基于深度学习的传感器故障检测电动汽车智能电池管理系统

IF 2.6 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Venkata Satya Rahul Kosuru, Ashwin Kavasseri Venkitaraman
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引用次数: 7

摘要

电池传感器数据的收集和传输对电池管理系统(BMS)至关重要。由于传感器故障、通信问题甚至网络攻击带来的不准确电池数据会对BMS造成严重危害,并对基于BMS的应用程序(如电动汽车)的整体可靠性产生不利影响,因此评估BMS中电池传感器和通信数据的耐用性至关重要。传感器数据对于BMS执行每项操作都是必要的。有效的传感器故障检测对于电动汽车电池系统的可持续性和安全性至关重要。这项研究提出了一种电池数据系统,尤其是锂离子电池,该系统允许基于深度学习的检测以及故障电池传感器和传输信息的分类。最初,我们收集传感器数据,并使用z分数归一化进行预处理。使用稀疏主成分分析(SPCA)提取特征,并使用增强型海洋捕食者算法(EMPA)进行特征选择。建议的基于初始蝙蝠优化深度残差网络(IB-DRN)的虚假电池数据识别和分类系统可以增强BMS的安全性和可靠性。使用MATLAB(2021a)、统计学、机器学习和深度学习工具箱以及实验研究进行模拟,以显示和评估所建议的策略的执行情况。它被证明优于传统方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Smart Battery Management System for Electric Vehicles Using Deep Learning-Based Sensor Fault Detection
Battery sensor data collection and transmission are essential for battery management systems (BMS). Since inaccurate battery data brought on by sensor faults, communication issues, or even cyber-attacks can impose serious harm on BMS and adversely impact the overall dependability of BMS-based applications, such as electric vehicles, it is critical to assess the durability of battery sensor and communication data in BMS. Sensor data are necessary for a BMS to perform every operation. Effective sensor fault detection is crucial for the sustainability and security of electric vehicle battery systems. This research suggests a system for battery data, especially lithium ion batteries, that allows deep learning-based detection and the classification of faulty battery sensor and transmission information. Initially, we collected the sensor data, and preprocessing was carried out using z-score normalization. The features were extracted using sparse principal component analysis (SPCA), and enhanced marine predators algorithm (EMPA) was used for feature selection. The BMS’s safety and dependability may be enhanced by the suggested incipient bat-optimized deep residual network (IB-DRN)-based false battery data identification and classification system. Simulations using MATLAB (2021a), along with statistics, machine learning, and a deep learning toolbox, along with experimental research, were used to show and assess how well the suggested strategy performs. It is shown to be superior to traditional approaches.
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来源期刊
World Electric Vehicle Journal
World Electric Vehicle Journal Engineering-Automotive Engineering
CiteScore
4.50
自引率
8.70%
发文量
196
审稿时长
8 weeks
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