This paper presents a review on the recent research and technical progress of electric motor systems and electric powertrains for new energy vehicles. Through the analysis and comparison of direct current motor, induction motor, and synchronous motor, it is found that permanent magnet synchronous motor has better overall performance; by comparison with converters with Si-based IGBTs, it is found converters with SiC MOSFETs show significantly higher efficiency and increase driving mileage per charge. In addition, the pros and cons of different control strategies and algorithms are demonstrated. Next, by comparing series, parallel, and power split hybrid powertrains, the series–parallel compound hybrid powertrains are found to provide better fuel economy. Different electric powertrains, hybrid powertrains, and range-extended electric systems are also detailed, and their advantages and disadvantages are described. Finally, the technology roadmap over the next 15 years is proposed regarding traction motor, power electronic converter and electric powertrain as well as the key materials and components at each time frame.

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In domains ranging from computer vision to natural language processing, machine learning models have been shown to exhibit stark disparities, often performing worse for members of traditionally underserved groups. One factor contributing to these performance gaps is a lack of representation in the data the models are trained on. It is often unclear, however, how to operationalize representativeness in specific applications. Here we formalize the problem of creating equitable training datasets, and propose a statistical framework for addressing this problem. We consider a setting where a model builder must decide how to allocate a fixed data collection budget to gather training data from different subgroups. We then frame dataset creation as a constrained optimization problem, in which one maximizes a function of group-specific performance metrics based on (estimated) group-specific learning rates and costs per sample. This flexible approach incorporates preferences of model-builders and other stakeholders, as well as the statistical properties of the learning task. When data collection decisions are made sequentially, we show that under certain conditions this optimization problem can be efficiently solved even without prior knowledge of the learning rates. To illustrate our approach, we conduct a simulation study of polygenic risk scores on synthetic genomic data-an application domain that often suffers from non-representative data collection.We find that our adaptive sampling strategy outperforms several common data collection heuristics, including equal and proportional sampling, demonstrating the value of strategic dataset design for building equitable models. CCS Concepts: • Computing methodologies → Machine learning; Artificial intelligence; • Theory of computation → Design and analysis of algorithms.

Abstract:Two disadvantages of the SRM are its torque ripple and acoustic noise. Previous work on vibration modes and resonant frequencies of the laminations of an 8-6 SRM is extended here to include the effects of the frame. Both a smooth frame and a ribbed frame are examined and the presence of numerous additional vibratory modes in the ribbed frame demonstrated. Accelerometer tests behind a pole verify some of the theoretical predictions.

Electronic techniques for controlling acoustic noise and vibration depend on an accurate knowledge of the stator resonant frequency. Analytical models are developed, which allows the calculation of the first several modal frequencies. The impact of the stator stack length on the accuracy of the formulae is also examined. Experimental validation is included.

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