Co-simulation Methodologies for Hybrid and Electric Vehicle Dynamics

In recent decades, full electric and hybrid electric vehicles have emerged as an alternative to conventional cars due to a range of factors, including environmental and economic aspects. These vehicles are the result of considerable efforts to seek ways of reducing the use of fossil fuel for vehicle propulsion. Sophisticated technologies such as hybrid and electric powertrains require careful study and optimization. Mathematical models play a key role at this point. Currently, many advanced mathematical analysis tools, as well as computer applications have been built for vehicle simulation purposes. Given the great interest of hybrid and electric powertrains, along with the increasing importance of reliable computer-based models, the author decided to integrate both aspects in the research purpose of this work. Furthermore, this is one of the first final degree projects held at the ETSII (Higher Technical School of Industrial Engineers) that covers the study of hybrid and electric propulsion systems. The present project is based on MBS3D 2.0, a specialized software for the dynamic simulation of multibody systems developed at the UPM Institute of Automobile Research (INSIA). Automobiles are a clear example of complex multibody systems, which are present in nearly every field of engineering. The work presented here benefits from the availability of MBS3D software. This program has proven to be a very efficient tool, with a highly developed underlying mathematical formulation. On this basis, the focus of this project is the extension of MBS3D features in order to be able to perform dynamic simulations of hybrid and electric vehicle models. This requires the joint simulation of the mechanical model of the vehicle, together with the model of the hybrid or electric powertrain. These sub-models belong to completely different physical domains. In fact the powertrain consists of energy storage systems, electrical machines and power electronics, connected to purely mechanical components (wheels, suspension, transmission, clutch…). The challenge today is to create a global vehicle model that is valid for computer simulation. Therefore, the main goal of this project is to apply co-simulation methodologies to a comprehensive model of an electric vehicle, where sub-models from different areas of engineering are coupled. The created electric vehicle (EV) model consists of a separately excited DC electric motor, a Li-ion battery pack, a DC/DC chopper converter and a multibody vehicle model. Co-simulation techniques allow car designers to simulate complex vehicle architectures and behaviors, which are usually difficult to implement in a real environment due to safety and/or economic reasons. In addition, multi-domain computational models help to detect the effects of different driving patterns and parameters and improve the models in a fast and effective way. Automotive designers can greatly benefit from a multidisciplinary approach of new hybrid and electric vehicles. In this case, the global electric vehicle model includes an electrical subsystem and a mechanical subsystem. The electrical subsystem consists of three basic components: electric motor, battery pack and power converter. A modular representation is used for building the dynamic model of the vehicle drivetrain. This means that every component of the drivetrain (submodule) is modeled separately and has its own general dynamic model, with clearly defined inputs and outputs. Then, all the particular submodules are assembled according to the drivetrain configuration and, in this way, the power flow across the components is completely determined. Dynamic models of electrical components are often based on equivalent circuits, where Kirchhoff’s voltage and current laws are applied to draw the algebraic and differential equations. Here, Randles circuit is used for dynamic modeling of the battery and the electric motor is modeled through the analysis of the equivalent circuit of a separately excited DC motor, where the power converter is included. The mechanical subsystem is defined by MBS3D equations. These equations consider the position, velocity and acceleration of all the bodies comprising the vehicle multibody system. MBS3D 2.0 is entirely written in MATLAB and the structure of the program has been thoroughly studied and understood by the author. MBS3D software is adapted according to the requirements of the applied co-simulation method. Some of the core functions are modified, such as integrator and graphics, and several auxiliary functions are added in order to compute the mathematical model of the electrical components. By coupling and co-simulating both subsystems, it is possible to evaluate the dynamic interaction among all the components of the drivetrain. ‘Tight-coupling’ method is used to cosimulate the sub-models. This approach integrates all subsystems simultaneously and the results of the integration are exchanged by function-call. This means that the integration is done jointly for the mechanical and the electrical subsystem, under a single integrator and then, the speed of integration is determined by the slower subsystem. Simulations are then used to show the performance of the developed EV model. However, this project focuses more on the validation of the computational and mathematical tool for electric and hybrid vehicle simulation. For this purpose, a detailed study and comparison of different integrators within the MATLAB environment is done. Consequently, the main efforts are directed towards the implementation of co-simulation techniques in MBS3D software. In this regard, it is not intended to create an extremely precise EV model in terms of real vehicle performance, although an acceptable level of accuracy is achieved. The gap between the EV model and the real system is filled, in a way, by introducing the gas and brake pedals input, which reflects the actual driver behavior. This input is included directly in the differential equations of the model, and determines the amount of current provided to the electric motor. For a separately excited DC motor, the rotor current is proportional to the traction torque delivered to the car wheels. Therefore, as it occurs in the case of real vehicle models, the propulsion torque in the mathematical model is controlled through acceleration and brake pedal commands. The designed transmission system also includes a reduction gear that adapts the torque coming for the motor drive and transfers it. The main contribution of this project is, therefore, the implementation of a new calculation path for the wheel torques, based on performance characteristics and outputs of the electric powertrain model. Originally, the wheel traction and braking torques were input to MBS3D through a vector directly computed by the user in a MATLAB script. Now, they are calculated as a function of the motor current which, in turn, depends on the current provided by the battery pack across the DC/DC chopper converter. The motor and battery currents and voltages are the solutions of the electrical ODE (Ordinary Differential Equation) system coupled to the multibody system. Simultaneously, the outputs of MBS3D model are the position, velocity and acceleration of the vehicle at all times. The motor shaft speed is computed from the output vehicle speed considering the wheel radius, the gear reduction ratio and the transmission efficiency. This motor shaft speed, somehow available from MBS3D model, is then introduced in the differential equations corresponding to the electrical subsystem. In this way, MBS3D and the electrical powertrain model are interconnected and both subsystems exchange values resulting as expected with tight-coupling approach.When programming mathematical models of complex systems, code optimization is a key step in the process. A way to improve the overall performance of the integration, making use of C/C++ as an alternative programming language, is described and implemented. Although this entails a higher computational burden, it leads to important advantages regarding cosimulation speed and stability. In order to do this, it is necessary to integrate MATLAB with another integrated development environment (IDE), where C/C++ code can be generated and executed. In this project, C/C++ files are programmed in Microsoft Visual Studio and the interface between both IDEs is created by building C/C++ MEX file functions. These programs contain functions or subroutines that can be dynamically linked and executed from MATLAB. This process achieves reductions in simulation time up to two orders of magnitude. The tests performed with different integrators, also reveal the stiff character of the differential equations corresponding to the electrical subsystem, and allow the improvement of the cosimulation process. When varying the parameters of the integration and/or the initial conditions of the problem, the solutions of the system of equations show better dynamic response and stability, depending on the integrator used. Several integrators, with variable and non-variable step-size, and for stiff and non-stiff problems are applied to the coupled ODE system. Then, the results are analyzed, compared and discussed. From all the above, the project can be divided into four main parts: 1. Creation of the equation-based electric vehicle model; 2. Programming, simulation and adjustment of the electric vehicle model; 3. Application of co-simulation methodologies to MBS3D and the electric powertrain subsystem; and 4. Code optimization and study of different integrators. Additionally, in order to deeply understand the context of the project, the first chapters include an introduction to basic vehicle dynamics, current classification of hybrid and electric vehicles and an explanation of the involved technologies such as brake energy regeneration, electric and non-electric propulsion systems for EVs and HEVs (hybrid electric vehicles) and their control strategies. Later, the problem of dynamic modeling of hybrid and electric vehicles is discussed. The integrated development environment and the simulation tool are also briefly described. The core chapters include an explanation of the major co-simulation methodologies and how they have been programmed and applied to the electric powertrain model together with the multibody system dynamic model. Finally, the last chapters summarize the main results and conclusions of the project and propose further research topics. In conclusion, co-simulation methodologies are applicable within the integrated development environments MATLAB and Visual Studio, and the simulation tool MBS3D 2.0, where equation-based models of multidisciplinary subsystems, consisting of mechanical and electrical components, are coupled and integrated in a very efficient way.

Computational and Experimental Modelling of Mooring Lines Dynamics for Offshore Floating Wind Turbines

El cálculo de cargas de aerogeneradores flotantes requiere herramientas de simulación en el dominio del tiempo que consideren todos los fenómenos que afectan al sistema, como la aerodinámica, la dinámica estructural, la hidrodinámica, las estrategias de control y la dinámica de las líneas de fondeo. Todos estos efectos están acoplados entre sí y se influyen mutuamente. Las herramientas integradas se utilizan para calcular las cargas extremas y de fatiga que son empleadas para dimensionar estructuralmente los diferentes componentes del aerogenerador. Por esta razón, un cálculo preciso de las cargas influye de manera importante en la optimización de los componentes y en el coste final del aerogenerador flotante. En particular, el sistema de fondeo tiene gran impacto en la dinámica global del sistema. Muchos códigos integrados para la simulación de aerogeneradores flotantes utilizan modelos simplificados que no consideran los efectos dinámicos de las líneas de fondeo. Una simulación precisa de las líneas de fondeo dentro de los modelos integrados puede resultar fundamental para obtener resultados fiables de la dinámica del sistema y de los niveles de cargas en los diferentes componentes. Sin embargo, el impacto que incluir la dinámica de los fondeos tiene en la simulación integrada y en las cargas todavía no ha sido cuantificada rigurosamente. El objetivo principal de esta investigación es el desarrollo de un modelo dinámico para la simulación de líneas de fondeo con precisión, validarlo con medidas en un tanque de ensayos e integrarlo en un código de simulación para aerogeneradores flotantes. Finalmente, esta herramienta, experimentalmente validada, es utilizada para cuantificar el impacto que un modelos dinámicos de líneas de fondeo tienen en la computación de las cargas de fatiga y extremas de aerogeneradores flotantes en comparación con un modelo cuasi-estático. Esta es una información muy útil para los futuros diseñadores a la hora de decidir qué modelo de líneas de fondeo es el adecuado, dependiendo del tipo de plataforma y de los resultados esperados. El código dinámico de líneas de fondeo desarrollado en esta investigación se basa en el método de los Elementos Finitos, utilizando en concreto un modelo ”Lumped Mass” para aumentar su eficiencia de computación. Los experimentos realizados para la validación del código se realizaron en el tanque del École Céntrale de Nantes (ECN), en Francia, y consistieron en sumergir una cadena con uno de sus extremos anclados en el fondo del tanque y excitar el extremo suspendido con movimientos armónicos de diferentes periodos. El código demostró su capacidad para predecir la tensión y los movimientos en diferentes posiciones a lo largo de la longitud de la línea con gran precisión. Los resultados indicaron la importancia de capturar la dinámica de las líneas de fondeo para la predicción de la tensión especialmente en movimientos de alta frecuencia. Finalmente, el código se utilizó en una exhaustiva evaluación del efecto que la dinámica de las líneas de fondeo tiene sobre las cargas extremas y de fatiga de diferentes conceptos de aerogeneradores flotantes. Las cargas se calcularon para tres tipologías de aerogenerador flotante (semisumergible, ”spar-buoy” y ”tension leg platform”) y se compararon con las cargas obtenidas utilizando un modelo cuasi-estático de líneas de fondeo. Se lanzaron y postprocesaron más de 20.000 casos de carga definidos por la norma IEC 61400-3 siguiendo todos los requerimientos que una entidad certificadora requeriría a un diseñador industrial de aerogeneradores flotantes. Los resultados mostraron que el impacto de la dinámica de las líneas de fondeo, tanto en las cargas de fatiga como en las extremas, se incrementa conforme se consideran elementos situados más cerca de la plataforma: las cargas en la pala y en el eje sólo son ligeramente modificadas por la dinámica de las líneas, las cargas en la base de la torre pueden cambiar significativamente dependiendo del tipo de plataforma y, finalmente, la tensión en las líneas de fondeo depende fuertemente de la dinámica de las líneas, tanto en fatiga como en extremas, en todos los conceptos de plataforma que se han evaluado. ABSTRACT The load calculation of floating offshore wind turbine requires time-domain simulation tools taking into account all the phenomena that affect the system such as aerodynamics, structural dynamics, hydrodynamics, control actions and the mooring lines dynamics. These effects present couplings and are mutually influenced. The results provided by integrated simulation tools are used to compute the fatigue and ultimate loads needed for the structural design of the different components of the wind turbine. For this reason, their accuracy has an important influence on the optimization of the components and the final cost of the floating wind turbine. In particular, the mooring system greatly affects the global dynamics of the floater. Many integrated codes for the simulation of floating wind turbines use simplified approaches that do not consider the mooring line dynamics. An accurate simulation of the mooring system within the integrated codes can be fundamental to obtain reliable results of the system dynamics and the loads. The impact of taking into account the mooring line dynamics in the integrated simulation still has not been thoroughly quantified. The main objective of this research consists on the development of an accurate dynamic model for the simulation of mooring lines, validate it against wave tank tests and then integrate it in a simulation code for floating wind turbines. This experimentally validated tool is finally used to quantify the impact that dynamic mooring models have on the computation of fatigue and ultimate loads of floating wind turbines in comparison with quasi-static tools. This information will be very useful for future designers to decide which mooring model is adequate depending on the platform type and the expected results. The dynamic mooring lines code developed in this research is based in the Finite Element Method and is oriented to the achievement of a computationally efficient code, selecting a Lumped Mass approach. The experimental tests performed for the validation of the code were carried out at the `Ecole Centrale de Nantes (ECN) wave tank in France, consisting of a chain submerged into a water basin, anchored at the bottom of the basin, where the suspension point of the chain was excited with harmonic motions of different periods. The code showed its ability to predict the tension and the motions at several positions along the length of the line with high accuracy. The results demonstrated the importance of capturing the evolution of the mooring dynamics for the prediction of the line tension, especially for the high frequency motions. Finally, the code was used for an extensive assessment of the effect of mooring dynamics on the computation of fatigue and ultimate loads for different floating wind turbines. The loads were computed for three platforms topologies (semisubmersible, spar-buoy and tension leg platform) and compared with the loads provided using a quasi-static mooring model. More than 20,000 load cases were launched and postprocessed following the IEC 61400-3 guideline and fulfilling the conditions that a certification entity would require to an offshore wind turbine designer. The results showed that the impact of mooring dynamics in both fatigue and ultimate loads increases as elements located closer to the platform are evaluated; the blade and the shaft loads are only slightly modified by the mooring dynamics in all the platform designs, the tower base loads can be significantly affected depending on the platform concept and the mooring lines tension strongly depends on the lines dynamics both in fatigue and extreme loads in all the platform concepts evaluated.

The peculiar electrical response of liquid crystal-carbon nanotube systems as seen by impedance spectroscopy

Conductive nanoparticles, especially elongated ones such as carbon nanotubes, dramatically modify the electrical behavior of liquid crystal cells. These nanoparticles are known to reorient with liquid crystals in electric fields, causing significant variations of conductivity at minute concentrations of tens or hundreds ppm. The above notwithstanding, impedance spectroscopy of doped cells in the frequency range customarily employed by liquid crystal devices, 100 Hz?10 kHz, shows a relatively simple resistor/capacitor response where the components of the cell can be univocally assigned to single components of the electrical equivalent circuit. However, widening the frequency range up to 1 MHz or beyond reveals a complex behavior that cannot be explained with the same simple EEC. Moreover, the system impedance varies with the application of electric fields, their effect remaining after removing the field. Carbon nanotubes are reoriented together with liquid crystal reorientation when applying voltage, but barely reoriented back upon liquid crystal relaxation once the voltage is removed. Results demonstrate a remarkable variation in the impedance of the dielectric blend formed by liquid crystal and carbon nanotubes, the irreversible orientation of the carbon nanotubes and possible permanent contacts between electrodes.