Modeling, Optimization and Realization of an Electrical Off-Road Motorbike

Modeling, Optimization and Realization of an Electrical Off-Road Motorbike Franz Pirker 1, Dragan Simic 1, Thomas Bäuml 1, Margit Noll 1 1 arsenal research, Giefinggasse 2, A-1210 Vienna - franz.pirker@arsenal.ac.at, dragan.simic@arsenal.ac.at, thomas.baeuml@arsenal.ac.at, margit.noll@arsenal.ac.at EET-2008 European Ele-Drive Conference International Advanced Mobility Forum Geneva, Switzerland, March 11-13, 2008 Abstract In this work the modeling, simulation and validation of an electrical two-wheel off-road motorbike is presented. All electrical motorbike components are developed in the simulation environment Dymola, using Modelica programming language. The power train concept of this electrical motorbike is based on a battery powered electric traction machine and a one gear transmission with chain. The variation of size and type of the electrical sources and drive components is implemented and explained. For evaluation of the electrical motorbike simulation model, all components of the electrical motorbike are measured and evaluated. Based on the measuring of the power train components and the simulation results performed at arsenal research a prototype of the electrical motorbike is constructed and realised. The entire way of development from modeling the electrical motorbike in Dymola using Modelica to the realization of the prototype will be explained. Keywords: modeling, simulation, optimization, energy source, electric drive 1 Introduction The development and production of an electrical two-wheel off-road motorbike and components for electrical motorbikes is an expensive and long process. Constantly increasing oil prizes and environmental issues are additional problems for the lifetime of conventional motorbikes driven with an internal combustion engine. Trends present an increasing electrification in vehicle industry due to new electrical safety concepts and increasing performance. The motivation for electrifying an off-road motorbike is on one hand the reduction of the exhaust emissions and furthermore the design of an off-road electrical motorbike with zero emission and on the other hand a significant reduction of noise emission. For shortening the development period and reducing costs, simulation is a crucial step in the design process. In this contribution two simulation libraries written in Modelica [1], the Smart- ElectricDrives (SED) library [2] and the Smart- PowerTrains (SPT) library [3], developed by arsenal research with focus on automotive applications, are used to model and simulate an electrical motorbike. The SED library is developed and implemented with the main focus on automotive applicacions such as electric and hybrid electric vehicles. The SPT library is developed on the basis of the mechanical part of the ModelicaStandardLibrary (MSL), according to [4], with emphasis on longitudinal simulations of vehicles and motorbikes. Modeling all kinds of power train applications like conventional vehicles, hybrid electric vehicles and electric motorbikes is covered. Due to strict modeling specifications compatibility of all

components and models of these two developed libraries with MSL is ensured. All electric components used in the simulation, such as electric machines, batteries, converters etc. are part of the SmartElectricDrives library. The mechanical components such as transmissions, chains, chassis, wheels, etc, are part of the SmartPowerTrains library. 2 Simulation Environment For modeling, simulation and optimization of different vehicle and motorbike concepts including different power train components an entire vehicle simulation has to be performed. A motorbike simulation model that consists of electrical, mechanical and operating strategy components allows to investigate the detailed interplay of the power train with the environment. Determining real requirement for the specific vehicle and motorbike components results from the simulations. The simulation tool used for that purpose is Modelica/Dymola (Dynamic Modeling Laboratory). This simulation environment, Dymola, is an ideal modeling and simulation basis for implementation of complex systems using the object-oriented modeling language Modelica [5]. Modelica contains component-oriented models of complex multidomain physical systems like systems containing mechanical, electrical, hydraulic, thermal, control. Modelica supports flexible, interdisciplinary and real-time capable implementing and simulation of all kinds of applications e.g. alternative vehicles and motorbikes. The implementation of Modelica models is based on algebraic and differential equations. Using the open source Modelica library MSL, a simulation environment was developed that is focused on modeling and simulating conventional, alternative and electric drive structures of vehicles and motorbikes [3, 6]. 2.1 SmartPowerTrains Library The SPT library is developed at arsenal research for modeling and simulating of longitudinal dynamic models of vehicles. All mechanical and thermal components of this library are modeled as object-oriented physical models using algebraic and differential equations. All models of the SPT library are parameterized using geometrical and mechanical data. For the modeling and investigation of the electrical motorbike treated in this paper, the SPT library is used. Only the mechanical components are taken from the SPT library, whereas the electrical components are included in the SED library. Apart from modeling the elementary mechanical components in the drive train it is also necessery to implement a virtual strategy model for controlling all mechanical and electrical components. Additionally virtual driver model has to be present. The strategy and the driver models control the entire vehicle, such as the states of the gas pedal, the brake pedal and the control values of the motor, in this case an electric one. The main components included in the SPT library are: Mechanical gear boxes Automatic gear boxes Chain and belt drives Wheels Chassis Driver Strategies Environmental effects. 2.2 SmartElectricDrives Library The SED library is developed at arsenal research for modeling and simulatingelectric drives. All electrical components of this library are modeled as object-oriented physical model using algebraic and differential equations. All models of the SED library are parameterized using parameters from data sheets. From the SED library all electrical components for the models treated in this paper are used. The SED library includes components and models for state-of-the-art electric drive systems. All components and models of the SED library are implemented in Modelica simulation language by using MSL components and models. The most important components included in the SED library are: Energy Sources Controlled electrical machines Converters Controllers.

Figure 1: Electric motorbike model in Dymola using Modelica language 2.3 Simulation Model Based on the SED and the SPT library an electrical motorbike concept is modeled and investigated. The complex simulation model with parameterized motorbike components is modeled in figure 1. This configuration consists of a front wheel (frontwheel) model, a rear wheel (rear wheel) model, a transmission (transmission) with one gear, a chain (chain) and a permanent magnet synchronous machine (machine). Attention is paid to energy consumption during a simulated drive cycle. Therefore the quasi stationary model of a permanent magnet synchronous machine with integrated converter and control system, including voltage and curent limitation as well as flux weakening is used here. For powering the electrical motorbike, an energy source (battery) is modeled using an idealized battery model consisting of a constant capacitor and a constant internal resistor. All mechanical components, such as brakes, chassis (chassis), strategy (strategy), drive cycle (cycle) and driver (driver) are taken from the SPT library as they are provided as ready-to-use models there. For controlling the electrical motorbike velocity (acceleration pedal position and brake pedal position) a virtual driver model (driver) was implemented. All models extend their parameters from a model identified as parameters, in figure 1. 2.3.1 Operating Strategy An operating strategy is implemented in the strategy model of the electrical motorbike. This operating strategy is modeled using the Modelica.StateGraph standard library, according to [4]. This operating strategy controls the reference torque of the electric machine. The reference torque of the electric machine is limited between maximum and zero torque. Therefore the reference torque is restricted to be positive only. In this case, only the drive of the electric machine is implemented, no recuperation occurs. 2.3.2 Variation Calculation The developed simulation model allows using an external script file. This script allows a variation of the parameters and feeding them to the model. Furthermore the script starts and stops the simulation. With this script file it is possible to accomplish the variation calculation of each electrical motorbike component. Using the developed simulation model of the motorbike, the size and type of each component is optimized. The optimization is accomplished using different drive cycles such as drive with constant velocity, real live cycles and the standardized European drive cycle (New European Driving Cycle (NEDC), Urban Driving Cycle (UDC), etc. 3 Parameterization of the Simulation For the optimization of the electrical motorbike different sizes and types of the same power train components are used. Three different power classes of the electric machine, four types of the battery, five gear series and many different driving cycles, such as NEDC, UDS, constant driv-

ing cycle and real driving cycle are provided to manage the variation calculations. All provided components need a set of parameters which have to be determined prior to the simulation. The parameters of the different battery types and different power classes of the electric machine have been determined through data sheets. All other components, such as electric motorbike chassis, the wheels, ect. are parameterized through measurement data. The Electric Machines The electric machine parameters are chosen such way, that it is able to support the power train with a minimum of 8 Nm at a speed of 6000 rpm. However, in this operating point the electric machine works in the flux weakening region. All three electric machines are permanent magnet synchronous machines. The parameters are determined through datasheets.. The parameters of the machine datasheets are listed in table 1. Table 1: Data sheets of the PMSM electric machines speed torque efficiency (rpm) (Nm) (%) PMSM A 1500 10.19 88.8 3000 9.78 91.6 4500 9.18 91.9 6000 8.27 91.3 PMSM B 1500 16.20 89.2 3000 15.56 91.9 4500 14.60 92.1 6000 13.16 91.5 PMSM C 1500 30.56 91.4 3000 29.34 93.5 4500 27.53 93.7 6000 24.82 93.1 The Batteries The battery parameters have to match the specifications of the electric machine and the operating strategy of the electrical motorbike. Depending on the chosen electric machine, 1, the battery must be capable of providing energy for the maximum possible electric torque (for PMSM C this is 24.82 Nm at a speed of 6000 rpm). Four different battery packs are parameterized using four different high power cells. The parameters of this high power cells are chosen as listed in table 2. The first battery consists of 200 Battery A high power cells. In this battery package 50 cells are connected in series and 4 cells in parallel. The nominal voltage of this battery package is 185 V at a weight of 23 kg. The second battery consists of 300 Battery B high power cells. In this battery package 50 cells are connected in series and 6 cells in parallel. The nominal voltage of this battery package is 185 V at a weight of 19.8 kg. The third battery consists of 50 Battery C high power cells. In this battery package 50 cells are connected in series. The nominal voltage of this battery package is 180 V at a weight of 18.5 kg. The fourth battery consists of 132 Battery D high power cells. In this battery package 132 cells are connected in series. The nominal voltage of this battery package is 158.4 V at a weight of 22 kg. 4 Components Validation The validation of the electrical motorbike model was executed first on component- an then on entire motorbike level. All electrical parameters of the electric machines and batteries, mechanical parameters of the electrical motorbike chassis and power train and the overall power consumption of the entire electrical motorbike were determined. The chassis driving resistances such as aerodynamic and rolling resistances were calculated based on the measured freewheeling curves. For validation of the electrical motorbikes power train, the electrical machines and the batteries data sheets were used. At arsenal research all three electric machines and all four high power cells were run on the motor- and battery testbenches were measured. 5 Simulation and Optimization For optimization of the electrical motorbike different configurations with varying size of the electric machines and different types of batteries were simulated. For covering numerous types of operations different driving cycles, such as cycles with constant speed, NEDC, UDC and real driving cycles were investigated. Figure 2 shows results of an optimization simulation for three battery types at constant motorbike speed. Five different driving cycles with different velocities (30, 40, 50, 60 and 70 km/h) were analyzed. They are identified as Con030, Con040, Con050, Con060 and Con070 in figure 2. The other components of the electrical motorbike were not varied in this simulation step. Similar to the variation of the battery, all other components

Table 2: Data sheets of the used high power cells technology Battery A Battery B Battery C Battery D lithium polymer lithium polymer lithium-ion nickel-metal hydrid weight (g) 115 66 370 167 capacity (mah) 4.8 2.0 6.8 6.5 voltage (V) 3.7 3.7 3.6 1.2 Figure 2: Simulation results of battery variation using PMSM A Figure 3: Integrated battery package using Battery A cells of the motorbike were varied, too. Finally, altogether about 2500 different motorbike concepts had to be simulated and evaluated. After the variation simulations it turned out, that the best concept is the one with the electric machine PMSM B and the battery package with 300 Battery A high power cells. with the measurement results. 6 Integration With the above gathered simulation and optimization results a real electrical motorbike was realized. Based on a conventional motorbike with an internal combustion engine (ICE) the new electrical motorbike was developed. The motorbike components, such as the chosen battery, electric machine, gear ratio, etc., are integrated in the chassis of the conventional motorbike. Figure 3 shows the realized, integrated battery module with 50 Battery A high power cells and figure 4 shows the PMSM A electric machine integrated in the motorbike chassis. The electric motorbike was tested on an off-road test track and measurements were accomplished. Finally the entire motorbike simulation was validated 7 Conclusions The presented electric motorbike simulation and optimization allows the determination of the best electric motorbike concept as well as the identification of the economic savings potential by integrating electric power train components. Using the Modelica libraries, SmartElectricDrives and SmartPowerTrains library, different concepts of an electric motorbike have been analyzed, simulated and optimized very quickly. Based on the implemented electric motorbike simulation model different potential concepts have been identified and analyzed under different design and drive cycle scenarios. A significant acceleration of the development process of the entire electrical motorbike and the power train components can be achieved and effort can be reduced. Due to the usage of quasi stationary machine models and simple battery models the electromechanical analysis is computed with very little effort. Simulations of entire electric vehicles and motorbikes such as the one presented have the potential to accelerate elec-

http://www.modelica.org, 2000. [Online]. Available: http://www.modelica.org [6] J. V. Gragger, D. Simic, C. Kral, H. Giuliani, V. Conte, and F. Pirker, A simulation tool for electric auxiliary drives in hevs the SmartElectricDrives library, World Automotive Congress, FISITA 06, Yokohama, Japan, 2006. Definitions, Acronyms, Abbreviations Figure 4: Integrated electric machine, PMSM B tric vehicles and hybrid electric vehicles development and design cycles considerably in the early concept phase. Acknowledgments Technical support from KTM-Sportmotorcycle AG, Austria for the practical investigations and measurements is gratefully acknowledged. Financial support from Federal Ministry of Transport, Innovation and Technology is gratefully acknowledged. References [1] P. Fritzson, Principles of Object-Oriented Modeling and Simulation with Modelica 2.1. Piscataway, NJ: IEEE Press, 2004. [2] J. Gragger, H. Giuliani, C. Kral, T. Bäuml, H. Kapeller, and F. Pirker, The SmartElectricDrives Library powerful models for fast simulations of electric drives, International Modelica Conference, 5th, Vienna, Austria, 2006. [3] D. Simic, H. Giuliani, C. Kral, and J. Gragger, Simulation of hybrid electric vehicles, International Modelica Conference, 5th, Vienna, Austria, 2006. [4] [Online]. http://www.modelica.org Available: [5] T. M. Association, Modelica A Unified Object-Oriented Language for Physical Systems Modeling, Tutorial, SED SmartElectricDrives SPT SmartPowerTrains MSL ModelicaStandardLibrary NEDC New European Driving Cycle UDC Urban Driving Cycle ICE internal combustion engine EV electric vehicle HEV hybrid electric vehicle

Authors Franz Pirker received the Dipl.-Ing. degree in electrical engineering from Vienna University of Technology, Vienna, Austria, in 1997. Since 1999, he has been the Head of Monitoring, Energy, and Drive Technologies at arsenal research, Vienna, Austria. In this area, the main research topics are hybrid electric vehicles especially development and design of electric drives. In these fields, arsenal research is developing new concepts for hybrid electric vehicles and electric driven auxiliaries. Dragan Simic received his PhD from the Vienna University of Technology, Austria, in 2007. Since 2002, he is scientific employee at arsenal research, Vienna, Austria. His research activities are focused on the longitudinal dynamics simulation of conventional and hybrid vehicles, including the simulation of auxiliaries. Thomas Bäuml received the Dipl. Ing. (FH) degree in mechatronics from the University of Applied Sciences Wiener Neustadt, Austria, in 2005. Since January 2006, he has been a research associate at arsenal research in the business unit Monitoring, Energy and Drive Technologies. His major research activities are focused on electric and thermal simulations of electric machines and vehicle simulations. Margit Noll obtained her PhD in physics from the University Vienna as well as a MBA in General Management from the Danube University Krems. Currently she is responsible for strategic research planning and research management in the field of electric drives technologies and alternative vehicle concepts at arsenal research.