Modeling of Electric Vehicles (EVs) for EV Grid Integration Study

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1 Modeling of Electric Vehicles (EVs) for EV Grid Integration Study Qiuwei Wu, Arne H. Nielsen, Jacob Østergaard, Seung Tae Cha, Francesco Marra and Peter B. Andersen Center for Electric Technology, Technical University of Denmark, Elektrovej 325, Kgs. Lyngby, DK 2800, , Abstract In order to successfully integrate EVs into power systems, it is necessary to develop a detailed EV model considering both the EV users driving requirements and the battery charging and discharging characteristics. A generic EV model was proposed which takes into account charging and discharging characteristics of EV batteries, the driving distance per trip and the availability of EVs for charging and providing grid service. The charging and discharging characteristics of EV batteries were used to determine the upper and lower limits of the state of charge (SOC) of EV batteries and to calculate the charging and discharging power. The driving distance per trip and availability of EVs were used to reflect the driving requirements and to implement intelligent charging and discharging management. Index Terms Electric Vehicles (EVs), EV model, state of charge (SOC), driving pattern I. INTRODUCTION With more and more renewable energy integrated into power systems, it has become a good option to use electric vehicles (EVs) to balance the uncertainties introduced by the intermittency of the renewable energy. At the same time, replacing conventional internal combustion engine (ICE) cars with EVs can reduce the green house gas emission from the transport sector. Therefore, the idea of replacing conventional ICE cars with EVs is expected to be realized in the near future with the development of battery technologies, efforts on the prototyping of more efficient EVs and the financial incentives provided by governments over the world. Denmark is a unique place regarding the renewable energy utilization and the use of EVs. At this moment, the wind power penetration level in Denmark is around 20%. The Danish government has set an aggressive target of using wind power that 50% of electricity consumption will be supplied by wind power in 2025.The average driving distance in Denmark is 42.7 km per day [1]. It is possible to meet the driving

2 requirement for a whole day with a fully charged 20 kwh battery. Therefore, from perspectives of balancing the uncertainties introduced by wind power and meeting driving requirements by EVs, it is a good idea to deploy a large amount of EVs in Denmark and integrate them into the Danish power system. The possibility of using vehicle to grid (V2G) to improve wind power integration was studied in [2]. The traffic data were used to calculate the vehicle fleet availability. It was concluded that it is possible to have EVs providing instantaneous disturbance and manual reserve to help integrate more wind power. The feasibility study of implementing V2G scenario in Denmark was done in [3]. The system constraints for integrating EVs into power systems were examined and the technical and economical viability of various possible V2G architectures were studied. In Ref. [4], the potential of using EVs in Denmark was investigated to identify the benefits for power systems with high wind power penetration with intelligent EV charging management. The research initiatives of the Edison project were also presented. A vehicle to grid demonstration project was implemented in AC Propulsion Inc. to evaluate the feasibility and practicality of EVs providing regulation service [5]. A test vehicle was fitted with a bi-directional grid power interface and wireless internet connectivity to carry out the demonstration. It was shown that it is feasible for EVs to provide regulation service from a technical and economical point of view. A three time-constant dynamic electric vehicle battery model for lead-acid, nickel metal hydride (Ni-MH), and lithium ion (Li-ion) batteries was proposed in [6] to represent the terminal voltage, state of charge (SOC) and power losses of each battery type. A generic battery model for dynamic simulation of hybrid electric vehicles was proposed in [7] which used the battery SOC as the state variable. It was shown that the proposed battery model can represent the battery discharge curves and the transient states. The naturalistic driving schedules obtained from field operational tests of passenger vehicles in southeast Michigan were used to predict energy usage as a function of trip length [8]. The analysis of naturalistic driving schedules can provide the times spent at given locations as well as the likely battery SOC at the time of arrival. Data from a fleet of PHEVs under normal operation were collected and analyzed to assess the impact of usage patterns on vehicle performance [9]. To the best knowledge of the authors, the existing EV models are focused on the battery dynamic performance for charging and discharging or the driving pattern analysis. Therefore, a generic EV model is proposed in this paper for the EV grid

3 integration study which reflects both the EV battery charging and discharging characteristics and the EV users driving requirements. The rest of the paper is arranged as follows: the requirements of an EV model for EV grid integration study are described in Section II and the charging and discharging characteristics of EV batteries are presented in Section III; in Sections IV and V, the driving pattern information is analyzed and an EV model for EV grid integration study is given; in the end, a brief conclusion is drawn. II. REQUIREMENTS OF AN EV MODEL FOR EV GRID INTEGRATION STUDY There are a few options to charge the EV batteries to meet the driving requirements: 1. Fast charging pull over EVs at the fast charging stations and charge the EV batteries within minutes 2. Battery swapping drive EVs to the battery swapping stations and exchange the used battery with a fully charged battery, the used batteries can be charged at the fast charging station or at the battery swapping stations 3. Low power charging charge EVs at homes, parking lots near home, working place or shopping malls The EV model proposed in this paper is for carrying out the grid integration study with the low power charging scenario. The EV battery supplies the energy needed for driving. Therefore, the charging power and time have to be modeled in the EV model. At the same time, the possibility of using EV batteries to provide power system regulating power makes it necessary to study the discharging characteristic of EV batteries. Moreover, the impact of charging and discharging on battery lifetime also has to be considered. The EV batteries characteristics that have to be included in the EV model are: charging power, discharging power, maximum SOC, minimum SOC/maximum depth of discharge (DOD) and charging time. In order to ensure that the EV users driving requirements are met, it is necessary to study the EV users driving pattern. The information of driving pattern that has to be reflected into the EV model is: initial SOC of EV batteries for the first trip of each day, starting time and ending time of each trip, destination of each trip, driving distance of each trip and energy consumption per km. III. CHARGING AND DISCHARGING ANALYSIS OF BATTERIES FOR EVS The EV batteries must store enough energy to meet the driving requirement. Leadacid batteries have low energy density typically around 20 Wh/kg whereas Ni-MH batteries have an energy density of about 70 Wh/kg. Although Ni-MH batteries have

4 considerably higher energy density than lead acid batteries, they have lower charging efficiency which is 90%. Li-ion batteries have energy density as high as 180 Wh/kg [10]. The comparison of volumetric and gravimetric energy density of different batteries shows that Li-ion batteries represent the most promising developments in the area of EVs. The expected life cycle performance of Li-ion, Ni-MH and lead-acid batteries is shown in Figure 1. The EV batteries need to meet the life time requirement. If it is assumed there is one time deep discharge per day, it means there are 4000 plus deep discharge cycles during the years battery lifetime. From Figure 1, the deep discharge should not be more than 80%. Figure 1 Cycle life Characteristic of Batteries [11] The charging stages of Li-ion batteries are illustrated in Figure 2. Figure 2 Charging Stages of Li-ion Batteries [12] Stage 1 is the constant current charging till the cell voltage limit is reached. Stage 2 starts after the cell voltage limit is reached and the charging current starts to drop as the full charge is approaching. Stage 2 terminates when the charging current is less than 3% of the rated current and takes a long time. It is recommended to ignore Stage 2. It means that the maximum SOC is 85% which is the SOC when the cell voltage limit is reached. The charging time can be calculated using Eq. (1).

5 . (1) where is the charging time, is the charged battery capacity expressed in Ah,. is the bulk charging current as per the charging rate. In order to obtain the charging power and the discharging power of EV batteries, the battery terminal voltage has to be calculated. As the Li-ion battery is the most promising battery for EVs, the battery model of Li-ion battery is used to calculate the charging and discharging power. Depending on the charging or discharging rate, the battery open circuit voltage of Lithium-Ion batteries can be obtained using Eq. (2) [13]. exp (2) where is the open circuit voltage at time t during charging or discharging, is the constant voltage, K is the polarization constant (Ah -1 ), Q is the maximum battery capacity (Ah), A is the expotional voltage, B is the expotional capacity (Ah -1 ). Due to the internal resistance of batteries, the battery terminal voltage can be determined by Eq. (3)... (3) where. is the battery terminal voltage at time t during charging or discharging, is the battery internal resistance,. is the battery current which is negative for charging and positive for discharging. The battery terminal voltage versus time and SOC during discharging and charging of a 1 Ah 3.6 V Li-Ion battery are illustrated in Figure 3. Figure 3 Battery Terminal Voltages of A Li-Ion Batteries for 1C, 2C and 5C Discharging and Charging The charging and discharging power can be determined by the battery terminal voltage, the battery current and the charging and discharging loss..., (4).., (5)

6 where, and, are the converter loss due to charging and discharging. The converter loss varies according to the converter size and the manufacture. The converter efficiency can reach 98.5%. This number is used to calculate the converter loss. The charging and discharging loss can be determined by Eq. (6) and Eq. (7)., % 1.5% (6),.. 1.5% (7) The discharging and charging power versus time and SOC of a 1 Ah 3.6 V Li-Ion battery are illustrated in Figure 4. Figure 4 Power of A Li-Ion Batteries for 1C, 2C and 5C Discharging and Charging IV. ANALYSIS OF DRIVING PATTERN INFORMATION In order to ensure that the EV users driving requirements are met, it is necessary to study the EV users driving pattern. Currently, the EVs on the road are very few. It is difficult to get the real EV driving pattern. But it is reasonable to assume that the EV users will more or less have the same driving pattern as the ICE car users. Therefore, the analysis of the driving data of ICE cars can be a good estimate of the EV driving pattern. The starting and ending time of each trip can be used to calculate all the possible time slots for EV charging and discharging. The destination of each trip gives more information of the availability of EVs for charging and discharging. Depending on the availability of charging facility in the destination, the available time slots for EV charging and discharging can be revised a little bit. The destination information also indicates where the EV is connected to the power system. The driving distance of each trip and the energy consumption per km can be used to calculate the SOC change of EV batteries due to the specific trip. The energy used

7 per km for a home passenger car is between 120 Wh/km and 180 Wh/km. If there is not detailed information for energy consumption, 150 Wh/km can be used to calculate the energy consumption with the driving distance. The information from the driving pattern that should be reflected in the EV model is illustrated in Table 1. Table 1 Driving Pattern Information Information Value or Parameter Energy used per km 150 Wh/km Starting time for Kth trip Ending time for Kth trip Location FA(t) V. EV MODEL FOR GRID INTEGRATION STUDY Based on the charging and discharging analysis of batteries and the driving pattern analysis, an EV model is proposed for the low power charging EV integration study which is depicted in Eq. (8) (8) 1 where is the EV charging power at time period t, is the battery state. If is 1, EV batteries are in the charging state; if is -1, the EV batteries are in the discharging state; equal to 0 means the EV batteries are in the idle state or EVs are driving. The constraint of is given Eq. (9) & 1 (9) 1 0 & where is the availability of EVs for charging and discharging, is 0 if EVs is driving or there is no charging spot when EVs are parked. and are given in Section III which are 20% and 85%, respectively. VI. CONCLUSION A generic EV model is proposed for the EV grid integration study with the low power charging scenario. The proposed EV model takes into account both the EV battery charging and discharging characteristics and the driving requirements. The charging and discharging characteristics of EV batteries were used to calculate the charging and discharging power. The driving pattern information was also reflected into the proposed EV model. The driving distance per trip was used to calculate the

8 SOC change of EV batteries and the starting and ending time of each trip was used to obtain the availability of EVs for charging and discharging. VII. ACKNOWLEDGEMENT The authors are grateful to the financial support from the project of Electric vehicles in a Distributed and Integrated market using Sustainable energy and Open Networks (EDISON) funded by the ForskEl program (ForskEL Project Number ). REFERENCES [1] Standardv;rider for traffic data til OSPM modellen, Tetraplan A/S, [2] Jean Brassin, Vehicle-to-Grid Improving Wind Power Integration, master thesis, Center for Electric Technology, DTU, [3] D. K. Chandrashekhara, J. Horstmann, J. Østergaard, E. Larsen, C. Kern, T. Wittmann and M. Weinhold, Vehicle to Grid (V2G) in Denmark-Feasibility Study, Center for Electric Technology, DTU, Jun [4] J. Østergaard, A. Foosnæs, Z. Xu, T. Mondorf, C. Andersen, S. Holthusen, T. Holm, M. Bendtsen and K. Behnke, Electric Vehicles in Power Systems with 50% Wind Power Penetration: the Danish Case and the EDISON programme, European Conference Smart Grids and Mobility, pp. 1-8, Jun [5] A. N. Brooks, Vehicle-to-Grid Demonstration Project: Grid Ancillary Service with A Battery Electric Vehicle, Dec [6] R.C. Kroeze and P.T. Krein, Electrical Battery Model for Use in Dynamic Electric Vehicles Simulations, IEEE Power Electronics Specialists Conference 2008, pp , Jun [7] O. Tremblay, L. A. Dessaint, and A. I. Dekkiche, A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles, IEEE Power Vehicle and Propulsion Conference 2007, pp , Feb [8] B. Adornato, R. Patil, Z. Filipi, Z. Baraket and T. Gordon, Characterizing Naturalistic Driving Patterns for Plug-in Hybrid Electric Vehicle Analysis, IEEE Power Vehicle and Propulsion Conference 2009, pp , Sep [9] S. M. Mohler, S. Ewing, V. Marano, Y. Guezennec, and G. Rizzoni, PHEV Fleet Data Collection and Analysis, IEEE Power Vehicle and Propulsion Conference 2009, pp , Sep [10] R. Hodkinson and J. Fenton, Light Weight Electric/Hybrid Vehicle Design, SAE International, [11] T. Markel, A. Brooker, J. Gonder, M. O. Keefe, A. Simpson and M. Thornton, Plug-in Hybrid Vehicle Analysis, National Renewable Energy Laboratory, [12] Batteryuniversity.com, Charging Lithium-Ion Batteries, [Online] available: [13] Battery model, SimPowerSystems, MATLAB.