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Abstract
Shift-scheduling calibration of automatic transmission (AT) vehicles is vital for both driving experience and automobile industry. Shifting schedules are usually calibrated with the consideration of fuel economy and drivability while neglecting the individual driving preference. In this work, we propose to exemplify the individual shifting preference by integrating the manual transmission (MT) shifting habit into the AT shift-scheduling calibration, where the habit is reflected as the shifting points and is available in most automated manual transmission (AMT) vehicles. The automated calibration of AT shifting schedules is directed by using the particle swarm optimization (PSO), during the virtual automobile cycle test, i.e., FTP-72. Candidate shifting-schedules are generated in the overlapped zone of MT shifting points and the space around the base map, and are evaluated on both shifting quality and fuel economy. Through iterations, the generated candidate shift schedules are tested and assessed until the overall performance reach the optimum. Experimental results are presented to show the effectiveness of the proposed method, which retains the shifting preferences as well as enhances the performance index by about 5%, 4%, and 2% for the drivers with aggressive, moderate, and mild styles, respectively.
Highlights
Calibration of shift-scheduling is an important part of automobile industry to enhance the customer experience
The shift-scheduling is calibrated in order to achieve high-performance in both fuel economy and shifting quality, the calibration task could be formulated as a goaldirected optimization process
To validate the feasibility and effectiveness of the proposed method, numerical experiments were conducted on the automatic transmission vehicle model (ATVM) to accomplish the shift scheduling calibration task
Summary
Calibration of shift-scheduling is an important part of automobile industry to enhance the customer experience. The gear shift map is automatically processed by employing the bionic-based optimization in [13] Ren He et al [14] expounded a gear shifting indication system where the interval algorithm and genetic algorithm were adopted to achieve least fuel consumption and shift frequency reduction. We proposed an effective way to integrate the MT driving preferences into the automated calibration of AT shifting schedules while reducing the labor burden and achieving satisfied performance in shift quality as well as fuel economy. This could be realized by means of personalized driver model and the bionic-based particle swarm optimization (PSO). Conclusions and future work are given in the final section
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