CONVENTIONALLY reduced order models are being

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1 Co-Simulation of an Electric Traction Drive Christoph Schulte and Joachim Böcker Abstract For the simulation of electrical drives, reducedorder models or simple look-up tables are often used in order to decrease the computation time. In the latter case, the quality of the results is reduced due to interpolation. This publication introduces a coupled simulation structure which includes the control, the power electronics and also a permanent magnet synchronous machine. Each of these components is simulated in different software environments. The coupling between the different software tools is realized by a defined data flow structure. Such a structure can be useful for accurate design and system optimization. Comparison between the simulation results and experimental measurements shows that a good agreement is obtained. I. INTRODUCTION CONVENTIONALLY reduced order models are being used for the simulation of electric drives, where the inverter for the motor is replaced by a sinusoidal voltage sourceandthemotormodelisreplacedbyasimplelook-uptable. While such an approach can give reasonable information about the system level performance of the drive, it cannot identify and investigate certain transient and harmonic effects, switching- and increased iron losses etc., which are necessary for optimization of the drive []. In order to maximize the quality of simulation results of an electrical drivetrain it is therefore essential to use a model which is as detailed as possible. But, making such a detailed model is not feasible using a single software tool []. This is because, the model which consists of a control structure, power electronics and a machine, is a heterogeneous model. The machine model is replaced by a finite element model to achieve high accuracy. Although the simulation effort is increased by the finite element analysis [], the present day computing hardware is capable of handling this. The requirement of a detailed model, coupled with the capabilities of modern computers, propel us towards realizing a more sophisticated simulation structure. This publication presents a coupled simulation structure(cosimulation) for an electric drive, where the control structure, power electronics and motor are modeled in different environments. A data flow between each model is realized using a defined structure. II. SIMULATION STRUCTURE The main focus is the development of a co-simulation structure of an electrical traction drive. Before going into the details Christoph Schulte and Joachim Böcker are with the department for Power Electronics and Electrical Drives, University of Paderborn, D-3395 Paderborn(Germany), {schulte, This work was developed in the course of the Collaborative Research Center Self-Optimizing Concepts and Structures in Mechanical Engineering University of Paderborn, and was published on its behalf and funded by the Deutsche Forschungsgemeinschaft. ofthedesignandthesystemsetupofthecoupledstructure,the basic principle is described. A co-simulation generally consists of several components, each equipped with a bidirectional information interface. This means that data or information can be received from and sent to the linked software tools. This results in a coupled simulation environment which works as a combined system. The system model of the electric drive discussed in this work consists of the following components: ) Control model in MATLAB/Simulink ) Power Converter in ANSYS Simplorer 3) MotormodelinANSYSMaxwellD Figure shows a system level representation of the entire drive scheme. A. Control (MATLAB/Simulink) The control structure used here is based on flux-oriented control (FOC) which ensures good dynamics, stability and high efficiency in the whole operation range as reported in [5]. T Operating Point Selection ψmax Modulation Control i d i q Control Scheme a Field Oriented Current Control ε ωel ua ub uc PWM Position Signal Processing sa sb sc ia ib ic IPMSM Fig.. Overview of a complete drive system containing of field-oriented control structure, power converter and motor model[5] The operation characteristics for the different working points are defined in the Operating Point Selection block by several parameters including speed of the motor and predeterminedd-andq-currentshapesordesiredtorque T.Theentire control scheme was implemented in MATLAB in accordance with[5].theinputstothiscontrolschemearethethreemotor currents i a,b,c, the motor angle ε and the DC link voltage u DC while it outputs the pulse-width-modulation (PWM) signals s a,b,c forthepowerelectronicinverter. T,n

2 B. Power Converter (ANSYS Simplorer) Figure shows the three-phase inverter[3] of the simulation structure which is implemented in ANSYS Simplorer. Here, idealswitchesareused,butitisalsopossibletoreplacethem with exact models of IGBTs. The PWM signals (s a, s b, s c ) generated from the control scheme in MATLAB are received through an interface (which will be described in the next section) and fed to the inverter switches S to S built up in Simplorer. q d S S S 3 Fig. 3. Sketch of an permanent magnet synchronous motor with embedded magnets and corresponding direct and quadrature axes R S L end u DC PWM S S 5 S s a s b s c III. CO-SIMULATION In order to execute the co-simulation, continuous data exchange needs to take place among all the individual components (MATLAB, Simplorer and Maxwell models) as shown in Fig. 5. The interface between MATLAB and Simplorer has been realized and implemented using an S-function(as shown in figure ) provided by the software manufacturer ANSYS. Fig.. 3-Phase Power Converter with additional winding resistance and end-winding inductance Besides the inverter, the model includes the concentrated phaseend-windinginductances L end andphasewindingresistances R S.Thesecomponentsareincludedinthismodelasit is not possible to integrate them in a two dimensional Finite Element Method(FEM) based motor model. C. Motor (ANSYS Maxwell D) The permanent magnet synchronous machine with interior magnets (IPMSM), which is exemplarily illustrated in Fig. 3, is realized as a two-dimensional Finite Element model in Maxwell D. A two-dimensional model is chosen, as it can generate almost all the information at a reduced computational burden. Such two dimensional models are extensively used in motor design analysis and optimization. IPMSM has been chosen for this particular co-simulation analysis, owing to the increasing interests of automotive manufactures on it. Such a motor presents advantages like an improvement in maximum torque to weight ratio as well as efficiency. The design of motors with embedded magnets results in a nonuniformreluctancesleadingtoinductances L d and L q.fora givenpermanentmagnetflux Ψ p andnumberofpolepairs p the torque T can be calculated as T = 3 p(ψ pi q +(L d L q )i d i q ) () where, i d is the direct axis current component and the i q is the quadrature axis component of the current. The torque T can be divided into two parts: the reaction and the reluctance torque.thereactiontorqueisafunctionofcurrent i q aswell asthepermanentmagnetflux Ψ p,whilethereluctancetorque canbedefinedasafunctionof L d, L q, i d and i q.asketchof amotormodelwithembeddedmagnetsisshowninfig.3. exported to Simplorer s a s b s c S-Function ε i a i b i c imported from Simplorer Fig.. Embedded MATLAB S-Function block showing exported and imported values The objective of this S-function is to transfer the switching signals s a,b,c totheinvertermodelinsimplorerandtoreceive the motor currents i a,b,c and rotor position ε from the motor model in Maxwell. Even other variables (like flux linkages, losses,etc.)canbeexchanged ifrequired.itactsasdatainterface between MATLAB and Simplorer as there is no direct data exchange between Maxwell and MATLAB. Information exchange between the models in MATLAB and Maxwell(like speed and torque information) therefore must be realized via Simplorer. This is because there exists only a configurable data interface module between Maxwell and Simplorer, which is provided by the software manufacturer ANSYS. With all links established, the co-simulation process can be started by executing the MATLAB model with a given initial position of the motor. All other software tools are then started automatically. The corresponding voltage pulse pattern is calculated by the control and forwarded to the power inverter model in Simplorer. In Simplorer, this pattern is then

3 s a,b,c MATLAB Simulink ANSYS Simplorer i a,b,c,ε u a,b,c ANSYS Maxwell Fig. 5. Dataflow of the co-simulation between all software tools transmitted to switches of the three phases. The resulting voltage waveforms of the inverter are then passed to the finiteelement model in Maxwell. The FEM model calculates the torque, the phase currents and the field quantities. In order to close the simulation cycle the current motor position ε and currents i a,b,c are transmitted from the motor model to the control scheme in MATLAB via Simplorer as shown in Fig. 5. The advantage of selecting such a co-simulation concept is that e.g. field quantities or the occurring iron losses, which are mainly influenced by the switching of the power converter, can be studied in detail. Furthermore, every part of the co-simulation can be replaced by a new model without affecting the simulation structure as long as the inputs and outputs of the interface remain the same. Hence, performance comparisons between several combinations of motors, control and power electronic circuit models can be generated easily. The behavior of such amodel willbe very closetothatofahardwaresetupasshowninthenextsection. IV. EXPERIMENTAL VALIDATION In order to validate the concept of a coupled co-simulation model, co-simulation results were compared with the measurement results from an equivalent test setup. Several operating points n {min,9min,min } were examined. At each case (refer to Fig. -), the response to a step change in the q component of the current i q was observed at t = ms. Then, 5ms later, the i q step demand was reduced to A. During these operations the d component of the current i d was kept unchanged at a constant value. To ensure the comparability of both the results, the simulation results were temporally adjusted according to the measurement hardware equidistant sampling rate of µs. In the following the focus of the several profiles lies in the observation of the several currents during the step changes. While the red dashed profile shows the demanded current, the blue and green profiles indicate the test bench measurement and simulation results respectively. Case: n = min, i d = A, i q = A A Fig.. Responsesof i q and i d forthestepchangein i q at min Case: n = 9min, i d = A, i q = A 5A Fig.7. Responsesof i q and i d forthestepchangein i q at 9min

4 Case3: n = min, i d = 3A, i q = A 5A Case: n = 9min, i d = A, i q = A 5A Fig.. Responsesof i q and i d forthestepchangein i q at min After adaption of all simulation results and comparison with the test bench measurements profiles a good agreement can be observed with the given plots. Due to the cross-coupling of the two current components, under- and overshoots can be identifiedinthemeasurementplotsaswellasintheprofiles of the co-simulation results. Similar to the current response, comparison from the experimental and simulated results, the measured T Meas,avg and simulated torque profiles T Co-Sim were examined for all the three cases. The following plots show the normalized results of the co-simulation. Case: n = min, i d = A, i q = A A Fig.9. Torqueprofileoftheco-simulationat min Fig.. Torqueprofileoftheco-simulationat 9min Case3: n = min, i d = 3A, i q = A 5A Fig.. Torqueprofileoftheco-simulationat min The dashed blue lines indicate the average steady state values of the measured torque of the equivalent test setup. The measured value from the torque sensor is only an indication of the average torque measured once the steady state has reached. To perform a validation of the simulation results a comparison with test bench measurements was done. While the co-simulation current profiles are directly compared with the test setup measurements the torque profiles are compared in thetime-rangems-5mswiththesteadystatetorqueof the measurement. The resulting difference for every case is givenintablei. TABLE I AVERAGE DIFFERENCE OF THE CURRENT COMPONENTS AND THE TORQUE WorkingPoint Diff.in i d Diff.in i q Diff.in T min.%.9%.5% 9min.5%.9%.7% min.%.33% 3.% The average difference of the co-simulation results compared to the measurement lies in the rage of.5% to.%.

5 Therefore, it can be seen that both results are in close coordination with each other. The other benefit of a co-simulation is the estimation of losses with any inverter fed motor. The iron loss P C,Co-Sim calculated with the co-simulation includes all harmonic losses due to the high switching frequencies of the inverter. In contrast the loss calculation P C,FEM of the common Finite Element Analysis (FEA) only the fundamental frequency is considered. Thus a holistic estimate of losses can be obtained from a co-simulation structure. This is also one of the main features of the structure. Table II gives an overview over the simulated iron losses for both cases. REFERENCES [] S. Kanerva, J. Kaukonen, A. Szucs, and T. Hautamaki. Coupled femcontrol simulation in the analysis of electrical machines and converters. In th International Power Electronics and Motion Control Conference,. [] U. Knorr and R. Juchem. A complete co-simulation-based design environment for electric and hybrid-electric vehicles, fuel-cell systems and drive trains. Technical report, Ansoft Corporation, Pittsburgh, 3. [3] R. Krishnan. Permanent Magnet Synchronous and Brushless DC Motor Drives. CRC Press,. [] F. Leonardi and B. Ionescu. Advancements in tools and methods for the design of permanent magnet integrated starter alternators. In IEEE International Conference on Electric Machines and Drives, 5. [5] W. Peters, T. Huber, and J. Böcker. Control realization for an interior permanent magnet synchronous motor(ipmsm) in automotive drive trains. In PCIM Europe,. TABLE II COMPARISON OF IRON LOSS FOR COMMON FEM AND CO-SIMULATION WorkingPoint P C,FEM P C,Co-Sim P C,Co-Sim P C,FEM min 37.W 55.37W.97% 9min 5.5W 7.7W.% min.7w 59.5W 35.% When considering the losses in both cases, a clear difference is apparent. The iron loss calculated with all voltage harmonics exceed the fundamental losses of the common FEA by approximately 35%. This result can be handful when the drive efficiency has to be considered. V. CONCLUSION AND FUTURE WORK The proposed co-simulation structure presents advantages in terms of accuracy and modularity. Such a coupled model can not only be useful for accurate motor design and optimization but, also in the optimization of power electronics. Further, it is possible to accurately study the system performance before realization of an expensive hardware. Other benefits include its usage for accurate design and optimization while being modular. As shown in three exemplary cases the difference liesintherangeof.3%to.9%.however,theproposedcosimulation structure suffers with a problem of same simulation step size for all the simulation components. This will greatly slow down the simulation because, the step time is usually decided based on the component with highest dynamics. Ideally, it would be greatly beneficial to increase the step timeforthemotormodelinordertospeedupthesimulation, without affecting the results. Future work will be concentrated towards finding a co-simulation method in which individual components are simulated with differing timesteps. Nevertheless, the proposed co-simulation structure provides when compared with the usual individual simulations the following benefits: Analysis of impact factors High degree of modularity Ability to optimize on system level

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