A Novel Structure for Comprehensive HEV Powertrain Modelling

The main aim of this paper is to study the potential impacts in hybrid and full electrical vehicles performance by utilising continuously variable transmissions. This is achieved by two stages. First, for Electrical Vehicles (EVs), modelling and analysing the powertrain of a generic electric vehicle is developed using Matlab/Simulink-QSS Toolkit, with and without a transmission system of varying levels of complexity. Predicted results are compared for a typical electrical vehicle in three cases: without a gearbox, with a Continuously Variable Transmission (CVT), and with a conventional stepped gearbox. Second, for Hybrid Electrical Vehicles (HEVs), a twin epicyclic power split transmission model is used. Computer programmes for the analysis of epicyclic transmission based on a matrix method are developed and used. Two vehicle models are built-up; namely: traditional ICE vehicle, and HEV with a twin epicyclic gearbox. Predictions for both stages are made over the New European Driving Cycle (NEDC).The simulations show that the twin epicyclic offers substantial improvements of reduction in energy consumption in HEVs. The results also show that it is possible to improve overall performance and energy consumption levels using a continuously variable ratio gearbox in EVs.

The paper proposes a Hybrid Electric Vehicle (HEV) design based on the installation of a fuel cell (FC) module in the existing Daewoo Tico electric vehicle to increase its range in urban areas. Installing an FC module supplied by a 2 kg hydrogen tank would not significantly increase the mass of the electric vehicle, and the charging time of the hydrogen tank is lower than the battery charging time. For design analysis, a model was created in the MATLAB/Simulink software package. The model simulates vehicle range at different HEV speeds for Absorbent Glass Mat (AGM) and Proton Exchange Membrane Fuel Cell (PEMFC) power sources. The greatest anticipated benefit derived from the model analysis relates to velocities ranging from 20 km/h to 30 km/h, although the optimal HEV velocity in an urban area is in the range of 30 km/h to 40 km/h. The results indicate that this conversion of Electric Vehicle (EV) to HEV would bring a benefit of 87.4% in terms of vehicle range in urban areas. Therefore, the result of the conversion in this case is a vehicle with sub-optimal characteristics, which are nevertheless very close to optimal.

This paper discusses the need for modeling and simulation of electric and hybrid vehicles. Comparison of conventional and hybrid electric vehicle has been presented in brief. The modeling and simulation capabilities of existing tools such as Advanced Vehicle Simulator (ADVISOR), is demonstrated through an example. Major role of both type of vehicles for future reference and problem solving techniques with the help of simulation so as to reduce cost, time and increase the experience of human kind has been examined. Hybrid electric vehicle are quite efficient in comparison to fuel type of vehicles and increases the efficiency, controllability & will be used as a promising future technologies. General Comparison and analysis of result found out for Conventional Vehicles & Hybrid Electric Vehicles has been shown. Hybrid electric vehicle are quite efficient in comparison to fuel type of vehicles and increase the efficiency, controllability & support future technologies.

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.

Full Electrical Vehicles (FEVs) and Hybrid Electrical Vehicles (HEVs) are vehicles with many electric components compared to conventional ones. In fact the power train consists of electrical machines, power electronics and electric energy storage system (battery, super capacitors) connected to mechanical components (transmissions, gear boxes and wheels) and, for HEV, to an Internal Combustion Engine (ICE). The approach for a new vehicle design has to be multidisciplinary in order to take into account the dynamic interaction among all the components of the vehicle and the power train itself. The vehicle designers in order to find the correct sizing of components, the best energy control strategy and to minimize the vehicle energy consumption need modeling and simulation since prototyping and testing are expensive and complex operations. Developing a simulation model with a sufficient level of accuracy for all the different components based on different physic domains (electric, mechanical, thermal, power electronic, electrochemical and control) is a challenge. Different commercial simulation tools have been proposed in literature and they are used by the automotive designer [1]. They have different level of detail and are based on different mathematical approaches. In paragraph 2 a general overview on different modeling approaches will be presented. In the following paragraphs the author approach, focused on the modeling of each component constituting a FEV or HEV will be detailed. The authors approach is general and is not based on vehicle oriented simulation tools. It represents a good compromise among model simplicity, flexibility, computational load and components detail representation. The chapter is organized as follows:

In this paper, a supervisory level power flow control model for a parallel hybrid electric vehicle (HEV) is developed. The HEV studied utilizes power flow from an internal combustion engine ICE and from an electric-motor-generator, EM-GEN, (30 kW) and battery pack consisting of thirty 25 Ah 12 V lead acid batteries connected in series. The EM-GEN unit is an induction machine operated under maximum torque/amp control. This paper summarizes the non EM-GEN aspects of the HEV power flow control model developed earlier but focuses primarily on the development of a viable EM-GEN model amenable to a supervisory level power flow control problem.

Over the past few years Hybrid Electric and Electric propulsion systems have found significant attention as the most plausible substitute to fossil fuel based engines. Hybrid Electric Vehicles (HEV) have been around for more than a decade and extensive research has been taken out to make these vehicles more efficient. With advances in technology, manufacturers such as Tesla and Chevrolet have successfully launched a number of Electric Vehicles (EV) in the past 5 years. In despite of all this success, HEVs and EVs currently face challenges in energy storage systems (ESS) with regard to a variety of parameters and to overcome these issues research has been done on different types of ESS systems to extend the range of such vehicles. Flywheel Energy Storage Systems (FESS) have regained interest in the last decade and the application of kinetic energy recovery system (KERS) in the Formula 1 has reinforced the case of using FESS in HEV and EV. In this study, the integration of an FESS system within a hybrid electric propulsion and an electric propulsion system is considered and with the help of Bond-Graphs as a multidisciplinary modelling tool the impact of this integration is analyzed and compared with each other.

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.

<div class="htmlview paragraph">Hybrid electric vehicles (HEV) and other advanced propulsion technologies offer greater fuel economy and lower pollutant and tailpipe emissions. Some experts regard the HEV technology as a practical, attractive solution to social concerns about fuel economy (and related fossil fuel conservation) and vehicle emissions. [<span class="xref">Maples 98</span>] The technology is practical; however, an HEV is more expensive to produce than a conventional internal combustion engine (ICE). In a world of limited resources and many petroleum users and emissions sources, the policy question is whether the best use of resources is to build HEV, to improve the fuel economy and lower emissions from other sources, or to devote the resources to other environmental projects.</div> <div class="htmlview paragraph">We compare the second generation (designed for the United States) Toyota Prius, to the conventional internal combustion engine Toyota Corolla. We examine both private (vehicle purchase price, maintenance, and fuel) and social (the pollutant [non-methane organic gases, carbon monoxide, nitrogen oxides] and carbon dioxide emissions) costs. For each of the vehicles, we evaluate lifetime vehicle exhaust emissions as well as upstream emissions from producing the fuel.</div> <div class="htmlview paragraph">We find that the second generation Prius is still not cost-effective in improving fuel-economy and lowering emissions. For the Prius to be attractive to U.S. consumers, the price of gasoline would have to be more than three times greater than the present level. To be attractive to regulators, the social value of abating tailpipe emissions would have to be 14 times greater than conventional values. Alternatively, the value of abating greenhouse gas emissions would have to be at least $220 per ton. We judge that any HEV would have a difficult time competing with the Corolla because of the Corolla's already high fuel economy and low pollutant emissions.</div> <div class="htmlview paragraph">We also examine the costs and benefits of making a GM Silverado pickup truck into a gasoline hybrid. Like the analysis of the Prius, we again find that the price of gasoline or social value of abating carbon dioxide would have to be higher than the base case values in order to payback the cost of converting the vehicle to an HEV. Finally, we find that at current gasoline prices and carbon dioxide valuation, a conventional vehicle would need to have a fuel economy of 13 mpg or less in order for a $4,000 cost of converting it to an HEV to get 30% better fuel economy would pay off. We conclude that HEV will not have significant sales unless fuel prices rise several-fold or unless regulators mandate them.</div>

8 - Digital Twin development and cloud deployment for a Hybrid Electric Vehicle

This paper discusses the modelling and simulation of electric and hybrid vehicles. Modelling method for electric and hybrid vehicle is discussed with powertrain components. Genetic Algorithm is used as optimization strategy for optimization of powertrain components and control system for fuel economy and reduction of the emission. The ADVISOR software developed by NREL is used for carrying out the simulation. The simulation results demonstrate that optimal selection of parameters and a decision can be made for satisfactory solution in conformity with statutory requirements for emission of electric and hybrid electric vehicle in this paper.

There is a growing interest in electric vehicles due to environmental concerns. Concerning the environment, EVs can provide emission-free transportation. Even taking into account the emissions from the power plants needed to fuel the vehicles, the use of EVs can still significantly reduce global air pollution. Recent efforts are directed toward developing an improved main component systems for the electric vehicle applications. Soon after the introduction of electric starter for internal combustion engine early this century, despite being high-energy efficient and nonpolluting, electric vehicle lost the battle completely to internal combustion engine due to its limited range and inferior performance. But, electric vehicles offer the most promising solutions to reduce the emission of vehicles. This paper describes a method for cost reduction estimation of electric vehicle (battery electric vehicle and hybrid electric vehicle). We used a cost reduction structure that consisted of five major subsystems for electric vehicle. Especially, we estimated the potential for cost reductions in electric vehicle as a function of time using the learning curve. Also, we estimated the potentials of cost by depreciation.

Minimization of power losses in hybrid electric vehicles in view of the prolonging of battery life

Compared to other transportation technologies, fuel cell hybrid electric vehicles have been widely considered as one of the most promising alternatives for transport applications, due to their zero-emission, low noise, and high efficiency. This paper presents an energy management strategy for a hybrid electric vehicle powered by a fuel cell as the main source and Li-Ion battery as a secondary source. Our proposed approach is based on fuzzy logic and aims to decrease hydrogen consumption while increasing the durability of power sources by respecting their dynamic constraints as well as the battery’s state of charge. The fuzzy logic controller takes as inputs the load power and the battery state of charge and outputs the fuel cell power. The simulation model of the hybrid electric vehicle and its energy management strategy are established using MATLAB/Simulink. The simulation results show the correct functioning of our model and demonstrate that the proposed control strategy shows a good performance in improving fuel economy and the effective distribution of energy between sources.

The Graduate Automotive Technology Education (GATE) Center at the University of Tennessee, Knoxville (UTK) offers courses addressing the simulation, modeling, and control system design of hybrid electric vehicles (HEV). In the Spring of 1999 such a course was conducted to support the UTK FutureCar Challenge entry for 1999. The vehicle modeled is a Dual-configuration Hybrid Electric Vehicle (DHEV) which uses a planetary power-split device similar to the Toyota Hybrid System used in the Toyota “Prius”. The goals of the course included the development of a real-time simulator that could incorporate actual vehicle control hardware in the simulator loop. This “control-hardware-in-the-loop” (CHIL) configuration was used for simulation, control system design, and troubleshooting. This approach allows the simulation of normal vehicle operating conditions as well as emergency fault handling situations in which it may not be desirable to subject the actual prototype vehicle to a given test condition. Additionally, it is possible to do a great deal of control system testing and development without an operating vehicle.