOC curves remain almost invariant at different aging levels [27,28], this method can apply to the whole life-cycle of the battery. (3) A fuzzy model is established to update R in real time to compensate the intrinsic model error, and the fuzzy rules are derived by using the verified prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries

et al. 2016

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Abstract:In the field of state of charge (SOC) estimation, the Kalman filter has been widely used for many years, although its performance strongly depends on the accuracy of the battery model as well as the noise covariance. The Kalman gain determines the confidence coefficient of the battery model by adjusting the weight of open circuit voltage (OCV) correction, and has a strong correlation with the measurement noise covariance (R). In this paper, the online identification method is applied to acquire the real model parameters under different operation conditions. A criterion based on the OCV error is proposed to evaluate the reliability of online parameters. Besides, the equivalent circuit model produces an intrinsic model error which is dependent on the load current, and the property that a high battery current or a large current change induces a large model error can be observed. Based on the above prior knowledge, a fuzzy model is established to compensate the model error through updating R. Combining the positive strategy (i.e., online identification) and negative strategy (i.e., fuzzy model), a more reliable and robust SOC estimation algorithm is proposed. The experiment results verify the proposed reliability criterion and SOC estimation method under various conditions for LiFePO 4 batteries.

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Battery open-circuit voltage estimation by a method of statistical analysis

Cited by 142 publications

References 4 publications

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

Developing Evaluation Module for Battery Lifetime

Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries

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