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Journal ArticleDOI

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

01 Jun 2013-Microelectronics Reliability (Pergamon)-Vol. 53, Iss: 6, pp 832-839
TL;DR: An improved GPR method is utilized—combination Gaussian Process Functional Regression (GPFR)—to capture the actual trend of SOH, including global capacity degradation and local regeneration, and results confirm that the proposed method can be effectively applied to lithium-ion battery monitoring and prognostics by quantitative comparison with the other GPR and GPFR models.
About: This article is published in Microelectronics Reliability.The article was published on 2013-06-01. It has received 375 citations till now. The article focuses on the topics: Prognostics & State of health.
Citations
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Journal ArticleDOI
Yaguo Lei1, Naipeng Li1, Liang Guo1, Ningbo Li1, Tao Yan1, Jing Lin1 
TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.

1,116 citations


Cites background from "Prognostics for state of health est..."

  • ...[351] improved long-term prediction performance of GPR by combining two covariance functions to capture the actual trends of both global degradation and local regeneration....

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Journal ArticleDOI
TL;DR: This review categorises data-driven battery health estimation methods according to their underlying models/algorithms and discusses their advantages and limitations, then focuses on challenges of real-time battery health management and discuss potential next-generation techniques.
Abstract: Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

538 citations


Cites methods from "Prognostics for state of health est..."

  • ...[141] utilized a combination of covariance functions and mean functions in GPR for multi-step-ahead prognostics....

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Journal ArticleDOI
19 Feb 2020-Joule
TL;DR: A timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches is presented, analyzed, and compared.

471 citations

Journal ArticleDOI
TL;DR: This work proposes Gaussian process (GP) regression for forecasting battery state of health, and highlights various advantages of GPs over other data-driven and mechanistic approaches.

399 citations


Cites background or methods from "Prognostics for state of health est..."

  • ...Batteries 5, 6, 7, and 18 (in the numbering of the online repository) were chosen to be analysed in the present work, since these have the most data-points; and because they have previously been chosen for analysis in earlier works [8,10], and hence the present selection facilitates a comparison with those works....

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  • ...[10] and used with the explicit mean function GP...

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  • ...[10] applied Gaussian process regression to battery capacity prediction, and showed that their predictive accuracy was improved when a linear or quadratic Explicit Mean Function (EMF; see Section 2....

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  • ...In particular, it is favourable over previous GP capacity estimation methods that use data from identical cells, which merely identify an optimal prior estimate for the parameters of a parametric model, which are then updated sequentially [10]....

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  • ...Kernel function selection is perhaps the most important aspect of GPmodelling, yet it has not been addressed in a principled manner in the aforementioned battery degradation literature [6,10,15]....

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Journal ArticleDOI
TL;DR: A novel Gaussian process regression (GPR) model based on charging curve is proposed in this paper, which has high SOH estimation accuracy and Covariance function design and the similarity measurement of input variables are modified so as to improve the SOH estimate accuracy.

393 citations

References
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Book
23 Nov 2005
TL;DR: The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
Abstract: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

11,357 citations


"Prognostics for state of health est..." refers background or methods in this paper

  • ...It can model the behavior of any system through the combination of the appropriate Gaussian process and realize prognostics combined with prior knowledge based on a Bayesian framework [28]....

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  • ...A periodic covariance function is generally used to model a function within a specific period [28]....

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  • ...Here we list the covariance functions that we applied in battery health prognostics [28,32]....

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  • ..., n for the training means [28] and for the test means u⁄....

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  • ...Generally, the hyper-parameters are needed to be optimized with the maximization of the log-likelihood function given by [28,32]:...

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Proceedings Article
27 Nov 1995
TL;DR: This paper investigates the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations.
Abstract: The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.

1,225 citations

01 Jan 1998
TL;DR: This chapter will assess whether the feedforward network has been superceded, for supervised regression and classification tasks, and will review work on this idea by Williams and Rasmussen (1996), Neal (1997), Barber and Williams (1997) and Gibbs and MacKay (1997).
Abstract: Feedforward neural networks such as multilayer perceptrons are popular tools for nonlinear regression and classification problems. From a Bayesian perspective, a choice of a neural network model can be viewed as defining a prior probability distribution over non-linear functions, and the neural network's learning process can be interpreted in terms of the posterior probability distribution over the unknown function. (Some learning algorithms search for the function with maximum posterior probability and other Monte Carlo methods draw samples from this posterior probability). In the limit of large but otherwise standard networks, Neal (1996) has shown that the prior distribution over non-linear functions implied by the Bayesian neural network falls in a class of probability distributions known as Gaussian processes. The hyperparameters of the neural network model determine the characteristic length scales of the Gaussian process. Neal's observation motivates the idea of discarding parameterized networks and working directly with Gaussian processes. Computations in which the parameters of the network are optimized are then replaced by simple matrix operations using the covariance matrix of the Gaussian process. In this chapter I will review work on this idea by Williams and Rasmussen (1996), Neal (1997), Barber and Williams (1997) and Gibbs and MacKay (1997), and will assess whether, for supervised regression and classification tasks, the feedforward network has been superceded.

795 citations

Journal ArticleDOI
TL;DR: Models of electrochemical processes in the form of equivalent electric circuit parameters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal framework to assess the remaining useful life of complex systems.
Abstract: This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochemical processes in the form of equivalent electric circuit parameters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal framework. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.

692 citations

Journal ArticleDOI
TL;DR: Li-ion battery prognostics and health monitoring have received more and more attention from a wide spectrum of stakeholders, including federal/state policymakers, business leaders, technical researchers, environmental groups and the general public.

601 citations