5G和4G通信在电动汽车电池管理系统中的集成:基于云的架构,用于增强性能和分析

IF 0.5 Q4 TELECOMMUNICATIONS
R. Suganya, L. M. I. Leo Joseph, Sreedhar Kollem
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引用次数: 0

摘要

针对电动汽车电池管理系统(BMS)中4G和5G通信的集成,提出了一种基于云的架构。本研究比较了支持向量机(SVM)、随机森林(RF)、卷积神经网络(CNN)和人工智能优化的BMS算法。人工智能优化的BMS记录了88%的健康状况(SoH),而传统的BMS只有72%的健康状况。此外,人工智能模型达到85%的能效、20毫秒的延迟和92%的故障检测准确率,超过了现有的方法。使用网络性能分析,5G具有2.5倍的吞吐量和更少的延迟。比4G低60%),实现实时监控。这可以使无线(OTA)更新在5G下的可靠性达到98%,在4G下的可靠性达到85%,确保软件更新的成功率。将这种基于人工智能的BMS系统与5G相结合,可以实现电池管理过程的高效自动化,提高电池寿命和能效,并实现故障检测、预测分析和远程电池更新。这种可扩展的、基于云的边缘解决方案是下一代电动汽车实施的理想选择,可通过低运营支出和最佳电池生命周期管理来扩展性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integration of 5G and 4G Communication in Battery Management Systems for Electric Vehicles: A Cloud-Based Architecture for Enhanced Performance and Analytics

The Cloud-Based Architecture is proposed for the Integration of 4G and 5G Communication in a Battery Management System (BMS) for Electric Vehicles (EV). This study compares Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and an AI-optimized BMS algorithm. The AI-optimized BMS has recorded 88% State of Health (SoH), with a good old traditional BMS only managing 72%. Furthermore, the AI model reaches 85% energy efficiency, 20 ms latency, and 92% fault detection accuracy, surpassing existing approaches. Using Network performance analysis, 5G has 2.5× more throughput, and less latency (approx. 60% less than 4G), empowering real-time monitoring. This can make Over-the-air (OTA) updates 98% reliable with 5G and 85% with 4G, ensuring the software updates success rate. Incorporating this AI-based BMS system with 5G provides efficient automation of the battery management process, improving battery lifespan, energy efficiency, and enabling fault detection, predictive analysis, and remote battery update. Ideal for next-gen EV implementations, this scalable and cloud-based edge solution extends performance with low operational expenditure and optimal battery lifecycle management.

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