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An end-cloud collaboration for state-of-charge estimation of lithium-ion batteries based on extended Kalman filter and convolutional neural network (CNN)—long short-term memory (LSTM)—attention mechanism (AM)
This paper introduces an innovative online state of charge (SOC) estimation method for lithium-ion batteries, designed to address the challenges of accurate and timely SOC estimation in electric vehicles under complex working conditions and computational limitations of on-board hardware. Central to this method is the concept of end-cloud collaboration, which harmonizes accuracy with real-time performance. The framework involves deploying a data-driven model on the cloud side for high-accuracy estimation, complemented by a fast model on the end side for real-time estimation. A crucial component of this system is the implementation of the extended Kalman filter on the end side, which fuses results from both ends to achieve high-accuracy and real-time online estimation. This method has been rigorously evaluated under various dynamic driving conditions and temperatures, demonstrating high accuracy, real-time performance, and robustness. The estimation results yield a root mean square error and mean absolute error of approximately 1.5% and 1%, respectively. Significantly, under the Cyber Hierarchy and Interactional Network framework, this method shows promising potential for extension to multi-state online cooperative estimation, opening avenues for advanced battery system management.
An end-cloud collaboration for state-of-charge estimation of lithium-ion batteries based on extended Kalman filter and convolutional neural network (CNN)—long short-term memory (LSTM)—attention mechanism (AM)
This paper introduces an innovative online state of charge (SOC) estimation method for lithium-ion batteries, designed to address the challenges of accurate and timely SOC estimation in electric vehicles under complex working conditions and computational limitations of on-board hardware. Central to this method is the concept of end-cloud collaboration, which harmonizes accuracy with real-time performance. The framework involves deploying a data-driven model on the cloud side for high-accuracy estimation, complemented by a fast model on the end side for real-time estimation. A crucial component of this system is the implementation of the extended Kalman filter on the end side, which fuses results from both ends to achieve high-accuracy and real-time online estimation. This method has been rigorously evaluated under various dynamic driving conditions and temperatures, demonstrating high accuracy, real-time performance, and robustness. The estimation results yield a root mean square error and mean absolute error of approximately 1.5% and 1%, respectively. Significantly, under the Cyber Hierarchy and Interactional Network framework, this method shows promising potential for extension to multi-state online cooperative estimation, opening avenues for advanced battery system management.
An end-cloud collaboration for state-of-charge estimation of lithium-ion batteries based on extended Kalman filter and convolutional neural network (CNN)—long short-term memory (LSTM)—attention mechanism (AM)
Jiang, Pengchang (author) / Wang, Hongxiang (author) / Huang, Guangjie (author) / Feng, Wenkai (author) / Xiong, Mengyu (author) / Zhao, Junwei (author) / Hua, Wei (author) / Zhang, Yong (author) / Wang, Wentao (author) / Zhu, Tao (author)
2024-03-01
12 pages
Article (Journal)
Electronic Resource
English
Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting
| DOAJ | 2019