Experimental testing of a heavy-duty Diesel engine at the dynamic test bench to assess the potential of regenerative braking.

MetroAutomotive(2023)

引用 0|浏览4
暂无评分
摘要
The kinetic energy recovery during braking is an essential feature of hybrid-electric vehicles, which significantly contributes to improve their fuel economy. In this work, an exhaustive performance analysis is conducted on the heavy-duty Diesel engine of a yard tractor used in port logistics, in order to estimate the potential of regenerative braking, which could be exploited in an electrified vehicle configuration. To this aim, experimental measurements are carried out at the dynamic test bench with reference to peculiar duty cycles, retrieved from an on-field measurement campaign. The on-field data are collected by means of a custom instrumentation, designed to gather real-time data from the CAN bus system of a yard tractor, equipped with the same engine, operating in the port of Salerno, Italy. First, a series of tests are carried out at the engine test bench in different steady-state and transient operating conditions, to experimentally characterize the engine performance in terms of torque, power output, and specific fuel consumption vs. engine speed and load request. Based on the engine characterization, the evaluation of engine efficiency and fuel consumption during the real duty cycles acquired on-field is performed. Furthermore, a comparison between the torque estimated by the engine control unit and the corresponding measurement by the test bench torsiometer is presented. Finally, the estimation of the kinetic energy recovery achievable by regenerative braking along the duty cycles under study is carried out, by analyzing the measured power and torque profiles. The results show that the energy recovery can significantly reduce the propulsion energy requested for performing the duty cycle.
更多
查看译文
关键词
Electrified vehicles, Hybrid-electric vehicles, Regenerative braking, Yard tractor, On-field data acquisition
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要