Predictive Transmission Shift Schedule for Improving Fuel Economy and Drivability Using Electronic Horizon

Lǚ, Jianbo; Hong, Sanghyun; Sullivan, Jonathan; Hu, Guopeng; Dai, Edward; Reed, Dennis; Baker, R. E.

doi:10.4271/2017-01-1092

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…The influence of the optimized transmission ratio on the shift smoothness for the entire vehicle is an important factor for determination of the constraint conditions for the transmission ratio, and the fast response characteristics of the motor can just overcome the difficulty in shifting caused by the excessive transmission ratio. 30 According to the gear shift strategy formulated in the gearbox module, when the motor peak speed constraint is taken into consideration, the motor speed at the loader’s gear switching point cannot exceed the peak speed, and the constraint conditions for the optimization variables are:…”

Section: Power System Parameter Optimizationmentioning

confidence: 99%

Power system parameter matching and particle swarm optimization of battery underground loader

Yang

Zhou

et al. 2021

Advances in Mechanical Engineering

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As part of the ongoing research into new energy technology, battery-powered underground loaders have emerged. However, there have been few studies on power system optimization and matching for these battery underground loaders to date. This paper, which takes a 3-m3 battery underground loader as its research object, determines the loader’s optimal operating point through study of the power response characteristics of the loader’s motor under various working conditions. The effects of different power batteries on the working conditions are analyzed, and the loader’s component parameters are matched. Additionally, an optimization model of the driving system of the battery underground loader is constructed. On the basis of the driving operation characteristics of the loader, the particle swarm optimization algorithm is proposed to optimize the operating conditions of the loader’s driving motor. The results show that the transmission ratio is reduced after optimization. The single-cycle energy consumption is reduced by approximately 1.98% and the number of cycles in the health status of the power battery’s state-of-charge increases by approximately 1.91%, which verifies the feasibility of use of the particle swarm algorithm in the loader optimization problem. This work can serve as a reference for related theoretical research on underground loaders.

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Section: Power System Parameter Optimizationmentioning

confidence: 99%

Power system parameter matching and particle swarm optimization of battery underground loader

Yang

Zhou

et al. 2021

Advances in Mechanical Engineering

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“…Therefore advanced knowledge of the road grade, obtained from 3D road maps, is very beneficial in predictive powertrain control as shown for instance in (Back et al, 2004;Zhang et al, 2010). Additionally, due to constraints on velocity, prior knowledge of road grade will allow more judicious use of available velocity band and gear selection (Terwen et al, 2004;Fröberg et al, 2006;Huang et al, 2008;Hellström et al, 2009Hellström et al, , 2010Kamal et al, 2011;Lu et al, 2017); for instance a vehicle can slow down in anticipation of a steep descent or speed up in preparation for a climb. The optimal solution can be non-trivial as shown for a heavy duty vehicle in (He et al, 2016).…”

Section: Anticipating State Of the Roadmentioning

confidence: 99%

Energy saving potentials of connected and automated vehicles

Vahidi

Sciarretta

2018

Transportation Research Part C: Emerging Technologies

396

126

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Connected and automated vehicles (CAV) are marketed for their increased safety, driving comfort, and time saving potential. With much easier access to information, increased processing power, and precision control, they also offer unprecedented opportunities for energy efficient driving. This paper is an attempt to highlight the energy saving potential of connected and automated vehicles based on first principles of motion, optimal control theory, and a review of the vast but scattered eco-driving literature. We explain that connectivity to other vehicles and infrastructure allows better anticipation of upcoming events, such as hills, curves, slow traffic, state of traffic signals, and movement of neighboring vehicles. Automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and save energy. Opportunities for cooperative driving could further increase energy efficiency of a group of vehicles by allowing them to move in a coordinated manner. Energy efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles.

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“…Under the model predictive control framework, Guo et al 8 obtained the optimal shift schedule by solving a nonlinear time-varying optimization problem, which also achieved an ideal fuel-saving effect. Lu et al 9 proposed a hybrid model predictive control-based optimization method for the drivetrain, and the simulation results show that the shifting strategy is effective in reducing fuel consumption and improving driving performance. Barbosa et al solved the optimization problem with the minimum fuel consumption and travel time as the optimization objectives and analyzed the results of gear optimization under different fuels.…”

Section: Introductionmentioning

confidence: 99%

Research on gear decision method of commercial vehicle based on predictive road information

Wang

Lei

et al. 2022

Advances in Mechanical Engineering

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As an essential part of the transportation industry, it is necessary to reduce the fuel consumption of commercial vehicles from the perspective of the environment and economy. Previous studies have shown that optimizing the gear sequence can reduce vehicle fuel consumption. This paper presents a gear decision method based on predictive road information. Under the model predictive control framework, the dynamic programming algorithm is used to solve the multi-objective optimization problem of gear decision. To solve the problem of the long calculation time of the dynamic programming algorithm, the nonlinear optimization algorithm is used to optimize the dynamic programming sub-problem, and the optimal gear sequence of fuel consumption is obtained. The final optimized gear sequence is obtained through the dynamic programming algorithm. The simulation analysis of the proposed gear shift decision method shows that the gear shift decision method can effectively reduce fuel consumption under fixed working conditions. Compared with the economic shift schedule, the fuel consumption is reduced by 5%, and the computing speed is improved compared with the dynamic programming algorithm.

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Predictive Transmission Shift Schedule for Improving Fuel Economy and Drivability Using Electronic Horizon

Cited by 16 publications

References 8 publications

Power system parameter matching and particle swarm optimization of battery underground loader

Power system parameter matching and particle swarm optimization of battery underground loader

Energy saving potentials of connected and automated vehicles

Research on gear decision method of commercial vehicle based on predictive road information

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