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.

Cancelación activa del ruido utilizando el KIT TMS320C5515 EZDSP

El control, o cancelación activa de ruido, consiste en la atenuación del ruido presente en un entorno acústico mediante la emisión de una señal igual y en oposición de fase al ruido que se desea atenuar. La suma de ambas señales en el medio acústico produce una cancelación mutua, de forma que el nivel de ruido resultante es mucho menor al inicial. El funcionamiento de estos sistemas se basa en los principios de comportamiento de los fenómenos ondulatorios descubiertos por Augustin-Jean Fresnel, Christiaan Huygens y Thomas Young entre otros. Desde la década de 1930, se han desarrollado prototipos de sistemas de control activo de ruido, aunque estas primeras ideas eran irrealizables en la práctica o requerían de ajustes manuales cada poco tiempo que hacían inviable su uso. En la década de 1970, el investigador estadounidense Bernard Widrow desarrolla la teoría de procesado adaptativo de señales y el algoritmo de mínimos cuadrados LMS. De este modo, es posible implementar filtros digitales cuya respuesta se adapte de forma dinámica a las condiciones variables del entorno. Con la aparición de los procesadores digitales de señal en la década de 1980 y su evolución posterior, se abre la puerta para el desarrollo de sistemas de cancelación activa de ruido basados en procesado de señal digital adaptativo. Hoy en día, existen sistemas de control activo de ruido implementados en automóviles, aviones, auriculares o racks de equipamiento profesional. El control activo de ruido se basa en el algoritmo fxlms, una versión modificada del algoritmo LMS de filtrado adaptativo que permite compensar la respuesta acústica del entorno. De este modo, se puede filtrar una señal de referencia de ruido de forma dinámica para emitir la señal adecuada que produzca la cancelación. Como el espacio de cancelación acústica está limitado a unas dimensiones de la décima parte de la longitud de onda, sólo es viable la reducción de ruido en baja frecuencia. Generalmente se acepta que el límite está en torno a 500 Hz. En frecuencias medias y altas deben emplearse métodos pasivos de acondicionamiento y aislamiento, que ofrecen muy buenos resultados. Este proyecto tiene como objetivo el desarrollo de un sistema de cancelación activa de ruidos de carácter periódico, empleando para ello electrónica de consumo y un kit de desarrollo DSP basado en un procesador de muy bajo coste. Se han desarrollado una serie de módulos de código para el DSP escritos en lenguaje C, que realizan el procesado de señal adecuado a la referencia de ruido. Esta señal procesada, una vez emitida, produce la cancelación acústica. Empleando el código implementado, se han realizado pruebas que generan la señal de ruido que se desea eliminar dentro del propio DSP. Esta señal se emite mediante un altavoz que simula la fuente de ruido a cancelar, y mediante otro altavoz se emite una versión filtrada de la misma empleando el algoritmo fxlms. Se han realizado pruebas con distintas versiones del algoritmo, y se han obtenido atenuaciones de entre 20 y 35 dB medidas en márgenes de frecuencia estrechos alrededor de la frecuencia del generador, y de entre 8 y 15 dB medidas en banda ancha. ABSTRACT. Active noise control consists on attenuating the noise in an acoustic environment by emitting a signal equal but phase opposed to the undesired noise. The sum of both signals results in mutual cancellation, so that the residual noise is much lower than the original. The operation of these systems is based on the behavior principles of wave phenomena discovered by Augustin-Jean Fresnel, Christiaan Huygens and Thomas Young. Since the 1930’s, active noise control system prototypes have been developed, though these first ideas were practically unrealizable or required manual adjustments very often, therefore they were unusable. In the 1970’s, American researcher Bernard Widrow develops the adaptive signal processing theory and the Least Mean Squares algorithm (LMS). Thereby, implementing digital filters whose response adapts dynamically to the variable environment conditions, becomes possible. With the emergence of digital signal processors in the 1980’s and their later evolution, active noise cancellation systems based on adaptive signal processing are attained. Nowadays active noise control systems have been successfully implemented on automobiles, planes, headphones or racks for professional equipment. Active noise control is based on the fxlms algorithm, which is actually a modified version of the LMS adaptive filtering algorithm that allows compensation for the acoustic response of the environment. Therefore it is possible to dynamically filter a noise reference signal to obtain the appropriate cancelling signal. As the noise cancellation space is limited to approximately one tenth of the wavelength, noise attenuation is only viable for low frequencies. It is commonly accepted the limit of 500 Hz. For mid and high frequencies, conditioning and isolating passive techniques must be used, as they produce very good results. The objective of this project is to develop a noise cancellation system for periodic noise, by using consumer electronics and a DSP development kit based on a very-low-cost processor. Several C coded modules have been developed for the DSP, implementing the appropriate signal processing to the noise reference. This processed signal, once emitted, results in noise cancellation. The developed code has been tested by generating the undesired noise signal in the DSP. This signal is emitted through a speaker simulating the noise source to be removed, and another speaker emits an fxlms filtered version of the same signal. Several versions of the algorithm have been tested, obtaining attenuation levels around 20 – 35 dB measured in a tight bandwidth around the generator frequency, or around 8 – 15 dB measured in broadband.

The Impact of Transit Oriented Development on the Travel Behaviors of Workers in Denver, Colorado

To combat unsustainable transportation systems characterized by reliance on petroleum, polluting emissions, traffic congestion and suburban sprawl, planners encourage mixed use, densely populated areas that provide individuals with opportunities to live, work, eat and shop without necessarily having to drive private automobiles to accommodate their needs. Despite these attempts, the frequency and duration of automobile trips has consistently increased in the United States throughout past decades. While many studies have focused on how residential proximity to transit influences travel behavior, the effect of workplace location has largely been ignored. This paper asks, does working near a TOD influence the travel behaviors of workers differently than workers living near a TOD? We examine the non-work travel behaviors of workers based upon their commuting mode and proximity to TODs. The data came from a 2009 travel behavior survey by the Denver Regional Council of Governments, which contains 8,000 households, 16,000 individuals, and nearly 80,000 trips. We measure sustainable travel behaviors as reduced mileage, reduced number of trips, and increased use of non-automobile transportation. The results of this study indicate that closer proximity of both households and workplaces to TODs decrease levels of car commuting and that non-car commuting leads to more sustainable personal travel behaviors characterized by more trips made with alternative modes.

The EU regime on biofuels in transport: Still in search of sustainability. Egmont Paper No. 68, July 2014

Like other regions of the world, the EU is developing biofuels in the transport sector to reduce oil consumption and mitigate climate change. To promote them, it has adopted favourable legislation since the 2000s. In 2009 it even decided to oblige each Member State to ensure that by 2020 the share of energy coming from renewable sources reached at least 10% of their final consumption of energy in the transport sector. Biofuels are considered the main instrument to reach that percentage since the development of other alternatives (such as hydrogen and electricity) will take much longer than expected. Meanwhile, these various legislative initiatives have driven the production and consumption of biofuels in the EU. Biofuels accounted for 4.7% of EU transport fuel consumption in 2011. They have also led to trade and investment in biofuels on a global scale. This large-scale expansion of biofuels has, however, revealed numerous negative impacts. These stem from the fact that first-generation biofuels (i.e., those produced from food crops), of which the most important types are biodiesel and bioethanol, are used almost exclusively to meet the EU’s renewable 10% target in transport. Their negative impacts are: socioeconomic (food price rises), legal (land-grabbing), environmental (for instance, water stress and water pollution; soil erosion; reduction of biodiversity), climatic (direct and indirect land-use effects resulting in more greenhouse gas emissions) and public finance issues (subsidies and tax relief). The extent of such negative impacts depends on how biofuel feedstocks are produced and processed, the scale of production, and in particular, how they influence direct land use change (DLUC) and indirect land use change (ILUC) and the international trade. These negative impacts have thus provoked mounting debates in recent years, with a particular focus on ILUC. They have forced the EU to re-examine how it deals with biofuels and submit amendments to update its legislation. So far, the EU legislation foresees that only sustainable biofuels (produced in the EU or imported) can be used to meet the 10% target and receive public support; and to that end, mandatory sustainability criteria have been defined. Yet they have a huge flaw. Their measurement of greenhouse gas savings from biofuels does not take into account greenhouse gas emissions resulting from ILUC, which represent a major problem. The Energy Council of June 2014 agreed to set a limit on the extent to which firstgeneration biofuels can count towards the 10% target. But this limit appears to be less stringent than the ones made previously by the European Commission and the European Parliament. It also agreed to introduce incentives for the use of advanced (second- and third-generation) biofuels which would be allowed to count double towards the 10% target. But this again appears extremely modest by comparison with what was previously proposed. Finally, the approach chosen to take into account the greenhouse gas emissions due to ILUC appears more than cautious. The Energy Council agreed that the European Commission will carry out a reporting of ILUC emissions by using provisional estimated factors. A review clause will permit the later adjustment of these ILUC factors. With such legislative orientations made by the Energy Council, one cannot consider yet that there is a major shift in the EU biofuels policy. Bolder changes would have probably meant risking the collapse of the high-emission conventional biodiesel industry which currently makes up the majority of Europe’s biofuel production. The interests of EU farmers would have also been affected. There is nevertheless a tension between these legislative orientations and the new Commission’s proposals beyond 2020. In any case, many uncertainties remain on this issue. As long as solutions have not been found to minimize the important collateral damages provoked by the first generation biofuels, more scientific studies and caution are needed. Meanwhile, it would be wise to improve alternative paths towards a sustainable transport sector, i.e., stringent emission and energy standards for all vehicles, better public transport systems, automobiles that run on renewable energy other than biofuels, or other alternatives beyond the present imagination.

TTIP’s Hard Core: Technical barriers to trade and standards. Paper No. 13 in the CEPS-CTR project ‘TTIP in the Balance’ and CEPS Special Report No. 117/August 2015

Michelle Egan and Jacques Pelkmans provide an overview of the TBT chapter in TTIP and the various issues between the US and the EU in this area, which in turn requires extensive expositions of domestic regulation in the US and the EU. TBTs, outside heavily regulated sectors such as chemicals, automobiles or medicines (which have separate chapters in TTIP), can be caused by divergent (voluntary) standards, technical regulations and conformity assessment. Indeed, in all three the US and the EU have long experienced frictions with considerable trading costs. The 1998 Mutual Recognition Agreement about conformity assessment only succeeded in two out of six sectors. The US and European standardisation traditions differ and this paper explains why it is so hard, also economically, to realise convergence. However, the authors reject the unproductive ‘stand-off’ between US and EU negotiators on standardisation and suggest to clarify the enormous economic ‘installed base’ of prominent US standards in the world economy and build a solution from there. As to technical regulation, the prospect of converging regulation (via harmonisation) is often dim, but equivalence (given similar levels of regulatory protection) can be an option.

Quantifying Non-Tariff Measures for TTIP. Paper No. 12 in the CEPS-CTR project ‘TTIP in the Balance’ and CEPS Special Report No. 116/July 2015

This paper provides an overview of methods employed to quantify non-tariff measures (NTMs) and then analyses their differences and looks at what these mean for the Transatlantic Trade and Investment Partnership (TTIP) negotiations. The authors find several similarities in the approaches taken. Because all studies conclude that NTMs matter, they argue that policy-makers are right to focus on ‘regulatory cooperation’ in TTIP. Given the significant differences in NTMs across sectors, policy-makers are urged to dive deep into sector-specific elements of NTMs and focus on those sectors where the largest potential gains can be made (i.e. where NTMs are highest, such as in agriculture, automobiles, steel, textiles and insurance services). An area identified for further research is the fact that unlike trade taxes (i.e. tariffs), regulatory barriers to trade are not generally targeted as the primary policy objective, but rather stem from other strategic policy concerns such as consumer safety and/or social and environmental protection. This element should be further investigated.

Swing Out

Daybook, image #28

Burton Tower from Rackham lobby

Albert Kahn, architect. Built 1936

Scene in front of Bus. Ad. Bldg. [Business Administration Building]

Lee Black & Kenneth Black, architects.

Chemistry [Building]

Architect: Smith, Hinchman & Grylls. Built 1909. Likely photographed from Burton Tower

Couzens Hall

Also known as Nurses Residence. Built 1925. Albert Kahn, architect. Addition 1954-1956. R.A. Calder, architect. On verso: Women's residence