Browse Topic: Hardware-in-the-loop (HIL)

Items (763)

CI/CD for Mission-Critical Systems: A Framework for Any Level of Rigor

2026-01-7529

9/22/2026

Modern mission-critical ground vehicle systems must adapt to rapidly evolving threats, deploying changes in months or days while maintaining reliable and safe operation. Historic manual development and testing methods cannot keep pace without compromising safety assurances. Continuous Integration and Continuous Deployment (CI/CD) pipelines offer proven approaches to accelerating development, but implementing them for mission-critical systems requires careful attention to verification rigor. This paper presents a practical framework for implementing CI/CD pipelines across any level of rigor, from rapid prototyping to DO-178C and ISO 26262 certified systems. Drawing on experience from aviation, medical device, and ground vehicle development, the framework provides guidance for each pipeline stage based on the system’s desired level of rigor. This framework includes an examination of the value of Software-in-the-Loop vs Hardware-in-the-Loop testing to optimize development timelines while maintaining software quality.

Lingg, Michael, Paul, Howard, Schulte, Brian, Wilkinson, Robert

Modeling Brake Torque Hysteresis for Chassis Controls Hardware in the Loop Simulation

2026-01-0824

9/14/2026

It is hardly a new trend for on road, vehicle intensive tuning and testing of chassis control features such as Anti-Lock Brakes, Traction Control, and Electronic Stability Control to move away from vehicle testing and towards non-vehicle test platforms such as Hardware-In the Loop (HIL) simulations and even further into pure math-based simulations. However, a significant acceleration of these activities has been occurring recently in the automotive industry, reducing or eliminating calibration time on vehicles and amplifying the demand for highly representative, non-vehicle test platforms to validate and even calibrate chassis controls features. In current state of the art HIL simulation, the input (brake pressure) to output (brake torque) of each wheel brake in a vehicle’s brake system is modeled relatively simplistically, including at most pressure and brake temperature sensitivities, usually in lookup table form. Each brake corner contains over 20 different friction interfaces, which in turn can cause hysteretic behavior (a difference in the output for a given input, depending on whether the brake is applying or releasing against the hysteretic friction). This hysteresis is neglected in most state of the art HIL simulations. Past studies by General Motors have shown that the importance of brake corner hysteresis in vehicle level, customer facing performance of chassis controls features can range from inconsequential to significant. With the crescendo-ing demand for high quality non-vehicle based methods for assessing chassis controls function, the effect of hysteresis is no longer academic. The present study starts with HIL based simulations, establishing the effect of brake corner hysteresis on one of the most visible chassis controls behaviors. An inertia dynamometer-based test was developed to exercises the subject brake corners through apply and release cycles, thus enabling any hysteretic behavior to be observed and characterized. Machine Learning models were trained with these data to represent brake corner hysteretic behavior and then deployed into an HIL simulation rig. The impact of these models – representing brake corner hysteretic behavior – was characterized for straight line stopping distance on low, medium, and high coefficient road surfaces.

Antanaitis, David, Ridenour, Nick, Miller, Bryan, Karnjate, Timothy

Research on High-Reliability Testing Systems for Intelligent and Connected Vehicle

2026-99-1111

9/11/2026

With the large-scale application of intelligent connected vehicles, the verification of their functional safety and reliability has become a core bottleneck in the industrial development. The traditional real- vehicle road test method can no longer meet the current demand for large-scale test verification due to problems such as high cost, low efficiency, and difficulty in reproducing dangerous scenarios. This paper studies the vehicle-in-the-loop simulation test system based on a digital twin. By constructing a virtual scenario highly consistent with the real world, physical-level multi-source perception signals are simulated and mapped to the system under test to enable high- reliability verification of real vehicles. In terms of lateral and longitudinal control functions, multiple sets of test cases are selected respectively for comparison between road tests and virtual simulation tests. The results show that the accuracy of key indicators is above 90%, which provides practical reference for the subsequent test and verification system of high-level autonomous driving.

Hou, Quanshan, Tang, Ke, Gao, Tian, Chen, Tao, Zhou, Si

Research on a Brake Energy Recovery Strategy Based on Improved ANFIS

2026-99-1134

9/11/2026

This paper addresses the issue of regenerative braking energy recovery in new energy vehicles and designs and optimizes a braking force distribution strategy. The strategy uses an ANFIS controller to dynamically optimize the proportion of front-axle regenerative braking force. The introduction of a pruning algorithm reduces computational complexity, thereby enabling a significant increase in mileage while maintaining stable driving performance. Co- simulations integrating Simulink and AVL Cruise, alongside Hardware-in-the-Loop (HiL) tests, the proof is that this strategy can still maintain excellent stability under different braking intensities. Moreover, it exhibits significantly higher energy recovery efficiency compared to benchmark strategies, while its effectiveness and real- time performance are successfully validated.

Lin, Hui, Zhao, Xuezhan, Tian, Jiahao

Data-Driven Robust Preview Control of Vehicle Active Suspension Considering Random Controller Area Network Communication Dropouts

10-11-01-0001

9/10/2026

With the shift to full-by-wire chassis architectures, active suspension control is progressively integrated into chassis domain controllers to achieve coordinated chassis management. However, random packet dropouts in controller area network (CAN) communication under high-load conditions can significantly degrade suspension control performance. To address this challenge, this study proposes a novel data-driven robust preview control method. First, the packet-dropout phenomenon in CAN communication is modeled as a Bernoulli random process, and an augmented state-space model of the active suspension system is constructed by incorporating road preview information. Second, based on zero-sum game theory, road disturbances and control inputs are modeled as adversarial players, leading to the formulation of a stochastic game algebraic Riccati equation (SGARE) for the suspension system. To improve data efficiency and reduce design complexity, a data-driven value iteration (VI) reinforcement learning algorithm is employed to approximate the optimal control solution, with rigorous proof of convergence. Simulation results demonstrate that the proposed algorithm provides effective and feasible solutions across different packet-dropout probabilities. Furthermore, hardware-in-the-loop simulations confirm the robustness and reliability of the proposed control scheme, showing that the active suspension system maintains stable performance even in the presence of random CAN communication losses.

Wang, Gang, Duan, Deyang, Zhou, Tingting, Liu, Suqi

Co-optimization of Energy Efficiency and Drivability for Dual-Motor Electric Commercial Vehicles: A Topology-Aware Hierarchical Approach

02-19-04-0026

7/29/2026

Dual-motor architectures provide additional operating degrees of freedom for electric commercial vehicles (ECVs), but the integration of automated manual transmissions (AMTs) introduces torque discontinuities during gear-related mode transitions. Existing energy management strategies usually focus on steady-state efficiency optimization, while the mechanical feasibility of mode transitions is often considered separately or neglected. To address this issue, this study proposes a topology-aware hierarchical control framework for dual-motor ECVs. The framework combines an offline global efficiency map with an online transition-feasibility arbitration mechanism. In the offline layer, the energy-oriented operating mode and torque split are extracted over the vehicle-speed and wheel-torque domain. In the online layer, a topology-based transition matrix is used to identify mechanically singular mode transitions, and potentially torque-interrupting commands are re-routed through feasible bridge modes. The proposed method embeds powertrain topology constraints into the real-time implementation of an offline optimal map, thereby complementing conventional global optimization methods with transition-feasibility arbitration. Simulation results under the CHTC driving cycle show that the proposed strategy improves torque continuity during mode transitions while retaining most of the energy-saving benefit of the unconstrained efficiency-oriented strategy. Compared with the rule-based strategy, the proposed method reduces SOC-equivalent energy consumption by 10.7%, and recovers 65.5% of the DP-achievable energy-saving potential. Hardware-in-the-Loop (HIL) results further demonstrate that the proposed online arbitration logic can be executed within the controller sampling period.

Song, Dafeng, Chen, Lexin, Zeng, Xiaohua, Ni, Lixin

Steering System Fault Diagnosis and Fault-Tolerant Control of Active Wheel-Corner Electric Vehicles

10-10-04-0036

7/9/2026

The Active Wheel-Corner (AWC) integrates driving, braking, steering, and suspension systems into the wheel end, forming a fully drive-by-wire, four-wheel independent steering and four-wheel independent driving (4WIS&4WID) vehicle platform. While improving vehicle control performance, the full by-wire architecture also places higher demands on system reliability and fault tolerance. The steer-by-wire system has electrical, communication, and software failure risks, which may cause the vehicle to lose steering capability and trigger severe traffic accidents. This article proposes a hierarchical active fault-tolerant control strategy based on fault information reconstruction (FAST-FTC), enabling fault diagnosis and active fault-tolerant control when the steer-by-wire system fails, effectively ensuring the steering maneuverability and lateral stability of the vehicle under fault conditions. First, the strategy designs an adaptive observer combined with the Dugoff tire model to estimate nonlinear tire forces, while introducing a fault factor to achieve quantitative grading of steering system faults. Second, a hierarchical controller is designed for steering system faults. The upper-level controller, based on Adaptive Super-Twisting Sliding Mode Control (AST-SMC), determines the generalized forces required to track the desired trajectory under different fault conditions. The lower-level controller, based on the Fault-Aware Model Predictive Control (FA-MPC) strategy, dynamically adjusts weight matrices according to the fault factor and tire reconstructed stiffness, coordinating the allocation of four-wheel driving and the steering of healthy wheels to ensure lateral stability. Finally, the effectiveness of the proposed active fault-tolerant control strategy is validated through Hardware-in-the-Loop (HIL) simulation and real-vehicle tests.

Xiao, Feng, Jiang, Yueyong, Cheng, Rui, Xu, Changhe, Tang, Xiangjiao, Gao, Fengling, Li, Jianhua

Feasibility Study on the Mechanical Adjustment of the Vibration Behaviour of a Powertrain Test Rig for Validating Longitudinal Vehicle Shuffle

2026-01-0735

7/1/2026

Vehicle manufacturers use Hardware-in-the-Loop (HiL) approaches to validate overall vehicle characteristics, including those dependent on the powertrain, at an early stage of vehicle development. A powertrain test rig is a typical example. In the specific setup, the vehicle engine and side shafts are mechanically coupled to the load machines of the test rig, eliminating the physical influence of the rims, tires and vehicle body. Adapting a specimen to the test rig changes some characteristics. This affects the specimen's vibration behaviour, making it more challenging to validate comfort-related characteristics. A particular example is longitudinal vehicle shuffle; the powertrain's first torsional natural frequency causes it. The natural frequencies of the real vehicle and device under test differ significantly, so a road-matching approach is not directly feasible. To account not only for tire-road contact but also for the missing vehicle mass, some scientific studies propose a purely model-based adjustment, without significant evidence. On the one hand, this has the advantage of flexible parameter adjustment, but on the other hand, the necessary computing technology and suitable parameterisation methods must be available. To investigate the extent to which the demand for a purely simulated adjustment is justified, this paper will consider a feasibility study that mechanically corrects for the missing vehicle influence. The method must determine the necessary target moment of inertia of real vehicles and the given one on the rig. This study presents a solution for reaching the target value. In addition, secondary constraints, such as manufacturing effort and costs, and safety aspects, must be considered. The approach should be flexible to accommodate variations in the most common vehicle and tire dimensions. Only by adapting the HiL to the target system, the actual vehicle, is it possible to perform road matching and thus validate driveability at an early stage in the development process.

Hübner, Carl, Prokop, Günther

Production-Ready Automated ECU Calibration Using Residual Reinforcement Learning

2026-01-0780

7/1/2026

Electronic Control Units (ECUs) have played a pivotal role in transforming motorcars of yore into the modern vehicles we see on our roads today. They actively regulate the actuation of individual components and thus determine the characteristics of the whole system. In this, the behavior of the control functions heavily depends on their calibration parameters which engineers traditionally design by hand. This is taking place in an environment of rising customer expectations and steadily shorter product development cycles. At the same time, legislative requirements are increasing while emission standards are getting stricter. Considering the number of vehicle variants on top of all that, the conventional method is losing its practical and financial viability. Prior work has already demonstrated that optimal control functions can be automatically developed with reinforcement learning (RL); since the resulting functions are represented by artificial neural networks, they lack explainability, a circumstance which renders them challenging to employ in production vehicles. In this article, we present an explainable approach to automating the calibration process using residual RL which follows established automotive development principles. Its applicability is demonstrated by means of a map-based air path controller in a series control unit using a hardware-in-the-loop (HiL) platform. Starting with a sub-optimal map, the proposed methodology quickly converges to a calibration which closely resembles the reference in the series ECU. The results prove that the approach is suitable for the industry where it leads to better calibrations in significantly less time and requires virtually no human intervention.

Kampmeier, Andreas, Badalian, Kevin, Koch, Lucas, Lee, Sung-Yong, Andert, Jakob

BO-NRPPO: A Bayesian Optimization–Tuned Proximal Policy Optimization Framework for Semi-Active Suspension Control

10-10-03-0025

6/25/2026

Semi-active suspension systems enhance ride comfort and handling performance by adaptively modulating damping characteristics. However, conventional model-based controllers often fail to maintain optimal performance under uncertain and time-varying vehicle conditions. This article proposes Bayesian Optimization–Tuned Proximal Policy Optimization with Non-Parametric Rewards (BO-NRPPO), a novel reinforcement learning (RL) framework that integrates Bayesian Optimization (BO) with Proximal Policy Optimization (PPO) and a non-parametric reward function (NRF). The proposed approach enables adaptive self-tuning, data-driven reward shaping, and uncertainty-aware policy learning. Moreover, a Trapezoidal Simple Moving Average (TSMA)–based reward normalization scheme is introduced to accelerate convergence and stabilize training. Simulation results across diverse driving scenarios demonstrate that BO-NRPPO outperforms the passive suspension, the classical Linear Quadratic Regulator (LQR), and PPO with parametric rewards. Specifically, compared to the passive suspension and the LQR baseline, BO-NRPPO achieves up to 6.63% and 5.14% improvements in handling stability, respectively. Concurrently, it delivers maximum enhancements of 46.96% and 42.55% in ride comfort over these two baselines. For real-world vehicle applications, this adaptive self-tuning capability significantly reduces the time-consuming manual calibration efforts typically required in chassis development. Furthermore, Hardware-in-the-loop (HiL) validation confirms its real-time applicability and robustness under uncertain driving conditions, highlighting its immense potential as a scalable intelligent suspension control solution.

Chen, Guoying, Wang, Xinyu, Wang, Jiaqi, Zhan, Xinwang, Bi, Chenxiao, Cong, Shiqi, Hua, Min, Sun, Tianjun, Gao, Zhenhai

Coordinated Path Tracking and Stability Strategy for Distributed Drive Electric Vehicles Using Model Predictive Control with Dual Feedback Adjustment

10-10-04-0034

6/3/2026

Distributed drive electric vehicles (DDEVs) provide enhanced maneuverability through independent wheel torque control, but coordinating precise path tracking with lateral stability remains challenging under aggressive driving conditions. This paper presents a coordinated control strategy that integrates model predictive control (MPC) for path tracking with a proportional gain controller for stability regulation. The proposed framework adopts a hierarchical design. The path tracking control leverages MPC to compute front steering commands while accounting for vehicle dynamics and preview errors. The stability adjustment uses dual proportional gain controllers to generate an additional yaw moment, which is adaptively balanced through a phase plane coordination mechanism, enhancing yaw stability during path tracking. The generated yaw moment is subsequently distributed to individual in-wheel motors with an optimization torque allocation method, respecting tire force limitations. The effectiveness of the proposed strategy is validated with hardware-in-the-loop (HIL) experiments under a double lane change maneuver. Results show that the coordinated approach improves path following and maintains yaw stability more effectively than conventional methods.

He, Yang, Zhu, Yuzheng, Guo, Ruixin, Zhu, Yueying, Xing, Chao, Liu, Shuangxi, Lin, Yier

Limit Stability Control for Corner Module Vehicles Based on Hierarchical Hybrid Game

10-10-04-0032

5/18/2026

Corner module vehicles (CMVs) achieve the decoupling of driving, braking, steering, and suspension, significantly enhancing vehicle handling potential, but under extreme operating conditions, the interactions between actuators severely constrain the improvement of vehicle handling performance. In order to mitigate conflicts between subsystems and enhance vehicle handling stability, a hierarchical hybrid game–based limit stability control method for CMVs is proposed in this article. Taking into account the handling potential of subsystems under limit conditions, a Stackelberg leader–follower game is designed by first designating Direct Yaw moment Control (DYC) as the leader and Active Rear Steering (ARS) as the follower. Subsequently, the DYC–ARS and Active Suspension System (ASS) were constructed into a non-cooperative game system, and the Nash equilibrium solution was solved through iteration. The lower-level controllers, respectively, established a tire force distribution model that minimizes the overall tire utilization rate and an active suspension force distribution model that does not affect the vehicle’s pitch, in order to enhance the safety margin of the vehicle under extreme conditions. Finally, the Hardware-in-the-Loop test results proved the effectiveness of the proposed controller.

Peng, Jinxin, Xiao, Feng, Ke, Yuan, Jin, Liqiang

An Active Collision Avoidance Method for Automobiles Based on Driving Intention Recognition and Improved Driving Safety Field

2026-99-0740

5/15/2026

Active collision avoidance methods are crucial components of vehicle active safety systems, which can effectively prevent collisions or mitigate collision-induced losses. To address the limitations of existing methods, particularly their insufficient foresight in dynamic traffic environments, this paper proposes an active collision avoidance control method based on driving intention recognition and an improved Driving Safety Field (DSF) model to enable more proactive and stable collision avoidance. First, a Hidden Markov Model (HMM) is trained using vehicle trajectory data from a public dataset to accurately identify the driving intentions of the obstacle vehicles, including Lane Change Left (LCL), Lane Keeping (LK), and Lane Change Right (LCR). Then, an improved potential field model is established, which incorporates vehicle acceleration to more comprehensively quantify the driving risk faced by the host vehicle within the DSF model framework. Subsequently, an active collision avoidance controller combining a longitudinal dual-PID braking controller and a lateral MPC steering controller is designed. This controller initiates corresponding avoidance actions based on the results of driving intention recognition and driving risk evaluation. Finally, co-simulation and hardware-in-the-loop (HIL) tests are conducted. The results demonstrate that, compared to conventional methods lacking driving intention recognition, the proposed method can initiate avoidance maneuvers approximately 1 s earlier, thereby more effectively avoiding potential collisions. Furthermore, key vehicle stability indicators, such as lateral acceleration and yaw rate, are significantly reduced during the avoidance process, indicating enhanced stability and satisfactory real-time performance. This method provides a solution for enhancing the active safety of vehicles in complex real traffic scenarios.

Pan, Yuxiang, Chen, Jin, Wang, Haitao, Bai, Xianxu

Robust Curvature Preview Drift Control Using STA-ISMC under Parameter Uncertainty

10-10-03-0021

5/6/2026

Autonomous vehicles exhibit extremely strong nonlinearity during drift. However, existing autonomous drift algorithms often neglect previewed path curvature and offer only limited consideration of road surface uncertainty because of the influence of vehicle nonlinear dynamics, which can affect tracking accuracy and robustness of drift control. To solve these problems, this study proposes a robust optimal drift control framework based on curvature preview. First, a preview vehicle kinematic model is constructed, and a preview model predictive control path-tracking controller that considers the forthcoming curvature is designed. Through the analysis of equilibrium points with additional yaw moment, a robust optimal drift controller is developed, which employs a three-degrees-of-freedom vehicle model with an additional yaw moment. This controller adopts integral sliding mode control with a super-twisting algorithm (STA) and exhibits good stability, which is verified through Lyapunov analysis. The proposed control algorithm is validated through hardware-in-the-loop experiments. The experimental results demonstrate that the proposed method significantly improves path-tracking accuracy and robustness under uncertain road surface conditions, thereby providing an effective control solution for drift-based path-tracking maneuvers.

Gan, Yurun, Song, Ziyu, Gu, Tongtong, Ding, Haitao, Xu, Nan, Zhang, Jianwei

Development and Validation of Torque Vectoring and Traction Control Strategies in a FSAE Vehicle Using a Dual-Axle Dynamometer HiL Platform

2026-01-0644

4/7/2026

This study presents the vehicle control optimization of a Formula SAE (FSAE) electric vehicle developed by National Taiwan University Racing Team (NTU Racing), utilizing a dual-axle dynamometer and a real-time Hardware-in-the-Loop platform from Chroma. The novelty of this work lies in the comprehensive system-level validation of independent torque control strategies, namely Torque Vectoring (TV) and Traction Control (TC), implemented directly within the vehicle control unit (VCU), and the high-fidelity simulation of dynamic driving scenarios based on the FSAE circuit. The vehicle features an independently controlled rear-axle, two-wheel drive (2WD) configuration, consisting of two in-wheel motors, self-developed inverters, and planetary gearboxes. During testing, a pre-built CarSim driver model provides throttle, brake, and steering inputs to the VCU via Controller Area Network (CAN) interface. The VCU, in turn, computes the independent torque commands according to the TV and TC strategies, which are then transmitted to the inverters and applied to the motors. The resulting torque output from the planetary gearboxes is measured and fed back into the CarSim vehicle model to simulate the rear wheel dynamics and command the dynamometers at the corresponding rotational speeds. The results show that with the dual-axle platform, the independent torque control strategies could be tuned effectively to improve vehicle dynamics, offering a more quantitative and precise approach for performance optimization compared to conventional Model-in-the-Loop (MiL) evaluations or driver-dependent feedback from track testing.

Hsiao, Tsung-Yu, Chen, Zhi-Ren, Jian, Rong-Wei, Chen, Tai-Hsiang, Wang, Tai-Jie, Hu, Wei-Zhe, Ho, Hui-Ting, Wu, Ting-Yu, Lin, Ting-He, Chiu, Joseph

AI-Enhanced Functional Safety in ADAS Controllers: Predictive Fault Management under ISO 26262

2026-01-0036

4/7/2026

Ensuring ISO 26262 functional safety in advanced driver assistance systems (ADAS) is increasingly complex as these platforms integrate artificial intelligence (AI) for perception, decision-making, and vehicle control. Traditional safety mechanisms are largely deterministic, but AI introduces non-determinism, creating challenges for verification, validation, and certification. Real-time vehicle telemetry, sensor outputs, and environmental inputs are processed through machine learning algorithms that forecast hardware and software faults before they escalate into hazardous conditions. These predictions are systematically integrated with ISO 26262 safety measures, enabling adaptive diagnostics, fault isolation, and rapid recovery strategies. The AI model introduces hazards such as data bias, model drift, opaque decision-making, and unsafe automation. A dedicated AI Hazard Analysis and Risk Assessment addresses data quality, validation, monitoring, explainability, and fail-safe mechanisms alongside system-level safety controls. The proposed approach demonstrates measurable improvements, including up to 25 % higher diagnostic coverage and fault-recovery times under 30 ms, while maintaining ASIL-D compliance and adhering to FTTI, SPFM, and DC requirements. Hardware-in-the-loop (HIL) simulations validate system performance and robustness under diverse operational scenarios. Future work focuses on uncertainty quantification and explainable AI integration, enhancing traceability and safety certification readiness for intelligent ADAS controllers. By demonstrating how AI can complement functional safety principles instead of conflicting with them, this study provides OEMs and Tier-1 suppliers with a roadmap for deploying certifiable, intelligent, and resilient ADAS platforms. This framework ensures safer, more reliable AI-enhanced vehicle systems while bridging the gap between emerging AI technologies and rigorous functional safety standards. This paper presents a predictive fault management framework that enhances functional safety in ADAS controllers by combining AI-driven predictive models with ISO 26262 safety mechanisms. This work uniquely bridges deterministic ISO 26262 workflows with predictive AI fault forecasting. In this framework, the AI model is used solely as a diagnostic enhancement and is not credited as an ISO 26262 safety mechanism; all safety decisions and fault reactions remain under deterministic safety-shell control.

Abdul Karim, Abdul Salam

Model-Based Design and dSPACE/MathWorks Environment Enable Innovative Engineering Education

2026-01-0073

4/7/2026

The exponentially growing complexity of engineering systems, such as robotic systems, autonomous vehicles, and unmanned aerial vehicles, require sophisticated control strategies that can efficiently coordinate system operation in various environments. The traditional control design approaches present significant challenges for control engineers to keep up with the increasing complexity and changing requirements. To advance embedded control system design, a paradigm shift from traditional development approaches toward more structured, systematic methodologies that can manage the multi-domain nature of control systems is critically needed. Model-based design approach is emerging as a solution for this demand. Model-based design approach uses a system model for control system development, from requirements capture to control system design, implementation, and testing. It provides an integrated environment for design, implementation, automatic code generation, and validation, which allows early error detection and continuous testing and verification. Model-based design reduces development time and cost, delivers higher-quality control systems, and enables collaboration among the engineers with different expertise. This paper presents a graduate-level course developed to train next generation of engineers with the skills for model-based embedded control system design. The development environment, including MATLAB/Simulink, dSPACE ConfigurationDesk, dSPACE ControlDesk, and dSPACE MicroAutobox III, is introduced. The course project, a control system for a hybrid powertrain, demonstrates the ability of students to develop a complicated control system using model-based design approach and perform validation through both Model-in-the-Loop (MIL) testing and Hardware-in-the- Loop (HIL) testing. The students’ feedback is very positive and shows that the course has prepared them well for their careers in industry and research institutions.

Repaka, Sindhura, Chen, Bo

Model-Based Development of the “Big 5” in Fuel Cell Systems

2026-01-0072

4/7/2026

Fuel cell systems are gaining traction across heavy-duty applications, driven by global decarbonization targets. Managing their inherent complexity and diverse architectural requirements, commonly organized into the “Big 5” fuel cell subsystems (stack, thermal, electric, anode, and cathode), necessitates advanced Model-Based Development (MBD) approaches. This paper presents and validates a constraint-graph-based, equation-oriented, acausal MBD methodology for fuel cell system (FCS) development, implemented in an industrial modeling environment. This methodology supports scalable functional and software development from 75 kW single-stack systems to twin-stack configurations exceeding 250 kW. It facilitates robust parameterization and reuse of consistently formulated, subsystem-level physical models across Model-in-the-Loop (MiL) to Hardware-in-the-Loop (HiL) environments, ensuring numerically robust software architectures and improved embedded control quality. Industrial application has demonstrated workflow benefits, including indicative reductions of approximately 30 percent in development time, 30 percent in calibration effort, and up to 15 percent in ECU memory utilization across multiple fuel cell system development programs. These improvements are primarily achieved through modular model formulation, systematic model reuse, and elimination of artificial delays enabled by the acausal modeling framework.

Bandi, Rajendra Prasad, Bleile, Thomas

MBSE-Driven XIL Workflow for Energy Evaluation of Automated Vehicles: From Simulation to VIL Testing

2026-01-0289

4/7/2026

With the increasing market penetration of automated vehicles, there is a critical need for credible and repeatable methods to quantify their energy impacts. This paper presents a Model-Based Systems Engineering (MBSE)-driven Anything-in-the-Loop (XIL) methodology for quantifying the powertrain energy consumption and potential savings from various controls for automated vehicles in realistic road scenarios while preserving high-fidelity powertrain behavior. The novelty of this approach lies in its use of a unified MBSE backbone (AMBER: Argonne National Laboratory’s [Argonne’s] MBSE-centric platform for transportation energy analysis) to automate the seamless and traceable progression from pure simulation to Vehicle-in-the-Loop (VIL) testing. This work utilizes Argonne's multi-vehicle simulation tool, RoadRunner, which automatically constructs closed-loop road scenarios (road geometry, vehicle sensors, other vehicles, and traffic controls) and connects them to Argonne’s validated, high-fidelity vehicle and powertrain models in Autonomie. The MBSE backbone in AMBER organizes requirements, interfaces, plant and controller models, and test scenarios into a single set of models that is maintained across pure simulation, Software-in-the-Loop (SIL), Processor-in-the-Loop (PIL), and VIL stages. Each stage has a clear role: simulation enables rapid development and validation of advanced models or controls across a large number of scenarios; SIL supports standalone algorithm verification and scenario down-selection; PIL validates real-time execution, inputs/outputs, and timing on the target processor; and VIL provides closed-loop evaluation with a real vehicle under controlled laboratory conditions. AMBER’s automated build and configuration enable rapid retargeting across platforms and repeatable scenario reproduction, making validation fast and cost-effective. To demonstrate its practical application, the workflow is used to validate the functionality and quantify the energy savings of an eco-driving control against a calibrated human driver model. Experiments show strong repeatability and consistent energy gains for the eco-driving strategy while preserving trip time, yielding average energy savings of 7.8% across the evaluated scenarios. Overall, the MBSE-guided XIL workflow shortens development time and reduces test cost by limiting on-road testing and lowering integration risk before track evaluation, while producing credible, closed-loop energy assessments traceable from requirements to test evidence.

Jeong, Jongryeol, Sharer, Phillip, Di Russo, Miriam, Das, Debashis, Zhang, Yaozhong, Karbowski, Dominik

Integrated Design and Validation of a Robust Lane Centering Controller for Automated Driving

2026-01-0037

4/7/2026

Lane centering is a critical active safety feature whose effectiveness depends on robust design and validation across diverse driving conditions. This paper presents the development of a Lane Centering Controller (LCC) using a structured model-based design workflow in MATLAB and Simulink. A kinematic bicycle model was employed to simulate vehicle dynamics and evaluate an angle based steering controller integrating both feedforward and feedback control paths. The controller was tested across multiple road geometries and speeds up to 65 mph to ensure tracking consistency and stability under nominal and perturbed conditions. Perception noise models for lane curvature and curvature rate were extracted from onboard camera data under controlled conditions, revealing Gaussian characteristics. No filtering was applied, allowing direct evaluation of the controller’s inherent robustness to raw signal variability. The LCC maintained a peak lateral offset within ±0.35 m and lateral jerk within ±9 m/s3, while respecting a steering comfort limit of ±3 Nm, thereby satisfying both functional and driver comfort requirements. The MATLAB based workflow also facilitated requirement traceability and automated test case validation, enabling quantitative comparisons of control response across different speeds and curvature transitions. These results establish a clear link between simulation fidelity and control performance, providing a reference for calibration transfer in higher fidelity environments. The paper concludes with discussion on extending the algorithm to real time Hardware-in-the-Loop (HIL) and Vehicle-in-the-Loop (VIL) platforms, demonstrating scalability toward full vehicle implementation and providing a validated framework for future high speed lane centering development.

Bijinepalli, Ravi Teja, Tambolkar, Pooja, Midlam-Mohler, Shawn

Characterizing the Impact of C-V2X PC5 Radio on Cooperative Driving Automation in Hardware-in-the-Loop Experiments

2026-01-0077

4/7/2026

The SAE J3216 standard defines Cooperative Driving Automation (CDA), which has received increasing attention in recent years as an umbrella framework encompassing a wide range of automated vehicle applications enabled by Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) technologies. Despite this growing interest, limited research has investigated the impact of Cellular Vehicle-to-Everything (C-V2X) on CDA applications, particularly with respect to agreement-seeking operations. This work presents a hardware-in-the-loop (HIL) experimental study designed to evaluate an Argonne National Laboratory designed CDA controller under different message configurations and varying C-V2X PC5 radio transmission frequencies. A three-vehicle car-following scenario was implemented in the Argonne-developed Roadrunner simulator, incorporating CDA agreement-seeking logic, vehicle powertrain models, and V2V communication modules. CDA messages were exchanged through two physical C-V2X PC5 radios, capturing realistic communication impairment caused by the hardware characteristics. Packet loss and cooperation ratio were evaluated as functions of transmission frequency and message scheduling strategy. To further investigate the role of packet loss in the agreement-seeking process, a four-state Markov chain model was applied to characterize mechanisms that reduce cooperation ratio. The results indicate that synchronous transmission of CDA messages introduces half-duplex constraints, leading to increased packet loss and reduced cooperative driving duration. Increasing the message transmission frequency improves overall cooperation time, although it results in higher packet loss rates. These findings provide insight into the behavior of C-V2X radios and their impact on CDA applications, with a particular emphasis on cooperation duration, while remaining agnostic to specific controller performance characteristics.

Zhan, Lu, Di Russo, Miriam, Das, Debashis, Stutenberg, Kevin, Misra, Priyash, Jeong, Jongryeol, Hyeon, Eunjeong

Control Strategy and Dynamic Characteristics of a Hydrogen Opposed-Cylinder Free-Piston Engine Generator

2026-01-5017

2/23/2026

Free-piston engine generator (FPEG), as a novel energy conversion device, has the advantages of good fuel adaptability and high energy utilization. Combustion variation between cycles poses a significant challenge to the running control of an FPEG. A hierarchical control strategy, including motion, combustion, and generation power controllers, is designed in this paper to achieve the stable and efficient running of a hydrogen-fueled opposed-cylinder FPEG prototype. Piston motion is controlled by adjusting the generation current, which is adjusted through iterative learning using piston displacement feedback and adaptive control using piston velocity feedback. Generating power is regulated by controlling the throttle opening angle, which is adjusted through iterative learning. A multidisciplinary joint mathematical model is developed to simulate the dynamic characteristics and verify the control strategy. The simulation results reveals that the dead center position accuracy can be maintained within ±0.3 mm when accounting for 25% combustion variation between cycles and misfires. The power generation can be adjusted between 20 kW and 30 kW, with the adjustment error maintained within ±0.3 kW. The prototype achieved an indicated power of 30.5 kW and an indicated thermal efficiency of 43.4% during the standard cycle. Hardware-in-the-loop testing was conducted for cold start, stable operation, and misfire conditions, confirming that the electronic controller meets the control requirements of the FPEG system.

Wang, Jiesheng, Liu, Liang, Xu, Zhaoping

An Integrated Model Approach for Predicting Vehicle Dynamics Performance in HiL Validation

2026-28-0044

2/12/2026

In the automotive industry, increasing noise regulations are influencing product sales and passenger comfort, creating a need for more effective noise testing methods. Hardware-in-Loop (HiL) based virtual acoustic testing serves as a critical step before Driver-in-Loop testing, allowing for the assessment of vehicle performance and noise levels inside and outside the vehicle under various conditions before physical prototype testing is performed. The Hardware-in-the-Loop (HiL) simulator setup is equipped with joystick control that requires a physical representation of the vehicle dynamics model provided as a Functional Mock-up Unit (FMU) in real-time format. In contrast, the vehicle control logic is implemented in C++ code. The simulator incorporates both lateral and longitudinal dynamics. Additional interfaces are integrated to support joystick input and virtual road visualization enabling realistic vehicle maneuvering and dynamic performance evaluation. However, performing all test protocols directly on the HiL setup can be time-consuming and costly. To address this limitation of full HiL testing, in this study, an offline Software-in-the-Loop (SiL) Co-simulation framework was developed as an alternative. This method replicates the HiL environment within MATLAB/Simulink, where joystick actions are simulated according to predefined driving protocols. The dynamic behavior of the vehicle during a reverse driving protocol, involving a 540° constant steering angle and 0–100% acceleration pedal input, was analyzed and compared between Offline SiL and HiL environments. Results demonstrated that 85% of key parameters exhibited strong correlation (R2 > 0.9), confirming that the offline SiL-based approach effectively replicates HiL performance. The remaining parameters also showed acceptable consistency. These findings indicate that the proposed Offline Co-simulation method is a promising, cost-effective, and scalable alternative for accurately predicting vehicle dynamic behavior, aligning well with current automotive industry needs for early-stage validation and optimization.

Visuvamithiran, Rishikesan, Chougule, Sourabh, Srinivasan, Rangarajan, Laurent, Nicolas

Research on Online Learning Architecture for Engine Combustion Prediction on Multi-Platform Simulation

03-19-01-0005

2/11/2026

To address the limitations of conventional offline data-driven models for engine parameter prediction in HIL testing, including poor generalization and inefficient use of supplementary data, this study develops an innovative cross-platform online learning architecture that integrates a pre-trained Python-based Wiebe parameter prediction model with high-fidelity MATLAB/Simulink engine simulation. The proposed framework incorporates five key functional modules (real-time data processing, online regression prediction, performance evaluation, incremental learning optimization, and engine simulation) to enable dynamic adaptation to varying engine conditions through seamless integration of Python’s incremental learning algorithms with Simulink’s simulation environment. By implementing a kth order polynomial decay learning rate strategy, the architecture significantly improves model convergence under limited training conditions while enhancing real-time performance and reliability in HIL testing scenarios. Experimental results demonstrate a 15% improvement in prediction accuracy compared to traditional offline methods, confirming the technical advantages of this MATLAB/Simulink/Python-based online learning approach for engine parameter prediction in industrial testing applications.

Wei, Mingxin, Shuai, Xiuyun, Wang, Zhaoyu, Zhao, Feiyang, Yu, Wenbin

TVF 2026 - eVTOL Progress : Advancing HIL and SIL Validation for eVTOL from Tip to Battery to Tail by Bloomy Controls

SM_EVTOL_2026-5233

1/27/2026

Advancing HIL and SIL Validation for eVTOL from Tip to Battery to Tail by Bloomy Controls

Blume, Peter

Fixed-Time Lateral Control of Autonomous Vehicles with Prescribed Performance

10-10-02-0012

1/27/2026

Autonomous vehicles regardless of the drivetrain configuration are highly sensitive to disturbances, uncertain dynamic parameters, and modeling errors. Neglecting these factors during trajectory-tracking or lane-keeping can cause the autonomous vehicle (AV) to deviate from its reference path, compromising safety and performance. In this work, a fixed-time prescribed performance backstepping controller integrated with a super-twisting-like algorithm is proposed to ensure fixed-time convergence of trajectory-tracking errors and robust stability under bounded uncertainty factors and external disturbances. A fixed-time prescribed performance approach is utilized to constrain the evolution of lateral and angular tracking errors, thereby limiting the risk of divergence and ensuring control stability. This framework is demonstrated by the Lyapunov-based stability analysis to demonstrate fixed-time stability in an arbitrarily small neighborhood around the origin. The framework is also validated through simulation on full-scale vehicle model. Moreover, virtual hardware-in-the-loop and real-time experiments are conducted on a reduced-scale QCar platform under uncertain parameters and external disturbances.

Bancel, Baptiste, Kali, Yassine, Nerguizian, Vahé, Saad, Maarouf

Accelerating Sunroof System Development with Modular Hardware-in-the-Loop Testing

2026-26-0519

1/16/2026

This paper presents a novel Hardware-in-the-Loop (HiL) testing framework for validating panoramic Sunroof systems independent of infotainment module availability. The increasing complexity of modern automotive features—such as rain-sensing auto-close, global closure, and voice-command operation—has rendered traditional vehicle-based validation methods inefficient, resource-intensive, and late in the development cycle. To overcome these challenges, a real-time HiL system was developed using the Real time simulation, integrated with Simulink-based models for simulation, control, and fault injection. Unlike prior approaches that depend on complete vehicle integration, this methodology enables early-stage testing of Sunroof ECU behavior across open, close, tilt, and shade operations, even under multi-source input conflicts and fault conditions. Key innovations include the emulation of real-world conditions such as simultaneous voice and manual commands, sensor faults, and environmental triggers using a software-controlled test environment. The system helps more than 60 automated test cases and makes regression testing easier without hardware reconfiguration, accelerating feedback cycles and enhancing software readiness. The results show that the framework efficiently identifies test case failures and speeds up validation timelines. The simulation model allows reuse for all ECU variants and streamlines test expansion for future functionalities. Simulation contributes a scalable and infotainment-free testing approach that enhances product quality, reduces dependency on physical prototypes, and supports continuous system integration in automotive control system.

Ghanwat, Hemant, Lad, Aniket Suryakant, Joshi, Vivek, More, Shweta

Advancing Automotive Radar Characterization: OTA HIL Testing for Enhanced ADAS Safety

2026-26-0043

1/16/2026

The precise validation of radar sensor is necessary due to surging demand for reliable Advanced Driver-Assistance Systems (ADAS) and autonomous driving technologies. Over-the-Air (OTA) Hardware-in-the-Loop approach is the optimal solution for the current challenges facing with traditional on road testing. This approach supports productive, controllable and repetitive environment because of its lab-based setup which will eliminates the drawbacks such as high costs, limited repeatability, safety related issues. Key parameters of radar such as accurate detection of objects, analysis of doppler velocity, range estimation, angle of arrival measurement, can be tested dynamically. And this test setup offers wide range of testing scenarios, including varying distance of target, relative speeds, simulation of objects and environmental effects also supported.OTA provides the flexibility to eliminate the physical test tracks or targets so that developers can simulate the errors, by introducing faults into the systems and validate the compliances as per the industry standards, OTA HIL testing completely reduces development time and costs through enhancing test coverages, which will increase radar performance. This paper describes the system architecture, test plans, experimental results, demonstrate the critical role of OTA HIL in advancing automotive radar workflows and ensuring reliable ADAS and autonomous driving functionalities.

Jadhav, Tejas, Karle, Ujjwala, Paul, Harshit, SNV, Karthik

NN FMU: Deep Learning Models for Next-Gen xiL Vehicle Simulation

2026-26-0452

1/16/2026

Functional Mock-up Units (FMUs) have become a standard for enabling co-simulation and model exchange in vehicle development. However, traditional FMUs derived from physics-based models can be computationally intensive, especially in scenarios requiring real-time performance. This paper presents a Python-based approach for developing a Neural Network (NN) based FMU using deep learning techniques, aimed at accelerating vehicle simulation while ensuring high fidelity. The neural network was trained on vehicle simulation data and trained using Python frameworks such as TensorFlow. The trained model was then exported into FMU, enabling seamless integration with FMI-compliant platforms. The NN FMU replicates the thermal behavior of a vehicle with high accuracy while offering a significant reduction in computational load. Benchmark comparisons with a physical thermal model demonstrate that the proposed solution provides both efficiency and reliability across various driving conditions. The paper discusses the workflow for model training and integration strategies for deep learning models within simulation tools like AMESIM and Simulink. With this approach significant time reduction is observed without affecting the accuracy when compared with the physical model. NN FMU also reduces efforts up to 40 % compared with traditional FMU conversion. CPU improvement from physical to NN FMU model achieved greater than 30 % reduction with the same accuracy. NN FMU maintains FMI compatibility and can be directly used in a wide range of XiL applications such as Model-in-the-Loop (MiL), Software-in-Loop (SiL), and Hardware-in-Loop (HiL) testing scenarios. This NN FMUs opens pathways for hybrid modelling approaches that combine data-driven and physics-based paradigms for automotive simulations.

Srinivasan, Rangarajan, Ashok Bharde, Pooja, Mhetras, Mayur, Chehire, Marc

Next Gen Automotive EDS Architecture Design Verification using Real-Time Function Mode Simulation & Failure Condition Analysis

2026-26-0454

1/16/2026

In the current automotive design and development of the Electrical Distribution System (EDS), at an earlier stage, before the physical prototyping is largely absent. Traditional methods for verification and validation of EDS are performed with HIL, SIL, MIL, prototype testing or physical vehicle trials reveal design errors at later stages in the development cycle, which may lead to redesign, prolonged timelines and increased failure rates at vehicle integration. Hence, there is a critical need for an early-stage simulation methodology that ensures robustness and reliability of E/E architecture with first-time-right readiness at the design stage itself. In this paper, a digital EDS architecture simulation introduces a mode-based structural behavioural approach where specific vehicle functions, failure conditions and malfunction scenarios are set up in a simulation environment with their corresponding electrical circuits for simulation. A function-specific truth table-based analysis model enabling the controller to control the electrical paths for different electrical loads dynamically. This methodology ensures digital verification of electrical loads behaviour at different operating conditions, power distribution and switching logics are accurately validated during the design stage, reducing production time issues and ensuring seamless transition to series production.

Jaisankar, Gokulnath, Warke, Umakant, Chakra, Pipun, Borole, Akash

Proactive AI-Enhanced Cybersecurity for Next-Gen Automotive Infotainment ECU

2026-26-0619

1/16/2026

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.

More, Shweta, Kulkarni, Shraddha, Kumar, Priyanshu, Ghanwat, Hemant, Joshi, Vivek

Vision-Based Framework for Automated Testing of Automotive HMI Systems Using Deep Learning Techniques

2026-26-0571

1/16/2026

Nowadays, digital instrument clusters and modern infotainment systems are crucial parts of cars that improve the user experience and offer vital information. It is essential to guarantee the quality and dependability of these systems, particularly in light of safety regulations such as ISO 26262. Nevertheless, current testing approaches frequently depend on manual labor, which is laborious, prone to mistakes, and challenging to scale, particularly in agile development settings. This study presents a two-phase framework that uses machine learning (ML), computer vision (CV), and image processing techniques to automate the testing of infotainment and digital cluster systems. The NVIDIA Jetson Orin Nano Developer Kit and high-resolution cameras are used in Phase 1's open loop testing setup to record visual data from infotainment and instrument cluster displays. Without requiring input from the system being tested, this phase concentrates on both static and dynamic user interface analysis, including screen transitions, animations, and error messages. Among the methods used are optical character recognition (OCR) for on-screen text validation, convolutional neural networks (CNNs) for screen classification, and object detection for user interface verification. Automated anomaly detection and interface behavior evaluation are made easier with this method. Phase 2 suggests integrating a Hardware-in-the-Loop (HIL) simulator to transform the system into a closed-loop testing environment. The vision-based system will assess system responsiveness and end-to-end behavior, while the HIL setup will produce simulated user inputs and vehicle network data (such as CAN, Ethernet). This thorough framework tackles important issues like complex system integration, multimodal interaction testing, and managing cognitive load. In order to support the creation of safer, more user-friendly infotainment and digital cluster systems that are in line with Advanced Driver Assistance Systems (ADAS) standards, it seeks to decrease the amount of manual testing effort, increase test coverage, and improve consistency.

Lad, Rakesh Pramod, Mehrotra, Soumya, Mishra, Arvind

Shaping the Future of Mobility with Innovative Tailgate Testing Solutions

2026-26-0473

1/16/2026

As vehicles evolve toward increased automation and comfort, Power Operated Tailgate (POT) have become a common feature, especially in premium and mid-segment vehicles. These systems, although user-friendly on the surface, involve complex interactions between electronic control units (ECUs), sensors, actuators, and mechanical systems. Ensuring the reliability, safety, and robustness of these features under diverse operating conditions presents a significant validation challenge. Traditional testing methods, which rely heavily on physical prototypes and manual interaction, are often time-consuming, expensive, and prone to human error. Moreover, testing certain safety [3] features, such as anti-pinch or stall protection, under real physical conditions poses inherent risks and limitations. This paper presents a Hardware-in-Loop (HiL)[1] based testing approach for POT [2] systems, offering a safer, faster, and more comprehensive alternative to conventional validation methods. The HiL platform is built around a real-time test environment using Real Time Software, framework, integrated with MATLAB/Simulink [5] based plant models representing motor behaviour, hall sensors, and tailgate dynamics. The ECU under test communicates via CAN [4] and other physical I/Os, while the plant models simulate realistic vehicle responses in real time. The HiL approach enables full automation of functional, diagnostic, and safety validation of the tailgate system including open/close commands, fault injections (open circuit, short faults), latch and sensor logic, and anti-pinch scenarios. This methodology significantly reduces prototype dependence, accelerates ECU software validation, and increases overall test coverage. Results show substantial improvements in fault detection, regression testing efficiency. The proposed solution demonstrates how HiL [1] testing is not only a cost-effective validation method but also a strategic enabler for scalable and safe development of automotive mechatronic systems. This paper concludes by discussing the long-term benefits and future scope of enhancing the HiL setup with remote diagnostics, and seamless integration with other systems. The automotive industry is undergoing a transformation with a growing emphasis on comfort, convenience, and automation. Power Operated Tailgate (POT) have become an integral part of modern vehicles, offering hands-free access, anti-pinch

More, Shweta, Ghanwat, Hemant, Shetti, Suraj, Jape, Akshay, Kulkarni, Shraddha, Jagdale, Nitin

Model Standardization for MBD throughout Entire Power Unit Development Process

2026-26-0428

1/16/2026

The distribution of mobility equipped with electrified power units is advancing towards carbon-neutral society. The electrified power units require an integration of numerous hardware components and large-scale software to optimize high-performance system. Additionally, a value-enhancement cycle of mobility needs to be accelerated more than ever. The challenge is to achieve high-quality performance and high-efficient development using Model-Based Development (MBD). The development process based on V-model has been applied to electrified power units in passenger vehicle. Traditionally, MBD has been primarily utilized in the left bank (performance design phase) of the V-model for power unit development. MBD in performance design phase has been widely implemented in research and development because it refines prototype performance and reduces the number of prototypes. However, applying the MBD to an entire power unit development process from performance design phase to performance verification phase is not achieved. This is because 1D models constructed during the performance design phase with the MBD are not intended for use in the performance verification phase. As a result, the MBD effect is insufficient for the high-efficient development. Therefore, uses of 1D models that made at the performance design phase for the left bank should be enhanced. In particular, the 1D system model that integrated sub-systems is important for applying the MBD to the verification phase. The 1D system model is able to use for a virtual calibration of control of a vehicle system. In addition, the verification with Hardware-in-the-Loop Simulation (HILS), Engine-in-the-Loop (EIL), and Power unit-in-the-Loop (PIL) is also achieved smoothly by using the sub-system models in the 1D system model. The 1D system model standardization is necessary for achieving the above high-efficient development process. This paper explains efforts to the standardize model assuming the use of MBD throughout the entire power unit development process and practical efforts of both performance design phase and the verification phase using the standardized model.

Ogata, Kenichiro, Katsuura, Akihiro, Tsuji, Minako, Matsumoto, Takumi, Iwase, Hiromu, Nakasako, Seiya, Takahata, Motoki

Integrated Quality Assurance Frameworks for Testing Advanced Automotive Systems in the Era of ADAS and E-Mobility

2026-26-0568

1/16/2026

Ensuring the safety and functionality of sophisticated vehicle technologies has grown more difficult as the automotive industry quickly shifts to intelligent, electric, and connected mobility. Software-defined architectures, electric powertrains, and advanced driver assistance systems (ADAS) all require strong quality assurance (QA) frameworks that can handle the multi domain nature of contemporary vehicle platforms. In order to thoroughly assess the functionality and dependability of next generation automotive systems, this paper proposes an integrated QA methodology that blends conventional testing procedures with model-based validation, digital twin environments, and real-time system monitoring. The suggested framework, which includes hardware-in-the-loop (HIL), software-in-the-loop (SIL), and over-the-air (OTA) testing techniques, concentrates on end-to-end traceability from specifications to validation. Simulating intricate situations for ADAS, electric vehicle battery temperature management, and dynamic system updates in connected platforms are prioritized. This study also outlines the main obstacles to integrating QA methods with changing regulatory environments and draws attention to discrepancies between operational performance in real-world scenarios and compliance benchmarks. Early fault detection, lifecycle validation, and continuous improvement are made possible by the QA process's transition from reactive to proactive through the integration of digital twins and predictive analytics. A strategic roadmap for QA specialists and test engineers to adjust to changing industry demands is presented in the paper's conclusion. In addition to promoting safety and dependability, the suggested framework speeds up time to market, lowers development costs, and increases consumer confidence in cutting-edge automotive technologies.

Komanduri, Arun Srinivas, Srivastava, Anuj

Human-Centric Automotive Interior Lighting Assessment: A Feedback-Based Approach

2026-26-0132

1/16/2026

In today’s world, automotive interior lighting systems not only need to meet rigorous internal test standards but also need to adapt with the changing customer’s expectation across different vehicle segments. As per technological advancements and consumer demands, these systems have become increasingly advanced and software driven. Traditionally, validation relied on physical integration with vehicle hardware, particularly infotainment system. However, this conventional approach presents several limitations, including dependency on mature hardware and software, challenges in testing and synchronization across multiple lighting modules, and constraints in design validation accuracy. To address these limitations, this paper introduces an innovative approach that employs real-time hardware-in-the-loop (HIL) simulation for virtual lamp testing. This method facilitates autonomous testing, enabling independent validation of interior lighting systems within a controlled virtual environment while eliminating the dependency on physical vehicle. By digitally controlling lighting systems, this approach provides several key advantages, including accelerated testing cycles, early-stage design validation, and integration testing and delivers higher validation accuracy through precise simulation of real-world scenarios. Additionally, the approach establishes an effective closed loop feedback mechanism for faster issue identification, contributing to significant reduction in overall testing time.

Shah, Kunal, Joshi, Vivek S., Mandloi, Prince

Robust Validation of Rotary Switch with TFT Display in Hardware in Loop Simulation

2026-26-0540

1/16/2026

Modern automotive systems are increasingly integrating advanced human-machine interfaces, including TFT displays, to enhance driver experience and functionality. Ensuring the reliability of these systems under diverse operating conditions is critical, especially given their role in vehicle control. This paper presents a Hardware-in-the-Loop (HIL) testing methodology for validation of rotary switch with TFT display. The HIL setup simulates real-world vehicle conditions, including CAN communication, power fluctuations and user interactions, enabling early detection of potential failure modes such as display flickering or communication loss. The results demonstrate improved robustness and reliability of the gear selection switch, supporting its deployment across multiple vehicle platforms.

Bhuyan, Anurag, Jahagirdar, Shweta, Khandekar, Dhiraj

Driving Thermal Intelligence – Pioneering Digital Twin-Driven Hardware-In-Loop for Smart Climate Control Modules

2026-26-0380

1/16/2026

Thermal comfort is increasingly recognized as a vital component of the in-vehicle user experience, influencing both occupant satisfaction and perceived vehicle quality. At the core of this functionality is the Climate Control Module (CCM), a dedicated embedded Electronic Control Unit (ECU) within automotive HVAC system [6]. The CCM orchestrates temperature regulation, airflow distribution, and dynamic environmental adaptation based on sensor inputs and user preferences. This paper introduces a comprehensive Hardware-in-the-Loop (HIL) [3] testing framework to validate CCM performance under realistic and repeatable conditions. The framework eliminates the dependencies on physical input devices—such as the Climate Control Head (CCH) and Infotainment Head Unit (HU)—by implementing virtual interfaces using real-time controller, and Dynamic System modelling framework for plant models. These virtual components replicate the behaviour of physical systems, enabling closed loop testing with high fidelity. Sensor data simulate critical environmental parameters including solar radiation load, outside air temperature (OAT), and evaporator temperature etc. Actuator of HVAC components such as blower motors, air flap actuators, and compressor control systems are used to represent real loads. The HIL setup supports real-time signal simulation, protocol emulation over LIN and CAN networks, and automated test execution. Additionally, fault injection capabilities allow for robust validation of diagnostic strategies and safety mechanisms. The framework facilitates early-stage validation, accelerates development cycles, and enhances product maturity by enabling exhaustive scenario testing without reliance on physical prototypes. Key outcomes include improved test coverage, reduced time-to-market, and scalable integration for future vehicle platforms. The paper also outlines future directions, including the incorporation of thermal intelligence through AI/ML algorithms, and the deployment of remote or cloud-based testing environments to support distributed development teams.

More, Shweta, Shinde, Vivek, Turankar, Darshana, Patel, Dafiya, Gosavi, Santosh, Ghanwat, Hemant

SEooC- Based Design Framework Integrated EV Power Conversion Systems under ISO-26262 Compliance

2026-26-0192

1/16/2026

The work completed on “System level concepts to test and design integrated EV system involving power conversion to satisfy ISO26262 functional safety requirement” is included in the paper. Integrating power conversion and traction inverter subsystems in EVs is currently popular since it increases dependability and improves efficiency and cost-effectiveness. Maintaining safety standards is at danger due to the growing safety requirements, which also raise manufacturing costs and time. The three primary components of integrated EV systems are the PDU, DC-DC converter, and onboard charger. Every part and piece of software is always changing and needs to be tested and validated in an economical way. Since the failure of any one of these components could lead to a disaster, the article outlines the economical approaches and testing techniques to verify and guarantee that the system meets the functional safety criterion.

Uthaman, Sreekumar, Mulay, Abhijit B, Gadekar, Pundlik

Testing Framework and Safety Evaluation for Vehicle-to-Vehicle (V2V) Charging Systems in Electric Mobility

2026-26-0176

1/16/2026

This paper presents a comprehensive testing framework and safety evaluation for Vehicle-to-Vehicle (V2V) charging systems, incorporating advanced theoretical modeling and experimental validation of a modern, integrated 3-in-1 combo unit (PDU, DCDC, OBC). The proliferation of electric vehicles has necessitated the development of resilient and flexible charging solutions, with V2V technology emerging as a critical decentralized infrastructure component. This study establishes a rigorous mathematical framework for power flow analysis, develops novel safety protocols based on IEC 61508 and ISO 26262 functional safety standards, and presents comprehensive experimental validation across 47 test scenarios. The framework encompasses five primary test categories: functional performance validation, power conversion efficiency optimization, electromagnetic compatibility (EMC) assessment, thermal management evaluation, and comprehensive fault-injection testing including Byzantine fault scenarios. Through systematic experimental validation using advanced power electronics simulation and hardware-in-the-loop (HIL) testing, we demonstrate 98.2% power conversion efficiency, sub-50ms fault detection response times, and compliance with automotive safety integrity level ASIL-D requirements. Our results establish the theoretical foundations and practical validation methodologies essential for next-generation V2V charging infrastructure deployment.

Uthaman, Sreekumar, Mulay, Abhijit B, Nikam, Sandip B.

Real-Time Multidimensional Vehicle Dynamic Stability Domain Calculation and Its Application in Intelligent Vehicles

2025-01-7324

12/31/2025

Vehicle stability is fundamental to the safe operation of intelligent vehicles, and real-time, high-accuracy calculation of the stability domain is crucial for maintaining control across the full range of driving conditions. Because the real stability domain is difficult to parameterize accurately and is shaped by multiple driving factors including vehicle-dynamics parameters and environmental conditions, existing approaches fail to capture the multidimensional couplings between time-varying driving inputs and the resulting stability boundaries. Moreover, these methods remain overly conservative owing to algorithmic limitations and cautious design assumptions, thereby restricting dynamic performance in complex scenarios. To address these limitations, this paper introduces a multidimensional vehicle dynamic stability region calculation framework under time-varying driving conditions and apply it into path tracking controller of intelligent vehicle. Sum-of-squares programming (SOSP) is enhanced with iterative shape functions to have a more precise description of the stability domain across discrete operating conditions. A feature analysis of the driving factors including key vehicle parameters and road conditions that influence stability-domain variation is conducted based on the SOSP. A multi-input-multi-output neural network is then used to continuously maps time-dependent driving factors to their stability-domain characteristics. Furthermore, a linear interpolation is adopted to parametrically represent the stability-domain boundary. Verification on a hardware-in-the-loop (HiL) platform demonstrates that the proposed method accurately captures the stability-domain characteristics in time-varying environments subject to multiple factors. Furthermore, the path-tracking controller equipped with the proposed model computes the stability-domain boundary in real time, improves maneuverability by limiting unnecessary interventions, and markedly reduces maximum tracking error.

Wang, Chengye, Zhang, Yu, Hu, Xuepeng, Qin, Haipeng, Wang, Guoli, Qin, Yechen

Optimizing Automotive Software Development for Hybrids through the V-Model Integrated with Functional Design and In-the-Loop Validation (SiL/HiL)

2025-36-0249

12/18/2025

This paper presents a comprehensive analysis of advanced methods for optimizing software development in hybrid vehicles, focusing on the V-Model methodology integrated with Model-Based Systems Engineering (MBSE), functional design techniques and In-the-Loop validation processes, and the incorporation of agile methodologies such as SAFe (Scaled Agile Framework). The increasing complexity of embedded systems in hybrid vehicles, driven by electrification and the introduction of autonomous and connected systems, demands systematic and rigorous approaches to ensure reliability, safety, and energy efficiency. Over the next sections, we will explore the fundamental principles of the V-Model, its adaptations to the context of hybrid vehicles, the implementation of functional design processes supported by MBSE, the application of Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) methodologies for system validation, and finally the integration of agile SAFe principles to manage complexity at scale. The goal is to provide a detailed technical overview that helps engineers and researchers implement more efficient development processes, reducing time to market while maintaining the robustness required for critical automotive systems.

Gomes, Cleber Willian, Natal, Icarus Lima

Development of a Hardware-In-the-Loop (HIL) Platform for Evaluating and Implementing Heated Injection Technology in GDI Engines

2025-36-0048

12/18/2025

Reducing pollutant emissions remains a major challenge for the automotive industry, driven by increasingly stringent environmental regulations. While solutions such as electric vehicles (EVs) and hybrid electric vehicles (HEVs) have been developed, internal combustion engines (ICEs) continue to dominate many markets, requiring additional emission control strategies. Traditional technologies like catalytic converters and advanced injection systems primarily optimize performance once the engine reaches its operating temperature. However, during the cold start phase, when engine temperatures are below optimal, combustion efficiency drops, resulting in increased emissions of non-methane organic gases (NMOG) and nitrogen oxides (NOx). This phase is further compromised by factors such as fuel droplet size and suboptimal catalyst performance. In response, this work presents the development of a Hardware-in-the-Loop (HiL) platform to study the impact of heated injection technology on cold start emissions in a 1.0L Gasoline Direct Injection (GDI) engine. By integrating simulation, modeling, and experimental validation, this research evaluates the potential of heated injectors to reduce harmful emissions during engine cold starts. The proposed system leverages vehicle downtime —such as door unlocking and prestart moments—to preheat the injectors, aiming for faster combustion stabilization compared to conventional solutions like heated catalytic converters. It is important to note that this project is still ongoing. The experimental phase is pending the arrival of new equipment, including heated injectors and dedicated instrumentation for accurate measurement and validation. Therefore, the current article focuses on the modeling and simulation phases, while the experimental results will be addressed in future work. Initial expectations suggest that this approach can significantly lower NMOG emissions, offering a promising and efficient pathway for improving the environmental performance of future ICE-powered vehicles.

Triviño, Juan David Parra, Teixeira, Evandro Leonardo Silva, De Lisboa, Fábio Cordeiro, Aguilar, Raul Fernando Sánchez, Oliveira, Alessandro Borges De Sousa

Thermal Control of Lithium-Ion Batteries Using a Model-Free Control Strategy

2025-36-0162

12/18/2025

The growth of the electric vehicle market has driven the advancement of technologies related to energy storage and lithium-ion cells, which stand out for their fast charge and discharge capabilities, high energy density, and long service life. This paper proposes a thermal control strategy for lithium-ion battery packs using the Active Disturbance Rejection Control (ADRC) method. The model is developed in Simcenter Amesim software, using cylindrical 21700 cells in a pack equipped with a water-cooling system, and was adapted for export in FMU format and integrated into MATLAB/Simulink, where the control algorithms were designed and simulated. From step input tests, a first-order transfer function was identified with a fitting of 97.67%, supporting the adoption of a first-order ADRC. The tests involved scenarios with changes in temperature reference and current disturbances typical of vehicle operation. Results indicate that ADRC performs satisfactorily in temperature tracking, even under actuator saturation, and particularly excels in disturbance rejection, outperforming the proportional-integral-derivative (PID) controller in speed and precision. Furthermore, ADRC proved robust to system degradation—an essential feature in the thermal management of batteries subject to aging. The proposed approach shows promise for real-world applications, offering thermal stability and extended system lifespan. For future work, experimental validation through Hardware-in-the-Loop (HiL) is suggested.

Leal, Gustavo Nobre, Fernandes, Lucas Pasqual, Ebner, Eric Rossini, Neto, Cyro Albuquerque, Leonardi, Fabrizio

Battery Thermal Control: A Comparison between Predictive and Reactive Controllers

2025-36-0159

12/18/2025

With the increase in hybrid and electric powertrains being developed, many concerns arise about the energy storage systems in all those vehicles. This unit supplies energy to every part, including its cooling system, so it becomes imperative that the BTMS balances the temperature and the energy spent on controlling it. This paper compares two fundamentally different control methods in four different test scenarios that simulate real situations faced in daily usage. The model is built digitally based on real NMC 21700 cells on Simcenter Amesim and then exported as an FMU file to MATLAB Simulink. The controllers were then created with the identified system and tuned to the FMU responses. The results indicate that the MPC can compensate for disturbances and act quickly on them, while the reactive nature of the PID takes longer to come into effect. However, the simulation with the MPC took much longer than the simpler PID, which can impact real-time situations, and the aggressive resulting nature of the predictive controller can lead to a higher cooling energy cost. Both had an acceptable response during all test cases and maintained the temperature despite the irregular heat generation profile in the cells. Future work will improve tuning and validate the controllers in real tests via hardware-in-the-loop methodology.

Fernandes, Lucas Pasqual, Ebner, Eric Rossini, Leal, Gustavo Nobre, Leonardi, Fabrizio, Neto, Cyro Albuquerque

Robust Lateral Trajectory Tracking of Intelligent Vehicles by Using Fuzzy Adaptive Dynamic Model Predictive Control

10-10-02-0009

12/3/2025

To further improve the smoothness and robustness of lateral trajectory tracking for intelligent vehicles under complex operating conditions, this study proposes and experimentally validates a fuzzy adaptive dynamic model predictive control (FADMPC) strategy on the basis of model predictive control (MPC) framework. Thereinto, a three-degrees-of-freedom vehicle dynamics model serves as the predictive model, and a recursive least-squares algorithm with a forgetting factor is used to estimate tire cornering stiffness, thereby improving model fidelity. A whale optimization algorithm (WOA)–based adaptive horizon scheduler is devised to address the sensitivity of the prediction horizon to vehicle speed and road friction, and a fuzzy regulator adjusts the weight on the lateral displacement error in the objective function in real time. Hardware-in-the-loop tests on jointed and split-road surfaces show that compared with adaptive dynamic MPC, traditional MPC, and linear quadratic regulator, the FADMPC markedly reduces the lateral tracking error and enhances vehicle stability while maintaining performance under variations in tire cornering stiffness and localization noise and satisfying on-board real-time constraints. As a unified control framework that combines model adaptation with online scheduling, the FADMPC offers an engineering pathway to robust trajectory tracking and provides theoretical and technical bases for on-board deployment and large-scale application.

Teng, Fei, Jin, Liqiang, Wang, Junnian, Yang, Chen, Fan, Jiapeng, Qiu, Neng, Li, Andong, Zhou, Yanbo

CARLA Hardware-in-the-Loop Simulation Environment for Autonomous Driving

12-09-02-0015

11/20/2025

This article suggests a validation methodology for autonomous driving. The goal is to validate front camera sensors in advanced driver-assist systems (ADAS) based on virtually generated scenarios. The outcome is the CARLA-based hardware-in-the-loop (HIL) simulation environment (CHASE). It allows the rapid prototyping and validation of the ADAS software. We tested this general approach on a specific experimental application/setup for a vehicle front camera sensor. The setup results were then proven to be comparable to real-world sensor performance. The CARLA simulation environment was used in tandem with a vehicle CAN bus interface. This introduced a significantly improved realism to user-defined test scenarios and their results. The approach benefits from almost unlimited variability of traffic scenarios and the cost-efficient generation of massive testing data.

Cardozo, Shawn Moses, Hlavác, Václav

Leveraging AI for Autonomous Vehicle Simulations in SIL and HIL Frameworks

2025-28-0271

11/6/2025

This paper offers recent ideas and its implementation on leveraging AI for off highway Autonomous vehicle Simulations in SIL and HIL frameworks. Our objective is to enhance software quality and reliability while reducing costs and efforts through advanced simulation techniques. We employed multiple innovative solutions to build a System of Systems Simulation. Physics based models are a prerequisite for detailed and accurate representation of the real-world system, but it poses challenges due to its computational complexity and storage requirements. Machine learning algorithms were used to create surrogate/reduced order models to optimize by preserving the expected fidelity of models. It helped to speed up simulation and compile model code for SIL & HIL Targets. Built AI driven interfaces to bridge windows, Linux and Mobile Operating systems. Time synchronization was the key challenge as multiple environments were needed for end-to-end solutions. This was resolved by reinforcement learning & optimization algorithms so that loss of information can be prevented. John Deere Operations Center™ Fleet management was integrated with vehicle simulators so that remote monitoring and control of machine configurations and settings for various autonomy mode could be validated in virtual environment. Gen AI based tools were used for creation of Test plan and its Automation to accumulate several hundred hours of test execution for autonomy related features. The team tested various SW & HW fault conditions to understand the impact and behavior of the system in autonomy mode. As part of the next steps this framework would be further scaled for future autonomy programs and product lines This adoption of AI-based methods has expedited the delivery of autonomous vehicles, ensuring they are technologically advanced and customized to meet customer needs.

Karegaonkar, Rohit P., Aole, Sumit, Dasnurkar, Swapnil, Singh, Vishwajeet, Saha, Soumyadeep

Automating Fault Detection Time Interval in Automotive Functional Safety Using HIL Simulation

2025-28-0274

11/6/2025

Functional safety is driven by number of standards like in automotive its driven by ISO26262, in Aerospace its driven by DO-178C, and in Medical its driven by IEC 60601. Automotive electronic controllers must adhere to state-of-the-art functional safety standard provided by ISO26262. A critical functional safety requirement is the Fault Handling Time Interval (FHTI), which includes the Fault Detection Time Interval (FDTI) and Fault Reaction Time Interval (FRTI). The requirements for FHTI are derived from Failure Mode Effect Analysis (FMEA) conducted at the system level. Various fault categories are analyzed, including electrical faults (e.g., short to battery, short to ground, open circuits), systemic faults (e.g., sensor value stuck, sensor value beyond range), and communication faults (e.g., incorrect CAN message signal values). Controllers employ strategies such as debouncing and fault time maturity to detect these faults. Numerous FDTI requirements must be verified to ensure compliance with FMEA-identified faults. Significant portion of total quantum of Test procedures of entire system are fault injection test cases, Manual testing of these cases is cumbersome, hence automating these tests is crucial for efficient regression testing. In HIL environment, ECU variables and communication signals are available for processing within tool which contains fault information which needs to be processed for FDTI calculations. The paper examines diverse strategies to handle the complexity of FDTI test cases in the HIL environment through automation, leveraging tool features, time trigger, time synchronization, post-processing techniques and real-time calculations during test execution to process FDTI calculations, ensuring thorough verification of functional safety requirements.

Lengare, Sunil, Yadav, Vikaskumar, Shiraskar, Pallavi

Validating Modular Applications in the FACE and GCIA Architectures

2025-01-0476

9/16/2025

Ground vehicle software continues to increase in cost and complexity, in part driven by tightly integrated systems and vendor lock-in. One method of reducing costs is reuse and portability, encouraged by the Modular Open Systems Approach and the Future Airborne Capability Environment (FACE) architecture. While FACE provides a Conformance Testing Suite to ensure portability between compliant systems, it does not verify that components correctly implement standard interfaces and desired functionality. This paper presents a layered test methodology designed to ensure that a FACE component correctly implements working communication interfaces, correctly handles the full range of data the component is expected to manage, and correctly performs all of the functionality the component is required to perform. This testing methodology includes unit testing of individual components, integration testing across multiple units, and full hardware in the loop system integration testing, offering a structured approach to validating FACE conformant components beyond formal conformance.

Lingg, Michael, Paul, Howard, Sullivan, Kyle, VanSolkema, William

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