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The 18th National Conference on Electrical Drives “CNAE 2016” Real-time Virtual Test-bench for Electric Vehicle Propulsion Systems Sebastian Vasile Ciceo1, Hunor Nagy1, Mircea Ruba1, Claudia Martis1, Horia Hedesiu1 1 Faculty of Electric Engineering, Technical University of Cluj-Napoca, Romania Abstract - This paper presents a virtual test-bench for the evaluation of two drivetrains for electric vehicles, consisting of a switched reluctance and a synchronous reluctance motor drive, simulated in real-time on FPGA, coupled via CAN bus with a forward-facing vehicle simulation model. In this way control algorithms can be assessed with results showing up in the vehicle's behaviour, providing a high fidelity hybrid method that combines rapid control prototyping with a real-time simulated plant model. Keywords – Rapid Control Prototyping, Electric Vehicle, Synchronous Reluctance Motor, Switched Reluctance Motor, Real-Time Simulation 1. INTRODUCTION The increasing shift in the automotive market towards electric powertrains and intelligent vehicles introduces the need of developing and testing mechatronic systems in a time and cost saving fashion [13]. By integrating the control and plant modelling from an early design stage in an virtual environment under the paradigm of Model-based System Engineering (MBSE), thus using a common design and validation environment available for different engineering domains, design and integration problems can be avoided efficiently [2], [13]. The conventional “V” development cycle used for automotive applications incorporates the following stages [3], [4]: Model-In-the-Loop (MIL) simulation were the plant and controller models are running together offline in order to validate the control algorithm or the system model. Software-In-the-Loop (SIL) were actual code is tested under the simulated plant model in order to validate code implementation. Hardware-In-the-Loop (HIL) simulation were physical hardware is tested in the presence of a realtime plant model in order to validate the hardware interactions with the plant. Rapid Control Prototyping (RCP) where closedloop tests are conducted using a physical plant and a prototype controller in order to assess the controller behaviour. This paper proposes a Model-based System Testing method (MBST) [5] where we combine the advantages of RCP with those of HIL simulation in the field of EV traction application. By coupling an FPGA Fig. 1 Virtual Test-bench system overview ACTA ELECTROTECHNICA, Volume 57, Number 3-4, 2016, Special Issue, ISSN 2344-5637 360 The 18th National Conference on Electrical Drives “CNAE 2016” based prototype controller using RCP and a real-time compatible virtual EV system plant model, the control algorithms can be tested in real-time before the physical test-bench is built and even before the final system model is fully configured, for example the final 3D motor model can be still under development [6]. By integrating multiple simulation and prototyping tools into a virtual test-bench where different subsystems depending on their physical domain (having different dynamic behaviour) can run at different time-rates, we are able to close the gap between testing and simulation. [2], [5]. Our focus is on decoupling the control and analytical model of two reluctance based electric machines drives [8], [9], [12] from the system model of the EV in order to test the real-time control algorithms in a safe way and check the interactions with the system model. By using this approach, extreme conditions and faulty behaviour can be tested without the risk of damaging physical equipment [2]. Because the virtual test-bench is easily reconfigurable, it is possible to compare different traction drive topologies and control algorithms and assess their performance behaviour at the system level. 2. METHODOLOGY 2.1. System overview The challenges of real-time simulation for EV traction drives consist in accurately representing fast non-linear transient response in discrete time domain, implying the need of a small integration step size. This occurs because the electrical system has a higher bandwidth compared to thermal or mechanical systems [6], [2]. In order to bypass this issue, some marketavailable solutions (discrete-time compensation methods) use interpolation algorithms and time stamping [2]. The solution adopted in this paper is using RCP for the controller and analytical representation of the power converter and machine model in NI LabVIEW FPGA Module implemented on dedicated prototyping hardware: NI PXI industrial controller with an R Series FPGA module. This removes the time-demanding task of programming in hardware description language and ensures a clock rate of above 1 MHz which is sufficient for ultra-fast transients [2]. The EV system model is designed in LMS Imagine.Lab Amesim – an integrated simulation platform for multi-domain mechatronic systems simulation, compiled as a Dynamic-link library (DLL) and integrated into NI VeriStand - a software environment for configuring real-time test applications. It runs on a second PXI controller that is able to run the EV model at a clock rate above 10 kHz, depending on the system model complexity. The communication between the electric drive real-time model and the EV real-time model is done via a CAN Bus interface, using the NI XNET API running on the real-time controller of the PXIs, ensuring deterministic data transfer up to 1 Mb/s. In this paper two electric drives using machines working on the principle of magnetic reluctance have been studied: the synchronous reluctance motor (SynRM) and the switched reluctance motor (SRM) (Fig. 1). 2.2. SRM model and control The SRM has a simple doubly salient structure, which gives an advantage in construction costs, with a working principle based on magnetic reluctance. Thus by applying voltage sequentially on its phases, magnetic flux is generated according to the rotor position and the current excitation. However due to the operating principle and structural characteristics the resulting magnetic saturation, flux linkage and generated torque have are highly non-linear [11]. ( ,) (1) = + ( , )= = ( , ) (2) , where V represents the supply voltage, R is the phase resistance, i the phase current, is the flux linkage, is the rotor position and T is the torque produced by the co-energy variation caused by the rotor movement [10]. Fig. 2 Current in function of rotor position and flux linkage When designing the control of the machine the described non-linarites have to be taken into account. In order to define the flux linkage and the output torque, estimation methods have been proposed in the literature [14]. A hybrid model was chosen, where the magnetic characteristics are defined beforehand by FEM simulations and stored in look-up tables (LUT) (Fig. 2) while the rest of the model is defined by analytical expressions, resulting in a fast and precise model at the cost of higher memory storage needed for FPGA implementation, thus being a superior approach to motor modeling [1]. The SRM drive consists of the before mentioned machine model based on LUTs, controlled by a hysteresis current controller using an asymmetric halfbridge converter. Positive, negative or zero voltage is applied on the machine phases, the values being a function of the firing angles compared to rotor position and current reference compared to a hysteresis bandwidth. ACTA ELECTROTECHNICA, Volume 57, Number 3-4, 2016, Special Issue, ISSN 2344-5637 The 18th National Conference on Electrical Drives “CNAE 2016” Fig. 4 Control diagram of the SynRM Fig. 3 Control diagram of the SRM The current reference input to the controller is calculated by a PI controller, relying on the error between the torque input and actual, computed torque (Fig 3). 2.3. SynRM model and control The SynRM has a three phase stator with distributed windings and an anisotropic rotor with flux barriers serving a saliency ratio between d-axis and qaxis inductances (Ld and Lq). Having these mechanical characteristics the rotor synchronously follows the rotating magnetic field in the air-gap, based on the principle of minimal reluctance. The governing equations of the machine defined in conventional dq frame are as following: = + = + =( (3) + ) 361 (4) (5) ,where V represents the applied voltage, stator phase resistance, L is the inductance, and i the current, with d and q marking the d- and q-axis orientation, while ω the angular velocity of the rotor. The applied control method is based on the field oriented control (FOC) method, with the dq current references being supplied by PI controllers, and by using the inverse Park transformation, voltage is applied by a three phase inverter controlled by a PWM controller (Fig. 4). 2.4. EV system model The EV model is a simple forward-facing vehicle model designed in LMS Imagine.Lab Amesim. The reason behind the model design simplicity is that it was built only in order to demonstrate the platform capabilities (more complex systems designs serving different purposes, such as energy consumption or driveability can be added later). The EV consists of a driver model (PI controller) that sends acceleration and braking commands to the Vehicle Control Unit (VCU), which is computing and sending the desired torque reference to the VeriStand communication block that interfaces the electric drive. 2.5. LabVIEW FPGA implementation Due to the high bandwidth required by the electrical loop because of the hysteresis controller (Fig. 5) and cascaded control structure, where the need of knowing the variables, in every iteration of the control program, NI LabVIEW FPGA Module has been used. For control purposes, the characteristics of the current as a function of flux linkage and rotor position ( ( , )) are needed together with the torque as a function of rotor position and current amplitude (T( , )). Therefore, data representing these variables have been stored as LUTs in the block memory of the FPGA as long data arrays. Accessing the elements from the memory was done by calculating the address in function of the two input variables. These are linearly increasing arrays in a LUT therefore, by dividing the input elements with the slope of their series, the queuing address can be calculated. Fig. 5 Hysteresis current control implemented in LV FPGA ACTA ELECTROTECHNICA, Volume 57, Number 3-4, 2016, Special Issue, ISSN 2344-5637 362 The 18th National Conference on Electrical Drives “CNAE 2016” By adopting this method the current and torque data can be acquired at every iteration in 18.75ns. The control algorithm was implemented by carrying out operations in Single Cycled Timed Loops (SCTL) having the advantage of minimizing the resource usage of the FPGA and increasing the execution speed. This is achieved because the calculations done in SCTLs store the logic blocks and the connection between them, in the FPGA hardware thus eliminating the need for using registers. Timing of the control algorithm was achieved by the use of a clock based counter, and setting the step size according to the sampling frequency. Differential equations were solved using the discrete Forward Euler method, while data operations were conducted by using fixed-point representation, where output precision is determined by setting the word and integer word length of every single operation in order to achieve a proper balance between precision and FPGA resource exploitation. Communication between the FPGA target and the real-time target has been done via read/write protocols, by which I/O can be written and read deterministic. 3. analytical models of the machines with full FEM models, as showed in [6]. ACKNOWLEDGMENT This work was supported by UEFISCDI PCCA 181/2012 ALNEMAD. REFERENCES 1. 2. 3. 4. 5. RESULTS AND CONCLUSIONS In order to validate the virtual test-bench, the first 600 seconds of a reduced scale New European Drive Cycle (NEDC) was given as test stimuli. Fig. 6 shows the output torque for the two simulated drives with their respective reference given by the VCU, during the first 200 seconds of the simulated run. 6. 7. 8. 9. 10. Fig. 6 Estimated torque for the SRM and SynRM during the imposed drive cycle As a conclusion, a virtual test-bench having the purpose of testing the interaction of control algorithms of electric drives with a simulated system model of an EV in real-time, thus combing the advantages of using RCP and HIL simulation. By adopting the FPGA implementation for the control and electric drive model numerical instabilities occurring in real-time are suppressed. As a future development, it is possible to validate the control algorithm by replacing the simulated electric drives with their physical counterparts and integrate them in a mechanical level HIL setup. Also, it is possible to refine the virtual test-bench by replacing the 11. 12. 13. 14. 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Nagy H., Ruba M., Hedesiu H., Martis, C. “FPGA based realtime simulation of a Switched Reluctance machine drive unit”. In 2016 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), pp. 1-5., IEEE, 2016. Nagy H., Ruba M., Hedesiu H., Martis, C. “Rapid control prototyping of a speed control strategy for a switched reluctance machine.” In press. Ponce, P., Ibarra, L., Molina, A. and MacCleery, B. “Real Time Simulation for DC and AC Motors Based on LabVIEW FPGAs” in Information Control Problems in Manufacturing, vol. 14, no. 1, pp. 1777-1784, 2014. Ramu. K. “Switched reluctance motor drives: modeling, simulation, analysis, design, and applications”, CRC Press, 2001. Ruba M., Hunor N., Hedesiu H., Martis, C. “FPGA based processor in the loop analysis of variable reluctance machine with speed control”. In Proceedings of IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), pp. 1-4, 2016. Van der Auweraer H., Anthonis J., De Bruyne S., Leuridan J. “Virtual engineering at work: the challenges for designing mechatronic products.”, Engineering with Computers 29, no. 3, pp. 389-40, 2013. Zhao S. W., Cheung N. C., Lee C. K., Yang X. Y.,Sun Z. G. “Survey of modeling methods for flux linkage of switched reluctance motor.” In Proceedings 4th International Conference on Power Electronics Systems and Applications (PESA), pp. 14, Hong Kong,, June 2011. ACTA ELECTROTECHNICA, Volume 57, Number 3-4, 2016, Special Issue, ISSN 2344-5637