Journal
APPLIED ENERGY
Volume 207, Issue -, Pages 354-362Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2017.05.139
Keywords
Electric vehicle; Battery; Fault diagnosis; Big data; Neural network
Categories
Funding
- State Key Program of National Natural Science Foundation of China [U1564206]
- Application and Demonstration of Innovative Methods in New Energy Vehicle Industry [2015IM030100]
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This paper presents a novel fault diagnosis method for battery systems in electric vehicles based on big data statistical methods. According to machine learning algorithm and 3 sigma multi-level screening strategy (36-MSS), the abnormal changes of cell terminal voltages in a battery pack can be detected and calculated in the form of probability. Applying the neural network algorithm, this paper combines fault and defect diagnosis results with big data statistical regulation to construct a more complete battery system fault diagnosis model. Through analyzing the abnormalities hidden beneath the surface, researchers can see the design flaws in battery systems and provide feedback on the upstream of designing. Furthermore, the local outlier factor (LOF) algorithm and clustering outlier diagnosis algorithm are applied to verifying the calculation results. To further validate the effectiveness of the diagnosis method, a corresponding analysis between statistical diagnosis results and actual vehicle is given. To test the big data diagnosis model, the diagnosis results based on the actual vehicle operating data for the whole year is shown. (C) 2017 Published by Elsevier Ltd.
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