International Conference on Ecologic Vehicles & Renewable Energies

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1 March 29 April 1 International Conference on Ecologic Vehicles & Renewable Energies Simulation and Control Aspects of a Plug In Hybrid Electric Vehicle Athanassios D. Karlis Department of Electrical and Computer Engineering, Democritus University of Thrace, Vas. Sofias 12, Xanthi, Greece akarlis@ee.duth.gr Reza Ghorbani Eric Bibeau Paul Zanetel Department of Mechanical and Manufacturing Engineering, University of Manitoba, Winnipeg, MB, Canada R3T 5V6 umghorba@cc.umanitoba.ca; eric_bibeau@umanitoba.ca; paul_zanetel@umanitoba.ca Copyright 2007 MC2D & MITI Abstract: The University of Manitoba, Canada, in close cooperation with Democritus University of Thrace, Greece, are developing a Renewable Energy Vehicle Simulator (REVS) that enables to simulate renewable energy vehicles using combination of propulsion system and fuels by adapting library modules to suit particular applications. The simulation software predicts the energy use of the vehicle, taking into account the duty cycle and driver habits. Library modules have been developed to simulate the Plug-In Hybrid Electric Vehicle (PHEV) architecture as this platform offers energy scenario for cars and buses allowing combination of energy sources that include renewable electricity and renewable biofuels. REVS was developed to address the next generations of vehicles, which will not rely exclusively on the use of fossil fuels burnt in an internal combustion engine. This paper will discuss the methodology for designing system level vehicles using the REVS package. A series parallel HEV with a conventional ICE and power split transmission system has been designed using the simulation package. A fuzzy controller has been developed to simulate the driver and to command the acceleration and brake pedal. Another fuzzy controller has been employed to manage the power flow in hybrid vehicle. The energy management strategy has been applied through a rulebased fuzzy approach. Several rules are used to determine, based on the power demand value, how much power to get from electric motor if the power demand is positive. Simulation results of the vehicle will be presented for high and low capacity battery in various driving schedules. Keywords: Plug-In Hybrid Electric Vehicle, Simulator, Transportations, Control Strategies, Energy Flow, Renewable Energy Vehicle Simulator. 1. Introduction The transportation energy sector is currently reliant on oil for mobility. Renewable biofuels like biodiesel and bioethanol contribute only a small percentage to the overall energy mix for mobility. Electricity use for transportation has limited commercial applications because of battery storage range issues although many successful demonstrations of electrical vehicles have been achieved. With production of oil predicted to decline, the number of transportation vehicles continuing to increase globally, and the realization that we live in a carbon constrained world, a transformation of the transportation sector is inevitable. Next generations of transportation vehicles will not rely exclusively on the use of fossil fuels burnt

2 in an internal combustion engine (ICE). Furthermore, the hydrogen fuel cell proposition is not as attractive as first believed as no gain is possible when the hydrogen is derived from electricity or fossil fuels. Recent advances in hybrid technologies have significantly increase vehicle efficiencies. More importantly, hybridisations now leads to a significant reduction in battery capacity allowing electricity to be used as the overall energy mix in the transportation sector by allowing cars to plug into the electrical grid directly to charge up and partially displace fossil fuels. This has created the Plug-in Hybrid Electrical Vehicles (PHEV) platform. Hybrid vehicles offer the promise of higher energy efficiency and reduced emissions when compared with conventional automobiles, but they can also be designed to overcome the range limitations inherent in a purely electric automobile by utilizing two distinct energy sources for propulsion. With hybrid vehicles, energy is stored as a petroleum fuel and in an electrical storage device, such as a battery pack, and is converted to mechanical energy by an internal combustion engine and electric motor, respectively. The electric motor is used to improve energy efficiency and vehicle emissions while the ICE provides extended range capability. Though many different arrangements of power sources and converters are possible in a hybrid power plant, the two generally accepted classifications are series and parallel [1]. Nowadays, researchers focus on understanding the dynamics of the hybrid vehicles by developing simulators [2], [3]. The results can be used to optimise the design cycle of hybrid vehicles by testing configurations and energy management strategies before prototype construction begins. Power flow management, optimisation of the fuel economy and reducing the emissions using intelligent control systems are part of the current research [4] [17]. Practical and experimental verification of the vehicle simulators is an important part of ongoing researches [18] [20]. The University of Manitoba, Canada, in close cooperation with Democritus University of Thrace, Greece, are developing a Renewable Energy Vehicle Simulator (REVS) that enables to simulate renewable energy vehicles using combination of propulsion system and fuels by adapting library modules to suit particular applications. The simulation software predicts the energy use of the vehicle, taking into account the duty cycle and driver habits. Library modules have been developed to simulate the PHEV architecture as this platform offers energy scenario for cars and buses allowing combination of energy sources that include renewable electricity and renewable biofuels. REVS was developed to address the next generations of vehicles, which will not rely exclusively on the use of fossil fuels burnt in an internal combustion engine. The modelling of the chemical reactions of the internal combustion engines is carried out by the commercially available software IDEAS. The modelling of the transmission system, dynamics of the vehicle, electrical motor (EM) and power drivers is done using Matlab and Simulink. REVS simulates different vehicles configurations to enable the optimisation of available renewable energy resources and minimize greenhouse gases. This paper discusses the methodology for designing system level vehicles using the REVS package. A series parallel HEV with a conventional ICE and power split transmission system has been designed using the simulation package. A fuzzy controller has been developed to simulate the driver and to command the acceleration and brake pedal. Another fuzzy controller has been employed to manage the power flow in hybrid vehicle. The energy management strategy has been applied through a rulebased fuzzy approach. Several rules are used to determine, based on the power demand value, how much power to get from electric motor if the power demand is positive. Regenerative braking is activated inherently by power split device in series parallel HEV configuration. Other rules are used to cause the battery to operate within an efficient range of state of charges and to ensure that some limits are not exceeded. These limits represent the maximum allowable charging and discharging powers of the battery, in addition to the maximum allowable regenerated power. Plug In HEV (PHEV) is a reasonable solution to increase the efficiency and performance of the ground transportation by adding an extra high capacity battery pack or ultracapacitor in vehicle configuration. The vehicle can be plugged in during night and can be used for the daytime. 2. Design Methodology of REVS REVS has been developed in Matlab/Simulink environment as well as IDEAS [21], [22]. Mainly, the modules and components related to drivetrains, dynamics modelling and control are developed in Simulink and the fluid and heat transfer systems are carried out by IDEAS. The communication module transfers the data between Simulink and IDEAS at each time step. A user can select the components of the vehicle

3 from the libraries and create a specific vehicle configuration. The vehicle can be constructed graphically by connecting the main component blocks (environment, drive cycle, controller, engine, motor/generator, transmission, batteries, vehicle dynamics and renewable energy resources) using the Simulink visual programming methodology through the connection of the appropriate input and output ports. On the other hand, user can set the heat/fluid system components (engine chemical reactions, fuel, solar, fuel cells) using the IDEAS. Energy flow and electrical signals are the main elements transferred between library modules. REVS implements three kinds of controls: direct control, vehicle system level control and component level control. Direct control governs the flow of information from block to block in the model. One block can control another block through output connectors; the same block can be controlled by another block through input connectors. Signal and energy flow from block to block in the model create a direct control network. Results such as engine, motor and vehicle speeds, torque, power and emissions are displayed using the graphical plotting tools that can consider transient responses. Vehicle characteristics such as size and weight, gear ratios, drag and friction coefficients, inertias and the environmental situations can be changed in an excel worksheet file to specify the drivetrain. A controller block is designed with conventional and fuzzy logic controller blocks, which create the signals required to control the individual system-level components. REVS has been designed to be flexible in adding on of more Matlab/Simulink toolboxes for optimisation purposes and virtual reality interfaces. In this paper, simulation results are presented for a series parallel HEV with low capacity battery and for a series parallel PHEV with high capacity battery in various driving schedules. 3. Design of the Series Parallel Hybrid Electric Vehicle In this section, the design and analysis of the model of Toyota Prius as a series parallel HEV drive train using the REVS is discussed. The Prius components such as ICE, motor, battery, and vehicle dynamics models were defined based on vehicle s available information. The model of the Power Split Device (PSD) and battery is explained in [23]. A description is given of the performance specifications, the control strategies and power plant developed for the vehicle design. A fuzzy controller is designed to manage the output power of the electric motor based on accelerating pedal and State of the Charge (SOC) of the battery. Another fuzzy controller parallel with a first order system has been employed to model the driver response to the vehicle velocity error. Simulation studies are performed for Prius using several vehicle velocities drive cycle. Various performance parameters of the vehicle, such as vehicle velocity, SOC and generated power by ICE and EM, during the simulation studies are graphically presented in this paper. A Problem Formulation In a typical series parallel drive train design, consisting of an ICE, an EM, a generator and a PSD, either the ICE or the EM can be considered the primary energy source depending on the vehicle design and energy management strategy. The PSD divides the output torque of the ICE, with a fixed torque ratio, into the wheels and generator. The output power of the ICE can be divided into an infinite ratio between the wheels and generator. This configuration is designed so that the ICE and electric motor are both responsible for propulsion or each is the prime mover at a certain time in the drive cycle. Also part of the power of the ICE transfers to the wheels while the other is used to recharge an energy accumulator, usually a battery pack. The general schematic of the series parallel configuration in REVS is shown in fig. 1. In addition, a schematic of the series parallel HEV power train is shown in fig. 2. Series parallel HEV consists of different elements with various configurations that make the vehicle modelling more complex by providing different number of choices and their effect on vehicle s performance for a special mission. The modelling of Prius drivetrain is shown in details in fig. 3. The ICE model was designed based on Prius torque/power/velocity data and threshold using lookup table. The permanent magnet synchronous AC motor of the Prius model is also modelled based on available data using lookup table by considering the motor power threshold. Capacity and number of cells of the battery assumed as an initial input parameter in the simulation. State of the charge of the battery and current load determine the DC bus voltage based on battery model [23]. Regenerative braking is inherently performed through PSD and generator whenever the driver decreases the velocity of the vehicle. A fuzzy controller manipulates the

4 power contributions of the electric motor that is explained in detain in the following section. 4. The Energy Management Strategy The energy management strategy to control the power flow of the vehicle is described in this section. The following criteria have been considered in developing the energy management block: 1. The driver inputs (from brake and accelerating pedals) are similar to a conventional vehicle (driving the series parallel HEV should not feel different from driving a conventional vehicle). 2. The state of charge of the battery is sufficient at all times. The power controller determines the power needed to drive the wheels and charge the batteries. It also commands the power required from electric motor. The batteries can be charged at the same time when power is assigned to the electric motor. The ICE can provide the power for both charging the batteries and driving the wheels using PSD. The next section discusses the power controller that implements the energy management strategy and uses fuzzy logic to compute the power flow. Figure 1: The series parallel HEV general scheme. A. Power Controller Figure 2: The series parallel hybrid electric vehicle configuration. Fig. 3 presents the block diagram of the vehicle model. As shown in fig. 3, a fuzzy logic controller determines the output power of the EM with regard to the inputs of accelerator pedal and the SOC of the battery. The acceleration pedal signal is normalized to a value between zero and one (zero: pedal is not pressed, one: pedal fully pressed). The normalized braking pedal signal is directly connected to the vehicle dynamic block to subtract braking forces from wheel forces. The output of the power controller block is the scaling factor of the EM power, which is normalized between zero and one. The scaling

5 Figure 3: REVS model of the series parallel HEV. factor is multiplied by the maximum available power of the EM in EM block. On the other hand, the normalized value of the acceleration pedal is multiplied by the maximum available power of the ICE. Finally, the total power of the vehicle is the ICE+EM power. By this way, the driver can command the complete range of available power at all times. The maximum available EM and ICE power depends on their speed and temperature, and is computed using a 2D look-up table with speed and temperature as inputs of the EM and ICE blocks. The EM scaling factor computed through fuzzy logic controller is close to zero when the SOC of the battery is too low. In that case the EM is not used to drive the wheels, in order to prevent battery damage. When the SOC is high enough, the scaling factor equals one. User can change the membership functions of the input and output signals. With respect to the SOC limitations, the scaling factor is proportional to the acceleration pedal. To illustrate the fuzzy logic rules, fig. 4 shows the scaling factor as a function of acceleration pedal and SOC. As it is shown in fig. 4, the scaling factor is zero when the SOC is below 0.8. Figure 4: 3D graph of the fuzzy controller rules for the power controller; SOC and Acceleration pedal are the inputs and Scaling factor is the output. B. Velocity Tracking Controller A combination of a low pass filter and a fuzzy controller is assumed to model the driver for tracking the desired velocity.

6 The vehicle velocity error is assumed as the input of the low pass filter of the form 0.1. On the other hand, a fuzzy controller, 0.05s + 1 parallel with the first order system, commands the acceleration pedal. When the velocity of the vehicle is lower than desired one, the driver fuzzy controller sets a positive value for acceleration pedal and the more velocity error the more acceleration pedal. The functionality of the rules of the driver fuzzy controller is presented in fig. 5. factor, acceleration pedal, electric motor power in KW, ICE power in KW and generator power in KW are shown in both figs. 6 and 7. Figure 5: Graph of the driver fuzzy controller rules; Velocity error is output and Acceleration pedal is input. 5. Simulation Results Figure 6: Results for an acceleration with battery capacity 2KWh. The vehicle has been simulated with REVS by velocity commands. The parameters of the vehicle are listed in Table 1. Different sets of desired vehicle velocity applied on the model are used to examine the response of the system in two cases, i.e. with battery capacity equal to 2KWh and to 25KWh (PHEV case) respectively. The results of the simulations are shown in the next figures. Table 1: Parameters used in paper. Definition Values Curb weight 1600 Kg Max ICE power 57 kw 5,000 r.p.m. MAX ICE torque 115 Nm 4,200 r.p.m EM power 50 kw r.p.m. Maximum voltage 500 V Maximum EM torque 400 N.m r.p.m. Figs. 6 and 7 respectively are referring to the case where the battery capacity is only 2KWh. The first sub case examines the vehicle on acceleration (fig. 6) while in the second sub case a cyclic desired vehicle velocity is applied on the model of the vehicle. By specified parameters of the system, the desired vehicle velocity shown by solid line, actual vehicle velocity shown by dashed line, SOC, scaling Figure 7: Results for a cyclic desired vehicle velocity with battery capacity 2KWh. The simulations in both sub cases show that the vehicle can reasonably track the desired velocity. It is also shown that the SOC is kept almost higher than 0.8. In addition, ICE provides the main power to the vehicle during the cruise period, after 16 and 110 second respectively, what is highly desirable. Figs. 8 and 9 respectively are referring to the case

7 where the battery capacity is 25KWh (PHEV case). well defined to minimize the velocity error. It can be seen in fig. 9 that the SOC increases during deceleration period that is also one of the important factors in hybrid electric vehicle designs to regenerate the power. Figs. 8 and 9 show that in cruise velocity, period of and sec respectively, ICE is the major source of energy. 6. Conclusions Figure 8: Results for an acceleration with battery capacity 25KWh. A renewable energy vehicle simulator REVS, for modelling, simulation, and analysis of a drive train, developed at University of Manitoba in close cooperation with Democritus University of Thrace, Greece, using Matlab/Simulink/IDEAS software has been presented in this paper. The goal of REVS is to study issues related to plug in hybrid electric vehicle design such as dynamics, energy management and fuel economy. REVS provides new libraries to model different vehicle configurations in addition to Matlab/Simulink standard toolboxes. The design of a series parallel electric vehicle, Toyota Prius, is presented. Fuzzy controller has been used to control the power flow as well as to track the vehicle velocity. The results of velocity tracking performance, SOC, acceleration pedal commanded by fuzzy controller and electric motor command are illustrated to examine the dynamics of the vehicle in two cases of series parallel HEV and series parallel PHEV. Figure 9: Results for a cyclic desired vehicle velocity with battery capacity 25KWh (PHEV case). In the first case (fig. 6), the acceleration period is longer than the second case (fig. 8) caused by lower battery capacity and respectively lower amount of electric motor power has been used during first case. Fig. 7 shows a periodic change in SOC during driving cycle, while in fig 9 this is not the case. The results of PHEV case in figs. 8 and 9 show that the battery was operated at a relatively high SOC (between 0.89 and the maximum 0.96) for the whole period of driving cycle. It also demonstrates that the controller is References [1] A. Kalberlah, Electric hybrid drive systems for passenger cars and taxis, SAE Tech. Rep , [2] H.Ed. Bargar, J. Li, D.J. Goering, and J. H. Lee, Modelling and Verification of Hybrid Electric HMMWV Performance, in IECON'03 proceedings, vol. 1, pp [3] S.R. Cikanek, K.E. Bailey and B.K. Powell, Parallel Hybrid Electric Vehicle Dynamic Model and Powertrain Control, in Proceedings of the American Control Conference Albuquerque, New Mexico June [4] L. Ippoliot, V. Loia and P. Siano, Extended Fuzzy C-Means and Genetic Algorithms to Optimize Power Flow Management in Hybrid Electric Vehicles, Fuzzy Optimization and Decision Making, 2, pp , [5] S.C. Tzeng, K.D. Huang, C.C. Chen, Optimization of the dual energyintegration mechanism in a parallel-type

8 hybrid vehicle, Applied Energy 80 (2005) pp [6] N.A. Kheir, M.A. Salman, N.J. Schouten, Emissions and fuel economy trade-off for hybrid vehicles using fuzzy logic, Mathematics and Computers in Simulation 66 (2004) pp [7] P. Bowles, H. Peng, X. Zhang, Energy Management in a Parallel Hybrid Electric Vehicle with a Continuously Variable Transmission, Proceedings of the American Control Conference, Chicago, Illinois June [8] J.S. Won, R.Langari and M.Ehsan, An Energy Management and Charge Sustaining Strategy for a Parallel Hybrid Vehicle With CVT, IEEE Transactions on Control Systems Technology, vol. 13, no. 2, march [9] H.D. Lee, S.K. Sul, H.S. Cho, J.M. Lee, Advanced gear--shifting and clutching strategies for a parallel hybrid vehicle, IEEE Industry Applications Magazine, November - December [10] B. Bates, On the road with a Ford HEV, IEEE Spectrum, pp , July [11] M. Hayashida et al., Study on series hybrid electric commuter-car concept, SAE J. SP-1243, Paper , Feb [12] A. Nikopoulos, H. Hong, and T. Krepec, Energy consumption study for a hybrid electric vehicle, SAE J. SP-1243, Paper , Feb [13] R. D. Senger, Validation of ADVISOR as a simulation tool for a series hybrid electric vehicle using the Virginia Tech future car Lumina, Master s thesis, Virginia Tech Univ., Blacksburg, [14] D. Hermance and S. Sasaki, Hybrid electric vehicles take the streets, IEEE Spectrum, pp , Nov [15] Y. Takehisa, S. Shoichi, and A. Tetsuya, Toyota hybrid system: Its concept and technologies, in Proc. FISITA World Automotive Conf., Paris, France, Sept [16] P.A. Abthoff and J.S. Kramer, The Mercedes-Benz C-class series hybrid, in Proc. Electric Vehicle Symp. 14, Orlando, FL, Dec [17] L.J. Oswald and G.D. Skellenger, The GM/DOE hybrid vehicle propulsion systems program: A status report, in Proc. Electric Vehicle Symp. 15, Orlando, FL, Dec [18] M. Alexander, et al., A Mid-Sized Sedan Designed for High Fuel Economy and Low Emissions: The UC Davis Future Car, Technical Report, [19] S. Sasaki, Toyota's Newly Developed Hybrid Powertrain, in 10th International Symposium on Power Semiconductor Devices and ICs, 1998, Kyoto, Japan. [20] C. Bourne, P. Faithfull and C. Quigley, Implementing Control of a Parallel Hybrid Vehicle, International Journal of Vehicle Design, (5-6), pp [21] M. McGarry and M. Watson, Dynamic simulation: a maturing technology generating real benefits, Ideas Simulation, available at [22] G.A. Pugh, Extending Simulation, Indiana University-Purdue University, [23] J. Liu, H. Peng, Z. Filipi, Modelling and Control Analysis of Toyota Hybrid System, in Proceedings of the International Conference on Advanced Intelligent Mechatronics, 2005 IEEE/ASME, pp