Electrical Vehicle Design and Modeling 19 3.2 Battery charging control During the charging of the battery, i.e., both due to the regenerative braking and the grid, it is very important that the maximum battery charging current and voltage not are exceeded. The maximum allowed cell charging current can be calculated from the inner and outer voltage of the battery cell, i.e., i Bat,cell,cha,max = ⎧ ⎨ ⎩ V Bat,max,cell −v Bat,int,cell R Bat,cell,cha , V Bat,max,cell −v Bat,int,cell R Bat,cell,cha ≤ I Bat,1,cell I Bat,1,cell , V Bat,max,cell −v Bat,int,cell R Bat,cell,cha > I Bat,1,cell . (73) In Equation 73 it is insured that neither the maximum allowed voltage or current are exceeded. The battery pack consist of N Bat,s series connected cells and N Bat,p parallel connected strings. The total voltage and current of the battery pack can therefore be calculated as v Bat = N Bat,s v Bat,cell (74) i Bat = N Bat,p i Bat,cell (75) i Bat,cha,max = N Bat,p i Bat,cell,cha,max . (76) During the charging of the battery the battery cell voltage v Bat,cell should not exceed V Bat,max,cell = 4.2 V and the maximum cell charging current should not be higher than I Bat,1,cell = 7 A (Saft, 2010). In order to charge the battery as fast as possible either the maximum voltage or maximum current should be applied to the battery. The requested battery charging current, i.e., the output current of the boost converter i BC , is therefore i ∗ BC = i Bat,cha,max , (77) which means that the requested output power of the boost converter is p ∗ BC = v Bat i ∗ BC . (78) The requested charging current insures that neither the maximum allowed voltage or current are exceeded. However, for a big battery pack the required charging power might be so high that a special charging station is necessary. The requested input current of the boost converter, i.e., the rectifier current i RF ,canbe calculated by Equation 31 and 78: i ∗ RF = −  V th,BC −v RF  −   V th,BC −v RF  2 −4R BC p ∗ BC 2R BC . (79) The grid RMS-current can therefore from Equation 34 be calculated as I Grid = ⎧ ⎪ ⎨ ⎪ ⎩  2 3 i ∗ RF ,  2 3 i ∗ RF < I Grid,max I Grid,max ,  2 3 i ∗ RF ≥ I Grid,max . (80) 19 Electrical Vehicle Design and Modeling 20 Will-be-set-by-IN-TECH Glider mass M glider 670 kg Wheel radius r w 0.2785 m Front area A front 1.68 m 2 Aerodynamic drag coefficient C drag 0.3 Table 4. Parameters of the vehicle used for the case study. Thereby it is ensured that the maximum RMS grid current is not exceeded. The actual values can therefore be obtained by calculating backwards, i.e., i RF =  3 2 I Grid (81) p RF = v RF i RF (82) p BC = p RF −R BC i 2 RF −V th,BC i RF (83) i BC = p BC v Bat . (84) 4. Case study 4.1 Driving cycle When different cars are compared in terms of energy consumption a standard driving cycle is used. An often used driving cycle is the New European Driving Cycle (NEDC) as this driving cycle contains both city driving with several start-and-stops and motorway driving, i.e., it is a good representation of a realistic driving environment. The NEDC has a maximum speed of 120 km/h, an average speed of 33.2 km/h, a duration of 1184 s, and a length of 10.9 km. The NEDC profile can be seen in Fig. 12. The input to the simulation will be the NEDC repeated 14 times as this should provide a driving distance of 153 km which is assumed to be an acceptable driving distance. 4.2 Vehicle parameters The energy consumption of a given vehicle depend on the physical dimensions and total mass of the vehicle. For this case study the parameters in Table 4 are used. The glider mass is the mass of the vehicle without motor, battery, power electronics, etc. It might be understood from the parameters in Table 4 that it is a rather small vehicle, i.e., similar to a Citroën C1. 4.3 Results In Fig. 13 the battery state-of-charge, current, voltage, and the power of the grid and battery can be seen. It is understood from Fig. 13(a) that the battery is designed due to its energy requirement rather than the power requirement as the state-of-charge reaches the minimum allowed value of SoC Bat,min = 0.2. In Fig. 13(b) and (c) the battery current and voltage are shown, respectively. It is seen that when the current becomes higher the voltage becomes lower as the power should be the same. In Fig. 13(d) the battery and grid power are shown. It is seen that the charging of the battery is limited by the maximum allowed grid power P Grid,max . After approximately two hours the battery reaches the maximum voltage, and it is therefore seen that the battery then is charged under constant-voltage approach, which means that the battery current and power and grid power slowly are decreased until the battery reaches its initial state-of-charge value. 20 Electric Vehicles – Modelling and Simulations Electrical Vehicle Design and Modeling 21 0 200 400 600 800 1000 0 20 40 60 80 100 120 Time [s] Speed [km/h] Fig. 12. New European Driving Cycle (NEDC). This driving cycle will be repeated 14 times and thereby serving as the input profile of the Matlab/Simulink simulation model. Due to the minimum battery pack voltage requirement N Bat,s = 216 series connected battery cells are required. The chosen vehicle is designed to be able to handle 14 repetitions of the NEDC. From Fig. 10 it is understood that N Bat,p = 5 parallel strings are demanded in order to fulfill this requirement. This means that the battery pack has a capacity of E Bat = V Bat,nom,cell N Bat,s Q Bat,1,cell N Bat,s 1000 Wh/kWh = 28.0 kWh. (85) The energy distribution of the vehicle can be seen in Fig. 14. During the 14 NEDC repetitions E t = 11.2 kWh is delivered to the surface between the driving wheels and the road, but E Grid = 22.7 kWh charging energy is taken from the grid. This means that only 49 % of the charging energy from the grid is used for the traction and that the grid energy consumption is 148.3 Wh/km. The rest of the energy is lost in the path between the wheels and the grid. The auxiliary loads are responsible for the biggest energy loss at 17 %. However, it is believed that this can be reduced significant by using diodes for the light instead of bulbs, and to use heat pumps for the heating instead of pure resistive heating. The battery is responsible for the second largest energy waist as 14 % of the grid energy is lost in the battery. The battery was only designed to be able to handle the energy and power requirements. However, in order to reduce the loss of the battery it might be beneficial to oversize the battery as the battery peak currents then will become closer to its nominal current 21 Electrical Vehicle Design and Modeling 22 Will-be-set-by-IN-TECH 0 1 2 3 4 5 6 7 0.2 0.4 0.6 0.8 0 1 2 3 4 5 6 7 0 20 40 0 1 2 3 4 5 6 7 700 800 900 0 1 2 3 4 5 6 7 −10 0 10 20 30 Battery state-of-charge SoC Bat [ − ] (a) (b) (c) (d) Battery current i Bat [ A ] Battery voltage v Bat [ V ] Power [ kW ] Battery p Bat Grid p Grid P Grid,max V Bat,max Time [ h ] Fig. 13. Simulation results of the vehicle with 14 repeated NEDC cycles as input. (a) Battery state-of-charge. (b) Battery current. (c) Battery voltage. (d) Power of the battery and grid. 22 Electric Vehicles – Modelling and Simulations Electrical Vehicle Design and Modeling 23 which will reduce the negative influence of the peukert phenomena. However, a heavier battery will also increase the traction power, so the gained reduction in battery loss should be higher than the increased traction power. A bigger battery will of course also make the vehicle more expensive, but these issues are left for future work. E t :49% E Loss,TS :4% E Loss,EM :10% E Loss,Inv :2% E Loss,BC :2% E Aux :17% E Loss,Bat :14% E Loss,RF :2% Fig. 14. Energy distribution in the vehicle relative to the grid energy. 5. Conclusion In this chapter a battery electric vehicle have been modeled and designed. The battery of the electric vehicle is designed in such a way that both the power and energy requirements are fulfilled for a given driving cycle. The design procedure is an iterative process as the power flow inside the vehicle depends on the parameters of each component of the power system between the grid and driving wheels. The loss of each component in the vehicle depend on the internal states of the vehicle, i.e., the voltages, currents, speed, torques, and state-of-charge. These states have been included in the modeling in order to obtain a realistic energy calculation of the vehicle. A case study with a small vehicle undergoing 14 driving cycles of type NEDC resulted in a grid energy consumption of 148.3 Wh/km with an efficiency of 49 % from the grid to the driving wheels. However, a relatively big part of the energy loss is due to the auxiliary loads, e.g., light, safety systems, comfort systems, etc., and the battery. For this work the only design constraint of the battery was the voltage limit, and the energy and power requirements. For future work it is recommended also to include the cost and overall efficiency as design parameters. It is also suggested to investigate how the loss due to the auxiliary loads can be reduced. 23 Electrical Vehicle Design and Modeling 24 Will-be-set-by-IN-TECH 6. References Casanellas, F. (1994). Losses in pwm inverters using igbts, IEE Proceedings - Electric Power Applications 141(5): 235 – 239. Chan, C. C., Bouscayrol, A. & Chen, K. (2010). Electric, hybrid, and fuel-cell vehicles: Architectures and modeling, IEEE Transactions on Vehicular Technology 59(2): 589 – 598. Ehsani, M., Gao, Y., Gay, S. E. & Emadi, A. (2005). Modern Electric, Hybrid Electric, and Fuel Cell Vehicles - Fundamentals, Theory, and Design, first edn, CRC Press LLC. Emadi, A. (2005). Handbook of Automotive Power Electronics and Motor Drives,firstedn,Taylor &Francis. Gao, D. W., Mi, C. & Emadi, A. (2007). Modeling and simulation of electric and hybrid vehicles, Proceedings of the IEEE 95(4): 729 – 745. Jensen, K. K., Mortensen, K. A., Jessen, K., Frandsen, T., Runólfsson, G. & Thorsdóttir, T. (2009). Design of spmsm drive system for renault kangoo, Aalborg University . Lukic, S. & Emadi, A. (2002). Performance analysis of automotive power systems: effects of power electronic intensive loads and electrically-assisted propulsion systems, Proc. of IEEE Vehicular Technology Conference (VTC) 3: 1835 – 1839. Mapelli, F. L., Tarsitano, D. & Mauri, M. (2010). Plug-in hybrid electric vehicle: Modeling, prototype realization, and inverter losses reduction analysis, IEEE Transactions on Industrial Electronics 57(2): 598 – 607. Mohan, N., Underland, T. M. & Robbins, W. P. (2003). Power electronics, third edn, John Wiley. Saft (2010). Saftbatteries. URL: http://www.saftbatteries.com Schaltz, E. (2010). Design of a Fuel Cell Hybrid Electric Vehicle Drive System, Department of Energy Technology, Aalborg University. UQM (2010). Uqm technologies. URL: http://www.uqm.com 24 Electric Vehicles – Modelling and Simulations 2 Modeling and Simulation of High Performance Electrical Vehicle Powertrains in VHDL-AMS K. Jaber, A. Fakhfakh and R. Neji National School of Engineers, Sfax Tunisia 1. Introduction Nowadays the air pollution and economical issues are the major driving forces in developing electric vehicles (EVs). In recent years EVs and hybrid electric vehicles (HEVs) are the only alternatives for a clean, efficient and environmentally friendly urban transportation system (Jalalifar et al., 2007). The electric vehicle (EV) appears poised to make a successful entrance to the personal vehicle mass market as a viable alternative to the traditional internal combustion engine vehicles (ICE): Recent advances in battery technology indicate decreasing production costs and increasing energy densities to levels soon acceptable by broad consumer segments. Moreover, excluding the generation of the electricity, EVs emit no greenhouse gases and could contribute to meeting the strict CO2 emission limits necessary to dampen the effect of global warming. Several countries around the world have therefore initiated measures like consumer tax credits, research grants or recharging station subsidies to support the introduction of the EV. Finally, the success alternative vehicles like the Toyota Prius Hybrid proves a shift in consumer interest towards cleaner cars with lower operating costs (Feller et al., 2009). Nonetheless, the EV will first need to overcome significant barriers that might delay or even prevent a successful mass market adoption. Permanent Magnet Synchronous Motor (PMSM) is a good candidate for EVs. In this work, a high level modelling and an optimization is reported for the determination of time response (Tr) and power (P) of Electric Vehicle. The electric constant of back- electromotive-force, stator d- and q- axes inductances, switching period, battery voltage, stator resistance and torque gear ratio were selected as factors being able to influence Tr and P. The optimization process was carried out with Doehlert experimental design (Jaber et al., 2010). The optimization is based on simulations of the chain of the electric vehicle; every block is simulated with a different abstraction level using the hardware description language VHDL-AMS. The chain of electric traction is shown in Figure 1. It consists of 4 components: Control strategy, Inverter, PMSM model and Dynamic model. A right combination of these four elements determines the performance of electric vehicles. VHDL (Very High Speed Integrated Circuit Hardware Description Language) is a commonly used modelling language for specifying digital designs and event-driven systems. The popularity of VHDL prompted the development of Analog and Mixed-Signal Electric Vehicles – Modelling and Simulations 26 (AMS) extensions to the language and these extensions were standardized as IEEE VHDL- AMS in 1999. Some of the main features of this ASCII-based language include Model Portability, Analog and Mixed-Signal modeling, Conserved System and Signal Flow Modeling, Multi-domain modeling, Modeling at different levels of abstraction, and Analysis in time, frequency and quiescent domains. Since VHDL-AMS is an open IEEE standard, VHDL-AMS descriptions are simulator-independent and models are freely portable across tools. This not only prevents model designers from being locked in to a single tool or tool vendor but also allows a design to be verified on multiple platforms to ensure model fidelity. Fig. 1. Model of traction chain VHDL-AMS is a strict superset of VHDL and inherently includes language support for describing event-driven systems such as finite state machines. The standard not only provides language constructs for digital and analog designs but also specifies the interactions between the analogue and digital solvers for mixed-signal designs. The analog (continuous time) extensions allow the description of conserved energy systems (based on laws of conservation) as well as signal-flow models (based on block diagram modeling). VHDL-AMS distinguishes between the interface (ENTITY) of a model and its behavior (ARCHITECTURE). VHDL-AMS allows the association of multiple architectures with the same entity and this feature is typically used to describe a model at different levels of abstraction. With VHDL-AMS, it is possible to specify model behaviour for transient, frequency and quiescent domain simulations. Depending on the user’s choice of an analysis type, the appropriate behavior is simulated. The language is very flexible in that it allows different modeling approaches to be used, both individually and collectively. It is possible to describe model behavior with differential algebraic equations, value assignments and subprograms at a very abstract and mathematical level (McDermott et al., 2006). The VHDL-AMS language is an undiscovered asset for FPGA designers—a powerful tool to define and verify requirements in a non-digital context. Modeling and Simulation of High Performance Electrical Vehicle Powertrains in VHDL-AMS 27 As an electric vehicle is a multidisciplinary system, the new standard VHDL-AMS is suitable for the modelling and the simulation of such system in the same software environment and with different abstraction levels (Jaber et al., 2009). 2. Dynamic model The first step in vehicle performance modelling is to write an electric force model. This is the force transmitted to the ground through the drive wheels, and propelling the vehicle forward. This force must overcome the road load and accelerate the vehicle (Sadeghi et al., 2009). For any mission profile, an electric road vehicle is subjected to forces that the onboard propulsion system has to overcome in order to propel or retard the vehicle. These forces are composed of several components as illustrated in Figure 2 .The effort to overcome these forces by transmitting power via the vehicle drive wheels and tyres to the ground is known as the total tractive effort or total tractive force. Fig. 2. Forces on a vehicle The rolling resistance is primarily due to the friction of the vehicle tires on the road and can be written as (Jalalifar et al., 2007): RR V fg M F =´ ´ (1) The aerodynamic drag is due to the friction of the body of vehicle moving through the air. The formula for this component is as in the following: 2 1 . 2 DA x S CV F =  (2) An other resistance force is applied when the vehicle is climbing of a grade. As a force in the opposite direction of the vehicle movement is applied: . g .sin Lv FM a= (3) The power that the EV must develop at stabilized speed is expressed by the following equation: ( ) . aRRDAL PVF F F=++ (4) Electric Vehicles – Modelling and Simulations 28 The power available in the wheels of the vehicle is expressed by: V PTr memm R wheels = (5) According to the fundamental principle of dynamics the acceleration of the vehicle is given by: PP ma M V v g - = (6) ( ) . em m wheels RR DA L vwheels Tr R F F F MR    (7) .( ) lwheelsRRDAL TR F F F (8) . m m wheels r d W Rdt g = (9) A VHDL-AMS model for the dynamic model is specified in an “architecture” description as show in Listing 1. ARCHITECTURE behav OF dynamic_model IS QUANTITY Speedm_s : REAL := 0.0; QUANTITY F_RR : REAL := 0.0; QUANTITY F_DA : REAL := 0.0; QUANTITY F_L : REAL := 0.0; BEGIN F_RR = = f*Mv*g; F_DA = = 0.5*da*Sf*Cx* Speedm_s * Speedm_s; F_L = = Mv*g*sin(alpha) Tl = = Rwheels*( F_RR + F_DA + F_L); Speedm_s 'dot = = (1.0/(Mv*Rwheels))*(rm*Tem-Tl); Speedkm_h = = 3.6 * Speedm_s; Wm = = (rm/Rwheels)*Speedm_s; END ARCHITECTURE behav; Listing 1. VHDL-AMS dynamic model 3. PMSM model A permanent magnet synchronous motor (PMSM) has significant advantages, attracting the interest of researchers and industry for use in many applications. [...]... 1 0 4.00 6.40 12. 64 4.71 12. 66 12. 66 12. 15 12. 65 12. 16 12. 02 12. 65 12. 66 4.00 6 .24 12. 64 3.96 12. 65 12. 64 6 .28 7.11 7 .26 6.71 9. 12 7 .24 7.18 9.14 6. 72 7 .24 7.13 12. 65 5.73 7.16 98.00 40.00 12. 68 60.00 12. 24 12. 28 20 .00 12. 27 50.00 21 .00 12. 27 12. 90 96.80 40.00 12. 34 101.00 12. 36 12. 47 40.40 46.18 44.77 60.40 31.00 45.15 45.30 30.55 58.37 45.50 46.39 12. 55 46.70 46.00 146.300 78.687 35 .29 8 110.310 35.000... 12. 63 12. 64 6.60 12. 64 13.08 52. 80 20 .00 12. 78 58.00 12. 72 12. 85 24 .70 35.5 82 60.600 40.973 35.358 107.710 35 .25 6 35. 422 43.770 149.974 35 .21 2 35.490 76.946 35.370 100.00 12. 50 13.00 39.86 12. 80 39 Modeling and Simulation of High Performance Electrical Vehicle Powertrains in VHDL-AMS 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 1 -1 1 -1 1 -1 1 -1... prototypes and a few mass produced vehicles are now available For example, there were 23 hybrid electric presented at the North American International Auto Show (NAIAS) in 20 00 [Wyczalek, 20 00] There are several configurations of electric and hybrid vehicles [Bayindir, 20 11, Ehsani, 20 05]: 1 electric vehicles equipped with electric batteries and/ or supercapacitors called BEV (Battery Electric Vehicles) , 2 hybrid... Tout  k1Tin1  k2Tin 2 , out  1 k1  2 k2 (1) where k1 and k2 are the constants determined by the parameters of torque coupling The speed coupling adds the speeds of the engine, in1 , and the electric motor, in 2 , together by coupling their speeds The output speed out and torque, Tout , can be described by: out  k1in 1  k2int 2 , Tout  Tin1 Tin 2  k1 k2 (2) where k1 and k2 are the constants... Modeling and Apllications to Circuit Design, Sm2ACD 20 10, pp 908-911, ISBN 978-1- 424 4-5090-9, Tunis-Gammarth, Tunisia, 20 10 40 Electric Vehicles – Modelling and Simulations Jaber, K.; Ben Saleh, B.; Fakhfakh , A & Neji, R (20 09) Modeling and Simulation of electrical vehicle in VHDL-AMS, 16 th IEEE International Conference on, Electonics, Circuits, and Systems, ICECS 20 09, pp 908-911, ISBN 978-1- 424 4-5090-9,... 1 2 3 4 5 6 7 8 9 10 11 12 13 Responses Y=α/Y1+βY2 Ke Ld=Lq [mH] Ts [µs] E [V] R [ohm] rm Y1(Tr) [s] Y2(P) [Kw] Response (Y) -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 -1 1 -1 -1 1 1 -1 -1 1 -1 12. 62 12. 10 12. 08 12. 64 4.80 12. 67 12. 63 12. 09 3.89 12. 63 12. 64 6.60 12. 64... hybrid electric drive trains, 3 Torque-coupling and speed-coupling parallel hybrid electric drive trains The torque coupling adds the torques of the engine Tin1, and the electric motor, Tin2, together or splits the engine torque into two parts: vehicle propelling and battery is charging In this case the output torque, Tout , and speed , out , can be described by 46 Electric Vehicles – Modelling and Simulations. .. disadvantages of the pure electric vehicles, whose engines are powered by electric batteries: the limited duration of use (low autonomy) and time recharging for batteries 2 Hybrid electric vehicles A hybrid electric vehicle is distinguee from a standard ICE driven by four different parts: a) a device to store a large amount of electrical energy, b) an electrical machine to convert electrical power into... Electric Vehicles) , 2 hybrid electric vehicles which combine conventional propulsion based on ICE engine with petroleum fuel and electric propulsion with motor powered by batteries or supercapacitors called HEV (Hybrid Electric Vehicles) , 3 electric vehicles equipped with fuel cells, called FCEV (Fuel Cell Electric Vehicles) Concept of hybrid electric vehicle with ICE -electric motor aims to overcome... engines of vehicles that are individual and scattered Power plants are usually located outside urban areas, their emissions affects fewer people living in these cities By using electric motors and controllers efficient, electric vehicles provide the means to achieve a clean and efficient urban transport system and a friendly environment Electric vehicles are zero emission vehicles, called ZEV type vehicles . -1 -1 -1 12. 67 12. 72 35 .25 6 7 -1 1 1 -1 -1 -1 12. 63 12. 85 35. 422 8 1 1 1 -1. -1 1 12. 09 24 .70 43.770 9 -1 -1 -1 1 -1 1 3.89 100.00 149.974 10 1 -1 -1 1 -1 -1 12. 63 12. 50 35 .21 2 11 -1 1. state-of-charge value. 20 Electric Vehicles – Modelling and Simulations Electrical Vehicle Design and Modeling 21 0 20 0 400 600 800 1000 0 20 40 60 80 100 120 Time [s] Speed [km/h] Fig. 12. New European. current 21 Electrical Vehicle Design and Modeling 22 Will-be-set-by-IN-TECH 0 1 2 3 4 5 6 7 0 .2 0.4 0.6 0.8 0 1 2 3 4 5 6 7 0 20 40 0 1 2 3 4 5 6 7 700 800 900 0 1 2 3 4 5 6 7 −10 0 10 20 30 Battery