Fuzzy gear shifting control optimisation to improve vehicle performance, fuel consumption and engine emissions

Eckert, Jony Javorski; Santiciolli, Fabio Mazzariol; Yamashita, Rodrigo Yassuda; Corrêa, Fernanda Cristina; Silva, Ludmila C. A.; Dedini, Franco Giuseppe

doi:10.1049/iet-cta.2018.6272

Cited by 48 publications

(19 citation statements)

References 25 publications

(32 reference statements)

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“…The i−AWGA technique performs a wide search for the best solution, and it is not limited to false minimums, as occurs in some other optimization techniques. Moreover, this method was applied in several previous works, regarding vehicle multi-objective optimization [32][33][34]59], reaching satisfactory results.…”

Section: Optimizationmentioning

confidence: 94%

“…Finally, the simulation parameters related to the applied gear shifting strategy have to be defined; they significantly influence the energy management [60], the vehicle performance, the fuel consumption and the emissions [32,33]. The use of an adequate gear shifting strategy improves the gains reached by the vehicle hybridization, once the electric motors decrease the ICE torque demand, allowing for the anticipation of the upshifts, moving the engine to a lower fuel consumption/better efficiency operation point [34].…”

Section: Gear Shifting Strategymentioning

confidence: 99%

“…The second optimization criterion f 2 (X) is the minimization of the engine carbon monoxide (CO (g/km)), nitrogen oxide (NOx (g/km)) and hydrocarbon (HC (g/km)) emissions. These emissions are combined in a single parameter named emission factor E F [32,33]. The E F value is defined by the adaptive-weight technique [62] according to Equation (30) as a function of the maximum and minimum values of each gas emission presented in the solution database.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The convergence is defined by the stagnation of the evolution process, which is characterized by the repetition of the Pareto frontier [33,59,65] for over 20 generations of 80 new simulated solutions (crossover and mutation). Figure 7 shows the optimization flowchart according to the applied method.…”

Section: Population Control and Convergence Criterionmentioning

confidence: 99%

“…The increase in the ICE warm-up period is amplified by the addition of the extra electric traction system, which supplies a parcel of the power demand and therefore decreases the ICE torque that can increase the generated emissions if the power split between ICE and EMs is not properly defined [31]. Modifications in the standard driving behavior, e.g., changing the gear shifting strategy, can decrease the ICE fuel consumption and emissions [32,33].…”

Section: Introductionmentioning

confidence: 99%

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Design of an Aftermarket Hybridization Kit: Reducing Costs and Emissions Considering a Local Driving Cycle

et al. 2020

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For decades, drivers and fleet managers have been impacted by the instability of fuel prices, the need to save resources and the duty to meet and attain environmental regulations and certifications. Aiming to increase performance and efficiency and reduce emissions and mileage costs, plug-in electric vehicles (PHEVs) have been pointed out as a viable option, but there are gaps related to tools that could improve the numerous existing conventional vehicles. This study presents the design of an aftermarket hybridization kit that converts a vehicle originally driven by a combustion engine into a PHEV. To achieve this goal, an optimization was conducted with the objective of decreasing the cost (regarding fuel consumption and battery charging) to perform a local driving cycle, while attenuating the tailpipe emissions and reducing the battery mass. The torque curves of the electric motors, the battery capacity, the parameters for a gear shifting strategy and the parameters for a power split control were the design variables in the optimization process. This study used the Campinas driving cycle, which was experimentally obtained in a real-world driving scenario. The use of a local driving cycle to tune the design variables of an aftermarket optimization kit is important to achieve a customized product according to the selling location. Among the optimum solutions, the best trade-off configuration was able to decrease the mileage cost in 22.55%, and reduce the tailpipe emissions by 28.4% CO, 33.55% NOx and 19.11% HC, with the addition of a 137 kg battery.

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Section: Optimizationmentioning

confidence: 94%