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Maged N.F. Nashed1, Said Wahsh1, Hamed Galal2 and Tarak Dakrory2
1. Electronic Research Institute, Giza, Egypt
2. Faculty of Engineering (at Shubra), Benha University, Egypt
Received: September 15, 2011 / Accepted: September 29, 2011 / Published: January 25, 2012.
Abstract: In this paper, implantation of fuzzy logic controller for parallel hybrid electric vehicles (PHEV) is presented. In PHEV the required torque is generated by a combination of internal-combustion engine (ICE) and an electric motor. The controller simulated using the SIMULINK/MATLAB package. The controller is designed based on the desired speed for driving and the state of speed error. In the other hand, performance of PHEV and ICE under different road cycle is given. The hardware setup is done for electric propulsion system; the system contains the induction motor, the three phase IGBT inverter with control circuit using microcontroller. The closed loop control system used a DC permanent generator whose output voltage is related to motor speed. Comparison between simulation and experimental results show accurate matching.
Key words: Hybrid electric vehicles-induction motor, internal combustion engine, fuzzy logic control.
List of Symbols
A0, ……, A8 Constant equation factors B Viscous constant Fout Output pf fuzzy controller g(Pm.) Function introduced in Eq. (11) Ner Error of speed ids & idr Direct stator and rotor phase current iqs Quadrature stator phase current iqr Quadrature rotor phase current ir Rotor current is Stator current J Friction constant Ls Stator per phase leakage inductance Lr Rotor per phase leakage inductance M Mutual magnetizing inductance n Motor speed (r.p.m) Nmes Error of speed measurement, Pamb Ambient pressure Pm Motor power (watts) rs & rr Stator and rotor/phase resistance windings Te Developed torque Tl Load torque Teng ICE torque TLoad Load torque Vds Stator direct axises voltage Vqs Stator quadrature axises voltage Vs & Vr Stator and Rotor voltage x1 Speed of engine x2 Manifold pressureθ Throttle angleλs & λr Flux linkage for stator & rotor
ω Rotor angular velocity
1. Introduction
The early air quality concerns in 1960s and energy crisis in 1970s have brought EV’s back again, besides air pollution, and ICE vehicles and their extremely low efficiency. Hence, the problem associated with ICE vehicles is environmental, economical as well as political. These concerns have forced governments all over the world to consider alternative vehicle concepts[1]. Nevertheless, regulations become ever stricter, until the State of California passed a law in 1990
requiring that EV account for certain percentage of vehicles sales, beginning with the 1998 model year. Considering that oil is a finite resource and will some-day disappear, finding sustainable alternative energy is perhaps the most important issue facing us in the long run. Hoping that 21st century the environmental century.
Nowadays there more than 600 million vehicle on the street and roads of the world. Of these, more than 100000 are EV [2]. EV’s and hybrid-electric vehicles(HEV’s) over the most promising solution to reduce vehicular emission. The short range associated with EV’s still remains limitations of this technology.
HEVs produce the power required to drive the vehicle by a combination of two sources, an ICE and an electric motor. HEVs seem to be viable alternative to the ICE at the present. They can be generally classified as series or PHEV. In series HEVs, same as EVs, all the torque required to drive the vehicle is provided by an electric motor. On the other hand, in PHEVs, the torque obtained from the ICE is mechanically coupled to the torque produced by an electric motor [3]. In PHEVs, operation of each sources (ICE or electric motor), and amount of their contribution in production of torque at any time, will be decided by a controller. The choice of an electrical motor is important for better HEV design, so comparisons were done for different motor types and at different power rating [4-6]. Due to its availability, low cost, good performance and simple control, IM is chosen in this work.
2. System Model Descriptions
In this section, the model of PHEV power train parts is presented. The main parts of the system are shown in Fig. 1. 2.1 Induction Motor (IM)
The stator and rotor equations that represent the IM are given in Refs. [7-8]: Applying Park’s transformation IM equations in D-Q reference frame are
A simple model of engine introduced in Ref. [10] is used. This model includes a two-state dynamic model, whose output is the ICE torque. The states of the model are the speed of engine (x1) and the manifold pressure(x2).
3. Speed Control of IM Using FLC
The two main types of speed control of IM are the scalar control and the vector control. Sinusoidal PWM(SPWM) used in this work. Utilizing the FLC and it has three main components [11-14]. The membership functions of FLC to control speed of an IM contain two inputs which are the speed error (Δω) and the change in error (Δω*) and one output (ωc) [15-17].
In this study, performance of IM drive system with FLC controller is given. The proposed method to deal with FLC is to start at the moment of making FLC as software program, the different components of FLC(membership functions, rules) takes a large size in the software program which complicate the controlling process. The idea is simply to model FLC as an equation between error speed and output control of FLC it is called arithmetic FLC (AFLC) [18-19]. Simulation block diagram of the IM drive system using FLC are shown in Fig. 2.
4. PHEV
PHEV system contains two main components, which are the IM and the ICE. A complete drive cycle(C-cycle), contains rise, fall, constant and stop driving profile with a total time of 55 seconds as shown in Fig. 3. In this drive cycle IM drive the vehicle alone in some intervals of it while in others to speed up the vehicle uses both the IM and the ICE. In some intervals of the driving cycles in non-urban areas PHEV is driven by ICE only. Therefore, the vehicle is simulated when driven by both IM and ICE. The results show that the vehicle speed using the ICE is higher than the case of the vehicle is driven by the electric motor only.
When using the electric motor only the transient performance of the vehicle speed to reach steady state is better. In some intervals of the driving cycles, the vehicle speed is higher than the maximum speed obtained when the vehicle driven by ICE. So, the electric motor is used to meet the required driving cycle.
The simulation results in this case for the three speeds (vehicle, electric motor, and ICE speeds) are shown in Fig. 4. In some other cases the vehicle is driven by the IM and the vehicle speed required increased according to the divining C cycle. So, the ICE is shared the IM to drive the PHEV.
Also, the fuel consumption and exhaust of CO, NOx results are shown in Fig. 5 which shows remarkable improvement.
5. System Experimental Setup
The experimental drive system of IM using AFLC, contains the power circuit, drive circuit and control as shown in Fig. 6. The different part of experimental set up as IM with mechanical load and DC generator are shown in Fig. 7. The flowchart of the algorithm for
control program used in this work is shown in Fig. 8. The input data are reference speed and measurement speed, while the output is PWM signal to drive circuit. In this program, we calculate the voltage and frequency of IM depend on the speed reference.
The output SPWM control signals of two IGBT switches in the same leg (connected with the same phase). Changing the reference speed signal changes will change the frequency of the SPWM signal. The
filter capacitor current and it is clear that the capacitor current contains spikes due to the charging and the discharging at high switching frequency. While the inductor current is shown in Fig. 9, Fig. 10 shows performance when changing speed of IM, from 800r.p.m to 300r.p.m and as shown the trace of the actual speed to reach steady state value behind the reference speed by very small time. In the mean time,
reduces largely size of the software program, and easier in simulation using SIMULINK/MATLAB software package. The controller was designed based on the desired speed for driving and the state of change of speed. AFLC used to obtain the desired speed then micro-control takes very short time to reach the actual value. Analysis of IM with FL at variable load torque and speed is given. Simulation of PHEV under different driving cycles appears that starting of the vehicle in all the driving cycles by the electric motor since it has better starting performance than ICE. The system performance is stable and has good dynamic response under different disturbance.
Using PHEV under different road cycles shows that the ICE is used for long operating periods in non-urban areas, but the IM is used for long operating periods in urban areas to minimize air pollutions and to achieve healthy environment. The experimental results show that the IM drive system using AFLC controller has a good dynamic response. Simulation and experimental results are much similar.
References
[1] K. Rajashekara, Propulsion system issues in electric and hybrid vehicle applications, in: IPEC’95 Conf., Yokohama, 1995.
[2] Y. Sugii, M. Yada, S. Koga, Applicability of various motor to electric vehicle, in: EVS 13 Conf., Osaka, Japan, 1993.
[3] I. Husain, Electric and Hybrid Vehicles: Design Fundamentals, CRC Press, 2003.
[4] M. Ehsani, M. Rahman, H. Toliyat, Propulsion system design of electric and hybrid vehicles, IEEE Trans. on Industrial Electronics 44 (1997) 19-27.
[5] J. Jelonkiewicz, S. Linnman, Comparative study of drives for battery powered light vehicles, in: EPE 95 Conf., Seville, Spain, 1995.
[6] P.F. Puleston, S. Spurgeon, X.Y. Lu, A nonlinear sliding mode control framework for engine speed control, in: Proc. of 14th International Symposium of Mathematical Theory of Networks and Systems, Perpignan, France, 2000.
[7] A. Benchaib, A. Rachid, E. Audrezet, Sliding mode input-output linearization and field orientation for real-time control of induction motors, IEEE Trans. Power Electronic 14 (1999) 3-13.
[8] P.Z. Grabowski, A simple direct torque neuro-fuzzy control of PWM-inverter–fed induction motor drive, IEEE Trans. on Industrial Electronics 47 (2000) 863-870.
[9] F. Barrero, A. Gonzalez, A. Torralba, E. Galvan, L.G. Franquelo, Speed control of induction motors using a novel fuzzy sliding-mode structure, IEEE Trans. Fuzzy Systems 10 (2002) 375-383.
[10] M. Amrhein, P.T. Krein, Dynamic simulation for analysis of hybrid electric vehicle system and subsystem interactions, including power electronics, IEEE Trans. on Vehicular Technology 54 (2005) 825-836.
[11] M. Mohebbi, M. Charkhgard, M. Farrokhi, Optimal neuro-fuzzy control of parallel hybrid electric vehicles, in: IEEE Vehicle Power and Propulsion Conf., 2005.
[12] R.I John, J. Coupland, Type-2 fuzzy logic: a historical view, IEEE Computational Intelligence Magazine 2 (2007) 57-62.
[13] O. Castillo, P. Melin, J.R. Castro, Computational intelligence software for interval type-2 fuzzy logic, in:
Proc. of the Workshop on Building Computational Intelligence and Machine Learning Virtual Organizations, 2008, pp. 9-13.
[14] M.Y. Hsiao, S.L. Hseng, J.Z. Lee, Fuzzy sliding-mode controller, Information Sciences 178 (2008) 1696-1716.
[15] J. M. Mendel, F. Liu, D. Zhai ,α-plane representation for type-2 fuzzy sets: theory and applications, IEEE Trans. on Fuzzy Systems Archive 17 (2009) 1189-1207.
[16] I. Robandi, B. Kharisma, Design of interval type-2 fuzzy logic based power system stabilizer, in: Proc. of World Academy of Science Engineering and Technology, 2008, pp. 2070-3740.
1. Electronic Research Institute, Giza, Egypt
2. Faculty of Engineering (at Shubra), Benha University, Egypt
Received: September 15, 2011 / Accepted: September 29, 2011 / Published: January 25, 2012.
Abstract: In this paper, implantation of fuzzy logic controller for parallel hybrid electric vehicles (PHEV) is presented. In PHEV the required torque is generated by a combination of internal-combustion engine (ICE) and an electric motor. The controller simulated using the SIMULINK/MATLAB package. The controller is designed based on the desired speed for driving and the state of speed error. In the other hand, performance of PHEV and ICE under different road cycle is given. The hardware setup is done for electric propulsion system; the system contains the induction motor, the three phase IGBT inverter with control circuit using microcontroller. The closed loop control system used a DC permanent generator whose output voltage is related to motor speed. Comparison between simulation and experimental results show accurate matching.
Key words: Hybrid electric vehicles-induction motor, internal combustion engine, fuzzy logic control.
List of Symbols
A0, ……, A8 Constant equation factors B Viscous constant Fout Output pf fuzzy controller g(Pm.) Function introduced in Eq. (11) Ner Error of speed ids & idr Direct stator and rotor phase current iqs Quadrature stator phase current iqr Quadrature rotor phase current ir Rotor current is Stator current J Friction constant Ls Stator per phase leakage inductance Lr Rotor per phase leakage inductance M Mutual magnetizing inductance n Motor speed (r.p.m) Nmes Error of speed measurement, Pamb Ambient pressure Pm Motor power (watts) rs & rr Stator and rotor/phase resistance windings Te Developed torque Tl Load torque Teng ICE torque TLoad Load torque Vds Stator direct axises voltage Vqs Stator quadrature axises voltage Vs & Vr Stator and Rotor voltage x1 Speed of engine x2 Manifold pressureθ Throttle angleλs & λr Flux linkage for stator & rotor
ω Rotor angular velocity
1. Introduction
The early air quality concerns in 1960s and energy crisis in 1970s have brought EV’s back again, besides air pollution, and ICE vehicles and their extremely low efficiency. Hence, the problem associated with ICE vehicles is environmental, economical as well as political. These concerns have forced governments all over the world to consider alternative vehicle concepts[1]. Nevertheless, regulations become ever stricter, until the State of California passed a law in 1990
requiring that EV account for certain percentage of vehicles sales, beginning with the 1998 model year. Considering that oil is a finite resource and will some-day disappear, finding sustainable alternative energy is perhaps the most important issue facing us in the long run. Hoping that 21st century the environmental century.
Nowadays there more than 600 million vehicle on the street and roads of the world. Of these, more than 100000 are EV [2]. EV’s and hybrid-electric vehicles(HEV’s) over the most promising solution to reduce vehicular emission. The short range associated with EV’s still remains limitations of this technology.
HEVs produce the power required to drive the vehicle by a combination of two sources, an ICE and an electric motor. HEVs seem to be viable alternative to the ICE at the present. They can be generally classified as series or PHEV. In series HEVs, same as EVs, all the torque required to drive the vehicle is provided by an electric motor. On the other hand, in PHEVs, the torque obtained from the ICE is mechanically coupled to the torque produced by an electric motor [3]. In PHEVs, operation of each sources (ICE or electric motor), and amount of their contribution in production of torque at any time, will be decided by a controller. The choice of an electrical motor is important for better HEV design, so comparisons were done for different motor types and at different power rating [4-6]. Due to its availability, low cost, good performance and simple control, IM is chosen in this work.
2. System Model Descriptions
In this section, the model of PHEV power train parts is presented. The main parts of the system are shown in Fig. 1. 2.1 Induction Motor (IM)
The stator and rotor equations that represent the IM are given in Refs. [7-8]: Applying Park’s transformation IM equations in D-Q reference frame are
A simple model of engine introduced in Ref. [10] is used. This model includes a two-state dynamic model, whose output is the ICE torque. The states of the model are the speed of engine (x1) and the manifold pressure(x2).
3. Speed Control of IM Using FLC
The two main types of speed control of IM are the scalar control and the vector control. Sinusoidal PWM(SPWM) used in this work. Utilizing the FLC and it has three main components [11-14]. The membership functions of FLC to control speed of an IM contain two inputs which are the speed error (Δω) and the change in error (Δω*) and one output (ωc) [15-17].
In this study, performance of IM drive system with FLC controller is given. The proposed method to deal with FLC is to start at the moment of making FLC as software program, the different components of FLC(membership functions, rules) takes a large size in the software program which complicate the controlling process. The idea is simply to model FLC as an equation between error speed and output control of FLC it is called arithmetic FLC (AFLC) [18-19]. Simulation block diagram of the IM drive system using FLC are shown in Fig. 2.
4. PHEV
PHEV system contains two main components, which are the IM and the ICE. A complete drive cycle(C-cycle), contains rise, fall, constant and stop driving profile with a total time of 55 seconds as shown in Fig. 3. In this drive cycle IM drive the vehicle alone in some intervals of it while in others to speed up the vehicle uses both the IM and the ICE. In some intervals of the driving cycles in non-urban areas PHEV is driven by ICE only. Therefore, the vehicle is simulated when driven by both IM and ICE. The results show that the vehicle speed using the ICE is higher than the case of the vehicle is driven by the electric motor only.
When using the electric motor only the transient performance of the vehicle speed to reach steady state is better. In some intervals of the driving cycles, the vehicle speed is higher than the maximum speed obtained when the vehicle driven by ICE. So, the electric motor is used to meet the required driving cycle.
The simulation results in this case for the three speeds (vehicle, electric motor, and ICE speeds) are shown in Fig. 4. In some other cases the vehicle is driven by the IM and the vehicle speed required increased according to the divining C cycle. So, the ICE is shared the IM to drive the PHEV.
Also, the fuel consumption and exhaust of CO, NOx results are shown in Fig. 5 which shows remarkable improvement.
5. System Experimental Setup
The experimental drive system of IM using AFLC, contains the power circuit, drive circuit and control as shown in Fig. 6. The different part of experimental set up as IM with mechanical load and DC generator are shown in Fig. 7. The flowchart of the algorithm for
control program used in this work is shown in Fig. 8. The input data are reference speed and measurement speed, while the output is PWM signal to drive circuit. In this program, we calculate the voltage and frequency of IM depend on the speed reference.
The output SPWM control signals of two IGBT switches in the same leg (connected with the same phase). Changing the reference speed signal changes will change the frequency of the SPWM signal. The
filter capacitor current and it is clear that the capacitor current contains spikes due to the charging and the discharging at high switching frequency. While the inductor current is shown in Fig. 9, Fig. 10 shows performance when changing speed of IM, from 800r.p.m to 300r.p.m and as shown the trace of the actual speed to reach steady state value behind the reference speed by very small time. In the mean time,
reduces largely size of the software program, and easier in simulation using SIMULINK/MATLAB software package. The controller was designed based on the desired speed for driving and the state of change of speed. AFLC used to obtain the desired speed then micro-control takes very short time to reach the actual value. Analysis of IM with FL at variable load torque and speed is given. Simulation of PHEV under different driving cycles appears that starting of the vehicle in all the driving cycles by the electric motor since it has better starting performance than ICE. The system performance is stable and has good dynamic response under different disturbance.
Using PHEV under different road cycles shows that the ICE is used for long operating periods in non-urban areas, but the IM is used for long operating periods in urban areas to minimize air pollutions and to achieve healthy environment. The experimental results show that the IM drive system using AFLC controller has a good dynamic response. Simulation and experimental results are much similar.
References
[1] K. Rajashekara, Propulsion system issues in electric and hybrid vehicle applications, in: IPEC’95 Conf., Yokohama, 1995.
[2] Y. Sugii, M. Yada, S. Koga, Applicability of various motor to electric vehicle, in: EVS 13 Conf., Osaka, Japan, 1993.
[3] I. Husain, Electric and Hybrid Vehicles: Design Fundamentals, CRC Press, 2003.
[4] M. Ehsani, M. Rahman, H. Toliyat, Propulsion system design of electric and hybrid vehicles, IEEE Trans. on Industrial Electronics 44 (1997) 19-27.
[5] J. Jelonkiewicz, S. Linnman, Comparative study of drives for battery powered light vehicles, in: EPE 95 Conf., Seville, Spain, 1995.
[6] P.F. Puleston, S. Spurgeon, X.Y. Lu, A nonlinear sliding mode control framework for engine speed control, in: Proc. of 14th International Symposium of Mathematical Theory of Networks and Systems, Perpignan, France, 2000.
[7] A. Benchaib, A. Rachid, E. Audrezet, Sliding mode input-output linearization and field orientation for real-time control of induction motors, IEEE Trans. Power Electronic 14 (1999) 3-13.
[8] P.Z. Grabowski, A simple direct torque neuro-fuzzy control of PWM-inverter–fed induction motor drive, IEEE Trans. on Industrial Electronics 47 (2000) 863-870.
[9] F. Barrero, A. Gonzalez, A. Torralba, E. Galvan, L.G. Franquelo, Speed control of induction motors using a novel fuzzy sliding-mode structure, IEEE Trans. Fuzzy Systems 10 (2002) 375-383.
[10] M. Amrhein, P.T. Krein, Dynamic simulation for analysis of hybrid electric vehicle system and subsystem interactions, including power electronics, IEEE Trans. on Vehicular Technology 54 (2005) 825-836.
[11] M. Mohebbi, M. Charkhgard, M. Farrokhi, Optimal neuro-fuzzy control of parallel hybrid electric vehicles, in: IEEE Vehicle Power and Propulsion Conf., 2005.
[12] R.I John, J. Coupland, Type-2 fuzzy logic: a historical view, IEEE Computational Intelligence Magazine 2 (2007) 57-62.
[13] O. Castillo, P. Melin, J.R. Castro, Computational intelligence software for interval type-2 fuzzy logic, in:
Proc. of the Workshop on Building Computational Intelligence and Machine Learning Virtual Organizations, 2008, pp. 9-13.
[14] M.Y. Hsiao, S.L. Hseng, J.Z. Lee, Fuzzy sliding-mode controller, Information Sciences 178 (2008) 1696-1716.
[15] J. M. Mendel, F. Liu, D. Zhai ,α-plane representation for type-2 fuzzy sets: theory and applications, IEEE Trans. on Fuzzy Systems Archive 17 (2009) 1189-1207.
[16] I. Robandi, B. Kharisma, Design of interval type-2 fuzzy logic based power system stabilizer, in: Proc. of World Academy of Science Engineering and Technology, 2008, pp. 2070-3740.