A method for online capacity estimation of lithium ion battery cells using the state of charge and the transferred charge

Einhorn, M.; Conte, Fiorentino Valerio; Kral, Christian; Fleig, Jürgen

doi:10.1109/icset.2010.5684928

Cited by 29 publications

(29 citation statements)

References 10 publications

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“…In hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs), the battery pack's SOH can be defined using resistance since power losses in the pack can lead to lower acceleration or high fuel consumption in the vehicle. Although capacity can be estimated using a number of different approaches [1][2][3], the central focus of this paper will be algorithms for online estimation of the battery impedance.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

et al. 2018

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Safe and efficient operation of a battery pack requires a battery management system (BMS) that can accurately predict the pack state-of-heath (SOH). Although there is no universal definition for battery SOH, it is often defined based on the increase in the battery's internal resistance. Techniques such as extended Kalman filter (EKF) and recursive least squares (RLS) are two frequently used approaches for online estimation of this resistance. These two methods can, however, be computationally expensive, especially in the case of a battery pack composed of hundreds of cells. In addition, both methods require a battery model as well as chemistry specific parameters. Therefore, this paper investigates the performance of a direct resistance estimation (DRE) technique that requires minimal computational resources and can be implemented without any training data. This approach estimates the ohmic resistance only when the battery experiences sharp pulses in current. Comparison of results from the three algorithms shows that the DRE algorithm can accurately identify a degraded cell under various operating conditions while significantly reducing the required computational complexity. The findings will further advance diagnostic techniques for the identification of a weak cell in a large battery pack.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

et al. 2018

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“…Figure 6 shows the intrinsic manifold of battery #5 embedded in the eightdimensional space constructed by features extracted from the DV data. In this study, the capacity is estimated using either CC or DV data based on the geodesic distance on the intrinsic manifold, as calculated by (14). The estimated results for all the demonstration data (batteries #5, #7, #29, and #54) under the different operating conditions closely track the measured capacity, as illustrated in Figure 7.…”

Section: Resultsmentioning

confidence: 79%

“…Moreover, these models are individually dependent on the specific type of battery in terms of production processes, electrolytes, and anode and cathode materials. State-of-charge-(SOC-) opencircuit-voltage-(OCV-) based methods for in-cycle capacity estimation are widely applied in many real-world applications [13,14]. However, the SOC-OCV-based methods rely on accurate SOC and OCV values, which are usually highly time consuming to obtain [10,15].…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition

2017

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This study introduces visual cognition into Lithium-ion battery capacity estimation. The proposed method consists of four steps. First, the acquired charging current or discharge voltage data in each cycle are arranged to form a two-dimensional image. Second, the generated image is decomposed into multiple spatial-frequency channels with a set of orientation subbands by using nonsubsampled contourlet transform (NSCT). NSCT imitates the multichannel characteristic of the human visual system (HVS) that provides multiresolution, localization, directionality, and shift invariance. Third, several time-domain indicators of the NSCT coefficients are extracted to form an initial high-dimensional feature vector. Similarly, inspired by the HVS manifold sensing characteristic, the Laplacian eigenmap manifold learning method, which is considered to reveal the evolutionary law of battery performance degradation within a low-dimensional intrinsic manifold, is used to further obtain a low-dimensional feature vector. Finally, battery capacity degradation is estimated using the geodesic distance on the manifold between the initial and the most recent features. Verification experiments were conducted using data obtained under different operating and aging conditions. Results suggest that the proposed visual cognition approach provides a highly accurate means of estimating battery capacity and thus offers a promising method derived from the emerging field of cognitive computing.

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“…Previous research shows battery SOC and capacity are related through a linear coulomb counting equation [8]. The fundamental charge equation, the relation between charge and SOC for a module is given by (3) where the Q 0 is the initial charged stored.…”

Section: Weighting Factor Based Strategymentioning

confidence: 99%

“…A more careful design could be improve this. Capacity Estimation: The method adopted in this paper to track the capacity is similar to [8] where the SOC against capacity coulomb counting equation was used to estimate the capacity when the SOC was known or obtained from a SOC-OCV look-up table. In this work, the same equation is used to estimate the capacity where the SOC is calculated from SOC-OCV relationship.…”

Section: Online Battery Parameter Trackingmentioning

confidence: 99%

Adaptive control of hybrid battery energy storage systems under capacity fade

Mukherjee

Strickland

Varnosfaderani

2014

2014 16th European Conference on Power Electronics and Applications

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There is an increasing call for applications which use a mixture of batteries. These hybrid battery solutions may contain different battery types for example; using second life ex-transportation batteries in grid support applications or a combination of high power, low energy and low power, high energy batteries to meet multiple energy requirements or even the same battery types but under different states of health for example, being able to hot swap out a battery when it has failed in an application without changing all the batteries and ending up with batteries with different performances, capacities and impedances. These types of applications typically use multi-modular converters to allow hot swapping to take place without affecting the overall performance of the system. A key element of the control is how the different battery performance characteristics may be taken into account and the how the power is then shared among the different batteries in line with their performance. This paper proposes a control strategy which allows the power in the batteries to be effectively distributed even under capacity fade conditions using adaptive power sharing strategy. This strategy is then validated against a system of three different battery types connected to a multi-modular converter both with and without capacity fade mechanisms in place.

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A method for online capacity estimation of lithium ion battery cells using the state of charge and the transferred charge

Cited by 29 publications

References 10 publications

Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition

Adaptive control of hybrid battery energy storage systems under capacity fade

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