scholarly journals Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization

2018 ◽  
Vol 13 (2) ◽  
pp. 123-137 ◽  
Author(s):  
Jackeline Rios-Torres ◽  
Jun Liu ◽  
Asad Khattak
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Hybrid Electric

Driving shaft lifetime enhancement by hybrid electrification

2018 ◽  
Vol 233 (3) ◽  
pp. 572-584
Author(s):  
Volkan Sezer
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Power Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Conventional Vehicle ◽  
Management Algorithm ◽  
Hybrid Electric

As a classical definition, the main aim of hybrid electric vehicle technology is to decrease the fuel consumption and emissions with the assistance of its power management algorithm. However, hybrid electric vehicles could also be optimized for fatigue minimization of the driving shaft to enhance its lifetime. To the best of our knowledge, there are no studies on hybrid electric vehicles regarding this concept. In this study, we model a conventional vehicle, convert it into hybrid electric vehicle in simulation environment, and optimize the power management algorithm by considering its driving shaft lifetime enhancement. The optimization is done by redesigning one of the previous equivalent cost minimization strategy studies, which includes a new state of charge sustaining approach. In this work, we reformulate the solution considering the assumptive torque–cycle life curve of the driving shaft instead of fuel consumption or emissions. Longitudinal vehicle model is prepared for simulations and the performance of the new strategy is compared with the conventional vehicle under the real driving cycle data. The results demonstrate a significant enhancement potential of 26% in driving shaft’s lifetime. Finally, we show the additional electric motor’s optimum torque tracking performance under a real driving cycle using the experimental testbed.

scholarly journals Organic Rankine Cycle Waste Heat Recovery for Passenger Hybrid Electric Vehicles

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4532 ◽  
Author(s):  
Wan Rashidi Bin Wan Ramli ◽  
Apostolos Pesyridis ◽  
Dhrumil Gohil ◽  
Fuhaid Alshammari
Keyword(s):  
Electric Vehicles ◽  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Hybrid Electric Vehicles ◽  
Waste Heat ◽  
Organic Rankine Cycle ◽  
Rankine Cycle ◽  
Hybrid Vehicle ◽  
Driving Cycle ◽  
Hybrid Electric

Electrification of road transport is a major step to solve the air quality problem and general environmental impact caused by the still widespread use of fossil fuels. At the same time, energy efficiency in the transport sector must be improved as a steppingstone towards a more sustainable future. Multiple waste heat recovery technologies are being investigated for low-temperature waste heat recovery. One of the technologies that is being considered for vehicle application is the Organic Rankine Cycle (ORC). In this paper, the potential of ORC is discussed in detail for hybrid vehicle application. The modelling and testing of multiple systems such as the hybrid vehicle, engine, and ORC waste heat recovery are performed using the computational approach in GT-SUITE software environment correlated against available engine data. It was found that the maximum cycle efficiency achieved from the ORC system was 5.4% with 2.02 kW of delivered power recovered from the waste heat available. This led to 1.0% and 1.2% of fuel economy improvement in the New European Driving Cycle (NEDC) and Worldwide Harmonised Light Vehicle Test Procedure (WLTP) driving cycle test, respectively. From the driving cycle analysis, Hybrid Electric Vehicles (HEV) and ORC are operative in a different part of the driving cycle. This is because the entire propulsion power is provided by the HEV system, resulting in less engine operation in some part of the cycle for the ORC system to function. Apart from that, a brief economic analysis of ORC Waste Heat Recovery (WHR) is also performed in this paper and a comparative analysis is carried out for different waste heat recovery technologies for hybrid vehicle application.

scholarly journals Real-Time Control Strategy for CVT-Based Hybrid Electric Vehicles Considering Drivability Constraints

2019 ◽  
Vol 9 (10) ◽  
pp. 2074 ◽  
Author(s):  
Hangyang Li ◽  
Yunshan Zhou ◽  
Huanjian Xiong ◽  
Bing Fu ◽  
Zhiliang Huang
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Continuously Variable Transmission ◽  
Speed Ratio ◽  
Driving Pattern ◽  
Variable Transmission ◽  
Equivalent Factor ◽  
Hybrid Electric

The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort.

Traffic Condition-Based Gain Scheduling of Electric Assist Control Strategy for Parallel Hybrid Electric Vehicles

2007 ◽  
Author(s):  
Amir Poursamad
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Gain Scheduling ◽  
Driving Cycle ◽  
Traffic Condition ◽  
Traffic Conditions ◽  
Parallel Hybrid ◽  
Fuel Consumption And Emissions

This paper presents gain scheduling of control strategy for parallel hybrid electric vehicles based on the traffic condition. Electric assist control strategy (EACS) is employed with different parameters for different traffic conditions. The parameters of the EACS are optimized and scheduled for different traffic conditions of TEH-CAR driving cycle. TEH-CAR is a driving cycle which is developed based on the experimental data collected from the real traffic condition in the city of Tehran. The objective of the optimization is to minimize the fuel consumption and emissions over the driving cycle, while enhancing or maintaining the driving performance characteristics of the vehicle. Genetic algorithm (GA) is used to solve the optimization problem and the constraints are handled by using penalty functions. The results from the computer simulation show the effectiveness of the approach and reduction in fuel consumption and emissions, while ensuring that the vehicle performance is not sacrificed.

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