It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
A reliable and timely thermal runaway alarming method is useful to avoid potential fires happening to electric vehicles (EVs). In this paper, fault diagnosis for the battery pack in EVs using thresholds for multiple safety indicators. Firstly, a deep neural network is used to predict temperature and voltage in the future. Then both voltage-related and temperature-related safety indicators are extracted, including extremum entropy, variance entropy, cumulative center distance, and temperature rise rate. Their corresponding thresholds are defined using a boxplot. Verification using real-world fired EV data shows that the proposed method could realize 10–13 min ahead thermal runaway alarming.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 China Automotive Engineering Research Institute Co., Ltd. , Chongqing 401122 , China