Research on Intelligent Shift Strategy for Heavy Vehicles Based on Predictive Information Tianjin University

Format:

Book

Conference/Event

Author/Creator:

Zhang, Junfeng, author.

Contributor:

Chen, Daxin

Wang, Gaoxiang

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

By installing an automated mechanical transmission (AMT) on heavy-duty vehicles and developing a reasonable shift strategy, it can reduce driver fatigue and eliminate technical differences among drivers, improving vehicle performance. However, after detaching from the experience of good drivers, the current shifting strategy is limited to the vehicle state at the current moment, and cannot make predictive judgment of the road environment ahead, and problems such as cyclic shifting will occur due to insufficient power when driving on the ramp. To improve the adaptability of heavy-duty truck shift strategy to dynamic driving environments, this paper first analyzes the shortcomings of existing traditional heavy-duty truck shift strategies on slopes, and develops a comprehensive performance shift strategy incorporating slope factors. Based on this, forward-looking information is introduced to propose a predictive intelligent shift strategy that balances power and economy. The vehicle power demand is calculated based on the possible future working conditions and road information, and the rotation speed, torque and impact degree are constrained to ensure the rationality of the shift range under the premise of meeting the vehicle dynamic performance. Finally, the optimal gear sequence planning is carried out with the cumulative fuel consumption in the next 5 seconds as the economic objective and output at the corresponding time. Based on GT-Suite simulation platform, the vehicle object model and gearshift control strategy are established. Under different driving conditions and road scenes, the gearshift strategy of the real vehicle is compared with the gearshift strategy built in this paper. The simulation results show that compared with the real vehicle gearshift strategy, the predictive intelligent gearshift strategy improves the fuel consumption by about 0.2% ~ 0.78%, and has better adaptability in the dynamic driving environment; Compared with the comprehensive performance shift strategy, this strategy reduces the frequent gear shift caused by the gradient change of the comprehensive shift strategy, and the fuel consumption is reduced by about 1.22% under the actual road test condition

Notes:

Vendor supplied data

Publisher Number:

2024-01-2140

Access Restriction:

Restricted for use by site license