Abstract
This research focused on real-time optimization control to improve the fuel consumption of power-split hybrid electric vehicles. Particle swarm optimization (PSO) was implemented to reduce fuel consumption for real-time optimization control. The engine torque was design-variable to manage the energy distribution of dual energy sources. The AHS II power-split hybrid electric system was used as the powertrain system. The hybrid electric vehicle model was built using Matlab/Simulink. The simulation was performed according to US FTP-75 regulations. The PSO design objective was to minimize the equivalent fuel rate with the driving system still meeting the dynamic performance requirements. Through dynamic vehicle simulation and PSO, the required torque value for the whole drivetrain system and corresponding high-efficiency engine operating point can be found. With that, the two motor/generators (M/Gs) supplemented the rest required torques. The composite fuel economy of the PSO algorithm was 46.8 mpg, which is a 9.4% improvement over the base control model. The PSO control strategy could quickly converge and that feature makes PSO a good fit to be used in real-time control applications.
Highlights
Since the industrial era, the demand for fossil fuels has increased and the burning of fossil fuels has led to an increase in global carbon dioxide emissions, which has increased global warming
10.5% of total fuel combustion emissions, industry accounted for 47.8%, transportation accounted for 14.6%, services accounted for 13.4%, residential emissions accounted for 12.6%, and agriculture accounted for 1.1% [1]
A V6 3.6 L engine was applied in this research. This engine had a manufacturer configuration for a midsize power-split hybrid electric vehicles (HEVs), and it was compatible with the power of
Summary
The demand for fossil fuels has increased and the burning of fossil fuels has led to an increase in global carbon dioxide emissions, which has increased global warming. They managed the power distribution to minimize fuel consumption, which includes the actual fuel consumed by the engine and an equivalent fuel converted from the electrical energy consumed by motors Their simulation maintained the battery SOC in a reasonable range by applying a penalty function to ensure battery life. Particle swarm optimization (PSO) is another real-time control algorithm for vehicle fuel consumption. Wu et al [12] applied PSO to plug-in HEVs. The main goal of their study was to optimize the control strategy to achieve the best fuel economy. Wang et al [14] applied a particle-swarm-optimization-based nonlinear model predictive control strategy on a series-parallel hybrid electric bus to optimize the fuel consumption. GAs model a fuzzy-logic controller the to optimize the fuel consumption a hybrid electric mining remove the the worst positionbetween at one time, keeps the worst particles, judgingthe theworst best solution.
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