Automotive Control Research Articles

Analysis of Reviews on Factors Affecting Thermal Sensitivity of Automotive Controllers

Analysis of Reviews on Factors Affecting Thermal Sensitivity of Automotive Controllers

Application and Implementation of Artificial Intelligence Technology for Intelligent Vehicle

With the development of artificial intelligence (AI) and the advancement of industrial technology, electric vehicles have become the main tools for people’s travel mobility. In order to cope with the faster rhythms of life and to meet people’s aspirations to live a better life, the integration of AI technology and automobiles is becoming closer and more popular. In this study, the transplantation of TensorFlow and Keras architecture on the embedded platform is implemented. An intelligent tracking car is designed and completed. It takes one i.MX RT1064 as the core processor, two electromagnetic rods as the tracking sensors, a steering gear and a motor as the motion control system, and has the application of neural network algorithm in the training and operation stages. The application of convolutional neural networks to the electromagnetic tracking of intelligent car is realized. Through the research of this smart car model, it helps to deeply understand the key technologies and automotive automation control systems of smart car manufacturing, and lay a solid foundation for designing practical smart cars in the future

Automotive engine idle speed controller: Nonlinear model predictive control utilizing the firefly algorithm

This paper developed an automotive engine idle speed controller using nonlinear model predictive control and the Firefly Algorithm for Idle Speed Engine (NMPC-FA-ISE). The designed NMPC-FA is implemented on a field-programmable gate array (FPGA). The Vivado HLS tool performs the complete design flow for FPGA platforms. It was adopted and used where different optimization techniques are applied to achieve the best performance. Moreover, the Firefly swarm optimization algorithm (FA) was employed to handle the nonlinearity of NMPC instead of traditional techniques. An Engine Idle Speed Controller (ISC) is used to demonstrate the suggested NMPC and FA implementation solution on Python Productivity for the ZYNQ platform (PYNQ). The experimental results of the proposed approach proved that the FPGA implementation of the proposed NMPC-FA achieved satisfactory control performance for the engine idle speed control with a fast response time and acceptable power consumption according to the area occupied on the board.

Exhaust Gases as Fluxing Agents for Palladium: Formation Enthalpies and Vapor Pressures of PdO, PdO2, Pd(CO)n (n = 1–3), Pd(NO)n (n = 1,2), Pd(OH)n (n = 1,2), HOPdNO, and 21 Other Species under Automotive Catalyst Aging Conditions

Catalyst deactivation by precious metal sintering is a major driver of cost in automotive emission control catalysts. In order to develop strategies to mitigate sintering and maintain higher activity from lower precious metal loading, it is necessary to first develop a more complete understanding of the sintering process. In particular, it is of interest to identify the mobile intermediates that are responsible for sintering via Ostwald ripening and the extent to which the identities and concentrations of these intermediates depend upon the composition of the contacting atmosphere during catalyst use. In this work, first-principles thermodynamic calculations have been conducted on a series of small palladium-containing molecules to assess the potential of these molecules to form over palladium catalysts in the presence of automotive exhaust and serve as mobile carriers during Ostwald ripening. Among the results obtained, three stand out: (1) while exposure to reducing atmospheres in general does not enhance palladium mobility, exposure to carbon monoxide in particular leads to a multiple order of magnitude increase in palladium mobility driven primarily by the formation of Pd(CO)2; (2) under hydrothermal aging conditions, the most abundant vapor species is neither Pd nor PdOx but PdOH; and (3) combination of the latter with traces of nitric oxide enables a significant increase in palladium mobility via the formation of HOPdNO. The impact of surface adsorption on stabilization of mobile intermediates is also considered; a simple non-specific adsorption model indicates that surface stabilization enhances Pd(OH)2 formation under hydrothermal aging conditions.

Adaptive Regulation of Block-Oriented Nonlinear Systems Using Binary Sensors With Applications to Automotive Engine Control

In this article, adaptive regulation of block-oriented nonlinear systems, i.e., Hammerstein and Wiener systems, with binary-valued measurements of the regulation errors is considered. Compared with the classical framework for stochastic adaptive control, the new feature here is that only binary-valued observations of regulation errors are available to the controller. An adaptive regulator based on the stochastic approximation algorithm is proposed and it is proved that the regulator is optimal in the sense that it minimizes the long-run average of the squared regulation errors almost surely. Numerical examples as well as real applications of the proposed algorithms to automotive engine control are given.

Signal Abstraction for Root Cause Identification of Control Systems Malfunctions in Connected Vehicles

Today’s automotive control systems have gained huge advantage from using integrated software and hardware to reliably manage the performance of vehicles. The integration of largescale software with many hardware components, however, have increased the complexity of diagnosis and root cause analysis for a detected malfunction. High level of expertise and detailed knowledge of the underlying software and hardware are typically required to analyze a large list of variables and precisely identify the root cause of the malfunction. In this paper, an abstraction method is presented to identify the most important signals for a root cause analysis by leveraging data collected from a connected fleet of field vehicles. A novel label propagation methodology is proposed to select the most relevant signals for the root cause analysis by detecting linear and nonlinear correlations between an observed malfunction and candidate test signals of the control system. The proposed label propagation method eliminates the requirement for a priori known correlation kernel that is needed for a regression analysis. The signal abstraction method is applied and successfully tested for abstracting signals in the fuel control system, with high degree of interconnection between software and hardware, using data from more than 5000 connected vehicles.

A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits

Traditional mechanical parts have been increasingly replaced by in-vehicle electronic control units (ECUs) that communicate via control area networks (CAN). For security reasons, the Database CAN (DBC) file, which defines the contents of CAN messages, is deemed confidential by original equipment manufacturers (OEMs). However, confidentiality has severely hindered research on automotive intrusion detection systems (IDS) and automotive control network testing, which makes automotive aftermarket device development difficult. Previous research has used tokenization algorithms, machine learning algorithms, and diagnostic information to obtain coarse CAN message contents. However, there is a large gap between the results obtained with these methods and the information contained in DBC files. In order to reverse CAN messages in a fine-grained manner, we propose a method to reverse a body-related CAN message based on tagged bits. This method tags data bits by collecting CAN traffic in different vehicle states. The test messages are obtained by fuzzing the CAN messages based on the tagging results, and the candidate messages are obtained by combining them with the results of a packet analysis. The final reverse result was based on the column AND bit-by-bit of the candidate messages. The reverse results showed that the method proposed in this paper could accurately locate the bits representing or controlling the body behavior with high reverse accuracy.

TCE-IDS: Time Interval Conditional Entropy- Based Intrusion Detection System for Automotive Controller Area Networks

Intelligent connected vehicle is rapidly growing with the 5-G technology; the diversity of functional interfaces has significantly expanded the avenues of attack, making automotive controller area network (CAN) more vulnerable to cyberthreats. Automotive CAN network attacks are a direct threat to traffic safety, and in this study, we explore the use of intrusion detection techniques for mitigating cyberattacks. However, most automotive CAN network intrusion detection technologies are not capable of defending against sophisticated attacks, making it extremely challenging for detecting intrusions in practice. In this article, we propose a novel time interval conditional entropy method for detecting intrusions in automotive CAN networks. The time interval conditional entropy intrusion detection method is not susceptible to interference and is capable of detecting a variety of attacks. In our experiments, the conditional entropy values of regular communication messages are collected and utilized to distinguish and detect the attacks. The time interval conditional entropy detection method is implemented and evaluated in our controller area net-work bus (CAN-BUS) network platform. The experiments show that our method has higher detection accuracy and is easier to deploy compared to existing automotive CAN network intrusion detection methods.

CAD Support for Security and Robustness Analysis of Safety-critical Automotive Software

Modern vehicles contain a multitude of electronic control units that implement software features controlling most of the operational, entertainment, connectivity, and safety aspects of the vehicle. However, with security requirements often being an afterthought in automotive software development, incorporation of such software features with intra- and inter-vehicular connectivity requirements often opens up new attack surfaces. Demonstrations of such security vulnerabilities in past reports and literature bring in the necessity to formally analyze how secure automotive control systems really are against adversarial attacks. Modern vehicles often incorporate onboard monitoring systems that test the sanctity of data samples communicated among controllers and detect possible attack/noise insertion scenarios. The performance of such monitors against security threats also needs to be verified. In this work, we outline a rigorous methodology for estimating the vulnerability of automotive CPSs. We provide a computer-aided design framework that considers the model-based representation of safety-critical automotive controllers and monitoring systems working in a closed loop with vehicle dynamics and verifies their safety and robustness w.r.t. false data injection attacks. Symbolically exploring all possible combinations of attack points of the input automotive CPS, the proposed framework tries to find out which sensor and/or actuation signal is vulnerable by generating stealthy and successful attacks using a formal method-based counter-example guided abstract refinement process. We also validate the efficacy of the proposed framework using a case study performed in an industry-scale simulator.

Economic Proof-Testing Intervals for E/E/Pe Safety-Related System with Harmful Risk

E/E/PE safety-related systems play an important role in ensuring the functional safety especially for the safety-critical systems. In fact, the E/E/PE safety-related systems are widely implemented in several types of systems, such as automotive and chemical plant control systems, for the safe operation of them. In the operation of the systems, proof-testing, which is known as scheduled periodic inspections or maintenance activities, is conducted for maintaining certain level of safety integrity required to the E/E/PE safety-related system. However, the proof-testing needs a lot of time and cost for checking and proofing whether the safety-related system satisfies the safety requirement in operation. We discuss economic strategies for supporting design of proof-testing interval by focusing on the trade-off relationship between the maintenance cost and the risk at harmful event occurrence.

Model predictive control allocation based on adaptive sliding mode control strategy for enhancing the lateral stability of four-wheel-drive electric vehicles

A novel hierarchical direct yaw moment controller is designed to enhance the lateral stability of the four-wheel-drive electric vehicle. The adaptive sliding mode control (ASMC) technique in the upper-layer controller is employed to compute an additional yaw moment. The lower-layer controller distributes this yaw moment into each independent wheel by utilizing model predictive control allocation (MPCA). The proposed MPCA aims to mitigate the performance deterioration induced by in-wheel motor dynamics and optimize the power consumption stemming from the additional yaw moment. Co-simulation and hardware-in-the-loop (HIL) test is conducted to verify the performance of the proposed controller. Validation results show that the proposed hierarchical ASMC-MPCA controller outperforms the sliding mode control MPCA (SMC-MPCA) and the integrated nonlinear model predictive control (NMPC) with the lowest root-mean-square errors [Formula: see text] of yaw rate, sideslip angle, lateral deviation, and lowest power consumption. Additionally, the chattering phenomenon in SMC-MPCA can be suppressed effectively by adaptively estimating the parameter uncertainties. The proposed ASMC-MPCA controller also consumes less computational resources than the NMPC and SMC-MPCA, which indicates that the ASMC-MPCA is more suitable for an automotive onboard controller. The comparison between hierarchical and integrated controller frameworks also shows that the hierarchical framework is more suitable for production vehicles under non-powerful vehicle control units.

Data-Driven Feedforward Compensation Tuning in Torque Vectoring Control

In automotive control, torque vectoring enhances a vehicle dynamical characteristic by independently allocating wheel torques. Torque vectoring algorithms are often based on some form of closed-loop yaw rate tracking, whose reference is generated according to the driver's steering input. Thus, unless the vehicle is equipped with steer-by-wire, from the torque vectoring point of view, the driver's input simultaneously acts as a reference and as a disturbance. This paper presents a method to explicitly consider this aspect in the torque vectoring design by means of a feedforward compensation. At first, we identify the plant model highlighting the effects of the driver input steer on the system. Then, we design the compensation term with H∞ techniques based on the identified model. This solution is then refined using a Data-Driven approach based on a Bayesian optimization algorithm. Results show that the compensation term can reduce the tracking error by 40% and the control effort by 35%.

Experimental investigation of active suspension force control of electric motor driven actuator with estimation and tracking control strategy

Owing to the fastest developments of automotive electronics and control, a considerable focus is being provided on application of such techniques in automotive suspension applications. This paper presents an active force control of suspension system with Electrical actuator. To supress the variation of the sprung mass, the Mamdani fuzzy logic is used. Moreover, the unknown disturbances are compensated by active force control loop. The fuzzy logic based disturbances compensation was performed by active force control loop (AFC). The effectiveness of Fuzzy controller and AFC loop controlled electrical actuator active suspension system was validated through numerical simulation and Rapid control prototyping experiment testing under the ISO-8608 suggested road surface. The quarter car model-electric actuator active control scheme was implemented to suppress the vibration of suspension system. The outcomes of the assessments exhibit that the recommended suspension can easily adopt its control scheme to attain a better enhancement than that obtained by conventional suspensions.

Evaluation of the technical condition of locomotives using modern methods and tools

Considering its real technical condition when planning the volume of repair of locomotive equipment is one of the most important bases for reducing the costs of using railways and the cost of transportation. Continuous monitoring of the technical condition of the locomotive in use is carried out using stationary and on-board technical diagnostic tools. The method of small deviations is now widely used in various fields of science. It is a method designed to determine the existence of a correlation between the changes in parameters describing a process or a physical phenomenon. The method is based on the fact that some of the parameters of the process, in their not so large deviations from their initial values, the relationship between these deviations can be adequately expressed using certain differential calculus ratios. The wide-band exhaust gases oxygen content sensor can continuously monitor the air excess coefficient in the locomotive diesel engine cylinders. This type of sensor is widely used in automotive diesel engine control systems. It means the indirect estimation of the engine cylinders mixture quality by the exhaust gases' oxygen content

Integration of optimal engine and driveline controllers to minimize driveline clunk and shuffle

Automotive control algorithms have been moving from traditional, rule-based control algorithms to optimal, model-based control algorithms. While the model-based algorithms have shown to provide better control performance over a wide range of use cases with reduced calibration efforts, it is challenging to integrate multiple model-based controllers that are developed independently to address different control objectives. In this work, a nonlinear model predictive engine controller designed for torque tracking is integrated with a reference governor-based driveline controller designed to reduce vehicle drivability problems known as clunk and shuffle. The design of both controllers is discussed and their individual performance is demonstrated for real-world test conditions and realistic driving scenarios. Then, the integration between the two optimal controllers is discussed and a proof of concept use case showing the coordinated control between the two optimal controllers is presented. The results illustrate that without coordination between the two controllers neither of them are able to meet their original control objectives.

Secure Time-Sensitive Software-Defined Networking in Vehicles

Current designs of future In-Vehicle Networks (IVN) prepare for switched Ethernet backbones, which can host advanced LAN technologies such as IEEE Time-Sensitive Networking (TSN) and Software-Defined Networking (SDN). In this paper, we present an integrated Time-Sensitive Software-Defined Networking (TSSDN) architecture that simultaneously enables control of synchronous and asynchronous real-time and best-effort communication for all IVN traffic classes. Despite the central SDN controller, we can validate that control can operate without a delay penalty for TSN traffic, provided protocols are properly mapped. We demonstrate how TSSDN adaptably and reliably enhances network security for in-vehicle communication. A systematic investigation of the possible control flow integrations with switched Ether-networks reveals that these strategies allow for shaping the attack surface of a software-defined IVN. We discuss embeddings of control flow identifiers on different layers, covering the range from a fully exposed mapping to deep encapsulation. We experimentally evaluate these strategies in a production vehicle, which we map to a modern Ethernet topology. Our findings indicate that visibility of automotive control flows on lower network layers enables isolation and access control throughout the network infrastructure. Such a TSSDN backbone can establish and survey trust zones within the IVN and reduce the attack surface of connected cars in various attack scenarios.

Effectiveness of Think Aloud Pair Problem Solving and Case Study based Active Learning Techniques for Engineering Classroom

Engineering and Technology is a very dynamic sector and change is very rapid. The knowledge gained in traditional classrooms, through online lectures, through search engines, online video books, and Wikipedia is just helping to understand the theoretical concepts. These information sources cannot develop abilities to apply the theoretical concept to real-world problems. This can be developed through experiential learning in workshops, industrial visits, field visits, etc. The advanced courses are not having this scope due to limited lab capacities, high instrument costs, a variety of instruments, quick revisions in technology, less number of industries, IP securities, and many more reasons. The Automobile sector is a vibrant one and the Automobile Engineering department is always facing these issues. This study presents the teaching-learning experience of the course Automotive Control Engineering which faces the above issues. The study was conducted for T.Y.B.Tech Automobile Engineering students for the years 2018-19. The aim of the study was to increase the concept implementation ability of the students. The theoretical knowledge that was being received through books, lectures, and search engines were used to correlate the working of advanced technology. Think Aloud pair problem-solving technique (TAPPS) and case study presentation of the real-world system tools have been used for the course delivery. The course attainment level of the experimental group increased by 11%. The students have shown enhancement in competencies like problem-solving, application of theoretical concepts, critical thinking, lifelong learning, etc. Learning from peers and in groups also improved classroom engagement and the joy of learning. Keywords— Active learning; Control Engineering; Lifelong learning; OBE; Problem based learning; Think Aloud Problem Solving Technique

Rise of the Automotive Health-Domain Controllers: Empowering Healthcare Services in Intelligent Vehicles

We are facing a global healthcare crisis today as the healthcare cost is ever climbing, but with the aging population, government fiscal revenue is ever dropping. To address this imminent problem, we can start by enabling affordable anywhere anytime healthcare access through delivering healthcare services on intelligent vehicles. The foundation upon which healthcare services can be provided on intelligent vehicles is an automotive health-domain controller (AHDC), which is missing today. In this article, for the first time, we explain the necessity and define the functionalities of AHDCs. In addition, we delve into the technical challenges, requirements, and feasibility of integrating multiple key features into AHDCs. It is hoped that this will help the community standardize on-vehicle healthcare services provisioning, and lead to the universal adoption of the revolutionary mobile healthcare system.

Forging of an age-hardenable Mg–Al–Ca–Mn–Zn alloy on industrial scale

Weight reduction plays an important role in transportation industries, directly impacting on fuel consumption and vehicle range. The use of multi-material mixes is common practice, allowing for an optimum application of specific material properties. Light metals, primarily aluminum alloys, are used in both, cast and wrought state, to good effect. On the other hand, magnesium alloys, which are still lighter by one third, are used in castings exclusively. While scientific research and development on Mg wrought alloys is progressing steadily, industrial implementation is still scarce. As a result, safety relevant and structural applications made from Mg wrought products are nearly nonexistent. To increase acceptance and facilitate industrial application for this interesting class of materials we investigated the forging process of an original-sized automotive control arm. To ease industrial access, the used age hardenable Mg–Al–Ca–Zn–Mn lean alloy, can be processed similarly to Al alloys, e.g. 6xxx series. This work describes the development sequence, starting with the analysis of the forming window, followed by laboratory forging trials and industrial sized part production, providing information on forming characteristics as well as possible difficulties.

Automating the Assembly Process of Passenger Car Gearboxes

Aims: The study aims to identify ways and potential solutions to automate the assembly and production process for passenger car gearboxes.
 Object of Research: Assembly and production process for car gearboxes.
 Subject of Research: Modern and evolutionary automation tools that have the potential to be implemented in the assembly processes of automotive transmission controls.
 Methodology: To achieve this goal, as part of this study, it is planned to apply methods of bibliometric analysis of leading scientometric databases to obtain correlation relationships and analytical conclusions regarding the vector of development of automation means of passenger car gearbox assembly process.
 Results: As a result of the research by means of scientometric analysis and correlation the vector of probabilistic technical solutions of integration and development of automation means of the sequence of production operations during the assembly of the transmission, as well as adaptive framework-design solutions for the implementation of tools of the fourth iteration of industrial-industrial progress in the production processes of assembly of the studied technical control means and logical-technological connection of the elements of the transmission system, which affect the overall process of automotive manufacturization.
 Conclusion: The passenger car gearbox is a multi-component, complex system whose assembly is a complex multi-operational process, and given the high responsibility of this machine element, there is an urgent need to introduce modern automation tools into the assembly and production processes, which will significantly optimize global automatofactoring. The practical results of the present study consist in the formation of a focus scientometric database of profile data, identification of a potential vector of development of means and systems of automation of assembly-production operations, identification and formation of solutions for the implementation of modern means of automated production in the actual global automotive manufacturing, which allows to get the optimum ratio of production costs/quality of products by improving the manufacturability, productivity and flexibility of processes of assembly of multi-element and multi-component automotive systems and structures.