Simulation Model Analysis - Coursework Example

Simulation Model Analysis Electric car and Climate analysis Institution name: Course code: Course title: Student no.: Student name: Executive Summary A first stage of this research we would like to obtain the characteristic of the electric car by modeling the flow of power in the system. Given that this model of the car relies heavily on the electrical energy from the battery, the power flow efficiency in the system of great importance and proper management should be done. To ensure that the amount of energy supplied meets the needs of the electric car this study will look at the power flow calculation. To be able to have a good power flow response in electric vehicle design models especially of small size, MATLAB/Simulink software was used in this study. The sample screenshots of the simulation graphs from the software will be used in the study. The purpose of this report is to develop methodologies for identifying and validating potential problem areas that are hybrid specific, as well as describing the performance measurement methods to be used in order to obtain data suitable for benchmarking with conventional vehicles. The methodologies developed should be used as tools to increase the performance and quality of the vehicle and thereby increase customer value. The methodologies are used to develop several high level test plans, but also low level tests regarding Zero Emission Driving and capability of a Battery Management systems capability. Those validation requirements that are same as for conventional power trains refer to the validation plans and specifications used for conventional vehicles. The introductions of electric drive systems in cars increases vehicle complexity and as a result validation complexity as well. This thesis addresses the problems involved when creating hybrid specific validation methods. Using a game-theoretic model, we examine the market impacts of a hypo thetic fleet of one million plug-in electric vehicles (PIEV) on the wrongly competitive German electricity market. We separately analyze the market effects of additional load and additional storage capacity on prices, welfare, and electricity generation. We also examine how different players in charge of electric vehicle operations differ regarding vehicle recharging and the utilization of storage capacity. In particular, the model allows to investigate the combined decisions of oligopolistic generating firms on generation, vehicle loading, and storage. We also analyze the utilization of excess vehicle battery capacity for arbitrage, i.e. storing electricity in periods of low prices and selling it back to the market in times of higher prices. We examine if arbitrage is a viable strategy in the light of existing pumped hydro storage and battery degradation costs. The analysis shows that the introduction of PIEV generally increases generator profits and decreases consumer surplus. This is particularly true if vehicles are recharged in an uncontrolled way. In case of controlled loading of the PIEV fleet, welfare distortions as well as vehicle loading costs decrease substantially. If battery capacity that is not needed for daily average driving requirements can be used for grid storage, welfare effects are very different: generator profits decrease, while consumer surplus and overall welfare substantially increase due to a price-smoothing effect of additional storage capacity. Yet battery degradation costs may diminish arbitrage opportunities and related welfare effects. In addition, strategic generating firms tend to under-utilize their battery storage capacity, which may have negative implications for consumers. In contrast, consumers may benefit from a market power mitigating effect of vehicle and storage loading on strategic generators. Finally, electric vehicles increase the utilization of emission-intensive low-cost technologies, in particular if an oligopolistic generator is in charge of PIEV operations. The first section will briefly review the relevant literature. Section 3 describes the model and the main assumptions. Sections 4 and 5 include all relevant data and define different cases of PIEV operation. The results section discusses the impacts of different players controlling PIEV fleet on market prices, welfare and electricity generation. We also perform a sensitivity analysis regarding battery degradation costs. The last section summarizes and concludes . INTRODUCTION To be able to know that there is success in development, there should be easy mobility of the people in a given region, this is the best indicator. Therefore mobility services should be increased to achieve development of any nation. Vehicles are commonly used as a means of transport both in the urban and rural areas. They are very important in the provision of services and goods of a given region or nation. Across the world oil fueled vehicles are being used as a means of transport. Overreliance on oil fuel has unstabilized economy of many nations especially countries that depend on oil importation. As many people have access to credit and have improved their economical status especially in developing world the demand of fuel consumption by the vehicles has increased. In contrast to previous studies, we explicitly model a real interaction of PIEV operations and the German wholesale market. We endogenously determine the timing of vehicle recharging and storage operations by profit-maximizing players while taking care of market price reactions. Moreover, we allow for imperfect competition, which complements earlier analyses that assume perfectly competitive markets, for example Göransson et al. (2010) for Denmark or Sioshansi et al. (2010) for the Ohio power system. Moreover, we explicitly quantify the effect of different players being in charge of PIEV operations. We thus quantitatively support the argument brought foreword by Andersen et al. (2009) and Guille and Gross (2009), according to which the ‘aggregator’ - i.e. the actor in charge of vehicle operations - plays a crucial role for integrating electric vehicle fleets into power markets. More generally speaking, our analysis also enlarges the understanding of how flexible resources are used in imperfect electricity markets. We not only study strategic electricity storage, as for example Schill and Kemfert (2009) or Sioshansi (2010) do, but also the strategic allocation of dispatchable load and its interaction with oligopolistic generation. Although the analysis is motivated by electric vehicles, results may be interpreted in a more general way, as additional storage and dispatchable demand could also be introduced to the market by other technologies. Another deficiency is air pollution which is caused by the high level of noise in this model, this is hazardous to environment and human health. The problems stated above necessities the need for change in sources of energy for the vehicles. Electric car if an effort by human is not only to ensure that oil fuel dependency is reduced but environmental pollution is well managed. The following are the major components of the electric vehicle; electric motor which is acts as drive systems, the energy sources which are electrical, central control which is the control systems, and power converter which is a device that converts electrical energy source by the switching devices with variable needs of the electric vehicle. Currently the main energy source in electric cars is the battery which as a limited life service and the capacity shortcomings. It is therefore recommended that an alternative source be of energy be put in place. To obtain the battery capacity required, it is important that a power flow model be designed because it will show how much energy a given vehicle consumes. To determine the battery capacity for the electric car, there are experiments carried out in this study. There was also experiments to for having a car have battery energy and flywheel as its energy sources were carried out by developing a car model called the hybrid electric vehicle. During the acceleration phase of the electric vehicle, the energy from the flywheel is used as an alternative energy. According to Powell, B. K., et al. (1998), dynamic model of a complete electric vehicle has the following main components; controllers, inverters, traction motors and batteries and braking system. Matlab/Simulink (Mathworks) model was used to design the dynamic model and the prediction model of electrical energy in electric vehicle with statistical methods. System Overview & System Behaviour For the purpose of the research, the model of the electric vehicle design is a small electric car best suited in the cities or urban areas where. Figure 3 below illustrates the Vehicles design where there are it is meant to be electrical car with induction machine which is the part of the car as the prime mover, the inverter interfaces between the dc source and the induction machines. The battery as energy source is shown in figure 3 designed using the Simulink/Matlab. Fig3. The model of electric vehicle in this study (adapted from International Journal of Computer Applications (0975 – 8887)) The following are some of the main parts of the electric car with their advantages. In the electrical vehicle model the Induction motor is one of the component that is used as a is used as the driver. This part has several advantages such as it’s of robustness, low cost, it has a constant velocity hence used commonly in electric vehicle, and it has a large inertia, and advantage is that there is no routine maintenance needed. Given that it is the driving, Induction motor is modeled in qdn coordinates this makes it very flexible compared to normal models. With this fact the model can be analyzed since it can operate with a voltage that is non sinusoidal even from the non symmetrical sources. Batteries as source of energy in electric source which are lead acid type are preferred because they are readily available in the market and they are relatively cheap. These batteries provide the high current as dc voltage/current needed when igniting the car engine. There is a PWM converter used in this model as the power supplier in the electric car to facilitate the flow of power within the system. Electric vehicle specification Size Weight(kgs) 900 Friction coefficient 0.19 Rotary coefficient 0.0048 Max. speed(km/jam) 20 Acceleration(km/hs) 0->60 Mileage (km) 60 Battery type Lead acid Battery voltage (v) 500 Motor capacity(kwh) 5.5 Converter type IGBT inverter Table 1. The specifications of electric vehicle (adapted from International Journal of Computer Applications (0975 – 8887)) Although hybrid vehicles are expensive, they are fuel efficient given the urban environment compared to conventional models. Sensitivity Analysis Sensitivity analysis of the hybrid electrical vehicle design parameters is very important for optimum design of the whole vehicle performance. In this paper according to the hybrid electrical vehicle characteristics and based on the simulation model of whole vehicle performance three sensitivity computing methods of power system parameters were discussed. Through instance analysis and comparison the sensitivity which was defined as the rate of the relative change of dependent variable to the relative change of independent variable was in accordance with the practical applications of hybrid electrical vehicle and this sensitivity computing method has practical engineering application value. Tractive Effort Modeling To be in a position to determine the suitable size of the electrical car inductor more and the capacity of the battery it is necessary to carry out an analysis. First it is important to do the modeling so that you can come up with the equation of tractive effort which enables one to determine the load of the car on a flat road conditions. The tractive effort model of the electric vehicle in this study will look at the force acceleration force, the rolling resistance force, aerodynamic drag, and hill climbing. The rolling resistance force is push into the front of the electric vehicle. As illustrated in figure 4 below this rolling resistance force is equivalent to the transmitting wheel. It is relative to the weight of the vehicle, given as: F mg rr rr (1) where Frr is the rolling resistance force, m is mass of vehicle, g is the gravity and μrr is the coefficient of rolling resistance. Fig4. Mathematical analysis adapted from International Journal of Computer Applications (0975 – 8887)) Aerodynamic of a vehicle causes a force called the aerodynamic drag. Aerodynamic drag force is determined by the shape of the surface of the vehicle (A), coefficient of form (Cd), velocity (v) and air density (ρ). The formula for this component is: Fad=1/2(pACdv2) (2) The force that is impacted on the vehicle to move upward with a slope is called Hill climbing force. It is given by the equation below: F hc =mg sin()(3) where Y is the slope. To increase the speed of the vehicle, a force is required which is known as the acceleration force. According to Newton's second law, the linear acceleration force of a vehicle is given as: F la = ma (4) To make angular acceleration, an angular acceleration force is required by the wheels. Angular acceleration (Fωa), is as follow: Fwa=I (G2/r2)a (5) where I is the moment of inertia, G is gear ratio, r is the radius of the tyre. To calculate tractive effort in a given car we sum all the five forces discussed above which gives the total tractive force as follow: Fte=Frr+Fad+Fhc+Fla+Fwa (6) This analysis also was required to analyse the battery capacity of the electric car. The equation below can be used to determine capacity of the battery; Pte=Fte* v (7) where v is the speed of vehicle. A vehicle representation can be modeled using the MATLAB/Simulink software from the above equations(1-7). The inputs in this model will be the acceleration, speed and slope of the road and the output of motor and currents as shown in Figure 5. From the car requirements (table 1), 527.6 N force is needed to move the electric car model. Fig5 :The power flow of electric vehicle model adapted from International Journal of Computer Applications (0975 – 8887)) From the specification of the electric car in table 1 the induction machine needed is 3899 Watts according to values of the maximum speed for testing, total mass, friction coefficient, the air density and aerodynamic factors, the surface area of vehicles, the gear ratio factor G, wheel radius, and maximum power efficiency. Fig6 ; Simulation of speed Equation 7 above facilitated us to uncover a significant relationship between the power needed by the induction machine and the lead acid batteries which is the source. 21060kWs of energy of the lead acid batteries was used in this study having the notion of highest power that is used by the induction machine to drive an electric vehicle as 3889W; the battery can provide energy for 1 hour 30 minutes. Fig7: Acceleration graph Electric Vehicle Acceleration Modelling The key performance indicator of the electric car is its ability to accelerate, therefore acceleration speed is very important despite the fact that there are no standard measure. The acceleration time is given according to the figure 7 above, it can be pointed out that a standard accelerations for electric vehicles are the 0–30 kph. The electric vehicles performance simulations are carried out at maximum torque. The maximum torque of an electric motor is the function of angular speed. The torque falls when the speed of the motor attains a critical value denoted as ωc but the power remains constant. The maximum torque is constant at low speeds. Gear ratio G and the radius of the drive wheel r are important factors in determining Maximum Torque given: v < r/G wc then T=Tmax (8) After the igniting phase, then either the power is constant, and we have the torque is: T=Tmax(rwc/Gv) (9) We can combine equation 6, 8, 9 in order to find the acceleration of an electric vehicle is: To find the electric car acceleration, we combine equations 6,8, and 9 as: dv/dt =(ngr2/ngr2+IG2) {GT/r -µrrmg-1/2pACdv2-mg sin(¥) (10) given that constants are known (table 1) the equation can be differentiated as shown in equation11 by replacing the constants with actual values. The model simulation of acceleration is shown in Figure 6. vn+1= vn+dt(3.11- 0.000113vn2 ) (11) Fig8: Simulation of acceleration of electric vehicle model. Electric Vehicle System Modelling The model that proposed in Figure 3 is to get a performance of the electric vehicle. The model uses lead acid batteries 375 Volt, PWM inverter and three phase induction machine 220 Volt, 60 Hz. The simulation of electric vehicle system modeling is shown in the Figure below.: Fig9 :Simulation of electric vehicle system model. The simulations above were used to find out the performance of electric vehicle in starting conditions and running with constant speed. CONCLUSION Modeling of electric vehicle system makes it easy to determine how much battery capacity required by an electric vehicle with certain specifications to achieve a certain distance as well. This model can be used to estimate how long the battery can be used in electric vehicles. The model can also be used to determine the performance of electric vehicle such as the starting process or running with constant speed. The validation of hybrid electric vehicles poses a complex problem, however by using the methodologies described in this thesis the work flow is made more natural and the work load is minimized. Nonetheless, the methodology is a process that continuously should be updated as the experience in this field grows. Zero Emission vehicle capability of the MHD2 vehicle in its current configuration clearly is below any commercially useful levels especially if battery wear is to be taken into consideration. In the simulations a range of 5.95 km was simulated during the MVEG-A city part drive cycle. This range was reached by using an extreme SOC swing of 80% (100-20%). The result may seem remarkable, but is to be compared to commercial vehicles having SOC swings of their batteries at +/- 10% to keep the life time requirements on the batteries. Refinements and future work The validation methodologies presented in this thesis have been developed in an iterative process continuously improving the workflow and the possibilities to generate adequate results. This iterative process is to be continued as experience of hybrid electric vehicle validation grows. Evaluating and performing the tests presented in this thesis should be made to improve quality, performance and security of future HEV products. The simulations regarding Zero Emission Vehicle (ZEV) driving should be improved especially with regards to the road model and driver model which are quite simple in their current form. Appendices Fig9: Model Screenshot Fig10: HEV Mode Logic Fig11; Vehicle Speed  Fig12: Speeds From Urban Cycle 1  Fig 13: Voltages From Urban Cycle 1 This subsystem uses a PI regulator with anti windup on the integrator to prevent saturation in the actuator. It is used to simulate a drivers behavior to follow the drive cycle using the difference in speed between drive-cycle speed and actual vehicle speed as input to the controller the output generated by the controller is desired wheel torque to achieve requested vehicle speed. The model is a simplification of driver behavior but it gives good results for comparative studies. Fig14:Currents From Urban Cycle 1 This subsystem is used to allow for adding transmission losses by increasing the power demand in motoring mode and decreasing the power in generator mode by an efficiency factor that is determined by the transmission connecting the RDU with the wheels. References [1] Bernstein, L., Intergovernmental Pa-nel on Climate Change Fourth Assessment Report Climate Change 2007: Synthesis Report Summary for Policymakers, A-vailable Jan. 2008: www.ipcc.ch [2] Dhameja, S., 2002, Electric Vehicle Battery Systems, Newnes, Uni-ted Stated. [3] Husain, I., 2003, Electric and Hybrid Vehicles Design Fundamentals, Pertama, CRC Press, United Stated. [4] Kim, S., Chung, S., Shin, W., Lee, J., A study of predicting model of an electrical energy balance for a conventional vehicle, Procee-dings of the 17th World Con-gress The International Federa-tion of Automatic Control Seoul, Korea, July 6-11, 2008. [5] Kunzli, N., Public-Health Impact of Outdoor and Traffic- Related Air Pollution: A European Assess-ment, The Lancet, Vol. 356, Number 9232, September 2000, pp. 795- 801. [6] Larminie, J., Lowry, J., 2003, Electric Vehicle Technology Explained, John Wiley & Son. [7] Lustenader, E. L., Guess, R. H., Richter, Turnbull, F. G., De-velopment of a Hybrid Flywheel /Battery Drive System for Elec-tric Vehicle Applications, IEEE Transactions on Vehicular Tech-nology, Vol. VT-26, May 1977, pp.135- 143. [9] Powell, B. K., Bailey, K. E., Cikanek, S. R., Dynamic Modeling and Control of Hybrid Electric Ve-hicle Powertrain Systems, IEEE Control Systems. October 1998. [10] Septimu, M., Liviu, T., Behavior of the Lead Acid Battery after the Rest Period, WSEAS TRAN-SACTIONS on POWER SYS-TEMS, Issue 3, Volume 3, March 2008. [11] Wood, J. H., Long, G. R., More-house, D. F., Long Term World Oil Supply Scenarios: The Future Is Neither as Bleak or Rosy as Some Assert, US De-partment of Energy, 2004. Ava-ilable Jan. 2008: http://www.eia. doe.gov/pub/oil_gas/petroleum/feature_articles/2004/world oilsupply/oilsupply04.html [12] Ying, S., Ding, S., Yang, J., Hung, R., Electrochemistry Theorem Ba-sed State-of-Charge Estimation of the Lead Acid Batteries for Electric Vehicles, WSEAS Tran-sactions on Systems, Issue 10, Volume 7, October 2008, pp.10-92- 1103. Read More

We also analyze the utilization of excess vehicle battery capacity for arbitrage, i.e. storing electricity in periods of low prices and selling it back to the market in times of higher prices. We examine if arbitrage is a viable strategy in the light of existing pumped hydro storage and battery degradation costs. The analysis shows that the introduction of PIEV generally increases generator profits and decreases consumer surplus. This is particularly true if vehicles are recharged in an uncontrolled way.

In case of controlled loading of the PIEV fleet, welfare distortions as well as vehicle loading costs decrease substantially. If battery capacity that is not needed for daily average driving requirements can be used for grid storage, welfare effects are very different: generator profits decrease, while consumer surplus and overall welfare substantially increase due to a price-smoothing effect of additional storage capacity. Yet battery degradation costs may diminish arbitrage opportunities and related welfare effects.

In addition, strategic generating firms tend to under-utilize their battery storage capacity, which may have negative implications for consumers. In contrast, consumers may benefit from a market power mitigating effect of vehicle and storage loading on strategic generators. Finally, electric vehicles increase the utilization of emission-intensive low-cost technologies, in particular if an oligopolistic generator is in charge of PIEV operations. The first section will briefly review the relevant literature.

Section 3 describes the model and the main assumptions. Sections 4 and 5 include all relevant data and define different cases of PIEV operation. The results section discusses the impacts of different players controlling PIEV fleet on market prices, welfare and electricity generation. We also perform a sensitivity analysis regarding battery degradation costs. The last section summarizes and concludes . INTRODUCTION To be able to know that there is success in development, there should be easy mobility of the people in a given region, this is the best indicator.

Therefore mobility services should be increased to achieve development of any nation. Vehicles are commonly used as a means of transport both in the urban and rural areas. They are very important in the provision of services and goods of a given region or nation. Across the world oil fueled vehicles are being used as a means of transport. Overreliance on oil fuel has unstabilized economy of many nations especially countries that depend on oil importation. As many people have access to credit and have improved their economical status especially in developing world the demand of fuel consumption by the vehicles has increased.

In contrast to previous studies, we explicitly model a real interaction of PIEV operations and the German wholesale market. We endogenously determine the timing of vehicle recharging and storage operations by profit-maximizing players while taking care of market price reactions. Moreover, we allow for imperfect competition, which complements earlier analyses that assume perfectly competitive markets, for example Göransson et al. (2010) for Denmark or Sioshansi et al. (2010) for the Ohio power system.

Moreover, we explicitly quantify the effect of different players being in charge of PIEV operations. We thus quantitatively support the argument brought foreword by Andersen et al. (2009) and Guille and Gross (2009), according to which the ‘aggregator’ - i.e. the actor in charge of vehicle operations - plays a crucial role for integrating electric vehicle fleets into power markets. More generally speaking, our analysis also enlarges the understanding of how flexible resources are used in imperfect electricity markets.

We not only study strategic electricity storage, as for example Schill and Kemfert (2009) or Sioshansi (2010) do, but also the strategic allocation of dispatchable load and its interaction with oligopolistic generation.

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