Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries

Liu, Kailong; Hu, Xiaosong; Wei, Zhongbao; Li, Yang; Jiang, Yan

doi:10.1109/tte.2019.2944802

Cited by 271 publications

(117 citation statements)

References 36 publications

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“…However, a future improvement will be pursued by exploring the possibility of implementing a Gaussian process regression (GPR) technique [28,29] for extracting an accurate estimate of the actual shape detected by the scanner from the underlying noisy data. In this way, we could get rid of the initial parabolic assumption measuring possible focal displacements due to the actual reflector shape in operating conditions.…”

Section: Discussionmentioning

confidence: 99%

Laser Scanner-Based 3D Digitization for the Reflective Shape Measurement of a Parabolic Trough Collector

et al. 2020

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In concentrated solar power technology, the precise shape of the reflective surfaces is crucial for efficiency. Considering the geometry and size of a parabolic trough collector, measuring the actual shape is not trivial and some techniques can only be adopted during the assembly operations, evaluating only the manufacturing and alignment processes. The method proposed and tested in this work exploits a laser scanner-based three-dimensional digitization technique that can be used without any marker or other tools, and is attached to the structure. This technique is particularly suitable for assessing the behavior and the optical efficiency of the collectors under load and for validating a finite element model of the structure. The method defines the shape of the parabolic surface by collecting a 3D point cloud of the parabolic surface using a laser scanner. The measured form can then be compared with the ideal shape obtained from a finite element analysis of the structure subject to the gravity field. The comparison can also be performed when the collector is loaded by known forces or torques, with the finite element model reproducing the actual loading scenario. The object of the case study of this work was a 12 m wide full-scale prototype trough collector manufactured at the Politecnico di Milano. The uncertainty of the 3D measurements, acquiring twelve images in different positions, was verified to be less than 3.6 mm.

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Section: Discussionmentioning

confidence: 99%

Laser Scanner-Based 3D Digitization for the Reflective Shape Measurement of a Parabolic Trough Collector

et al. 2020

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“…2) ENTROPY CALCULATION Given a set of features F={ f 1 , f 2 ,…, f k } R nk , where k is the number of features, and n is the length of a feature. The MIC matrix of the set M(F) is defined as follows: 12 1…”

Section: ) Maximal Information Correlationmentioning

confidence: 99%

“…That is, the initial yardstick E is updated as the minimum value of E, N decreases by 1, and the while loop continues. Otherwise, the while loop ends (Steps [11][12][13][14][15][16][17][18][19]. Ultimately, the indexes of the selected features through the implementation of Algorithm 2 are outputted in I 2 (Steps 20-21).…”

Section: Algorithm 2 Function Inter_corr_check(pi1)mentioning

confidence: 99%

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State-of-Health Prognosis for Lithium-Ion Batteries Considering the Limitations in Measurements via Maximal Information Entropy and Collective Sparse Variational Gaussian Process

Xiang

Zhang

et al. 2020

IEEE Access

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Prognostics and health management (PHM) for electronic devices is intricate yet crucial in an era of electricity. The impending future of electric vehicles and clean energy requires more in-depth scrutiny and monitoring of their storage elements, lithium-ion batteries. State-of-health (SOH), as one of the most evident indicators for battery degradation quantifications, is a matter of vital importance that worths more attention. In this regard, this paper proposes a novel SOH prognostics framework for lithium-ion batteries considering the limitations in the recorded measurements through the use of linear statistical knearest neighbors (LSKNN) data interpolation, maximal information entropy search (MIES), and collective sparse variational Gaussian process regression (CSVGPR). First, the incomplete charging measurements are processed by LSKNN to infer the missing data points and suppress the unanticipated noises in the extracted temporal features, which indicate the trend of degradation. Then, the MIES scheme is proposed to filter the features that are extraneous to the SOHs of the corresponding batteries and that greatly correlate to the other features in the feature set. Finally, the CSVGPR model, considering the uncertainties within each of the sparse variational Gaussian processes, is utilized to implement SOH prognosis. The proposed framework is verified by a subset of the repository from NASA. In the test, multiple prognostics comparisons of innerbattery tests, cross-battery tests, and tests with other statistical learning methods are presented. The experiment results lend support to the superiority and effectiveness of the work. INDEX TERMS Lithium-ion batteries, prognostics and health management (PHM), maximal information entropy (MIE), sparse variational Gaussian process, k-nearest neighbors, state-of-health.

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“…Data-driven approaches, which generate the future predictions based on the collected data, have also been widely adopted. Through using advanced machine learning techniques such as support vector regression [26], Gaussian process regression [31,32] and neural network (NN) [33]- [35], these methods do not assume any battery degradation mechanism apriori, and turn out to be suitable for general battery types. With the development of vehicle data centers and cloud platforms [36], data-driven methods would gain a foreseeable popularity.…”

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confidence: 99%

Model Migration Neural Network for Predicting Battery Aging Trajectories

Tang

Liu

Wang

et al. 2020

IEEE Trans. Transp. Electrific.

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Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries

Cited by 271 publications

References 36 publications

Laser Scanner-Based 3D Digitization for the Reflective Shape Measurement of a Parabolic Trough Collector

Laser Scanner-Based 3D Digitization for the Reflective Shape Measurement of a Parabolic Trough Collector

State-of-Health Prognosis for Lithium-Ion Batteries Considering the Limitations in Measurements via Maximal Information Entropy and Collective Sparse Variational Gaussian Process

Model Migration Neural Network for Predicting Battery Aging Trajectories

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