Search Results (6)

Hybrid electric vehicles (HEVs) have emerged as a trendy technology for reducing over-dependence on fossil fuels and a global concern of gas emissions across transportation networks. This research aims to design the hybridized drivetrain of a Volkswagen (VW) Jetta MK5 vehicle on the basis of its mathematical background description and a computer-aided simulation (MATLAB/Simulink/Simscape, MATLAB R2023b). The conventional car operates through a five-speed manual gearbox, and a 2.0 TDI internal combustion engine (ICE) is first assessed. A comparative study evaluates the optimal fuel economy between the conventional and the hybrid versions based on a proportional-integral-derivative (PID) controller, whose optimal set-point is predicted and computed by a genetic algorithm (GA). For realistic hybridization, this research integrated a Parker electric motor and the diesel engine of a VW Crafter hybrid vehicle from the faculty of engineering to reduce fuel consumption and optimize the system performance of the proposed car. Moreover, a VCDS measurement unit is developed to collect vehicle data based on real-world driving scenarios. The simulation results are compared with experimental data to validate the model’s accuracy. The simulation results prove the effectiveness of the proposed energy management strategy (EMS), with an approximately 89.46% reduction in fuel consumption for the hybrid powertrain compared to the gas-powered traditional vehicle, and 90.05% energy efficiency is achieved. Full article

One of the major challenges in implementing the equivalent fuel consumption minimisation strategy in hybrid electric vehicles is the adaptation of the equivalence factor to real-world driving. In this paper, a novel adaptive equivalent fuel consumption minimisation strategy (A-ECMS) has been developed for a hybrid two-wheeler to further improve fuel savings by predicting the drive cycles and thereby estimating and adapting the equivalence factor online for the ECMS energy management control. A learning vector quantitative neural network (LVQNN)-based classifier was first proposed to recognise the real-world driving cycle based on a fixed time window of past driving information. Along with standardised drive cycles, real-world driving data were used in the learning process to increase the robustness of the learning. The A-ECMS is then capable of regulating its equivalence factors online based on the LVQNN controller output. Numerical simulation results indicated that there was considerable improvement in fuel economy of the vehicle with the proposed methodology, up to 10.7%, compared to the use of traditional ECMS which was manually optimised for a single drive cycle. The average improvement in fuel economy over the ten drive cycles considered for testing is 3.93%. Full article

Automatic clutch engagement control is essential for all kinds of vehicle power transmissions. The controllers for vehicle power transmissions may include model-based or model-free approaches and must provide high transmission efficiency, fast engagement and low jerk. Most vehicle automatic transmissions are using torque converters with transmission efficiencies up to 96%. This paper presents the use of fuzzy logic control for a dry clutch in parallel hybrid electric vehicles. This controller can minimize the loss of power transmission since it can offer a higher transmission efficiency, up to 99%, with faster engagement, lower jerk and, thus, higher driving comfortability with lower cost. Fuzzy logic control is one of the model-free schemes. It can be combined with AI algorithms, neuro networks and virtual reality technologies in future development. Fuzzy logic control can avoid the complex modelling while maintaining the system’s high stability amid uncertainties and imprecise information. Experiments show that fuzzy logic can reduce the clutch slip and vibration. The new system provides 2% faster engagement speed than the torque converter and eliminates 70% of noise and vibration less than the manual transmission clutch. Full article

Overcoming terrain obstacles presents a major problem for people with disabilities or with limited mobility who are dependent on wheelchairs. An engineering solution designed to facilitate the use of wheelchairs are assisted-propulsion systems. The objective of the research described in this article is to analyze the impact of the hybrid manual–electric wheelchair propulsion system on the kinematics of the anthropotechnical system when climbing hills. The tests were carried out on a wheelchair ramp with an incline of 4°, using a prototype wheelchair with a hybrid manual–electric propulsion system in accordance with the patent application P.427855. The test subjects were three people whose task was to propel the wheelchair in two assistance modes supporting manual propulsion. The first mode is hill-climbing assistance, while the second one is assistance with propulsion torque in the propulsive phase. During the tests, several kinematic parameters of the wheelchair were monitored. An in-depth analysis was performed for the amplitude of speed during a hill climb and the number of propulsive cycles performed on a hill. The tests performed showed that when propelling the wheelchair only using the hand rims, the subject needed an average of 13 ± 1 pushes on the uphill slope, and their speed amplitude was 1.8 km/h with an average speed of 1.73 km/h. The climbing assistance mode reduced the speed amplitude to 0.76 km/h. The torque-assisted mode in the propulsive phase reduced the number of cycles required to climb the hill from 13 to 6, while in the climbing assistance mode the number of cycles required to climb the hill was reduced from 12 to 10 cycles. The tests were carried out at various values of assistance and assistance amplification coefficient, and the most optimally selected parameters of this coefficient are presented in the results. The tests proved that electric propulsion assistance has a beneficial and significant impact on the kinematics of manual wheelchair propulsion when compared to a classic manual propulsion system when overcoming hills. In addition, assistance and assistance amplification coefficient were proved to be correlated with operating conditions and the user’s individual characteristics. Full article

Conventional vehicles tend to consume considerable amounts of fuel, which generates exhaust gases and environmental pollution during intermittent driving cycles. Therefore, prospective vehicle designs favor improved exhaust emissions and energy consumption without compromising vehicle performance. Although pure electric vehicles feature high performance and low pollution characteristics, their limitations are their short driving range and high battery costs. Hybrid electric vehicles (HEVs) are comparatively environmentally friendly and energy efficient, but cost substantially more compared with conventional vehicles. Hydraulic hybrid vehicles (HHVs) are mainly operated using engines, or using alternate combinations of engine and hydraulic power sources while vehicles accelerate. When the hydraulic system accumulator is depleted, the conventional engine reengages; concurrently, brake-regenerated power is recycled and reused by employing hydraulic motor–pump modules in circulation patterns to conserve fuel and recycle brake energy. This study adopted MATLAB Simulink to construct complete HHV and HEV models for backward simulations. New European Driving Cycles were used to determine the changes in fuel economy. The output of power components and the state-of-charge of energy could be retrieved. Varying power component models, energy storage component models, and series or parallel configurations were combined into seven different vehicle configurations: the conventional manual transmission vehicle, series hybrid electric vehicle, series hydraulic hybrid vehicle, parallel hybrid electric vehicle, parallel hydraulic hybrid vehicle, purely electric vehicle, and hydraulic-electric hybrid vehicle. The simulation results show that fuel consumption was 21.80% lower in the series hydraulic hybrid vehicle compared to the series hybrid electric vehicle; additionally, fuel consumption was 3.80% lower in the parallel hybrid electric vehicle compared to the parallel hydraulic hybrid vehicle. Furthermore, the hydraulic–electric hybrid vehicles consumed 11.4% less electricity than the purely electric vehicle did. The simulations indicated that hydraulic-electric hybrid vehicle could provide the best energy cost among all the configurations studied. Full article

A clutch is a mechanical device which provides engine torque to the drive shaft of a vehicle. A dry clutch control for AMT (Automated Manual Transmission) systems has been an important issue to improve fuel economy and drivability in hybrid electric systems. In this paper, we propose a dry clutch control system to apply to the parallel hybrid electric bus. In order to analysis dynamic performance of the target vehicle, a vehicle dynamic model including engine, clutch, motor, transmission and vehicle is designed. For gear shifting simulation, the shifting maps for the hybrid electric bus are applied from the analysis results of DP (Dynamic Programming) theory that is one of the optimal control methods. The shifting maps consist of a pure electric mode and a hybrid electric mode calculated by using driving cycles for commercial vehicles. From vehicle dynamic equations, the control algorithm for a dry clutch is organized by using feedback loops based on the value of an engine, a clutch speed, a clutch release travel and an estimated clutch torque. Simulations are performed to analyze the dynamic performance of the proposed clutch control system during gear shifting. As a result, the vehicle model with the designed clutch controller compares to one with only the lockup controller in energy dissipation during gear shifting. Full article