A SystemC-AMS Framework for the Design and Simulation of Energy Management in Electric Vehicles

Driving range is one of the most critical issues for electric vehicles (EVs): running out of battery charge while driving results in serious inconvenience even comparable to a vehicle breakdown, as an effect of long fuel recharging times and lack of charging facilities. This may discourage EVs for current and potential customers. As an effect, the dimensioning of the energy subsystem of an EV is a crucial issue: the choice of the energy storage components and the policies for their management should be validated at design time through simulations, so to estimate the vehicle driving range under reference driving profiles. Thus, it is necessary to build a simulation framework that considers an EV power consumption model that accounts for the characteristics of the vehicle and the driving route, plus accurate models for all power components, including batteries and renewable power sources. The goal of this paper is to achieve such an early EV simulation, through the definition of a SystemC-AMS framework, which models simultaneously the physical and mechanical evolution, together with energy flows and environmental characteristics. The proposed solution extends the state-of-the-art framework for the simulation of electrical energy systems with support for mechanical descriptions and the AC domain, by finding a good balance between accuracy and simulation speed and by formalizing the new information and energy flows. The experimental results demonstrate that the performance of the proposed approach in terms of accuracy and simulation speed w.r.t. the current state-of-the-art and its effectiveness at supporting EV design with an enhanced exploration of the alternatives

SystemC-AMS Simulation of Energy Management of Electric Vehicles

Electric vehicles (EV) are rapidly invading the market, since they are clean, quiet and energy efficient. However, there are many factors that discourage EVs for current and potential customers. Among them, driving range is one of the most critical issues: running out of battery charge while driving results in serious inconvenience even comparable to vehicle breakdown, as an effect of long fuel recharging times and lack of charging facilities. As a result, the dimensioning of the energy subsystem of an EV is a crucial activity. The choice of the power components and of the adopted policies should thus be validated at design time through simulations, that estimate the vehicle driving range under reference driving profiles. It is thus necessary to build a simulation framework that takes into account an EV power consumption model, dependent on the characteristics of the vehicle and of the driving route, plus accurate models for all power components, including batteries and green power sources. The goal of this paper is to achieve early EV simulation, so that the designer can estimate at design time the driving range of the vehicle, validate the adopted components and policies and evaluate alternative configurations

The importance of accurate battery models for power assessment in smart energy systems

The smart energy system is characterized by a broader combination of various energy sources and energy storage devices with smart control management and increased attention to optimization for increasing energy efficiency. The fundamental dimension in the smart energy system design is the power assessment of the possible design architecture. This demand imposes a need for accurately tracking the system’s power flow, simulating and validating the system’s behavior, and applying additional optimization and exploration during the design time. Thus, it is evident that simulation is a critical step in the design flow of a smart energy system. One essential element to enable such accurate simulation is the precise model of the power generation and consumption. While sophisticated models for energy sources exist, the power flow in the system does not perfectly match the power drawn from the energy storage devices because the battery, as the primary energy storage device in the smart energy system, has non-ideal discharge characteristics. We propose adopting an elaborate battery model for the smart energy system’s accurate power assessment in this work. We show the importance of battery model accuracy when conducting a power assessment using two different case studies

Cost-aware design and simulation of electrical energy systems

One fundamental dimension in the design of an electrical energy system (EES) is the economic analysis of the possible design alternatives, in order to ensure not just the maximization of the energy output but also the return on the investment and the possible profits. Since the energy output and the economic figures of merit are intertwined, for an accurate analysis it is necessary to analyze these two aspects of the problem concurrently, in order to define effective energy management policies. This paper achieves that objective by tracking and measuring the energy efficiency and the cost effectiveness in a single modular framework. The two aspects are modeled separately, through the definition of dedicated simulation layers governed by dedicated virtual buses that elaborate and manage the information and energy flows. Both layers are simulated concurrently within the same simulation infrastructure based on SystemC-AMS, so as to recreate at runtime the mutual influence of the two aspects, while allowing the use of different discrete time scales for the two layers. Thanks to the tight coupling provided by the single simulation engine, our method enables a quick estimation of various cost metrics (net costs, annualized costs, and profits) of any configuration of EES under design, via an informed exploration of the alternatives. To prove the effectiveness of this approach, we apply the proposed strategy to two EES case studies, we explored various management strategies and the presence of different types and numbers of power sources and energy storage devices in the EES. The analysis proved to allow the identification of the optimal profitable solutions, thereby improving the standard design and simulation flow of EES

Battery-aware electric truck delivery route exploration

The energy-optimal routing of Electric Vehicles (EVs) in the context of parcel delivery is more complicated than for conventional Internal Combustion Engine (ICE) vehicles, in which the total travel distance is the most critical metric. The total energy consumption of EV delivery strongly depends on the order of delivery because of transported parcel weight changing over time, which directly affects the battery efficiency. Therefore, it is not suitable to find an optimal routing solution with traditional routing algorithms such as the Traveling Salesman Problem (TSP), which uses a static quantity (e.g., distance) as a metric. In this paper, we explore appropriate metrics considering the varying transported parcel total weight and achieve a solution for the least-energy delivery problem using EVs. We implement an electric truck simulator based on the EV powertrain model and nonlinear battery model. We evaluate different metrics to assess their quality on small size instances for which the optimal solution can be computed exhaustively. A greedy algorithm using the empirically best metric (namely, distance × residual weight) provides significant reductions (up to 33%) with respect to a common-sense heaviest first package delivery route determined using a metric suggested by the battery properties. This algorithm also outperforms the state-of-the-art TSP heuristic algorithms, which consumes up to 12.46% more energy and 8.6 times more runtime. We also estimate how the proposed algorithms work well on real roads interconnecting cities located at different altitudes as a case study

Modeling and Simulation of Cyber-Physical Electrical Energy Systems with SystemC-AMS

Modern Cyber-Physical Electrical Energy Systems (CPEES) are characterized by wider adoption of sustainable energy sources and by an increased attention to optimization, with the goal of reducing pollution and wastes. This imposes a need for instruments supporting the design flow, to simulate and validate the behavior of system components and to apply additional optimization and exploration steps. Additionally, each system might be tested with a number of management policies, to evaluate their economic impact. It is thus evident that simulation is a key ingredient in the design flow of CPEES. This paper proposes a framework for CPEES modeling and simulation, that relies on the open-source standard SystemC-AMS. The paper formalizes the information and energy flow in a generic CPEES, by focusing on both AC and DC components, and by including support for mechanical and physical models that represent multiple energy sources and loads. Experimental results, applied to a complex CPEES case study, will prove the effectiveness of the proposed solution, in terms of accuracy, speed up w.r.t. the current state of the art Matlab/Simulink, and support for the design flow

Addressing the Smart Systems Design Challenge: The SMAC Platform

This article presents the concepts, the organization, and the preliminary application results of SMAC, a smart systems co-design platform. The SMAC platform, which has been developed as Integrated Project (IP) of the 7th ICT Call under the Objective 3.2 \u201cSmart components and Smart Systems integration\u201d addresses the challenges of the integration of heterogeneous and conflicting domains that emerge in the design of smart systems. SMAC includes methodologies and EDA tools enabling multi-disciplinary and multi-scale modelling and design, simulation of multidomain systems, subsystems and components at different levels of abstraction, system integration and exploration for optimization of functional and non-functional metrics. The article presents the preliminary results obtained by adopting the SMAC platform for the design of a limb tracking smart system

Knowledge Representation in Engineering 4.0

This dissertation was developed in the context of the BMBF and EU/ECSEL funded projects GENIAL! and Arrowhead Tools. In these projects the chair examines methods of specifications and cooperations in the automotive value chain from OEM-Tier1-Tier2. Goal of the projects is to improve communication and collaborative planning, especially in early development stages. Besides SysML, the use of agreed vocabularies and on- tologies for modeling requirements, overall context, variants, and many other items, is targeted. This thesis proposes a web database, where data from the collaborative requirements elicitation is combined with an ontology-based approach that uses reasoning capabilities. For this purpose, state-of-the-art ontologies have been investigated and integrated that entail domains like hardware/software, roadmapping, IoT, context, innovation and oth- ers. New ontologies have been designed like a HW / SW allocation ontology and a domain-specific "eFuse ontology" as well as some prototypes. The result is a modular ontology suite and the GENIAL! Basic Ontology that allows us to model automotive and microelectronic functions, components, properties and dependencies based on the ISO26262 standard among these elements. Furthermore, context knowledge that influences design decisions such as future trends in legislation, society, environment, etc. is included. These knowledge bases are integrated in a novel tool that allows for collabo- rative innovation planning and requirements communication along the automotive value chain. To start off the work of the project, an architecture and prototype tool was developed. Designing ontologies and knowing how to use them proved to be a non-trivial task, requiring a lot of context and background knowledge. Some of this background knowledge has been selected for presentation and was utilized either in designing models or for later immersion. Examples are basic foundations like design guidelines for ontologies, ontology categories and a continuum of expressiveness of languages and advanced content like multi-level theory, foundational ontologies and reasoning. Finally, at the end, we demonstrate the overall framework, and show the ontology with reasoning, database and APPEL/SysMD (AGILA ProPErty and Dependency Descrip- tion Language / System MarkDown) and constraints of the hardware / software knowledge base. There, by example, we explore and solve roadmap constraints that are coupled with a car model through a constraint solver.Diese Dissertation wurde im Kontext des von BMBF und EU / ECSEL gefördertem Projektes GENIAL! und Arrowhead Tools entwickelt. In diesen Projekten untersucht der Lehrstuhl Methoden zur Spezifikationen und Kooperation in der Automotive Wertschöp- fungskette, von OEM zu Tier1 und Tier2. Ziel der Arbeit ist es die Kommunikation und gemeinsame Planung, speziell in den frühen Entwicklungsphasen zu verbessern. Neben SysML ist die Benutzung von vereinbarten Vokabularen und Ontologien in der Modellierung von Requirements, des Gesamtkontextes, Varianten und vielen anderen Elementen angezielt. Ontologien sind dabei eine Möglichkeit, um das Vermeiden von Missverständnissen und Fehlplanungen zu unterstützen. Dieser Ansatz schlägt eine Web- datenbank vor, wobei Ontologien das Teilen von Wissen und das logische Schlussfolgern von implizitem Wissen und Regeln unterstützen. Diese Arbeit beschreibt Ontologien für die Domäne des Engineering 4.0, oder spezifischer, für die Domäne, die für das deutsche Projekt GENIAL! benötigt wurde. Dies betrifft Domänen, wie Hardware und Software, Roadmapping, Kontext, Innovation, IoT und andere. Neue Ontologien wurden entworfen, wie beispielsweise die Hardware-Software Allokations-Ontologie und eine domänen-spezifische "eFuse Ontologie". Das Ergebnis war eine modulare Ontologie-Bibliothek mit der GENIAL! Basic Ontology, die es erlaubt, automotive und mikroelektronische Komponenten, Funktionen, Eigenschaften und deren Abhängigkeiten basierend auf dem ISO26262 Standard zu entwerfen. Des weiteren ist Kontextwissen, welches Entwurfsentscheidungen beinflusst, inkludiert. Diese Wissensbasen sind in einem neuartigen Tool integriert, dass es ermöglicht, Roadmapwissen und Anforderungen durch die Automobil- Wertschöpfungskette hinweg auszutauschen. On tologien zu entwerfen und zu wissen, wie man diese benutzt, war dabei keine triviale Aufgabe und benötigte viel Hintergrund- und Kontextwissen. Ausgewählte Grundlagen hierfür sind Richtlinien, wie man Ontologien entwirft, Ontologiekategorien, sowie das Spektrum an Sprachen und Formen von Wissensrepresentationen. Des weiteren sind fort- geschrittene Methoden erläutert, z.B wie man mit Ontologien Schlußfolgerungen trifft. Am Schluss wird das Overall Framework demonstriert, und die Ontologie mit Reason- ing, Datenbank und APPEL/SysMD (AGILA ProPErty and Dependency Description Language / System MarkDown) und Constraints der Hardware / Software Wissensbasis gezeigt. Dabei werden exemplarisch Roadmap Constraints mit dem Automodell verbunden und durch den Constraint Solver gelöst und exploriert

Multi-Domain Fault Models Covering the Analog Side of a Smart or Cyber-Physical System

Over the last decade, the industrial world has been involved in a massive revolution guided by the adoption of digital technologies. In this context, complex systems like cyber-physical systems play a fundamental role since they were designed and realized by composing heterogeneous components. The combined simulation of the behavioral models of these components allows to reproduce the nominal behavior of the real system. Similarly, a smart system is a device that integrates heterogeneous components but in a miniaturized form factor. The development of smart or cyber-physical systems, in combination with faulty behaviors modeled for the different physical domains composing the system, enables to support advanced functional safety assessment at the system level. A methodology to create and inject multi-domain fault models in the analog side of these systems has been proposed by exploiting the physical analogy between the electrical and mechanical domains to infer a new mechanical fault taxonomy. Thus, standard electrical fault models are injected into the electrical part, while the derived mechanical fault models are injected directly into the mechanical part. The entire flow has been applied to two case studies: a direct current motor connected with a gear train, and a three-axis accelerometer

Analyzing the Impact of Roadmap and Vehicle Features on Electric Vehicles Energy Consumption

Electric Vehicles (EVs) market penetration rate is continuously increasing due to several aspects such as pollution reduction initiatives, government incentives, cost reduction, and fuel cost increase, among others. In the vehicular field, researchers frequently use simulators to validate their proposals before implementing them in real world, while reducing costs and time. In this work, we use our ns-3 network simulator enhanced version to demonstrate the influence of the map layout and the vehicle features on the EVs consumption. In particular, we analyze the estimated consumption of EVs simulating two different scenarios: (i) a segment of the E313 highway, located in the north of Antwerp, Belgium and (ii) the downtown of the city of Antwerp with real vehicle models. According to the results obtained, we demonstrate that the mass of the vehicle is a key factor for energy consumption in urban scenarios, while in contrast, the Air Drag Coefficient (C-d) and the Front Surface Area (FSA) play a critical role in highway environments. The most popular and powerful simulations tools do no present combined features for mobility, realistic map-layouts and electric vehicles consumption. As ns-3 is one of the most used open source based simulators in research, we have enhanced it with a realistic energy consumption feature for electric vehicles, while maintaining its original design and structure, as well as its coding style guides. Our approach allows researchers to perform comprehensive studies including EVs mobility, energy consumption, and communications, while adding a negligible overhead