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.

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.

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 Modeling and active control of power-split hybrid electric vehicle launch vibration

2018 ◽  
Vol 38 (2) ◽  
pp. 592-607 ◽  
Author(s):  
Rong Guo ◽  
Hao Chen ◽  
Meng-Jia Wang
Keyword(s):  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Planetary Gear ◽  
System Response ◽  
Control Effect ◽  
Wave Superposition ◽  
Power Split ◽  
Hybrid Electric

One of the key challenges with the development of hybrid electric vehicles is the noise, vibration, and harsh behavior, specifically the uncomfortable ride experience during launch. This paper focuses on the driveline vibration caused by the quick response of the traction motor in the launch condition of hybrid electric vehicles. A torsional vibration differential equation for frequency analysis, including a Ravigneaux planetary gear set, a reducer, a differential, half shafts, and wheels, is thus built. Based on the equation, many components of the power-split system are simplified to make the controller design easy. Finally, wave superposition control strategy has been proposed to suppress the vibration, in which the concept is delaying part of the input to superimpose with the original input to eliminate the output wave. In order to optimize the control effect, parameters of the controller are chosen according to the system response. The simulation outcomes demonstrate that wave superposition control strategy is effective in attenuating the vibration generated by hybrid electric vehicles during launch conditions.

Multiple-Objective Optimal Control of Hybrid Electric Vehicle

2013 ◽  
Vol 753-755 ◽  
pp. 1659-1664
Author(s):  
Jun Yan
Keyword(s):  
Optimal Control ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Optimal Control Strategy ◽  
Parallel Hybrid Electric Vehicle ◽  
Hybrid Electric ◽  
Fuel Consumption And Emissions

To reduce the fuel consumption and exhaust (HC, CO) emissions of parallel hybrid electric vehicle, the control strategy of the hybrid electric vehicle is studied in this paper. First it briefly analyzes the structure and working principle of the parallel hybrid electric vehicle drive system. Then a cost function is proposed which explains the fuel consumption and emissions. According to the minimum principle the minimum of the cost function can be got, consequently, the optimal control strategy can be obtained. Furthermore, in order to verify the effectiveness of the optimal control strategy, in MATLAB environment, it establishes a dynamic simulation model for hybrid electric vehicles. Through a comparative study between the optimal control strategy on and the traditional rules control strategy, the results of experiment it reveals that the optimal control strategy can effectively reduces fuel consumption and emissions of hybrid electric vehicles.

Characterizing Plug-In Hybrid Electric Vehicle Consumers Most Influenced by California’s Electric Vehicle Rebate

2017 ◽  
Vol 2628 (1) ◽  
pp. 23-31 ◽  
Author(s):  
Clair Johnson ◽  
Brett Williams
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Positive Association ◽  
Interest Level ◽  
Consumer Perspective ◽  
Hybrid Electric ◽  
The Relationship ◽  
Lower Household Income

California’s Clean Vehicle Rebate Project (CVRP) provides cash rebates to make plug-in and fuel-cell electric vehicle purchases and leases more financially attractive. Self-reported evidence provided by CVRP participants provides a unique opportunity to examine the influence of the incentive from the consumer perspective. With evidence from a voluntary survey offered to all individual CVRP participants, this inquiry used logistic regression to examine the relationship between consumer factors and the influence of CVRP on consumers’ acquisition decisions. In other words, would they have purchased their vehicle without the rebate? This initial analysis examined a set of consumers who adopted plug-in hybrid electric vehicles between fall 2012 and spring 2015 ( n = 7,345). Factors considered for inclusion encompassed transaction, household, and demographic characteristics, motivations for adopting plug-in hybrid electric vehicles, and measures of experience with plug-in electric vehicles (PEVs). Findings indicated, as expected, that several characteristics and experiences are associated with a greater likelihood that a consumer would consider the rebate essential. These characteristics and experiences include having lower household income, being younger, adopting less-expensive vehicles, being more motivated to adopt a PEV by a desire to save money, being less motivated to adopt a PEV by a desire to reduce environmental impact, and reporting a lower initial interest level in adopting a PEV. Less straightforward, but informative, results included a positive association between rebate influence and identification with a nonwhite ethnicity or as male. Additionally, the lack of significance of some predictors was unexpected; in particular, no housing characteristics were associated with the influence of the rebate.

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.

scholarly journals Intelligent Control of Parallel Hybrid Electric Vehicles

1999 ◽  
Author(s):  
Bradley Glenn ◽  
Gregory Washington ◽  
Giorgio Rizzoni
Keyword(s):  
Fuzzy Logic ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Logic Controller ◽  
Hybrid Electric Vehicles ◽  
Conventional Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Reduce Emissions

Abstract Currently Hybrid Electric Vehicles (HEV) are being considered as an alternative to conventional automobiles in order to improve efficiency and reduce emissions. To demonstrate the potential of an advanced control strategy for HEV’s, a fuzzy logic control strategy has been developed and implemented in simulation in the National Renewable Energy Laboratory’s simulator Advisor (version 2.0.2). The Fuzzy Logic Controller (FLC) utilizes the electric motor in a parallel hybrid electric vehicle (HEV) to force the ICE (66KW Volkswagen TDI) to operate at or near its peak point of efficiency or at or near its best fuel economy. Results with advisor show that the vehicle with the Fuzzy Logic Controller can achieve (56) mpg in the city, while maintaining a state of charge of .68 for the battery pack, compared to (43) mpg for a conventional vehicle. This scheme has also brought to light various rules of thumb for the design and operation of HEV’s.

scholarly journals Effective optimal control strategy for hybrid electric vehicle with continuously variable transmission

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401882481 ◽  
Author(s):  
Hangyang Li ◽  
Xiaolan Hu ◽  
Bing Fu ◽  
Jiande Wang ◽  
Feitie Zhang ◽  
...  
Keyword(s):  
Cost Function ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Continuously Variable Transmission ◽  
Optimal Control Strategy ◽  
Variable Transmission ◽  
Hybrid Electric

Hybrid electric vehicles equipped with continuously variable transmission show dramatic improvements in fuel economy and driving performance because they can continuously adjust the operating points of the power source. This article proposes an optimal control strategy for continuously variable transmission–based hybrid electric vehicles with a pre-transmission parallel configuration. To explore the fuel-saving potential of the given configuration, a ‘control-oriented’ quasi-static vehicle model is built, and dynamic programming is adopted to determine the optimal torque split factor and continuously variable transmission speed ratio. However, a single-criterion cost function will lead to undesirable drivability problems. To tackle this problem, the main factors affecting the driving performance of a continuously variable transmission–based hybrid electric vehicle are studied. On that basis, a multicriterion cost function is proposed by introducing drivability constraints. By varying the weighting factors, the trade-off between fuel economy and drivability can be evaluated under a predetermined driving cycle. To validate the effectiveness of the proposed method, simulation experiments are performed under four different driving cycles, and the results indicate that the proposed method greatly enhanced the drivability without significantly increasing fuel consumption. Compared to a single-criterion cost function, the use of multiple criteria is more representative of real-world driving behaviour and thus provides better reference solutions to evaluate suboptimal online controllers.

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