Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation
The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemica...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/10/10/373 |
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| author | Hongyu Shen Yuefeng Liu Qiyan Zhao Guoyue Xue Tiange Zhang Xiuying Tan |
| author_facet | Hongyu Shen Yuefeng Liu Qiyan Zhao Guoyue Xue Tiange Zhang Xiuying Tan |
| author_sort | Hongyu Shen |
| collection | DOAJ |
| description | The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemical systems challenging. In this work, a novel Mamba network architecture called BMPTtery (Bidirectional Mamba Predictive Battery Temperature Representation) is proposed to overcome these challenges. First, a two-step hybrid model of trajectory piecewise–polynomial regression and exponentially weighted moving average is created and used to an operational dataset of EVs in order to handle the problem of noisy and incomplete time-series data. Each time series is then individually labeled to learn the representation and adaptive correlation of the multivariate series to capture battery performance variations in complex dynamic operating environments. Next, a prediction method with multiple steps based on the bidirectional Mamba is suggested. When combined with a failure diagnosis approach, this scheme can accurately detect heat failures due to excessive temperature, rapid temperature rise, and significant temperature differences. The experimental results demonstrate that the technique can accurately detect battery failures on a dataset of real operational EVs and predict the battery temperature one minute ahead of time with an MRE of 0.273%. |
| format | Article |
| id | doaj-art-c5e151c4c3544e098878daecf93a8db1 |
| institution | OA Journals |
| issn | 2313-0105 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-c5e151c4c3544e098878daecf93a8db12025-08-20T02:11:01ZengMDPI AGBatteries2313-01052024-10-01101037310.3390/batteries10100373Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive CorrelationHongyu Shen0Yuefeng Liu1Qiyan Zhao2Guoyue Xue3Tiange Zhang4Xiuying Tan5School of Digital and Intelligence Industry, Inner Mongolia University of Science & Technology, Baotou 014000, ChinaSchool of Digital and Intelligence Industry, Inner Mongolia University of Science & Technology, Baotou 014000, ChinaSchool of Digital and Intelligence Industry, Inner Mongolia University of Science & Technology, Baotou 014000, ChinaSchool of Digital and Intelligence Industry, Inner Mongolia University of Science & Technology, Baotou 014000, ChinaSchool of Digital and Intelligence Industry, Inner Mongolia University of Science & Technology, Baotou 014000, ChinaSchool of Digital and Intelligence Industry, Inner Mongolia University of Science & Technology, Baotou 014000, ChinaThe battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemical systems challenging. In this work, a novel Mamba network architecture called BMPTtery (Bidirectional Mamba Predictive Battery Temperature Representation) is proposed to overcome these challenges. First, a two-step hybrid model of trajectory piecewise–polynomial regression and exponentially weighted moving average is created and used to an operational dataset of EVs in order to handle the problem of noisy and incomplete time-series data. Each time series is then individually labeled to learn the representation and adaptive correlation of the multivariate series to capture battery performance variations in complex dynamic operating environments. Next, a prediction method with multiple steps based on the bidirectional Mamba is suggested. When combined with a failure diagnosis approach, this scheme can accurately detect heat failures due to excessive temperature, rapid temperature rise, and significant temperature differences. The experimental results demonstrate that the technique can accurately detect battery failures on a dataset of real operational EVs and predict the battery temperature one minute ahead of time with an MRE of 0.273%.https://www.mdpi.com/2313-0105/10/10/373electric vehiclesmambatemperature predictionfailure diagnosismulti-step ahead prediction |
| spellingShingle | Hongyu Shen Yuefeng Liu Qiyan Zhao Guoyue Xue Tiange Zhang Xiuying Tan Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation Batteries electric vehicles mamba temperature prediction failure diagnosis multi-step ahead prediction |
| title | Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation |
| title_full | Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation |
| title_fullStr | Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation |
| title_full_unstemmed | Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation |
| title_short | Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation |
| title_sort | multi step temperature prognosis of lithium ion batteries for real electric vehicles based on a novel bidirectional mamba network and sequence adaptive correlation |
| topic | electric vehicles mamba temperature prediction failure diagnosis multi-step ahead prediction |
| url | https://www.mdpi.com/2313-0105/10/10/373 |
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