Explore the vast range of titles available.

\"Understanding fuel injection and engine management systems is the key to extracting higher performance from today's automobiles in a safe, reliable, and driveable fashion. Turbochargers, superchargers, nitrous oxide, high compression ratios, radical camshafts: all are known to make horsepower, but without proper understanding and control of fuel injection and other electronic engine management systems, these popular power-adders will never live up to their potential and, at worst, can cause expensive engine damage. Drawing on a wealth of knowledge and experience and a background of more than 1,000 magazine articles on the subject, engine-control expert Jeff Hartman explains everything from the basics of fuel injection to the building of complex project cars. Hartman covers the latest developments in fuel-injection and engine management technology applied by both foreign and domestic manufacturers, including popular aftermarket systems. No other book in the market covers the subject of engine management systems from as many angles and as comprehensively as this book. Through his continuous magazine writing, author Jeff Hartman is always up-to-date with the newest fuel-injection and engine management products and systems. \"-- Provided by publisher.

This e-book details state-of-the-art drive by wire technology. The eBook presents a systematic analysis of these issues and points out the fundamental reasons for the limited applications of these systems to dateNon-technological issues such as consumer perception of drive by wire systems, trade-off analysis of drive by wire system from Automotive OEMs and suppliers’ perspective are also reviewed in this volume.

Long description: Die Arbeit behandelt die Untersuchung transienter Betriebszustände an einem aufgeladenen Ottomotor mit Direkteinspritzung. Im Fokus steht die Untersuchung von verschiedenen Lastaufschaltungen, die unter Einsatz einer Engine-in-the-Loop-Entwicklungsumgebung im virtuellen Fahrversuch analysiert werden. Die Ergebnisse zeigen, dass für die Spitzenwerte der Rohemissionen in transienten Betriebsphasen meist eine geringe Anzahl aufeinanderfolgender Arbeitsspiele ursächlich ist. Zur Ermittlung der Entstehungsmechanismen werden die innermotorischen Prozesse, ausgehend von Gemischbildung, Verbrennung bis zur Schadstoffentstehung arbeitsspiel- bzw. kurbelwinkelaufgelöst analysiert. Zu diesem Zweck werden verschiedene Verfahren der Verbrennungsdiagnostik und Abgasmesstechniken mit kurzen Ansprechzeiten simultan eingesetzt. Für die Emissionsentstehung in transienten Betriebsphasen spielt die präzise Regelung des Luftverhältnisses eine zentrale Rolle. Hierzu wird das lokale Brennraumluftverhältnis durch Einsatz einer Sonderzündkerze auf Basis der Infrarotabsorptionsspektroskopie kurbelwinkelaufgelöst ermittelt. Durch eine neu definierte Kennzahl wird der Homogenisierungsgrad von Einzelarbeitsspielen in transienten Betriebsphasen quantifiziert und mit verschiedenen Einspritzparametern korreliert. Im Kontext eines virtuellen P2-Hybridkonzeptes werden die Erkenntnisse aus der Einzelarbeitsspielanalyse dazu genutzt, um durch eine Kurzzeitentkopplung des Verbrennungsmotors eine signifikante Reduktion der Maximalwerte für die PN-Rohemissionen zu realisieren. Dazu werden zwei Ansätze beschrieben. Im ersten Ansatz erfolgt eine Phlegmatisierung des Verbrennungsmotors ohne Applikationseingriff und im zweiten Ansatz eine gezielte Vorsteuerung der Motorparameter während der Kurzzeitentkopplung. Durch beide Ansätze werden ungünstige Übergangszustände umgangen und dadurch die Synergien aus Verbrennungs- und Elektromotor im Hybridtriebkonzept verbessert. Abschließend wird eine echtzeitfähige optische Analyse der Verbrennungsstrahlung mittels einer Prototypen-Lichtwellenleiterzündkerze zur adaptiven Regelung des Einspritzbeginns in die Motorsteuerung integriert. Dadurch passt sich die Motorapplikation von Arbeitsspiel zu Arbeitsspiel an die sich ändernden Randbedingungen für die verschiedenen Ordnungen des transienten Motorbetriebs an. Die Ergebnisse zeigen, dass die Maximalwerte für PN-Rohemissionen während der Lastaufschaltung signifikant reduziert werden können. Zusammenfassend beschreibt die Arbeit verschiedene neue Ansätze zur Reduzierung der Rohemissionen im transienten Betrieb eines Ottomotors mit Direkteinspritzung. Dies beinhaltet sowohl Ansätze, die ausschließlich durch das Brennverfahren verfolgt werden als auch Ansätze, die den Verbrennungsmotor in einem hybridisierten Gesamtsystem berücksichtigen.

This paper proposes a hierarchical control strategy to enhance the lateral stability of distributed drive electric vehicles. In the upper layer, the extended kalman filter (EKF) is employed for real-time estimation of critical vehicle states, including the sideslip angle and yaw rate. In the intermediate layer, a direct yaw-moment control (DYC) system based on integral terminal sliding mode control (ITSMC) is designed, which utilizes the deviation between the EKF-estimated states and their desired values to calculate the required additional yaw moment for stability compensation. In the lower layer, an optimal control–based torque allocation strategy is adopted to distribute the driving torque among the four in-wheel motors. Unlike many existing direct yaw moment control strategies that assume ideal state availability or suffer from control chattering and limited wheel-level realizability, this study explicitly addresses the coupled problem of state estimation uncertainty, robust yaw-moment generation, and practical torque realization under nonlinear tire dynamics. Simulation results demonstrate that the proposed EKF-based state estimation achieves high accuracy, while the ITSMC-DYC controller significantly improves lateral stability, trajectory tracking capability, and driving safety. Furthermore, hardware-in-the-loop (HIL) tests validate the effectiveness of the hierarchical control strategy under realistic scenarios, confirming its potential for practical applications.

Electric vehicles (EV) have gained importance in recent years due to environmental pollution and the future scarcity of fossil resources. They have been the subject of study for many years, where much work has focused on batteries and the electric motor (EM). There are several types of motors in the market but the most widely used are induction motors, especially squirrel cage motors. Induction motors have also been extensively studied and, nowadays, there are several control methods used—for example, those based on vector control, such as field-oriented control (FOC) and direct torque control (DTC). Further, at a higher level, such as the speed loop, several types of controllers, such as proportional integral (PI) and model predictive control (MPC), have been tested. This paper shows a comparison between a Continuous Control Set MPC (CCS-MPC) and a conventional PI controller within the FOC method, both in simulation and hardware in the loop (HIL) tests, to control the speed of an induction motor for an EV powered by lithium-ion batteries. The comparison is composed of experiments based on the speed and quality of response and the controllers’ stability. The results are shown graphically and numerically analyzed using performance metrics such as the integral of the absolute error (IAE), where the MPC shows a 50% improvement over the PI in the speed tracking performance. The efficiency of the MPC in battery consumption is also demonstrated, with 5.07 min more driving time.

This paper presents a direct robust adaptive tracking control strategy for the Iongitudinal motion of electric vehicles (EVs) subject to parametric uncertainties, nonlinear dynamics, and external disturbances. The vehicle longitudinal dynamics are formulated as a second-order nonlinear system with unknown nonlinearities. Unlike conventional indirect adaptive approaches that first identify unknown system dynamics, a radial basis function neural network (RBFNN) is employed to directly approximate the ideal feedback control law derived from sliding mode theory and Lyapunov synthesis. A robust adaptive law incorporating σ -modification is designed for online neural network weight update, enhancing robustness against approximation errors and bounded disturbances without requiring prior knowledge of their bounds. Lyapunov-based stability analysis rigorously demonstrates that all closed-loop signals are uniformly ultimately bounded (UUB), with tracking error converging to a tunable residual set around zero. The controller achieves model-independent operation, requiring no exact knowledge of vehicle nonlinear dynamics. Comprehensive simulations under step commands, and multi-frequency trajectories, together with parametric variations and road grade disturbances, validate the effectiveness of the proposed scheme in achieving accurate velocity tracking and superior robustness compared to conventional PID and sliding mode control. The main source code of this paper, including all simulation scripts and neural network modules, can support information found in S1 Pdf file.

Integrating embedded systems into next-generation vehicles is proliferating as they increase safety, efficiency, and driving comfort. These functionalities are provided by hundreds of electronic control units (ECUs) that communicate with each other using various protocols that, if not properly designed, may be vulnerable to local or remote attacks. The paper presents a vehicle-security operation center for improving automotive security (V-SOC4AS) to enhance the detection, response, and prevention of cyber-attacks in the automotive context. The goal is to monitor in real-time each subsystem of intra-vehicle communication, that is controller area network (CAN), local interconnect network (LIN), FlexRay, media oriented systems transport (MOST), and Ethernet. Therefore, to achieve this goal, security information and event management (SIEM) was used to monitor and detect malicious attacks in intra-vehicle and inter-vehicle communications: messages transmitted between vehicle ECUs; infotainment and telematics systems, which provide passengers with entertainment capabilities and information about the vehicle system; and vehicular ports, which allow vehicles to connect to diagnostic devices, upload content of various types. As a result, this allows the automation and improvement of threat detection and incident response processes. Furthermore, the V-SOC4AS allows the classification of the received message as malicious and non-malicious and acquisition of additional information about the type of attack. Thus, this reduces the detection time and provides more support for response activities. Experimental evaluation was conducted on two state-of-the-art attacks: denial of service (DoS) and fuzzing. An open-source dataset was used to simulate the vehicles. V-SOC4AS exploits security information and event management to analyze the packets sent by a vehicle using a rule-based mechanism. If the payload contains a CAN frame attack, it is notified to the SOC analysts.

The proliferation of electric vehicle (EV) racing competitions, such as Formula electric vehicle (FEV) competitions, has intensified the quest for high-performance electric propulsion systems. High-speed permanent magnet synchronous motors (PMSMs) for FEVs necessitate an optimized control strategy that adeptly manages the complex interplay between electromagnetic torque production and minimal power loss, ensuring peak operational efficiency and performance stability across the full speed range. This paper delves into the optimization of high-speed PMSM, pivotal for its application in FEVs. It begins with a thorough overview of the FEV motor’s basic principles, followed by the derivation of a detailed mathematical model that lays the groundwork for subsequent analyses. Utilizing MATLAB/Simulink, a simulation model of the motor drive system was constructed. The proposed strategy synergizes the principles of maximum torque per ampere (MTPA) with the flux weakening control technique instead of conventional zero direct axis current (ZDAC), aiming to push the boundaries of motor performance while navigating the inherent limitations of high-speed operation. Covariance matrix adaptation evolution strategy (CMA-ES) was deployed to determine the optimal d-q axis current ratio achieving maximum operating torque without overdesign problems. The implementation of the optimized control strategy was rigorously tested on the simulation model, with subsequent validation conducted on a real test bench setup. The outcomes of the proposed technique reveal that the tailored control strategy significantly elevates motor torque performance by almost 22%, marking a pivotal advancement in the domain of high-speed PMSM.

Driven by advancements in emerging technologies and data-driven innovations, the global automotive industry is focusing on intelligent and connected vehicles (ICVs), which involve complex electronic systems and vast data interactions. Safety concerns have expanded beyond traditional safety measures to include functional safety, safety of the intended functionality (SOTIF), and cybersecurity. Despite their interconnected nature, current methods often address these domains separately, risking incomplete safety assessments. This paper introduces a fusion safety analysis method that evaluates the three domains collectively. By identifying safety attributes and mapping unsafe behaviors to hazardous scenarios, it quantitatively assesses integrated safety risks. An illustrative case study on adaptive cruise control (ACC) highlights the method’s effectiveness, stressing the importance of addressing multi-dimensional safety issues to enhance ICVs safety.