Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression

Liu, Datong; Pang, Jingyue; Zhou, Jianbao; Yu, Peng; Pecht, Michael

doi:10.1016/j.microrel.2013.03.010

Cited by 396 publications

(175 citation statements)

References 19 publications

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“…Goebel et al [55] put forward a Gaussian process model to assess battery capacity degradation. Liu et al [46] constructed a battery data Gaussian process. They not only adopted the Gaussian process regression (GPR) method to give the uncertain interval of the RUL prediction and build the method system of lithium-ion battery online RUL prediction, but also set a RUL prediction verification and assessment experiment through NASA lithium battery validation data.…”

Section: Rul Prognostics Methodologies Based On the Stochastic Degradmentioning

confidence: 99%

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Guan

2016

Applied Sciences

158

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Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL) prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.

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Section: Rul Prognostics Methodologies Based On the Stochastic Degradmentioning

confidence: 99%

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Guan

2016

Applied Sciences

158

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“…5, 6 and 7. The prediction errors of published methods are obtained from reference [15,25,28]. It is clear that the proposed RTPF has much better prediction performance than the eight published methods.…”

Section: Prediction and Comparisonmentioning

confidence: 99%

“…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%

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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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“…The operation of a battery is dynamic, and the battery's performance is strongly influenced by the ambient temperature and different load conditions [11]. Battery-life prediction metrics are, most importantly, used to evaluate the state of health (SOH) [12][13][14][15] of batteries; the SOH measures the stored energy and the ability to deliver the available power. Battery failure could lead to reduced performance, operational impairment, and even catastrophic failure.…”

Section: Introductionmentioning

confidence: 99%

Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression

Wang

Zhao

et al. 2014

Energies

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Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is important for battery management systems. Traditional empirical data-driven approaches for RUL prediction usually require multidimensional physical characteristics including the current, voltage, usage duration, battery temperature, and ambient temperature. From a capacity fading analysis of lithium-ion batteries, it is found that the energy efficiency and battery working temperature are closely related to the capacity degradation, which account for all performance metrics of lithium-ion batteries with regard to the RUL and the relationships between some performance metrics. Thus, we devise a non-iterative prediction model based on flexible support vector regression (F-SVR) and an iterative multi-step prediction model based on support vector regression (SVR) using the energy efficiency and battery working temperature as input physical characteristics. The experimental results show that the proposed prognostic models have high prediction accuracy by using fewer dimensions for the input data than the traditional empirical models.

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Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression

Cited by 396 publications

References 19 publications

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

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

Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression

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