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Pan Q, Fan X, Li J. Automatic creation of molecular substructures for accurate estimation of pure component properties using connectivity matrices. Chem Eng Sci 2023. [DOI: 10.1016/j.ces.2022.118214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Feng S, Wang A, Cai J, Zuo H, Zhang Y. Health State Estimation of On-Board Lithium-Ion Batteries Based on GMM-BID Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:9637. [PMID: 36560004 PMCID: PMC9786921 DOI: 10.3390/s22249637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/02/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
As a single feature parameter cannot comprehensively evaluate the health status of a battery, a multi-source information fusion method based on the Gaussian mixture model and Bayesian inference distance is proposed for the health assessment of vehicle batteries. The missing and abnormal data from real-life vehicle operations are preprocessed to extract the sensitive characteristic parameters which determine the battery performance. The normal state Gaussian mixture model is established using the fault-free state data, whereas the Bayesian inference distance is constructed as an index to quantitatively evaluate the battery performance state. In order to solve the problem that abnormal data may exist in the measured data and introduce errors into evaluation results, the determination rules of abnormal data are formulated. The verification of real-life vehicle operation data reveals that the proposed method can accurately evaluate the onboard battery state and reduce safety hazards of electric vehicles during the normal operation process.
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Affiliation(s)
- Shirui Feng
- College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Anchen Wang
- College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Jing Cai
- School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Hongfu Zuo
- School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Ying Zhang
- College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
- School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
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Jin XB, Yu XH, Su TL, Yang DN, Bai YT, Kong JL, Wang L. Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy. ENTROPY (BASEL, SWITZERLAND) 2021; 23:219. [PMID: 33670098 PMCID: PMC7916859 DOI: 10.3390/e23020219] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/03/2021] [Accepted: 02/07/2021] [Indexed: 11/16/2022]
Abstract
Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement's causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network's over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system's big measurement data to improve prediction performance.
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Affiliation(s)
- Xue-Bo Jin
- Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China; (X.-H.Y.); (Y.-T.B.); (J.-L.K.)
- China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University, Beijing 10048, China
| | - Xing-Hong Yu
- Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China; (X.-H.Y.); (Y.-T.B.); (J.-L.K.)
- China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University, Beijing 10048, China
| | - Ting-Li Su
- Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China; (X.-H.Y.); (Y.-T.B.); (J.-L.K.)
- China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University, Beijing 10048, China
| | - Dan-Ni Yang
- Electrical and Information Engineering College, Tianjin University, Tianjin 300072, China;
| | - Yu-Ting Bai
- Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China; (X.-H.Y.); (Y.-T.B.); (J.-L.K.)
- China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University, Beijing 10048, China
| | - Jian-Lei Kong
- Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China; (X.-H.Y.); (Y.-T.B.); (J.-L.K.)
- China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University, Beijing 10048, China
| | - Li Wang
- Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China; (X.-H.Y.); (Y.-T.B.); (J.-L.K.)
- China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University, Beijing 10048, China
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