Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

Saha, Bhaskar; Goebel, Kai; Poll, Scott; Christophersen, Jon P.

doi:10.1109/tim.2008.2005965

Cited by 693 publications

(334 citation statements)

References 7 publications

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“…One is analyzing HIs under a constant discharge process, and the other is achieved under a constant charge process. For example, Saha et al [19] used the battery's impedance as the HI for the SOH of battery, and Tong et al [20] considered the open-circuit voltage, both under the circumstances of constant discharge. Moreover, Liu et al [21] discovered that the time interval of equal discharging voltage difference in each discharge cycle can be used as a HI to represent the capacity degradation; Li et al [22] used the temperature-change rate as the battery's HI.…”

Section: Algorithms/methods Advantages Disadvantagesmentioning

confidence: 99%

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Tang

et al. 2017

Energies

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Lithium-ion batteries are widely used in many systems. Because they provide a power source to the whole system, their state-of-health (SOH) is very important for a system's proper operation. A direct way to estimate the SOH is through the measurement of the battery's capacity; however, this measurement during the battery's operation is not that easy in practice. Moreover, the battery is always running under randomized loading conditions, which makes the SOH estimation even more difficult. Therefore, this paper proposes an indirect SOH estimation method that relies on indirect health indicators (HIs) that can be measured easily during the battery's operation. These indicators are extracted from the battery's voltage and current and the number of cycles the battery has been through, which are far easier to measure than the battery's capacity. An empirical model based on an elastic net is developed to build the quantitative relationship between the SOH and these indirect HIs, considering the possible multi-collinearity between these HIs. To further improve the accuracy of SOH estimation, we introduce a particle filter to automatically update the model when capacity data are obtained occasionally. We use a real dataset to demonstrate our proposed method, showing quite a good performance of the SOH estimation. The results of the SOH estimation in the experiment are quite satisfactory, which indicates that the method is effective and accurate enough to be used in real practice.

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Section: Algorithms/methods Advantages Disadvantagesmentioning

confidence: 99%

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Tang

et al. 2017

Energies

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“…As a steadily growing subject, prognostics have advanced expertise in various disciplines [2]. Many breakthroughs in remaining useful life estimation can be found in complex engineering systems such as electronics [3,4], batteries [5,6], actuators [7], turbofan engines [8,9] and NASA's launch vehicles and spacecraft systems [10].…”

Section: Introductionmentioning

confidence: 99%

Reducing Dimensionality of Multi-regime Data for Failure Prognostics

Bektaş

Alfudail

Jones

2017

J Fail. Anal. and Preven.

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Over the last decade, the prognostics and health management literature has introduced many conceptual frameworks for remaining useful life predictions. However, estimating the future behavior of critical machinery systems is a challenging task due to the uncertainties and complexity involved in the multi-dimensional condition monitoring data. Even though many studies have reported promising methods in data processing and dimensionality reduction, the prognostics applications require integration of these methods with remaining useful life estimations. This paper describes a multiple linear regression process that reduces the number of data regimes under consideration by obtaining a set of principal degradation variables. The process also extracts health indicators and useful features. Finally, a state-space model based on frequencydomain data is used to estimate remaining useful life. The presented approach is assessed with a case study on turbofan engine degradation simulation dataset, and the prediction performance is validated by error-based prognostic metrics.

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“…As a result, data-driven techniques draw more and more attention in SOH prognostics. In the literature, statistical, computational and artificial intelligence algorithms, such as autoregressive model [12], particle filter (PF) [13,14], Gaussian process regression [15], Wiener process [16], relevance vector machine (RVM) [17], Bayesian approach [18], support vector machine (SVM) [19] and neural networks [20,21] have been used for battery SOH and remaining useful life (RUL) prognostics in various applications. Capacity fade and impedance increase are the two most used health indicators of batteries.…”

Section: Introductionmentioning

confidence: 99%

A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

et al. 2016

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Abstract:State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framework.

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Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

Cited by 693 publications

References 7 publications

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Reducing Dimensionality of Multi-regime Data for Failure Prognostics

A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

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