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.

Evaluating the Performances of Advanced Powertrains

Volume 2 ◽  
2004 ◽  
Author(s):  
Massimo Feola ◽  
Fabrizio Martini ◽  
Stefano Ubertini
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Hybrid Vehicles ◽  
Propulsion System ◽  
Road Vehicles ◽  
Hybrid Propulsion ◽  
Hybrid Electric

Over the last few decades a tremendous effort has been made to reduce road vehicles engines contribution to air pollution and fuel consumption. Due to the more stringent limits imposed by governments, various manufactures started working in the incorporation of alternative powertrain configurations, such as pure electric vehicles (EV), hybrid electric vehicles (HEV) and fuel cell vehicles (FCV), in the automotive consumer market. In order to appreciate the advantages and disadvantages of these new vehicles over conventional vehicles a comparison must be performed in terms of efficiency and pollutant emissions. However, hybrid vehicles comprise many components with at least two different energy conversion devices (i.e. internal combustion engine and electric machine) drawing energy from at least two different energy storage devices (i.e. fuel tank and battery). In recent times, many hybrid propulsion system configurations have emerged and many others can be imagined comprising multiple reversible and irreversible energy paths. Therefore, considering that in a hybrid vehicle at least two different forms of energy (i.e. fuel chemical energy and electricity) are consumed, fuel consumption alone is no more sufficient to give a measure of the effectiveness of a hybrid propulsion system. This paper presents a first attempt to give a general mathematical form of the traction energy, the global efficiency and the specific fuel consumption of a hybrid electric vehicle that recovers as particular cases the thermal vehicle and the series hybrid electric vehicle. To evaluate the efficiency of the generic propulsion system the complete process from fuel energy and electricity to power available at the wheels is considered. The introduced concept of equivalent fuel consumption can be the basis for the comparison between road vehicles whatever the powertrain is pure thermal or hybrid. In order to get a better understanding of the mathematical analysis and its potential effectiveness some numerical simulations of hybrid vehicles virtual prototypes are performed through a suitable simulation model. The aim of the present analysis is to provide an instrument that allow a quick evaluation of the performances of hybrid electric vehicles.

Multirate Integration in Hybrid Electric Vehicle Virtual Proving Grounds

1998 ◽  
Author(s):  
Dario Solis ◽  
Chris Schwarz
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Time Scales ◽  
Electric Vehicles ◽  
Time Scale ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Differential Algebraic Equations ◽  
Vehicle Systems ◽  
Hybrid Electric

Abstract In recent years technology development for the design of electric and hybrid-electric vehicle systems has reached a peak, due to ever increasing restrictions on fuel economy and reduced vehicle emissions. An international race among car manufacturers to bring production hybrid-electric vehicles to market has generated a great deal of interest in the scientific community. The design of these systems requires development of new simulation and optimization tools. In this paper, a description of a real-time numerical environment for Virtual Proving Grounds studies for hybrid-electric vehicles is presented. Within this environment, vehicle models are developed using a recursive multibody dynamics formulation that results in a set of Differential-Algebraic Equations (DAE), and vehicle subsystem models are created using Ordinary Differential Equations (ODE). Based on engineering knowledge of vehicle systems, two time scales are identified. The first time scale, referred to as slow time scale, contains generalized coordinates describing the mechanical vehicle system that includs the chassis, steering rack, and suspension assemblies. The second time scale, referred to as fast time scale, contains the hybrid-electric powertrain components and vehicle tires. Multirate techniques to integrate the combined set of DAE and ODE in two time scales are used to obtain computational gains that will allow solution of the system’s governing equations for state derivatives, and efficient numerical integration in real time.

Control Strategy of Hybrid Electric Vehicles Based on Driving Style Identification

2014 ◽  
Vol 945-949 ◽  
pp. 1587-1596
Author(s):  
Xian Zhi Tang ◽  
Shu Jun Yang ◽  
Huai Cheng Xia
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Entropy Theory ◽  
Driving Style ◽  
Identification Error ◽  
Identification Method ◽  
Hybrid Electric

The driving style comprehensive identification method based on the entropy theory is presented. The error and error proportion of each identification result are calculated. The entropy and the variation degree of the identification error of each identification method are calculated based on the definition of information entropy. According to the entropy and the variation degree of the identification error, the weight of each kind of identification method can be determined in the comprehensive identification method, and the driving style comprehensive identification algorithm is derived. The control strategy of hybrid electric vehicles based on the driving style identification is proposed. The economic control strategy and dynamic control strategy are established. Depending on the results of driving style identification, aiming at different kinds of drivers, the mode of control strategies can be adjusted, so the demands of different kinds of drivers can be satisfied. The hybrid electric vehicle simulation model and control strategy model are built, and the simulations have been done. Due to the simulation results, the drivers’ intention comprehensive identification method based on the entropy theory is proved to represent the driver’s driving style systematically and comprehensively, and the hybrid electric vehicle control strategy based on the driving style identification can make the vehicles satisfy different drivers’ demands.

scholarly journals Lifetime Model Development for Integration in Power Management of HEVs By Terms of Minimizing Fuel Consumption and Battery Degradation

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Nejra Beganovic ◽  
Bedatri Moulik ◽  
Ahmed Mohamed Ali ◽  
Dirk S¨offker
Keyword(s):  
Fuel Consumption ◽  
Power Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Operating Conditions ◽  
Battery Lifetime ◽  
Conflicting Objectives ◽  
Battery Degradation ◽  
Lifetime Model ◽  
Hybrid Electric

Along with increasingly frequent use of electric and hybrid electric vehicles, the constraints and demands placed on the them become stricter. The most noticeable challenge considering Hybrid Electric Vehicles (HEVs) is to provide an optimalpower flow between multiple electric sources alongside provided as less as possible aging of energy storage components. To provide efficient battery usage with respect to batteries lifetime, it becomes unavoidable to develop battery lifetime models, which do not only reflect the State-of-Heath (SoH) but also allow battery lifetime prediction. The lifetimeoriented battery models have to be integrated in power management. To be used efficiently and to provide optimal power split ensuring mitigation of battery degradation without sacrificing desired power consumption, accurate modeling of battery degradation is of utmost importance. This implies that gradual battery degradation, which is directly affected by applied loading profiles, has to be monitored and used as additional control input. Moreover, the lifetime model developed in this case has to provide model outputs also in the timeframe of power management. In this contribution, a machine state-based lifetime model for electric battery source is developed. In this particular case, different degradation states as well as machine state transitions are identified in accordance to current operating conditions. Here, the change in charging/ discharging rate (C-rate), overcharging/undercharging of the battery (depth-of-discharge), and the temperature are taken in consideration to define machine model states. The End-of-Lifetime (EoL) is defined as deviation between nominal and current ampere-hour (Ah)-throughput. The proposed machine state-based lifetime model is verified based on existing battery lifetime models using simulation setup. The developed lifetime model in this way serve as a prerequisite forits integration into power management with an aim to provide the trade-off between aforementioned conflicting objectives; fuel consumption and battery degradation.

Parametric Vehicular Simulator in the Evaluation of HELVIS Mini-HEV EDS Control

2011 ◽  
Author(s):  
Rafael C. B. Sampaio ◽  
Gabriel S. de Lima ◽  
Vinicius V. M. Fernandes ◽  
Andre´ C. Hernandes ◽  
Marcelo Becker
Keyword(s):  
Artificial Intelligence ◽  
Experimental Data ◽  
Real Time ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Differential System ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Hybrid Electric ◽  
The Universe

HELVIS (Hybrid Electric Vehicle In Low Scale) is a mini-HEV platform used on the research of HEVs (Hybrid Electric Vehicles), through which students of all degrees have the opportunity to be introduced to the universe that surrounds HEVs in many aspects. In this work the HELVIS-Sim is presented. HELVIS-Sim is a full dynamic & kinematic vehicular simulator for the HELVIS platform, consisting of a Simulink™ environment through which the states of a large number of variables related to the vehicle can be observed and analyzed. Specially in this paper, the focus is in the control of HELVIS EDS (Electronic Differential System), presenting classic, A.I.-based (Artificial Intelligence) and optimal robust controllers in the problem of the adjustment of the rear angular speeds. HELVIS-Sim results are then compared to experimental data obtained from the real HELVIS EDS, with the aid of a dSpace™ real time interface board.

scholarly journals Support vector machine–based driving cycle recognition for dynamic equivalent fuel consumption minimization strategy with hybrid electric vehicle

2018 ◽  
Vol 10 (11) ◽  
pp. 168781401881102
Author(s):  
QIN Shi ◽  
Duoyang Qiu ◽  
Lin He ◽  
Bing Wu ◽  
Yiming Li
Keyword(s):  
Support Vector Machine ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Driving Cycle ◽  
Support Vector ◽  
Equivalent Fuel ◽  
Hybrid Electric ◽  
Equivalent Fuel Consumption

For a great influence on the fuel economy and exhaust, driving cycle recognition is becoming more and more widely used in hybrid electric vehicles. The purpose of this study is to develop a method to identify the type of driving cycle in real time with better accuracy and apply the driving cycle recognition to minimize the fuel consumption with dynamic equivalent fuel consumption minimization strategy. The support vector machine optimized by the particle swarm algorithm is created for building driving cycle recognition model. Furthermore,the influence of the two parameters of window width and window moving velocity on the accuracy is also analyzed in online application. A case study of driving cycle in a medium-sized city is introduced based on collecting four typical driving cycle data in real vehicle test. A series of characteristic parameters are defined and principal component analysis is used for data processing. Finally, the driving cycle recognition model is used for equivalent fuel consumption minimization strategy with a parallel hybrid electric vehicle. Simulation results show that the fuel economy can improve by 9.914% based on optimized support vector machine, and the fluctuations of battery state of charge are more stable so that system efficiency and batter life are substantially improved.

Eco-Trajectory Planning with Consideration of Queue along Congested Corridor for Hybrid Electric Vehicles

2019 ◽  
Vol 2673 (9) ◽  
pp. 277-286 ◽  
Author(s):  
Zhen Yang ◽  
Yiheng Feng ◽  
Xun Gong ◽  
Ding Zhao ◽  
Jing Sun
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicles ◽  
Trajectory Planning ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Urban Traffic ◽  
Vehicle Speed ◽  
Speed Profile ◽  
Driving Model ◽  
Hybrid Electric

At signalized intersections, vehicle speed profile plays a vital role in determining fuel consumption and emissions. With advances of connected and automated vehicle technology, vehicles are able to receive predicted traffic information from the infrastructure in real-time to plan their trajectories in a fuel-efficient way. In this paper, an eco-driving model is developed for hybrid electric vehicles in a congested urban traffic environment. The vehicle queuing process is explicitly modeled by the shockwave profile model with consideration of vehicle deceleration and acceleration to provide a green window for eco-vehicle trajectory planning. A trigonometric speed profile is applied to minimize fuel consumption and maximize driving comfort with a low jerk. A hybrid electric vehicle fuel consumption model is built and calibrated with real vehicle data to evaluate the energy benefit of the eco-vehicles. Simulation results from a real-world corridor of six intersections show that the proposed eco-driving model can significantly reduce energy consumption by 8.7% on average and by 23.5% at maximum, without sacrificing mobility.

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