Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

Tang, Shengjin; Chen, Yu; Wang, Xue; Guo, Xiaoqing; Si, Xiaosheng

doi:10.3390/en7020520

Cited by 229 publications

(177 citation statements)

References 53 publications

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“…Thus, the data from Battery No. 5's current states are used to determine the iterative multi-step prediction model parameters by using Equations (16) and (17), whereas Battery No. 5 is used for testing to show the capacity fading trend compared to the forecast at 40, 60, and 80 cycles due to Battery No.…”

Section: Prognostic Results and Discussionmentioning

confidence: 99%

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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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Section: Prognostic Results and Discussionmentioning

confidence: 99%

“…Battery failure could lead to reduced performance, operational impairment, and even catastrophic failure. Therefore, estimating the end of life (EOL) or providing the remaining useful life (RUL) [16,17] estimates of lithium-ion batteries plays a significant role in PHM.…”

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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“…He et al [48] put forward the multi-scale Gaussian process modeling method in wavelet analysis, which was based on the degradation data of lithium batteries in constant-current discharge to predict the RUL. Tang et al [49] proposed a RUL prediction method based on measure errors of the Wiener process, which can better predict the battery RUL.…”

Section: Rul Prognostics Methodologies Based On the Stochastic Degradmentioning

confidence: 99%

“…Now, existing research on lithium battery degradation modeling and RUL is aimed at fixed loads that build a relationship model of cycles and degradation through the constant current discharge to predict the RUL. Although studies [38,48,49,51] have considered several groups of lithium battery RUL prediction problems under different constant discharge currents, they did not consider the random variation of the current under actual operation process. Random-variable current affects battery degradation rate, which results in the fact that lithium battery degradation function is a nonlinear time-varying function influenced by random effects.…”

Section: Problem Analysismentioning

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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“…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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Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

Cited by 229 publications

References 53 publications

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

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

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

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