Abordagem de Anotações para o Suporte da Gestão Energética de Software em Modelos AMALTHEA

The automotive industry is continuously introducing innovative software features to provide more efficient, safe, and comfortable solutions. Despite the several benefits to the consumer, the evolution of automotive software is also reflected in several challenges, presenting a growing complexity that hinders its development and integration. The adoption of standards and appropriate development methods becomes essential to meet the requirements of the industry. Furthermore, the expansion of automotive software systems is also driving a considerable growth in the number of electronic components installed in a vehicle, which has a significant impact on the electric energy consumption. Thus, the focus on non-functional energy requirements has become increasingly important. This work presents a study focused on the evolution of automotive software considering the development standards, methodologies, as well as approaches for energy requirements management. We propose an automatic and self-contained approach for the support of energy properties management, adopting the model-based open-source framework AMALTHEA. From the analysis of execution or simulation traces, the energy consumption estimation is provided at a fine-grained level and annotated in AMALTHEA models. Thus, we enable the energy analysis and management of the system throughout the entire lifecycle. Additionally, this solution is in line with the AUTOSAR Adaptive standard, allowing the development of energy management strategies for automatic, dynamic, and adaptive systems.A indústria automotiva encontra-se constantemente a introduzir funcionalidades inovadoras através de software, para oferecer soluções mais eficientes, seguras e confortáveis. Apesar dos diversos benefícios para o consumidor, a evolução do software automóvel também se reflete em diversos desafios, apresentando uma crescente complexidade que dificulta o seu desenvolvimento e integração. Desta forma, a adoção de normas e metodologias adequadas para o seu desenvolvimento torna-se essencial para cumprir os requisitos do setor. Adicionalmente, esta expansão das funcionalidades suportadas por software é fonte de um aumento considerável do número de componentes eletrónicos instalados em automóveis. Consequentemente, existe um impacto significativo no consumo de energia elétrica dos sistemas automóveis, sendo cada vez mais relevante o foco nos requisitos não-funcionais deste domínio. Este trabalho apresenta um estudo focado na evolução do software automotivo tendo em conta os padrões e metodologias de desenvolvimento desta área, bem como abordagens para a gestão de requisitos de energia. Através da adoção da ferramenta AMALTHEA, uma plataforma open-source de desenvolvimento baseado em modelos, é proposta uma abordagem automática e independente para a análise de propriedades energéticas. A partir da análise de traços de execução ou de simulação, é produzida uma estimativa pormenorizada do consumo de energia, sendo esta anotada em modelos AMALTHEA. Desta forma, torna-se possível a análise e gestão energética ao longo de todo o ciclo de vida do sistema. Salienta-se que a solução se encontra alinhada com a norma AUTOSAR Adaptive, permitindo o desenvolvimento de estratégias para a gestão energética de sistemas automáticos, dinâmicos e adaptativos

Framework For The Successful Set-up Of A Common Data Model In The Context Of An Industry 4.0-ready Plant Design Process

The production plant design process consists of a multitude of individual engineering disciplines, which rely on a variety of digital models. The individual tasks build up on each other, while each discipline consumes information from the previous processes. However, sharing relevant data across multiple companies is challenging and susceptible to miscommunication and delays. Furthermore, integrating diverse software systems, tools, and technologies create compatibility issues and hinder seamless integration. As a result, a heterogeneous, non-automated data and information landscape is created, characterized by a high level of manual data transfer. This represents a major problem on the way towards Industry 4.0. The goal of this paper is to provide a framework for the successful set-up of a common data model in the context of an Industry 4.0-ready plant design process across and along the value chain. For this purpose, a literature review of current problems in the cross-company and cross-departmental collaboration in the plant design process is provided and requirements for the framework are derived. Existing solutions and research projects are compiled and evaluated against the requirements, from which the framework's structure is concluded. The framework itself is intended to be holistic and must therefore not only include technical aspects (e.g. data interfaces, semantics), but also enable the entire organization and value chain to implement the common data model as part of the digital transformation process (e.g. employee skills, business strategy, legal conditions). Based on this, the framework is further elaborated by deducing calls for action for a successful set-up of a common data model within the research project DIAMOND (Digital plant modeling with neutral data formats). The focus should be on employees and their competencies, as these are prerequisites for shaping digital transformation. Future research must prioritize these actions to enhance technology readiness and organizational Industry 4.0 preparation

Uvođenje tehnika zasnovanih na modelu u razvoj aplikacija za ugradbene sustave s vremenskim ograničenjima

This paper investigates the feasibility of integrating legacy software processes and tools into the paradigm of model-based development of industrial real-time embedded systems. Research has been conducted on the example of using legacy assembly code for automatic code generation scheme inside MATLAB/Simulink environment. A sample Simulink model has been presented, code has been generated from it and its correctness has been validated by back-to-back comparison with the simulation results.Ovaj rad ispituje mogućnost integriranja naslije.enih procesa i alata za razvoj programske podrške namijenjene industrijskim ugradbenim računalnim sustavima s nametnutim vremenskim ograničenjima u paradigmu razvoja zasnovanog na modelu. Istraživanje je provedeno na primjeru korištenja naslijeđenog asemblerskog programskog koda pri automatskoj generaciji izvršnog koda unutar MATLAB/Simulink okruženja. Prikazan je primjer Simulink modela iz kojega je generiran kod čija je ispravnost utvrđena usporedbom s rezultatima simulacije

Model-based resource analysis and synthesis of service-oriented automotive software architectures

Context Automotive software architectures describe distributed functionality by an interaction of software components. One drawback of today\u27s architectures is their strong integration into the onboard communication network based on predefined dependencies at design time. The idea is to reduce this rigid integration and technological dependencies. To this end, service-oriented architecture offers a suitable methodology since network communication is dynamically established at run-time. Aim We target to provide a methodology for analysing hardware resources and synthesising automotive service-oriented architectures based on platform-independent service models. Subsequently, we focus on transforming these models into a platform-specific architecture realisation process following AUTOSAR Adaptive. Approach For the platform-independent part, we apply the concepts of design space exploration and simulation to analyse and synthesise deployment configurations, i. e., mapping services to hardware resources at an early development stage. We refine these configurations to AUTOSAR Adaptive software architecture models representing the necessary input for a subsequent implementation process for the platform-specific part. Result We present deployment configurations that are optimal for the usage of a given set of computing resources currently under consideration for our next generation of E/E architecture. We also provide simulation results that demonstrate the ability of these configurations to meet the run time requirements. Both results helped us to decide whether a particular configuration can be implemented. As a possible software toolchain for this purpose, we finally provide a prototype. Conclusion The use of models and their analysis are proper means to get there, but the quality and speed of development must also be considered

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

Towards a Secure DevOps Approach for Cyber-Physical Systems: An Industrial Perspective

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