实时监控和老化检测算法设计及在基于 SiC 的汽车动力驱动系统中的应用

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Pierpaolo Dini, Giovanni Basso, Sergio Saponara, Claudio Romano
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引用次数: 0

摘要

文章介绍了一种创新方法,用于设计和实验验证监测与异常检测算法,特别关注老化现象,这种现象与现代高性能电气化车辆中电力电子系统开关设备中 Rds(on)$R_{ds_{(on)}}$ 的异常变化有关。案例研究涉及一种用于全电气化汽车的电力驱动装置,其中使用了集成到轮毂电机(Elaphe)中的三相轴向磁通同步电机,以及采用 SiC 技术(碳化硅)设计的高效三相逆变器。文章分四个阶段对创新的老化监测和检测系统进行了设计和验证。第一阶段包括创建电力驱动的实时模型,通过 WLTP(全球统一轻型汽车测试程序)测试期间直接推断的实验数据进行验证。第二阶段包括通过异常注入程序创建一个代表老化现象的虚拟数据集,用电机电流相位上的缩放因子(取决于 Rds(on)$R_{ds_{(on)}}$ 值)来模拟老化现象,该因子与 SiC 器件受影响的逆变器分支有关。第三阶段是设计基于 ANN(人工神经网络)回归模型的 Rds(on)$R_{ds_{(on)}}$ 估计器,其中包括使用特征提取和缩减技术的数据处理阶段。第四也是最后一个阶段是通过 PIL(Processor-In-the-Loop)测试对该方法进行实验验证,在 NXPs32k144 嵌入式平台(基于 Cortex-M4)上集成监控算法(由实时模型和基于人工智能的回归模型组成),使该算法与应用异常注入的电力驱动模型进行交互。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Real-time monitoring and ageing detection algorithm design with application on SiC-based automotive power drive system

Real-time monitoring and ageing detection algorithm design with application on SiC-based automotive power drive system

Real-time monitoring and ageing detection algorithm design with application on SiC-based automotive power drive system

The article describes an innovative methodology for the design and experimental validation of monitoring and anomaly detection algorithms, with a particular focus on the aging phenomenon, linked to the anomalous modification of the R d s ( o n ) $R_{ds_{(on)}}$ , in devices switching in power electronic systems integrated into modern high-performance electrified vehicles. The case study concerns an electric drive for fully electrified vehicles, in which a three-phase axial flux synchronous motor integrated into a wheel motor (Elaphe) is used and in which a high-efficiency three-phase inverter, designed with SiC technology (silicon carbide). The article proposes the design and validation of the innovative aging monitoring and detection system, in four consecutive phases. The first phase involves the creation of a real-time model of electric drive, validated through experimental data extrapolated directly during a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) test. The second phase consists of the creation of a virtual dataset representative of the aging phenomenon, via an anomaly injection procedure, emulating this phenomenon with a scaling factor (depending on the value of the R d s ( o n ) $R_{ds_{(on)}}$ ) on the current phase of the motor, relating to the inverter branch whose SiC device is affected. The third phase concerns the design of an estimator of the R d s ( o n ) $R_{ds_{(on)}}$ , based on an ANN (Artificial Neural Network) regression model, and involves a data manipulation phase with features extraction and reduction techniques. The fourth and final phase, involves the experimental validation of the method, through PIL (Processor-In-the-Loop) tests, integrating the monitoring algorithm (consisting of a real-time model and AI-based regression model) on the NXPs32k144 embedded platform (based on Cortex-M4), making the algorithm interact with the electric drive model on which anomaly injection is applied.

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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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