A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery

Liu, Kailong; Shang, Yunlong; Ouyang, Quan; Widanage, Widanalage Dhammika

doi:10.1109/tie.2020.2973876

Cited by 399 publications

(171 citation statements)

References 30 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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“…Due to the uncertain aging effect of lithium battery, the RUL prediction of lithium battery has a great challenge [64][65][66][67][68]. The RUL prediction approaches of lithium battery are mainly divided into three types: particle filter, artificial intelligence and stochastic process modeling.…”

Section: B a Practical Case Studymentioning

confidence: 99%

Remaining Useful Life Prediction Under Imperfect Prior Degradation Information

et al. 2020

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The remaining useful life (RUL) prediction is the core of equipment maintenance and decisionmaking. Accurate RUL prediction can make effective maintenance before the failure occurs to reduce the probability of equipment failure. However, in industrial practice, we often face the situation that prior information is insufficient or inaccurate, which could influence our prediction of RUL and reduce the prediction accuracy severely. To solve this problem, we study the issue of RUL prediction under imperfect prior degradation data by reasonably fusing the prior information and the field degradation data. Firstly, based on the linear Wiener degradation process, we prove a conclusion that the random parameter estimation results by the joint Bayesian algorithm and the expectation-maximization (EM) algorithm are the same as that by the maximum likelihood estimation (MLE) method. It shows that the joint estimation method completely overcomes the influence of prior information, and the more iterations, the smaller proportion of prior information. Secondly, we also prove that the random parameter joint updating method for the nonlinear Wiener degradation process has the same characteristics. Then, a heuristic algorithm that reasonably fuses the prior information and the field degradation data is proposed, which controls the number of iterations of the joint updating algorithm based on the credibility of the prior information. It is also applied to the nonlinear degradation process by not iterating the nonlinear coefficients. Finally, the correctness of the conclusion of this paper is verified by the simulated degradation data, and the effectiveness of the proposed heuristic updating method is verified by the actual lithium battery degradation data.

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“…Furthermore, distributed Kalman filtering is used for realizing the coordination and fusion of error estimation, which is an important scheme to solve the state estimation for large-scale systems [28]. A data-driven approach combining the RBF neural network (NN) and the extended Kalman filter (EKF) was proposed to estimate the internal temperature for lithium-ion battery thermal management [29,30].…”

Section: Introductionmentioning

confidence: 99%

Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturbances and Transmission Time Delays

Liu

Yao

et al. 2020

Complexity

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The dynamic positioning system of unmanned underwater vehicles (UUVs) is a complex and large-scale system mainly due to the nonlinear dynamics, uncertainty in model parameters, and external disturbances. With the aid of the bio-inspired computing (BIC) method, the designed three-dimensional (3D) spatial positioning system is used for enlarging communication constraints and increasing signal coordination processing. With the growing of measurement scales, the issue of the networked high-precision positioning has been developed rapidly. Then, an information fusion estimation approach is presented for the distributed networked systems with data random transmission time delays and lost and disordered packets. To reduce the communication burden, an adaptive signal selection scheme is employed to reorganize the measurement sequence, and the parameter uncertainties as well as cross-correlated noise are used to describe the uncertain disturbances. Moreover, a reoptimal weighted fusion state estimation is designed to alleviate the information redundancy and maintain higher measurement accuracy. An illustrative example obtained from the 3D spatial positioning system is given to validate the effectiveness of the proposed method.

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A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery

Cited by 399 publications

References 30 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

Remaining Useful Life Prediction Under Imperfect Prior Degradation Information

Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturbances and Transmission Time Delays

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