Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Dependable Digitally-Assisted Mixed-Signal IPs Based on Integrated Self-Test & Self-Calibration

Heterogeneous SoC devices, including sensors, analogue and mixed-signal front-end circuits and the availability of massive digital processing capability, are being increasingly used in safety-critical applications like in the automotive, medical, and the security arena. Already a significant amount of attention has been paid in literature with respect to the dependability of the digital parts in heterogeneous SoCs. This is in contrast to especially the sensors and front-end mixed-signal electronics; these are however particular sensitive to external influences over time and hence determining their dependability. This paper provides an integrated SoC/IP approach to enhance the dependability. It will give an example of a digitally-assisted mixed-signal front-end IP which is being evaluated under its mission profile of an automotive tyre pressure monitoring system. It will be shown how internal monitoring and digitally-controlled adaptation by using embedded processors can help in terms of improving the dependability of this mixed-signal part under harsh conditions for a long time

Design of software-oriented technician for vehicle’s fault system prediction using AdaBoost and random forest classifiers

Detecting and isolating faults on heavy duty vehicles is very important because it helps maintain high vehicle performance, low emissions, fuel economy, high vehicle safety and ensures repair and service efficiency. These factors are important because they help reduce the overall life cycle cost of a vehicle. The aim of this paper is to deliver a Web application model which aids the professional technician or vehicle user with basic automobile knowledge to access the working condition of the vehicles and detect the fault subsystem in the vehicles. The scope of this system is to visualize the data acquired from vehicle, diagnosis the fault component using trained fault model obtained from improvised Machine Learning (ML) classifiers and generate a report. The visualization page is built with plotly python package and prepared with selected parameter from On-board Diagnosis (OBD) tool data. The Histogram data is pre-processed with techniques such as null value Imputation techniques, Standardization and Balancing methods in order to increase the quality of training and it is trained with Classifiers. Finally, Classifier is tested and the Performance Metrics such as Accuracy, Precision, Re-call and F1 measure which are calculated from the Confusion Matrix. The proposed methodology for fault model prediction uses supervised algorithms such as Random Forest (RF), Ensemble Algorithm like AdaBoost Algorithm which offer reasonable Accuracy and Recall. The Python package joblib is used to save the model weights and reduce the computational time. Google Colabs is used as the python environment as it offers versatile features and PyCharm is utilised for the development of Web application. Hence, the Web application, outcome of this proposed work can, not only serve as the perfect companion to minimize the cost of time and money involved in unnecessary checks done for fault system detection but also aids to quickly detect and isolate the faulty system to avoid the propagation of errors that can lead to more dangerous cases

Multi-Dimensional Model Based Engineering for Performance Critical Computer Systems Using the AADL

International audienceThe Architecture Analysis & Design Language, (AADL), Society of Automotive Engineers (SAE), AS5506, was developed to support quantitative analysis of the runtime architecture of the embedded software system in computer systems with multiple critical operational properties, such as responsiveness, safety-criticality, security, and reliability by allowing a model of the system to be annotated with information relevant to each of these quality concerns and AADL to be extended with analysis-specific properties. It supports modelling of the embedded software runtime architecture, the computer system hardware, and the interface to the physical environment of embedded computer systems and system of systems. It was designed to support a full Model Based Engineering lifecycle including system specification, analysis, system tuning, integration, and upgrade by supporting modelling and analysis at multiple levels of fidelity. A system can be automatically integrated from AADL models when fully specified and when source code is provided for the software components

The SAE Architecture Analysis & Design Language (AADL) A Standard for Engineering Performance Critical Systems

International audienceThe Society of Automotive Engineers (SAE) Architecture Analysis & Design Language, AS5506, provides a means for the formal specification of the hardware and software architecture of embedded computer systems and system of systems. It was designed to support a full Model Based Development lifecycle including system specification, analysis, system tuning, integration, and upgrade over the lifecycle. It was designed to support the integration of multiple forms of analyses and to be extensible in a standard way for additional analysis approaches. A system can be automatically integrated from AADL models when fully specified and when source code is provided for the software components. Analysis of large complex systems has been demonstrated in the avionics domain

GRU-based denoising autoencoder for detection and clustering of unknown single and concurrent faults during system integration testing of automotive software systems

Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques

A machine learning-based approach to optimize repair and increase yield of embedded flash memories in automotive systems-on-chip

Nowadays, Embedded Flash Memory cores occupy a significant portion of Automotive Systems-on-Chip area, therefore strongly contributing to the final yield of the devices. Redundancy strategies play a key role in this context; in case of memory failures, a set of spare word- and bit-lines are allocated by a replacement algorithm that complements the memory testing procedure. In this work, we show that replacement algorithms, which are heavily constrained in terms of execution time, may be slightly inaccurate and lead to classify a repairable memory core as unrepairable. We denote this situation as Flash memory false fail. The proposed approach aims at identifying false fails by using a Machine Learning approach that exploits a feature extraction strategy based on shape recognition. Experimental results carried out on the manufacturing data show a high capability of predicting false fails

A novel framework for vehicle functions identification by exploiting machine learning techniques

openNowadays vehicles architectures exploit various automotive network protocols that bring information between the implemented Electronic Central Units (ECUs). Exchanged data are encoded and only Original Equipment Manufacturers (OEMs) and T1 (Tier One) producers know their meaning and how decode them. A software model will be developed in order to detect vehicles functions without having database files associated to network signals. Furthermore, the model will behave like an ECU by producing output signals related to input ones. Machine Learning techniques will be exploited, in particular Clustering task will be exploited to understand not a priori known vehicle functions and a Neural Network will be implemented to emulate an ECU behavior. Signals will be grouped in five different types of vehicle functions and the model will predict the ECU’s output data with high accuracy. Applications concerning the developed project are, in primis, to fix up possible vehicles electronics faults. In addiction, vehicle predictive maintenance could be done. Another application, could be to check by OEMs if T1 manufacturers comply the required specification.Nowadays vehicles architectures exploit various automotive network protocols that bring information between the implemented Electronic Central Units (ECUs). Exchanged data are encoded and only Original Equipment Manufacturers (OEMs) and T1 (Tier One) producers know their meaning and how decode them. A software model will be developed in order to detect vehicles functions without having database files associated to network signals. Furthermore, the model will behave like an ECU by producing output signals related to input ones. Machine Learning techniques will be exploited, in particular Clustering task will be exploited to understand not a priori known vehicle functions and a Neural Network will be implemented to emulate an ECU behavior. Signals will be grouped in five different types of vehicle functions and the model will predict the ECU’s output data with high accuracy. Applications concerning the developed project are, in primis, to fix up possible vehicles electronics faults. In addiction, vehicle predictive maintenance could be done. Another application, could be to check by OEMs if T1 manufacturers comply the required specification