Browse Topic: On-board energy sources
Evaluating the impact of software changes on fuel consumption and emissions is a critical aspect of transmission development. To evaluate the trade-offs between performance improvements and potential negative effects on efficiency, a forward-looking Software-in-the-Loop (SiL) simulation has been developed. Unlike backward calculations that derive fuel consumption based solely on cycle speed and engine speed, this approach executes complete driving cycles as the Worldwide Harmonized Light-Duty Vehicle Test Cycle (WLTC) within a detailed SiL environment. By considering all relevant influencing factors in a dynamic simulation, the method provides a more accurate assessment of fuel consumption and emission differences between two versions of the transmission software. The significant contribution of this work lies in the high-fidelity integration of a real virtual Transmission Control Unit (vTCU) software within a comprehensive, validated forward-looking SiL environment. This approach
Yamaha Motor Engineering Co., Ltd. provides plastic processing technology based on fuel tank press forming technology, and is developing various plastic processing methods, including forging, and developing mold equipment to realize them. This time, the core parts of the YECVT unit mounted on Yamaha Motor Co., Ltd.'s small premium scooter "NMAX" were not made by welding individual parts to each other, but by integrally forming them from a single thick plate using the cold forming method, resulting in lightweight, compact, high-strength, high-precision parts. By incorporating a composite plastic processing method that takes advantage of the characteristics of the material while making full use of analysis technology and mold technology, we were able to develop a composite plastic processing method (plate forging method) that creates new added value and mass produce it. In addition,this development has made it possible to achieve a thickness increase of 1.7 times the standard material
The calibration of automotive electronic control units is a critical and resource-intensive task in modern powertrain development. Optimizing parameters such as transmission shift schedules for minimum fuel consumption traditionally requires extensive prototype testing by expert calibrators. This process is costly, time-consuming, and subject to variability in environmental conditions and human judgment. In this paper, an artificial calibrator is introduced – a software agent that autonomously tunes transmission shift maps using reinforcement learning (RL) in a Software-in-the-Loop (SiL) simulation environment. The RL-based calibrator explores shift schedule parameters and learns from fuel consumption feedback, thereby achieving objective and reproducible optimizations within the controlled SiL environment. Applied to a 7-speed dual-clutch transmission (DCT) model of a Mild Hybrid Electric Vehicle (MHEV), the approach yielded significant fuel efficiency improvements. In a case study on
Items per page:
50
1 – 50 of 27326