Browse Topic: Electronic control units
This paper proposes a nonlinear and robust State-Dependent Riccati Equation (SDRE) combined with H∞ control architecture for brake- by-wire systems, specifically designed to handle severe tire-road friction variations and μ-split scenarios. The primary objective is to maximize deceleration capabilities while rigorously maintaining yaw stability, trajectory tracking, and passenger comfort through jerk limitation. Situated within the domain of active safety, this research addresses robustness against real-world uncertainties by utilizing a high-fidelity 14-degree-of-freedom vehicle model that accounts for longitudinal, lateral, and yaw dynamics, suspension-induced pitch and roll effects, and nonlinear tire behavior with explicit load transfer. To ensure near-optimal slip tracking under variable surface conditions, the system employs online friction estimation via Extended and Unscented Kalman Filters (EKF/UKF) fusing wheel and IMU data to adaptively adjust slip targets. The control strategy is bifurcated: the SDRE component manages dominant nonlinearities through state-dependent gains to prevent wheel lock-up, while the H∞ component provides robust disturbance rejection against parametric uncertainties such as mass variations and sensor noise. Control efforts are distributed via a Quadratic Programming (QP) torque allocator featuring anti-windup mechanisms and explicit saturation handling to compensate for lateral drift during μ-split braking. Validation is conducted through a Model- in-the-Loop (MIL) to Software-in-the-Loop (SIL) pipeline using scenarios including wet surfaces and panic braking. Simulation results demonstrate enhanced yaw stability and controlled deceleration profiles compared to conventional baselines, ensuring computational feasibility for automotive Electronic Control Units (ECUs).
Software-defined vehicles (SDVs) are reshaping automotive control architectures by shifting intelligence to embedded systems, where computational efficiency is paramount. This paper presents a systematic evaluation of control strategies (PID, LQR, MPC) for the classical control problem involving inverted pendulum on a cart under strict embedded constraints representative of software-defined vehicle ECUs. The objective is to evaluate and compare the performance of advanced control algorithms under varying control objectives when deployed on microcontrollers with constrained computational and memory resources, representative of the limitations encountered in embedded platforms used for SDVs. Furthermore, the study illustrates systematic optimization strategies that enable these algorithms to achieve real-time execution within such resource-constrained environments. Each control strategy is implemented with careful consideration of algorithmic complexity, real-time responsiveness, and resource utilization. Performance is evaluated across key metrics, enabling a comparative analysis that highlights trade-offs between control fidelity and hardware efficiency. By demonstrating how advanced control logic can be effectively deployed on constrained hardware, this work supports the broader goal of enabling intelligent, responsive vehicle behavior through software-centric design. The findings are particularly relevant for automotive and embedded engineers developing control systems for SDVs, where balancing performance and resource constraints is critical to achieving scalable, safe, and adaptive vehicle functionality.
Modern vehicles require sophisticated, secure communication systems to handle the growing complexity of automotive technology. As in-vehicle networks become more integrated with external wireless services, they face increasing cybersecurity vulnerabilities. This paper introduces a specialized Proxy based security architecture designed specifically for Internet Protocol (IP) based communication within vehicles. The framework utilizes proxy servers as security gatekeepers that mediate data exchanges between Electronic Control Units (ECUs) and outside networks. At its foundation, this architecture implements comprehensive traffic management capabilities including filtering, validation, and encryption to ensure only legitimate data traverses the vehicle's internal systems. By embedding proxies within the automotive middleware layer, the framework enables advanced protective measures such as intrusion detection systems, granular access controls, and protected over-the-air (OTA) update channels. This strategy enhances both data security and system isolation, creating protective boundaries between critical vehicle operations and potential external attacks. The architecture particularly excels in supporting Vehicle-to-Everything (V2X) connectivity, facilitating seamless information exchange between vehicles, roadside infrastructure, and pedestrians. This capability is essential for enhancing roadway safety, optimizing traffic flow, and supporting autonomous driving technologies. The system incorporates dedicated proxy modules for specialized protocols including Trivial File Transfer Protocol (TFTP), Diagnostic Over Internet Protocol (Doip), and Message Queuing Telemetry Transport (MQTT), each fulfilling specific functions in vehicle diagnostics, software updates, and telemetry data management. Performance evaluations will measure latency and throughput metrics to validate the architecture's efficiency and reliability. The framework's modular design aims to provide scalability and adaptability to accommodate both technological advancements and emerging security challenges. The proxy-based security framework presented offers a holistic and forward-looking approach to safeguarding in-vehicle networks. It provides automotive manufacturers with the tools to develop connected vehicles that combine intelligence and efficiency with robust protection against diverse cybersecurity threats.
With the rapid advancement of connected vehicle technologies, infotainment Electronic Control Units (ECUs) have become central to user interaction and connectivity within modern vehicles. However, this enhanced functionality has introduced new vulnerabilities to cyberattacks. This paper explores the application of Artificial Intelligence (AI) in enhancing the cybersecurity framework of infotainment ECUs. The study introduces AI-powered modules for threat detection and response, presents an integrated architecture, and validates performance through simulation using MATLAB, CANoe, and NS-3. This approach addresses real-time intrusion detection, anomaly analysis, and voice command security. Key benefits include zero-day exploit resistance, scalability, and continuous protection via OTA updates. The paper references real-world automotive cyberattack cases such as OTA vulnerability patches, Connected Drive exploits, and Uconnect hack, emphasizing the critical need for AI-enabled proactive cybersecurity frameworks.
The automotive industry is undergoing a transformational shift with the addition of Virtual ECU in the development of software and validation. The Level 3 Virtual ECU concept will lead to the transformation in the SDLC process, as early detection of defects will have a significant impact on cost and effort reduction. This paper explains the application of a Level 3 virtual ECU which can enable to perform testing in initial period considering the Shift Left Strategy, which will significantly reduce development time. This paper demonstrates various development and validation strategies of virtual ECU and how it can impact project timeline.
This study introduces a novel Large Language Model (LLM)-driven approach for comprehensive diagnosis and prognostics of vehicle faults, leveraging Diagnostic Trouble Codes (DTCs) in line with industry-standard automation protocols. The proposed model asks for significant advancement in automotive diagnostics by reasoning through the root causes behind the fault codes given by DTC document to enhance fault interpretability and maintenance efficiency, primarily for the technician and in few cases, the vehicle owner. Here LLM is trained on vehicle specific service manuals, sensor datasets, historical fault logs, and Original Equipment Manufacturer (OEM)-specific DTC definitions, which leads to context-aware understanding of the vehicle situation and correlation of incoming faults. Approach validation has been done using field level real-world vehicle dataset for different running scenarios, demonstrating model’s ability to detect complex fault chains and successfully predicting the associated root cause. By utilizing time series based future projection of the vehicle pattern, this approach could also predict the probable future faults as well as the requisite steps needed to prevent them. Overall, key contributions of this work include: (1) a modular diagnostic framework that seamlessly integrates different electronic control unit (ECU) architectures for sequential root cause analysis of vehicle faults, (2) cross-platform compatibility allowing utility across varied vehicle models and platforms, and (3) a user-friendly interface that eliminates the need for technical expertise by generating output data into simple, actionable insights. This work was benchmarked against traditional rule-based diagnostic tools and showed 50-70% reduction in the troubleshooting time for Root cause analysis (RCA). In the prognosis front, model could predict upcoming possible faults in the Battery behavior with significant accuracy. The framework also supports continuous learning by integrating new fault patterns, ensuring adaptability over time. This paper establishes the potential of integrating advanced language models into the automotive diagnostics pipeline and provides a scalable, intelligent, and intuitive solution for next-generation vehicle fault management.
With the rapid adoption of electric vehicles (EVs), ensuring the reliability, safety, and cost-effectiveness of power electronic subsystems such as onboard chargers, DC-DC converters, and vehicle control units (VCUs) has become a critical engineering focus. These components require thorough validation using precise calibration and communication protocols. This paper presents the development and implementation of an optimized software stack for the Universal Measurement and Calibration Protocol (XCP), aimed at real-time validation of VCUs using next-generation communication methods such as CAN, CAN-FD, and Ethernet. The stack facilitates read/write access to the ECU’s internal memory in runtime, enabling efficient diagnostics, calibration, and parameter tuning without hardware modifications. It is designed to be modular, platform-independent, and compatible with microcontrollers across different EV platforms. By utilizing the ASAM-compliant protocol architecture, the proposed system significantly reduces dependency on expensive proprietary tools, offering a cost-effective alternative for the Indian EV industry.
The rapid evolution of in-vehicle electronic systems toward zonal based architectures introduces a new layer of complexity in automotive diagnostics. Traditional architectures, built on Controller Area Network (CAN) and Local Interconnect Network (LIN) protocols, operate on a uniform Real-Time Operating System (RTOS), enabling simplified and consistent diagnostic workflows across Electronic Control Units (ECUs). However, next-generation platforms must accommodate diverse communication protocols (e.g., CAN, LIN, DoIP, SOME/IP) and heterogeneous operating systems (e.g., RTOS, Linux, QNX), resulting in fragmented and inflexible diagnostic processes. This paper presents a Diagnostic controller that addresses these challenges by enabling unified, scalable, and adaptive diagnostic capabilities across modern vehicle platforms. The proposed system consolidates protocol handling at the application level, abstracts diagnostic complexities, and allows cross-platform communication through hypervisor-based services. Diagnostic configurations are decoupled from static software builds and delivered dynamically as configuration files, supporting real-time adaptability to software updates and Over-The-Air (OTA) changes. This architecture also facilitates seamless interoperability across operating systems and enables service-based diagnostics in line with the industry’s move toward software-defined vehicles. The result is a robust, future-ready diagnostic solution optimized for high software variability, platform heterogeneity, and increasing system complexity in modern automotive ecosystems.
This paper addresses the challenge of increasing hardware complexity, long development cycles and high costs associated with integrating multiple systems. The research explores the potential of Large Language Models (LLMs) when applied as chatbots to revolutionize the design and development of automotive electrical hardware systems, encompassing areas such as convenience features, safety systems, advanced lighting, vehicle body control and modular electronic control units. A key focus is on how LLMs can automate cost-reduction design tasks, including design optimization, requirements verification and component validation, ultimately driving down expenses without compromising performance or reliability. Furthermore, the research investigates how LLMs can assist in decision-making by providing data-driven insights that inform critical design choices and facilitate enhanced team collaboration, leading to improved productivity through innovative tools and streamlined workflows. In that sense, the project’s scope includes creating a Data Warehouse with relevant design, features offering, testing and quality feedback data to train the LLMs. A research tool based on LLMs will be developed to offer optimization recommendations, such as identifying oversizing, suggesting cost reduction and hardware modularization. The tool will be seamlessly integrated into the cost-reduction engineering teams’ workflow, promoting agility and modernization. The expectation is that this research will drive innovation, enhance competitiveness and promote sustainability within the automotive sector, leading to accelerated time-to-market for new vehicle models, improved engineering efficiency in the development of electrical hardware systems and a strengthened market position for automotive OEMs. Ultimately, the goal is to create more efficient, safer and feature-rich automotive experiences while simultaneously optimizing cost-effectiveness in the design and production of advanced electrical systems.
Simulation has become mission-critical for ADAS development. Model-based systems engineering can integrate modeling and simulation from the start of the design process. Advanced Driver Assistance Systems (ADAS) are transforming vehicle safety, acting as the bridge between conventional driving and full autonomy. From adaptive cruise control to emergency braking and blind-spot detection, these technologies rely on a dense network of radar sensors, antennas, electronic control units and software. What unites them is the need for precise functionality under complex real-world situations. Achieving full reliability requires more than testing on the road; it demands a virtual approach grounded in simulation. Simulation has become mission-critical for ADAS development. As new vehicles integrate dozens of sensors into tightly constrained spaces, even subtle design decisions can affect system performance. Radar solutions, in particular, present unique challenges, especially as vehicle surfaces grow more complex and the number of onboard systems increases.
The calibration of automotive electronic control units is a critical and resource-intensive task in modern powertrain development. Optimizing parameters such as transmission shift schedules for minimum fuel consumption traditionally requires extensive prototype testing by expert calibrators. This process is costly, time-consuming, and subject to variability in environmental conditions and human judgment. In this paper, an artificial calibrator is introduced – a software agent that autonomously tunes transmission shift maps using reinforcement learning (RL) in a Software-in-the-Loop (SiL) simulation environment. The RL-based calibrator explores shift schedule parameters and learns from fuel consumption feedback, thereby achieving objective and reproducible optimizations within the controlled SiL environment. Applied to a 7-speed dual-clutch transmission (DCT) model of a Mild Hybrid Electric Vehicle (MHEV), the approach yielded significant fuel efficiency improvements. In a case study on a 4.7 km Worldwide harmonized Light-Duty vehicles Test Cycle (WLTC) driving segment, the RL-optimized shift strategy reduced fuel consumption from a baseline of 0.46 L to 0.37 L. Furthermore, when starting from an already optimized shift map representative of a series production vehicle’s calibration, the artificial calibrator further enhanced fuel efficiency, achieving approximately a 0.6 % reduction in fuel consumption for the 4.7 km segment and nearly a 5 % reduction for the full WLTC. The artificial calibrator thus demonstrates a promising methodology to frontload calibration tasks in simulation, thereby offering the potential to reduce reliance on resource-intensive physical testing and to significantly accelerate the development of fuel-efficient powertrain control software.. The direct compatibility of parameter files with real vehicle Electronic Control Unit (ECUs) and the validated SiL behavior suggest high transferability of learned strategies, offering the potential for minimal fine-tuning on physical vehicles post-simulation.
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