4.5 Article

Experimental Study for the Assessment of the Measurement Uncertainty Associated with Electric Powertrain Efficiency Using the Back-to-Back Direct Method

期刊

ENERGIES
卷 11, 期 12, 页码 -

出版社

MDPI
DOI: 10.3390/en11123536

关键词

measurement of efficiency; uncertainty of the efficiency; electric power train; brushless electric motor; simulation of the driving cycle

资金

  1. Ministry of Economic Development of Italy, Innovation Industry [MS01_00038]

向作者/读者索取更多资源

Brushless electric motors are used intensively in the industrial automation sector due to the motors low inertia and fast response. According to the International Electrotechnical Commission, IEC 60034-2-1, the efficiency of a three-phase electric machine (excluding machines for traction vehicles) can be determined by direct or indirect techniques. In the case of small traction motors (<10 kW), direct methods are used extensively by manufacturers, even if no standard has been published or scheduled by the IEC. In this paper, we evaluated the accuracy of the (direct) back-to-back method for the estimation of the energy performance of a 3 kW brushless AC electric motor used in a light electric vehicle. We measured the efficiencies of a pair of motors and inverters, as well as the overall efficiency of the entire power train. The results showed that the methodology was sufficiently accurate and comparable with other indirect methods available in existing literature. Moreover, we developed a Simulink model that used the powertrain efficiency map as the input to perform the simulation of a standard urban driving cycle. The simulation was run 500 times to calculate the probability density function associated with the total range of the vehicle, considering the uncertainty of the efficiency that was determined experimentally. The simulation results confirmed the low deviation of the distribution standard compared to the average value of the range of the vehicle.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据