Automotive Control Research Articles

Container-Based Electronic Control Unit Virtualisation: A Paradigm Shift Towards a Centralised Automotive E/E Architecture

The past 40 years have seen automotive Electronic Control Units (ECUs) move from being purely mechanical controlled to being primarily digital controlled. While the safety of passengers and efficiency of vehicles has seen significant improvements, rising ECU numbers have resulted in increased vehicle weight, greater demands placed on power, more complex hardware and software, ad hoc methods for updating software, and subsequent increases in costs for both vehicle manufacturers and consumers. To address these issues, the research presented in this paper proposes that virtualisation technologies be applied within automotive electrical/electronic (E/E) architecture. The proposed approach is evaluated by comprehensively studying the CPU and memory resource requirements to support container-based ECU automotive functions. This comprehensive performance evaluation reveals that lightweight container virtualisation has the potential to welcome a paradigm shift in E/E architecture, promoting consolidation and enhancing the architecture by facilitating power, weight, and cost savings. Container-based virtualisation will also enable efficient and robust online dynamic software updates throughout a vehicle’s lifetime.

Effects of Ni cation ratio and sintering temperature on microstructure and electrical properties of NiMnCoO4-based ceramics

Negative temperature coefficient (NTC) thermistors as temperature sensors play an important role in various electrical/electronic devices and are widely applied in the fields such as, automotive sensor modules, home appliances and control applications. The electrical properties of NTC thermistors can be effectively controlled by regulating the content of their chemical components or changing the microstructures. Therefore, this study investigated the effects of Ni cation ratio and sintering temperature on microstructures, crystal structures, and electrical properties of NiMnCoO4-based ceramics. With increasing Ni content, it was found that sinterability of NiMnCoO4-based ceramics was decreased with forming rock-salt phase of NiO, resulting in degrading resistivity. Furthermore, the spinel structures were detected by X-ray diffraction analysis regardless of sintering temperatures. Notably, values of average grain size for sintered samples were increased with increasing sintering temperature. Besides, it was clarified that electrical properties were significantly influenced by average grain size. The room temperature resistivity was increased from approximately 300 kΩ⋅cm for the sintered sample at 1100°C to about 600 kΩ⋅cm for for the sintered sample at 1300oC. On the other hand, the B(25/85) constant was not significantly influenced by the sintering temperature. Accordingly, it is expected that the results of this study are useful for controlling electrical properties of MnCoNiO4-based ceramics.

A hybrid framework for latency compensation in remote testing of automotive electronic control units

As the number of electronic control units (ECUs) in vehicles continues to grow, exchanging physical components between original equipment manufacturers (OEMs) and ECU suppliers presents logistical challenges, impeding the pace of development and leading to accumulation of costs. In this work, we introduce a conceptual framework that enables remote testing of geographically dispersed ECUs over the Internet. Pertinent to the Internet of Things (IoT) sphere, where interconnection of distributed components is hampered by the inherent challenges of latency, we propose a hybrid synchronous methodology that combines asynchronous test management with time synchronization mechanisms to mitigate the delay impact within the distributed environment. Additionally, we discuss challenges and prospects associated with this approach.

Multi-Stream Scheduling of Inference Pipelines on Edge Devices - a DRL Approach

Low-power edge devices equipped with Graphics Processing Units (GPUs) are a popular target platform for real-time scheduling of inference pipelines. Such application-architecture combinations are popular in Advanced Driver-assistance Systems for aiding in the real-time decision-making of automotive controllers. However, the real-time throughput sustainable by such inference pipelines is limited by resource constraints of the target edge devices. Modern GPUs, both in edge devices and workstation variants, support the facility of concurrent execution of computation kernels and data transfers using the primitive of streams , also allowing for the assignment of priority to these streams. This opens up the possibility of executing computation layers of inference pipelines within a multi-priority, multi-stream environment on the GPU. However, manually co-scheduling such applications while satisfying their throughput requirement and platform memory budget may require an unmanageable number of profiling runs. In this work, we propose a Deep Reinforcement Learning (DRL)-based method for deciding the start time of various operations in each pipeline layer while optimizing the latency of execution of inference pipelines as well as memory consumption. Experimental results demonstrate the promising efficacy of the proposed DRL approach in comparison with the baseline methods, particularly in terms of real-time performance enhancements, schedulability ratio, and memory savings. We have additionally assessed the effectiveness of the proposed DRL approach using a real-time traffic simulation tool IPG CarMaker.

Atomic-Scale Behavior of Perovskite-Supported Ir-Pd-Ru Nanoparticles under Redox Atmospheres.

An advanced materials solution utilizing the concept of "smart catalysts" could be a game changer for today's automotive emission control technology, enabling the efficient use of precious metals via their two-way switching between metallic nanoparticle forms and ionic states in the host perovskite lattice as a result of the cyclical oxidizing/reducing atmospheres. However, direct evidence for such processes remains scarce; therefore, the underlying mechanism has been an unsettled debate. Here, we use advanced scanning transmission electron microscopy to reveal the atomic-scale behaviors for a LaFe0.95Pd0.05O3-supported Ir-Pd-Ru nanocatalyst under fluctuating redox conditions, thereby proving the reversible dissolution/exsolution for Ir and Ru but with a limited occurrence for Pd. Despite such selective dissolution during oxidation, all three elements remain cooperatively alloyed in the subsequent reduction, which is a key factor in preserving the catalytic activity of the ternary nanoalloy while displaying its self-regenerating functionality and control of particle agglomeration.

Design and development of an intelligent zone based master electronic control unit for power optimization in electric vehicles

The development of electric vehicles (EVs) has been incremental because EVs satisfy a significant demand for energy sources. Electronic control unit (ECU) is an important component that processes the electric signals received from various sensors for generating the control signals for the actuators. Automotive control systems were initially operated manually throughout the automotive revolution based on the responses of input signals received from ECUs and drivers. Most of the functions in EV are controlled by the ECU and every ECU consumes power at all times even if it is not in use. The larger power consumption of passive ECUs like adaptive lighting systems (ALS), automatic wiper systems (AWS) brake light systems (BLS), etc., affect the life of ECUs and the range of EVs. This article is primarily concerned with limiting power consumption by switching the power supply to the passive ECUs based on their requirements. Hence, to achieve the objective, the intelligent zone (i-zone) based master ECU is triggered to activate the slave ECUs. Designing suites including Proteus and KiCAD were used for designing the circuits including master as well as slave ECU. This prototype is built using three secondary ECUs such as ALS & AWS and BLS which are controlled using i-zone-based master ECU. The performance of this implemented design is evaluated, and it is discovered that almost 40% of the battery consumption is reduced. This i-zone-based master ECU and all its slave ECUs manage power while ensuring the safety and reliability of EVs.

Teaching Strategies for Automotive Engine Electronic Control System Testing and Maintenance Course under the “1+X” Certificate System

With the continuous development of China’s economy, cars have become the most common means of transportation for people. However, due to this impact, the contradiction between the market demand for after-sales services such as car testing and maintenance and the actual supply of professional technical personnel has become increasingly prominent. Therefore, strengthening the education reform of automotive inspection and maintenance professional technology has become an important task for major vocational colleges. The “1+X” certificate system has become a new norm in vocational education reform in China due to its significant advantages in cultivating students’ professional knowledge and skills to obtain professional skills certificates. Based on this, this article takes the course of automotive engine electronic control system testing and maintenance as an example to explain the practical significance and strategies of course teaching based on the “1+X” certificate system, aiming to further promote professional teaching reform and provide some reference for colleagues.

A Multivariate Time Series Prediction Method for Automotive Controller Area Network Bus Data

This study addresses the prediction of CAN bus data, a lesser-explored aspect within unsupervised anomaly detection research. We propose the Fast-Gated Attention (FGA) Transformer, a novel approach designed for accurate and efficient prediction of CAN bus data. This model utilizes a cross-attention window to optimize computational scale and feature extraction, a gated single-head attention mechanism in place of multi-head attention, and shared parameters to minimize model size. Additionally, a generalized unbiased linear attention approximation technique speeds up attention block computation. On three datasets—Car-Hacking, SynCAN, and Automotive Sensors—the FGA Transformer achieves predicted root mean square errors of 1.86 × 10−3, 3.03 × 10−3, and 30.66 × 10−3, with processing speeds of 2178, 2768, and 3062 frames per second, respectively. The FGA Transformer provides the best or comparable accuracy with a speed improvement ranging from 6 to 170 times over existing methods, underscoring its potential for CAN bus data prediction.

Exploring the correlation and synchronicity between environmental factors and occupant thermal response in dynamic outdoor cabin environments

To improve the cabin thermal environment and explore advanced automotive air-conditioning control methods, it is essential to understand the key factors affecting occupant thermal response as well as the synchronicity of the thermal response to environmental changes in outdoor conditions. Given the current gaps in research, this study conducted outdoor experiments in summer to analyze the correlation and synchronicity between the cabin thermal environment and occupant thermal sensation as well as facial skin temperature. Results showed that, during the cooling phases of experiments with high initial cabin air temperatures, occupant thermal sensation improved significantly within 7 min. After 15 min, facial skin temperature and thermal sensation reached quasi-steady states. Thermal sensation was primarily influenced by air temperature, followed by solar radiation, and exhibited significant synchronicity with changes in these factors. When the air temperature stabilized at around 26 °C, every 200 W/m2 of solar radiation exposure increased the thermal sensation unit during the 40-min experiment period. Cheek and nose skin temperatures were significantly correlated with air temperature and solar radiation, and were sensitive to environmental changes, synchronizing with changes in air temperature and relative humidity. Further analysis showed the feasibility of using cheek and nose skin temperatures to characterize the occupant thermal sensation in a cabin. Additionally, this study found sex differences in the occupant thermal response in a cabin. The results provide insight into the key optimization parameters for comfort-oriented cabin thermal environment design and offer support for future air conditioning control in cabins based on thermal imaging.

Design of four-wheel steering system based on automotive electronic control technology

Abstract With the rapid development of the automobile industry, people’s requirements for automobile handling performance and driving safety are getting higher and higher. As an important automobile power system, four-wheel steering technology can improve the maneuverability, stability, and safety of vehicles by controlling the steering angles of front and rear wheels. Therefore, the design and research of the four-wheel steering system has become one of the hotspots in the field of automotive engineering. In this paper, a two-degree-of-freedom dynamics model is established, and based on the PID control strategy of joint feedback of center-of-mass lateral deflection and traverse angular velocity, the vehicle model of a four-wheeled steering vehicle is constructed by using TruckSim, and the minimum turning radius test, serpentine test, and steering wheel angle step test project are carried out. The results show that compared with the two-wheel steering vehicle, the minimum turning diameter of the four-wheel steering vehicle is reduced by 1/3, which effectively improves the maneuverability at low speeds as well as the safety and stability at high speeds, and has better transient response.

Continuity of GNSS as a critical attribute for safety applications in land transport

The Global Navigation Satellite System (GNSS) is widely used for air traffic management—more than 150,000 aircraft and 5000 airports worldwide are equipped with SBAS (Satellite-based augmentation system) technology, which contributes to safer and more efficient air operations. The next challenge is to extend GNSS positioning to maritime, autonomous cars and railway control systems preserving their safety requirements. The main parameter is the integrity of the GNSS positioning, although the time for which the integrity is guaranteed, defined by continuity, the most demanding requirement for aviation applications, has not been sufficiently investigated for land transportation. The aim of this paper is to close this gap by clarifying: (1) where the requirement for GNSS continuity comes from, (2) why GNSS continuity is needed in land transport, and (3) how GNSS-based applications can be made more reliable when needed. Using a comparative analysis, the continuity requirements in aviation, rail, maritime, and road transport have been investigated showing their importance for railways and automotive control, paving the way to eventually update the current EN 50126 (RAMS) and ISO/TR 4804 standards respectively for railways and automated cars. One of the main findings, through Markov modeling, is the improvement of the Mean Time to System Failure (MTTFsys) that for the railway safety-of-life applications can be significantly increased from about 521 h up to 5 × 105 h. These results can contribute to accelerating the adoption of GNSS positioning for automated land transportation, by exploiting the extensive experience brought by the aviation sector where GNSS was introduced 20 years ago.

Multi-Output Physically Analyzed Neural Network for the Prediction of Tire–Road Interaction Forces

<div>This article introduces an innovative method for predicting tire–road interaction forces by exclusively utilizing longitudinal and lateral acceleration measurements. Given that sensors directly measuring these forces are either expensive or challenging to implement in a vehicle, this approach fills a crucial gap by leveraging readily available sensor data. Through the application of a multi-output neural network architecture, the study focuses on simultaneously predicting the longitudinal, lateral, and vertical interaction forces exerted by the rear wheels, specifically those involved in traction. Experimental validation demonstrates the efficacy of the methodology in accurately forecasting tire–road interaction forces. Additionally, a thorough analysis of the input–output relationships elucidates the intricate dynamics characterizing tire–road interactions. This research underscores the potential of neural network models to enhance predictive capabilities in vehicle dynamics, offering insights that are valuable for various applications in automotive engineering and control systems.</div>

CAN Interface Insights for Electric Vehicle Battery Recycling

Road transportation is a significant worldwide contributor to greenhouse gases, and electrifying the driveline of road vehicles is essential in overcoming the evident challenge of climate change. A sustainable transition to electric vehicles requires efficient and safe methods for recycling and repurposing used electric vehicle batteries. While various testing methods have been explored for assessing battery state of health and state of risk for recycling and reuse, a research gap exists concerning using data from integrated battery monitoring systems in the recycling process of electric vehicle batteries. This study addresses the research gap by presenting an approach to extract data from the monitoring system integrated into the battery using the automotive standard controller area network interface. In addition, methods to use this interface to ensure the optimal state of charge of the batteries for storage are presented. The benefits, challenges, and limitations set by the proprietary nature of the data to assess the state of risk and health of electric vehicle batteries for recycling and repurposing are presented, discussed, and evaluated. Finally, the influence of battery regulations and the battery passport proposal on electric vehicle battery recycling and repurposing are discussed to provide future perspectives.

Systematic Risk Characterisation of Hardware Threats to Automotive System

The increasing dependence of modern automotive systems on electronics and software poses cybersecurity risks previously not factored into design and engineering of such systems. Attacks on hardware components, communication modules and embedded software – many of which are purposefully designed for automotive control and communications – are the key focus of this paper. We adopt a novel approach to characterise such attacks using Gajski-Kuhn Y-charts to represent attack manipulation across behavioural, structural and physical domains. Our selection of attacks is evidence-driven demonstrating threats that have been demonstrated to be feasible in the real-world. We then risk assess impact of such threats using the recently adopted ISO/SAE 21434 standard for automotive cybersecurity risk assessment, including mitigations for potential adoption. Our work serves to provide unique insights into the complex dynamic of hardware vulnerabilities and how the industry may address system-level security and protection of modern automotive platforms.

Text Recognition for the Speed Control of the Vehicle

An automatic vehicle human-voice-based safety control is proposed to decrease the happening of vehicle accidents due to an eye or hand activities while driving. The design is implemented using an Arduino microcontroller. One of the most important safety control features is the automotive speed control (ASC). In this safety control function, which prevents a vehicle sudden hit, the necessary action is issued based on the vehicle-to- vehicle distance measure. To prevent the driver from attention withdraw and maintain a proper road and situation focus (as especially is the case at night), several other automated safety control operations are provided (i.e., radio, flasher, left flasher, right flasher, wipers, drive, and stop). These features are controlled according to human voice commands using an EasyVR module. The proposed model has been designed, built, and tested providing suitable results as desired.

Deep reinforcement learning implementation on IC engine idle speed control

Efficient control of automotive engine idle speed is crucial for achieving better fuel economy and smoother engine running. This paper presents a comparison between proportional-integral-derivative (PID) control and Reinforcement Learning (RL) using the Deep Q-Network (DQN) algorithm as a high-level control method for minimizing idle speed fluctuations caused by changes in engine irregularities, and the response time and accuracy of the throttle control mechanism. In addition to low-level PID control for the throttle valve position, MATLAB/Simulink was employed to build the simulation environment, incorporating an engine model and an electronic throttle body model, and observing the engine's current speed. The results demonstrated the superiority of RL-based control over PID in reducing idle speed fluctuations and enhancing engine performance in simulations and real-world experiments. This study advances automotive engine control strategies.

Time Domain Analysis of Ride Comfort and Energy Dissipation Characteristics of Automotive Vibration Proportional–Integral–Derivative Control

<div>A time domain analysis method of ride comfort and energy dissipation characteristics is proposed for automotive vibration proportional–integral–derivative (PID) control. A two-degrees-of-freedom single wheel model for automotive vibration control is established, and the conventional vibration response variables for ride comfort evaluation and the energy consumption vibration response variables for energy dissipation characteristics evaluation are determined, and the Routh stability criterion method was introduced to assess the impact of PID control on vehicle stability. The PID control parameters are tuned using the differential evolution algorithm, and to improve the algorithm’s adaptive ability, an adaptive operator is introduced, so that the mutation factor of differential evolution algorithm can change with the number of iterations. The PID control parameter optimization method presented in this article is versatile and can be used to optimize PID control parameters under different conditions. This article provides the results of PID control parameter optimization under different conditions. Based on PID control and its parameter tuning, a time domain solution method for two types of vibration response variables, their root mean square values, and the average power of energy consumption vibration of automotive vibration PID control is proposed. Two kinds of simulation test schemes are designed under urban driving conditions, which are commonly used Class B road and pulse road, with a commonly used vehicle speed of 60 km/h. The ride comfort and energy dissipation characteristics of passive suspension and PID control are compared. The results show that introducing energy consumption vibration response variables as a supplement to the conventional vibration response variables is beneficial for expanding the research and application scope of the automotive vibration and its control; with the sprung mass acceleration deviation as the control input, PID control can effectively improve the vehicle ride comfort, but it makes the vibration energy dissipation characteristics worse.</div>

Automotive wheel torque control systems

The approaches of foreign specialists to improving electronic control systems for the dynamic state of vehicles (traction, directional stability, rollover resistance) to improve their safe operation are analyzed. The three main components of the Active Transmission Torque Management (ADTM) system are considered: the center clutch, the electronic limited slip differential (ELSD), and the torque vectoring system. The possibilities and limitations of using active torque control systems to ensure vehicle safety are shown. Keywords: electronic control, vehicle dynamics, wheel torque, torque vectoring, active safety. alexey.scherbin@nami.ru

Energy, exergy, sustainability, thermoeconomic, exergoeconomic, environmental and environmental-economic effects of novel boron-containing open cell geopolymer filter of a diesel engine on exhaust emissions

In this study, the energy, exergy, sustainability, thermoeconomic, exergoeconomic, environmental and environmental-economic based analyses are applied to the experimentally developed (3 % boric acid based) boron-doped geopolymer filter used for the diesel engine to assess it in a large engineering point of view. In this context, novel boron-doped filter exhaust after treatment system (BDF EAS) is compared with catalytic exhaust after treatment system (catalytic EAS) and unfiltered option (filterless) with engine loads of 50 Nm, 75 Nm and 100 Nm, while engine speeds are 1500 rpm, 1700 rpm, and 1900 rpm. The utilization of the newly developed BDF EAS has a significant effect on the exhaust emission particles. It effectively filters the particles compared to the original catalytic EAS of the engine. On the other hand, the developed BDF EAS has effective oxidation process for the emissions. So, the newly developed boron-doped filter can be used as a single device that has both of the Diesel Oxidation Catalyst (DOC) and Diesel Particulate Filter (DPF) specifications together. Hence, the new product of BDF EAS is environmentally benign system for the diesel engines and boron-doped geopolymer material can be used as an innovative material in the automotive industry's exhaust emission control system.

Improving the labor safety of drivers of technological vehicles

The purpose of this work was to analyze existing ways to improve the labor safety of drivers of technological vehicles and other vehicles, and to develop proposals for its effective improvement. To do this, an analysis of existing means of ensuring road safety was carried out in the following areas: improving the Rules of the Road, optimizing the work schedule of drivers, increasing the legal liability of drivers for violations of traffic rules, the use of driver condition monitoring systems, the use of automotive traffic control systems and advanced emergency braking. The results of the work performed showed that in each of the areas there are certain ways to improve them, which will improve the safety of drivers of technological and other vehicles. Keywords safety, control, emergency braking, fatigue, driver, transport, traffic