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

Systems modelling and simulation in the product development process for automotive powertrains : executive summary

This submission is a summary of the ten submissions that form the Engineering Doctorate Portfolio. The aim of the portfolio is to demonstrate the benefit of applying systems modelling and simulation in a modified powertrain product development process. A description is given of the competitive pressures that are faced by motor manufacturers in the global automotive business environment. Competitive pressures include a requirement for reduced time to market, exacting product quality standards, manufacturing over-capacity that increases fixed costs and compromises profit margins, and legislation that is increasingly difficult to meet. High-level strategic responses that are being made by manufacturers to these pressures are presented. Each strategic response requires organisational changes and improved approaches to the way in which day-to-day business is conducted. Computer Aided Engineering (CAE) is presented as an approach that can help to improve the competitiveness of motor manufacturers by reducing product development time and the level of hardware prototyping that is required. An investigation in five engineering companies yielded a number of observations about the use of CAE and its integration into product development. Best practice in the implementation of CAE in the product development process is defined. The use of CAE by a leading motor manufacturer in powertrain development is compared with the best practice model, and it is identified that there is a lack of coherence in the application of CAE. It is used to tackle specific problems but the use of CAE is not integrated into the product development process. More importantly, it was found that there is limited application of systems modelling and simulation, which is a critical technique for the effective integration of vehicle systems and the development of on-board vehicle control systems. Before systems modelling and simulation can be applied III powertrain development, an appropriate set of tools and associated modelling architecture must be determined. An appraisal of a range of different tools is undertaken, each tool being appraised against a set of criteria. A combination of DymolaIModelica and MATLAB/Simulink tools is recommended as the optimum solution. DymolaIModelica models of the vehicle plant should be embedded into Simulink models that also contain controller and driver models. MATLAB should be used as the numerical engine and for the creation of user environments. Transmission calibration is selected as a suitable pilot example for applying systems modelling and simulation in powertrain development. Best practice in CAE implementation and the systems modelling and simulation architecture are validated using this example. Simulation models of vehicles equipped with CVT and discrete ratio automatic transmissions are presented. A full description of the operation of the transmission system, of the simulation model itself, and of the validation of the model is presented in each case. The potential benefit of the CVT model in transmission calibration is demonstrated. A Transmission Calibration Simulation Tool (TCST) is described within which the discrete ratio simulation model is encapsulated. The TCST includes a user environment in which the simulation model can be parameterised, a variety of simulation runs can be specified, and simulation results are processed. Development of the TCST requires an objective measure of driveability effects that are influenced by the transmission shift schedule. A method for objective assessment of driveability is developed, correlated, and implemented as an integral part of the TCST. This element of the TCST allows trade-off exercises to be conducted between fuel economy and driveability. The development of a transmission calibration based on experimental testing is compared with a similar exercise based on simulation testing. This study shows that, if the TCST is properly integrated into the transmission calibration process, the vehicle test time taken to optimise the calibration for fuel economy could be reduced by six weeks, and a week of calibrator time could be saved. Thus, the aim of the submission is fulfilled, since the benefit of applying systems modelling and simulation in the powertrain development process has been demonstrated. It is concluded that a consistent approach is required for effectively integrating systems modelling and simulation into the product development process. A model is proposed that clarifies how this can be achieved at a local level. It is proposed that in the future, the model is applied whenever systems modelling and simulation is introduced into a powertrain department

CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties

The increasing processing power of today’s HW/SW platforms leads to the integration of more and more functions in a single device. Additional design challenges arise when these functions share computing resources and belong to different criticality levels. The paper presents the CONTREX European project and its preliminary results. CONTREX complements current activities in the area of predictable computing platforms and segregation mechanisms with techniques to consider the extra-functional properties, i.e., timing constraints, power, and temperature. CONTREX enables energy efficient and cost aware design through analysis and optimization of these properties with regard to application demands at different criticality levels

Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop

The process of developing control functions for embedded systems is resource-, time-, and data-intensive, often resulting in sub-optimal cost and solutions approaches. Reinforcement Learning (RL) has great potential for autonomously training agents to perform complex control tasks with minimal human intervention. Due to costly data generation and safety constraints, however, its application is mostly limited to purely simulated domains. To use RL effectively in embedded system function development, the generated agents must be able to handle real-world applications. In this context, this work focuses on accelerating the training process of RL agents by combining Transfer Learning (TL) and X-in-the-Loop (XiL) simulation. For the use case of transient exhaust gas re-circulation control for an internal combustion engine, use of a computationally cheap Model-in-the-Loop (MiL) simulation is made to select a suitable algorithm, fine-tune hyperparameters, and finally train candidate agents for the transfer. These pre-trained RL agents are then fine-tuned in a Hardware-in-the-Loop (HiL) system via TL. The transfer revealed the need for adjusting the reward parameters when advancing to real hardware. Further, the comparison between a purely HiL-trained and a transferred agent showed a reduction of training time by a factor of 5.9. The results emphasize the necessity to train RL agents with real hardware, and demonstrate that the maturity of the transferred policies affects both training time and performance, highlighting the strong synergies between TL and XiL simulation

Code Generation for Safety-Critical Systems

International audienceThe number of safety-critical systems in vehicles is rapidly increasing. A few years ago, the failure of a computersystem in a vehicle would in the worst case mean the loss of a function, but in the systems of the future, the wrongreaction to a fault may be a safety hazard for the vehicle’s occupants and other road users

CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties

The increasing processing power of today’s HW/SW platforms leads to the integration of more and more functions in a single device. Additional design challenges arise when these functions share computing resources and belong to different criticality levels. CONTREX complements current activities in the area of predictable computing platforms and segregation mechanisms with techniques to consider the extra-functional properties, i.e., timing constraints, power, and temperature. CONTREX enables energy efficient and cost aware design through analysis and optimization of these properties with regard to application demands at different criticality levels. This article presents an overview of the CONTREX European project, its main innovative technology (extension of a model based design approach, functional and extra-functional analysis with executable models and run-time management) and the final results of three industrial use-cases from different domain (avionics, automotive and telecommunication).The work leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2007-2011 under grant agreement no. 611146

Harnessing the Power of Digital Twins for Enhanced Material Behavior Prediction and Manufacturing Process Optimization in Materials Engineering

The advent of Industry 4.0 and the digital revolution have brought forth innovative technologies such as digital twins, which have the potential to redefine the landscape of materials engineering. Digital twins, virtual representations of physical entities, can model and predict material behavior, enabling enhanced design, testing, and manufacturing of materials. However, the comprehensive utilization of digital twins for predictive analysis and process optimization in materials engineering remains largely uncharted. This research intends to delve into this intriguing intersection, investigating the capabilities of digital twins in predicting material behavior and optimizing manufacturing processes, thereby contributing to the evolution of advanced materials manufacturing. Our study will commence with a detailed exploration of the concept of digital twins and their specific applications in materials engineering, emphasizing their ability to simulate intricate material behaviors and processes in a virtual environment. Subsequently, we will focus on exploiting digital twins for predicting diverse material behaviors such as mechanical properties, failure modes, and phase transformations, demonstrating how digital twins can utilize a combination of historical data, real-time monitoring, and sophisticated algorithms to predict outcomes accurately. Furthermore, we will delve into the role of digital twins in optimizing materials manufacturing processes, including casting, machining, and additive manufacturing, illustrating how digital twins can model these processes, identify potential issues, and suggest optimal parameters. We will present detailed case studies to provide practical insights into the implementation of digital twins in materials engineering, including the advantages and challenges. The final segment of our research will address the current challenges in implementing digital twins, such as data quality, model validation, and computational demands, proposing potential solutions and outlining future directions. This research aims to underline the transformative potential of digital twins in materials engineering, thereby paving the way for more efficient, sustainable, and intelligent material design and manufacturing processes