Multi-Objective Genetic Algorithm for an automatic transmission gear shift map

Fofana, Adama; Haas, Olivier C.L.; Ersanilli, Vince; Burnham, Keith J.; Mahtani, Joe; Woolley, C. Leonard; Vithanage, Karen

doi:10.1016/j.ifacol.2016.07.021

Cited by 11 publications

(8 citation statements)

References 5 publications

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“…The traditional methods mainly include weighted measurement method, ε-constraint method, and target programming method, while the modern heuristic intelligent algorithms mainly include the genetic algorithm, the particle swarm algorithm, the simulated annealing algorithm, reinforcement learning, etc. [30][31][32][33][34][35][36][37].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

“…At present, as one of the hot issues in current research, intelligent algorithms have been applied to solve MOO problems [30][31][32][33][34][35][36][37], in most cases, the algorithm implementation program is relatively intuitive and easy to modify, but the algorithm depends on the actual problem, the designer's experience, and technology, so there are still many problems. For example, in some cases it cannot be guaranteed that the solution is the optimal one, while some global optimal solutions may not be available in practice, or the performance is not stable.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

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Parameter Matching Optimization of a Powertrain System of Hybrid Electric Vehicles Based on Multi-Objective Optimization

Zhang

Tang

et al. 2019

Electronics

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Aiming at problems of large computational complexity and poor reliability, a parameter matching optimization method of a powertrain system of hybrid electric vehicles based on multi-objective optimization is proposed in this paper. First, according to the vehicle basic parameters and performance indicators, the parameter ranges of different components were analyzed and calculated; then, with the weight coefficient method, the multi-objective optimization (MOO) problem of fuel consumption and emissions was transformed into a single-objective optimization problem; finally, the co-simulation of AVL Cruise and Matlab/Simulink was achieved to evaluate the effects of parameter matching through the objective function. The research results show that the proposed parameter matching optimization method for hybrid electric vehicles based on multi-objective optimization can significantly reduce fuel consumption and emissions of a vehicle simultaneously and thus provides an optimized vehicle configuration for energy management strategy research. The method proposed in this paper has a high application value in the optimization design of electric vehicles.

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Section: Multi-objective Optimizationmentioning

confidence: 99%

Section: Multi-objective Optimizationmentioning

confidence: 99%

Parameter Matching Optimization of a Powertrain System of Hybrid Electric Vehicles Based on Multi-Objective Optimization

Zhang

Tang

et al. 2019

Electronics

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“…The constructed gear shifting strategy improved the fuel economy by 20% and guaranteed the vehicle dynamic performance. Fofana et al 10 utilized GA to optimize the gear shifting strategy for AT of a conventional internal combustion vehicle in the New European Driving Cycle (NEDC), considering the CO 2 emissions, drivability, and transmission durability. Kim et al 11 built a simulation model for a truck and utilized dynamic programming (DP) to obtain the gear shifting strategy of AT with the best energy economy for different demanded powers.…”

Section: Introductionmentioning

confidence: 99%

Development of adaptive gear shifting strategies of automatic transmission for plug-in hybrid electric commuting vehicle based on optimal energy economy

Shen

Zhao

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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Gear shifting strategy of automatic transmission has an important impact on the fuel economy of vehicles. For plug-in hybrid electric commuting vehicles, to develop the driving cycle and working mode adaptive gear shifting strategy of automatic transmission is of great significance to improve the vehicle energy economy. Three main efforts have been made to distinguish our work from exiting research. First, based on the fixity and repeatability of the driving cycle for plug-in hybrid electric commuting vehicles, the typical driving cycle of plug-in hybrid electric commuting vehicle is constructed by the self-organized mapping and K-means clustering methods. Second, in the typical plug-in hybrid electric commuting vehicle driving cycle constructed herein, the working points with the best energy economy are calculated by improved dynamic programming for each working mode of the plug-in hybrid electric commuting vehicle, considering the working efficiency of the power components and transmission. On this basis, the optimal gear shifting strategy of automatic transmission for the plug-in hybrid electric commuting vehicle in each working mode is extracted. Third, the simulation tests are conducted which compare the formulated adaptive gear shifting strategies with the traditional engine fuel economy-based gear shifting strategy. The simulation results illustrate that the adaptive gear shifting strategies increase the energy economy by 3.32%. The proposed gear shifting strategy development method can provide a reference for further optimization of automatic transmission gear shifting strategies of plug-in hybrid electric commuting vehicle for real applications.

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“…Used for solving both constrained and unconstrained optimization problems that are based on natural selection and is a balancing act between exploration (global) of the solution space and excitation (local) of the solution space. Genetic algorithms will often find the optimal result, however in some applications the process is time consuming [16,17].…”

Section: Various Algorithm Objectives and Overviewmentioning

confidence: 99%

“…Fuel Consumption in Simulation and Real Vehicle Platform(Shen, et al) [13] mentioned above, one desired outcome to gear shift map formulation is to reduce greenhouse gas emissions. Fofana et.al [17]. have developed a Multi-Objective Genetic Algorithm (MOGA) to generate a set of non-dominated, equally optimal solutions that optimize reduced emissions, drivability and durability.…”

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confidence: 99%

Generation and Sensitivity Analysis of Transmission Shift Schedule for Hybrid-Electric Vehicle

Connelly¹

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The increased concern of global climate change and lack of sustainability of fossil fuels in the projected future has prompted further research into alternative fuel vehicles, or advanced vehicles, in an effort to combat vehicle emissions and fuel consumption. One of the many areas of advanced vehicles being researched includes the electrification and hybridization of vehicles. As the technology for hybrid-electric vehicles has increased, so has the need for more advanced control scheme for the vehicles. This includes the development and optimization of a shift schedule for the automatic transmission in a hybrid powertrain. The focus of this work is to demonstrate how to develop and analyze the benefits and shortcomings of two different shift schedules for a position 3 parallel hybrid-electric vehicle: a traditional two-parameter shift schedule that operates as a function of the driver's accelerator position and the vehicle's speed (SOC independent shift schedule), and a three-parameter shift schedule that also adapts to fluctuations in the state of charge of the high voltage batteries (SOC dependent shift schedule). The shift schedules were generated using an exhaustive search coupled with a fitness function to evaluate all possible vehicle operating points. The generated shift schedules were then tested in the software-in-the-loop (SIL) environment and the vehicle-in-the-loop (VIL) environment and compared to each other, as well as to the stock 8L45 8-speed transmission shift schedule. The results show that both generated shift schedules improved upon the stock transmission shift schedule used in the hybrid powertrain in component efficiency, vehicle efficiency, engine fuel economy, and vehicle fuel economy. However, there were few differences between the two shift schedules. A sensitivity analysis was then performed on the generated SOC dependent shift schedule by varying the initial SOC in the SIL environment in an attempt to explore more of the shift schedule's solution space. The sensitivity analysis showed little difference in vehicle energy consumption, engine fuel economy, and vehicle fuel economy during the executed driving cycle as initial SOC varied. Additionally, the analysis showed that the gear commanded from the SOC dependent shift schedule between the three cases were almost identical with the exception of at the start of the simulation. Once the control algorithm achieved and sustained the target SOC, the SOC dependent shift schedule contributed little as deviations in SOC were minute. iii DEDICATION This thesis is dedicated to my old family, new family, and friends. I want to thank my dad, James Connelly, for his patience with me and constant support through my college career. Without him I would not be where I am today. I want to thank my mom, Judi Connelly, who has been a pillar of support and always believed I can be better than I am. I want to thank my little sister, Sarah, for always keeping me on the right track.

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Multi-Objective Genetic Algorithm for an automatic transmission gear shift map

Cited by 11 publications

References 5 publications

Parameter Matching Optimization of a Powertrain System of Hybrid Electric Vehicles Based on Multi-Objective Optimization

Parameter Matching Optimization of a Powertrain System of Hybrid Electric Vehicles Based on Multi-Objective Optimization

Development of adaptive gear shifting strategies of automatic transmission for plug-in hybrid electric commuting vehicle based on optimal energy economy

Generation and Sensitivity Analysis of Transmission Shift Schedule for Hybrid-Electric Vehicle

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