This paper presents an approach utilizing Nonlinear Model Predictive Control (NMPC) and Unscented Kalman Filter (UKF) to predict system state and control the trajectory of the vehicle with dual trailers in an intersection turn scenario. The UKF estimates vehicle and trailers’ lateral traversal velocity states and the NMPC controls the vehicle acceleration and steering to maintain the vehicle’s desired heading through the turn. The vehicle’s lateral traversal velocity function is formulated using Lyapunov based method which is used as a propagation function in the UKF to improve the estimation accuracy. The lateral traversal velocity is then used as one of the constraints in the NMPC problem. The overall estimation and the control scheme are formulated and assessed in the simulation environment. The simulation results show good tracking and curb avoidance performance.

Malla, Rijan

AI Model Creation and System Verification of “Telemotion” Infrastructure-Cooperative Autonomous Driving System Using Digital Twin

2026-01-0255

4/7/2026

The concept of the vehicle has changed as a result of many innovations over the last decade in the fields of connected, autonomous/automated, shared, and electric (CASE) technologies. At the same time, labor shortages in Japan are becoming more serious due to a decline in the working population. To help resolve these issues, a remote-controlled autonomous vehicle driving system called Telemotion has been developed that automates the movement of vehicles in production plants. This system is an autonomous driving and transportation system in which the recognition, judgment, and operation functions of driving are handled by a control system outside the vehicle that communicates wirelessly with the vehicle. This system utilizes artificial intelligence (AI) and other advanced technologies to realize safe unmanned autonomous driving, and is already in operation in production plants. Currently, efforts are under way to build a digital twin environment and conduct AI learning using computer

Hatano, Yasuyoshi, Iwazaki, Noritsugu, Nagafuchi, Yuhei, Iwahori, Kento, Tanaka, Atsushi, Uezu, Satoru, Kanou, Takeshi, Inoue, Go, Okamoto, Yuki, Oka, Yuhei, Kakuma, Daisuke, Chiba, Hiroya, Egashira, Kazuki, Ishikuro, Megumi, Sawano, Takuro

Development of a Model-Aware Data-Driven Control Algorithm for a Drive-by-Wire System for a Level 4 Automated Vehicle

2026-01-0043

4/7/2026

Precision control in Level 4 Automated Vehicles is essential for enhancing operational efficiency, accuracy, and safety. This work, conducted as part of ARPA-E’s NEXTCAR program, focuses on developing a robust hardware and software control solution to enable drive-by-wire functionality. A previous publication by the authors presented the hardware solutions for overtaking stock vehicle controls. This paper focuses on a model-based and data-driven control algorithm to enable drive-by-wire functionality for longitudinal and lateral motion control for a 2021 Honda Clarity Plug-In Hybrid Electric Vehicle. This vehicle was equipped with a set of sensors and an onboard processing unit to enable Level 4 automation. For lateral controls, an algorithm was developed to command steering torque to the electronic power steering module, ensuring the vehicle could attain the desired steering angle position at varying speeds. The system leveraged feedforward and feedback mechanisms. Feedback controller

Adsule, Kartik, Bhagdikar, Piyush, Drallmeier, Joseph, Alden, Joshua, Gankov, Stanislav

A Latent Space Framework for Modeling Transient Engine Emissions Using Joint Embedding Predictive Architectures

2026-01-0423

4/7/2026

Accurately modeling and controlling vehicle exhaust emissions, particularly during highly transient events such as rapid acceleration, is crucial for meeting stringent environmental regulations and optimizing modern powertrain systems. While conventional data-driven modeling methods, such as Multilayer Perceptrons (MLPs) and Long Short-Term Memory (LSTM) networks, have improved upon earlier phenomenological or physics-based models, they often struggle to capture the complex nonlinear dynamics of emission formation. These monolithic architectures attempt to learn from all available data, which increases their sensitivity to dataset variability. They often require increasingly deep and complex architectures to improve performance, thereby limiting their practical utility. This paper introduces a novel approach that overcomes these limitations by modeling emission dynamics in a structured latent space. Using a rich dataset combining real-world driving data from a Portable Emission

Sundaram, Ganesh, Gehra, Tobias, Ulmen, Jonas, Heubaum, Mirjan, Görges, Daniel, Günthner, Michael

A Comparison of Road Grade Preview Signals from Lidar and Maps

2026-01-0026

4/7/2026

Road grade can impact the energy efficiency, safety, and comfort associated with automated vehicle control systems. Currently, control systems that attempt to compensate for road grade are designed with one of two assumptions. Either the grade is only known once the vehicle is driving over the road segment through proprioception, or complete knowledge of the oncoming road grade is known from a pre-made map. Both assumptions limit the performance of a control system, as not having a preview signal prevents proactive grade compensation, whereas relying only on map data potentially subjects the control system to missing or outdated information. These limits can be avoided by measuring the oncoming grade in real-time using on-board lidar sensors. In this work, we use point returns accumulated during travel to estimate the grade at each waypoint along a path. The estimated grade is defined as the difference in height between the front and rear wheelbase at a given waypoint. Kalman filtering

Schexnaydre, Logan, Poovalappil, Aman, Robinette, Darrell, Bos, Jeremy

Efficient Embedded Control of Nonlinear Systems for Software-Defined Vehicles

2026-01-0075

4/7/2026

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

Vupparige, Varun, Pandya, Vidit

Vehicle Test Platform with Experimentally Correlated Tire Model for Torque Vectoring Control

2026-01-0622

4/7/2026

This paper presents a testing platform for the development of lateral stability control systems in independent motor electric vehicles (EVs). A 10 degree of freedom (DOF) vehicle simulation and a radio control test vehicle are constructed to enable controls validation scalable to full size vehicles. These vehicle simulations, or ‘digital twins’, have been widely adopted throughout the automotive industry due to their lower operating costs and ease of implementation. Virtual models are not perfect representations of reality, however, and physical testing is still necessary to validate systems for use in the real world. This is especially true when testing safety-critical features such as stability control. As a result, a simulation environment working in conjunction with a test vehicle represents an optimal hybrid approach. In this work, a high fidelity vehicle model is constructed in the Matlab/Simulink environment. To capture the effect of suspension, the digital twin is capable of

Petersen, Nicholas Conner, Robinette, Darrell

Backward Fuzzy Driving Control for 6×4 Off-Road Vehicles

2026-01-0148

4/7/2026

Off-road autonomous vehicle systems must be able to operate across unstructured and variable terrain while avoiding obstacles. This presents significant challenges in vehicle and control system design, especially for less conventional platforms such as 6×4 vehicles. While forward driving autonomy has developed and matured in recent years, effective reverse navigation remains an under-explored area of vehicle co-design. Reversing 6×4 vehicles have limited rear steering authority, an extended wheelbase, and asymmetric traction, which introduce complex dynamics into any control system that is used. To address this need, a robust and experimentally validated fuzzy logic control architecture for 6×4 reverse navigation was developed during the course of this project. This architecture incorporates both near-field and long-range path data with adaptive outputs controlling steering and velocity based on a rule base that covers the whole vehicle state space. This method has low computational

Dekhterman, Samuel R., Sreenivas, Ramavarapu S., Norris, William R., Patterson, Albert E., Soylemezoglu, Ahmet, Nottage, Dustin

A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)

2026-01-0147

4/7/2026

The shared autonomy framework has become an option with great potential in the field of autonomous vehicles. Human and machine control decisions typically demonstrate strengths in different scenarios. As a result, the robustness of systems can be enhanced by the collaboration between humans and autonomy. A shared autonomy architecture that takes into account both human and environmental factors was proposed in this work. The authority distribution between the human operator and the autonomy algorithm was determined by the Shared Autonomy Arbiter (SAB). Designed with a two-tier structure, the SAB incorporated a policy-level decision module, as well as a numerical-level arbitration tuning module. A fuzzy inference system (FIS) was incorporated to enhance the noise tolerance of the policy selection module. Furthermore, the human factor was taken into account by applying a projection to the users’ control input. The human operator’s control decision was projected by the Adaptive

Sang, I-Chen, Norris, William, Patterson, Albert, Sreenivas, Ramavarapu S., Soylemezoglu PhD, Ahmet, Nottage, Dustin S.

Multi-Objective Reinforcement Learning Framework for Transmission Shift Schedule Optimization

2026-01-0162

4/7/2026

Building upon previous work that successfully employed a Reinforcement Learning (RL) agent for the autonomous optimization of transmission shift programs to enhance fuel efficiency, this paper addresses a critical limitation of that approach: the neglect of human-centric factors. While the prior methodology achieved substantial fuel consumption reductions by training an RL agent in a Software-in-the-Loop (SiL) environment, it did not explicitly account for aspects such as driver comfort and preferences, which are paramount for real-world user acceptance and drivability. This work presents a multi-objective optimization framework extending the artificial calibrator to simultaneously maximize fuel efficiency and enhance driver comfort. The method introduces a modified RL reward function that penalizes undesirable shift behavior to ensure a smooth driving experience (drivability). This new methodology also incorporates a mechanism to capture and integrate driver preferences, moving beyond

Kengne Dzegou, Thierry Junior, Schober, Florian, Rebesberger, Ron, Henze, Roman, Sturm, Axel

Navigation Integrated End-To-End Path Planning Development and Vehicle Testing

2026-01-0032

4/7/2026

In order to achieve fully autonomous driving, point to point autonomous navigation is the most important task. Most existing end-to-end models output a short-horizon path which makes the decision process hard to interpret and unreliable at intersections and complex driving scenarios. In this research, we build a navigation-integrated end-to-end path planner on top of an openpilot open source model. We created a navigation branch that encodes route polyline geometry, distance-to-next-maneuver, and high-level instructions and combines with path plan branch using residual blocks and feed-forward layers. By adding minimal parameters, new model keeps the original openpilot tasks unchanged and have the path output based on the navigation information. The model is trained on diverse urban scenes’ intersections, and it shows improved route performance in vehicle testing. The proposed model is validated in a Comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test results

Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin

Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning

2026-01-0041

4/7/2026

This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging reinforcement learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating

Zhang, Yaozhong, Ammourah, Rami, Han, Jihun, Moawad, Ayman, Shen, Daliang, Karbowski, Dominik

Embedded Real-Time Control of a Distributed Trailer Propulsion System with Regenerative Braking for Net Neutral Load Towing

2026-01-0065

4/7/2026

Towing imposes substantial efficiency penalties on both battery-electric vehicles (BEVs) and internal combustion engine (ICE) vehicles, reducing range by 30-50%. This paper presents a proof-of-concept embedded control architecture for distributed trailer propulsion that actively regulates drawbar force to reduce towing loads. Unlike proprietary e-trailer systems requiring specialized hardware, the proposed implementation demonstrates feasibility using commercial off-the-shelf (COTS) components and open-source software. The distributed architecture employs dual Raspberry Pi 4B single-board computers communicating via ROS 2 at 20 Hz. The trailer-mounted controller executes a Simulink-generated control node coordinating load cell acquisition (HX711 ADC), motor CAN bus telemetry, and throttle commands to a 5 kW BLDC traction motor powered by a 5 kWh LiFePO4 battery pack. A vehicle-mounted controller logs OBD-II/CAN validation data. The control pipeline implements cascaded EWMA/Hampel

Joshi, Gaurav, Adelman, Ian, Liu, Jun, Donnaway, Ruthie

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

Repaka, Sindhura, Chen, Bo

Neural Surrogates and Reinforcement Learning for Heavy-Duty Diesel Torque Control

2026-01-0199

4/7/2026

Accurate torque-trace reproduction on regulatory drive cycles is central to heavy-duty diesel certification and development testing. Conventional controllers such as Proportional Integral Derivative (PID or PI) can be enhanced with gain scheduling and feedforward (FF) maps to satisfy requirements but require extensive calibration and are sensitive to nonlinearities and delay. This paper evaluates a data-driven control framework comprising a recurrent neural surrogate of engine torque (specifically an LSTM – long short-term memory) trained on engine/dynamometer data and a reinforcement learning (RL) policy trained using this surrogate (“world model”) to track requested torque while regularizing control effort. The RL policy (specifically TD3 – twin delayed deep deterministic) is benchmarked against tuned PID and PID+FF baselines on the Environmental Protection Agency’s Heavy Duty Federal Test Procedure (HD-FTP) segments using EPA regression criteria (slope, |intercept|, R2) and tracking

Cook, James, Puzinauskas, Paulius, Bittle, Joshua, Hall, Spencer

Development of Honda S+ Shift Utilizing e:HEV System

2026-01-0418

4/7/2026

Honda is promoting mobility electrification to realize a carbon-neutral society by 2050. Hybrid vehicles will remain advantageous over electric vehicles in terms of manufacturing cost and driving range until renewable energy usage increases, charging infrastructure is sufficiently developed, and battery costs are reduced. In response to this situation, Honda has developed a new control system, “Honda S+ Shift”, which further enhances the “emotional value of driving pleasure” inherent to the e:HEV system and creates new value for hybrid vehicles. Honda S+ Shift synchronizes the engine and vehicle speed and selects a virtual gear position according to the driver's operation such as acceleration, cornering, and deceleration. Subsequently, the system achieves the required system output in cooperation with a dedicated energy management system. It also works with each vehicle system, such as drive force control, sound control, and meter cluster, to stimulate all five senses of the driver

Murata, Naoya, Narimoto, Ryosuke, Saito, Masatoshi, Ishida, Daichi, Gunji, Hiroki, Mitogawa, Terumasa, Ukai, Yohei, Kurachi, Shinobu, Nagakura, Akari, Shiki, Kazuki, Maeda, Sadaharu

Integrated DBSCAN-Based Segmentation of Tractor Activity into Productive and Non-Productive States from GPS Data

2026-01-0169

4/7/2026

Accurate identification of Productive and Non-Productive States or tractor duty cycles—comprising working, idle, and transport states—is critical for performance analysis, fuel optimization, and emissions modeling in agriculture machinery and fleet monitoring. This study explores the application of integrated unsupervised machine learning (ML) techniques to classify duty cycles using GPS-derived parameters such as speed, location variance, and temporal patterns. Unlike supervised approaches, the proposed method does not rely on several labeled engine and vehicle parameters, making it scalable and adaptable across diverse operational contexts. Clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) in integration with hybrid rule-based and a road feature is employed to segment GPS data into distinct behavioral states. Feature engineering focuses on extracting motion signatures and spatial-temporal features that correlate with operational modes

Maharana, Devi prasad, Gangsar, Purushottam, Dharmadhikari, Nitin, Pandey, Anand Kumar

Sideslip Angle Estimation for Mining Trucks Based on Real-Time Cornering Stiffness Identification

2026-01-0618

4/7/2026

High-precision estimation of key vehicle–road state parameters is crucial for ensuring the accurate and safe control of mining trucks (MT), as well as for reliable trajectory tracking. Among these parameters, the vehicle sideslip angle is particularly critical for assessing and predicting lateral stability. However, its direct measurement is challenging, and its estimation typically depends on an accurate characterization of tire cornering stiffness. For MT, large variations in loading conditions (from empty to fully loaded) pose significant challenges to sideslip angle estimation due to the resulting nonlinearity and variability of tire cornering stiffness. To address this issue, a novel joint estimation framework integrating the Moving Horizon Estimation (MHE) and Square-Root Cubature Kalman Filter (SCKF) is proposed to simultaneously achieve high-precision estimation of both tire cornering stiffness for each tire and vehicle sideslip angle. In this framework, the cornering stiffness

Xia, Xue, Shen, Peihong, Jiao, Leqi, Li, Tao, Chen, Huiyong, Zhao, Kun, Jiao, Leqi, Zhao, Zhiguo

Adaptive Economic Model Predictive Control for Engine Emissions Management with Compensation for Performance Drift

2026-01-0287

4/7/2026

This paper addresses the changes in engine emissions due to in-use component changes through the synergistic application of predictive control, machine learning, and onboard adaptation. In particular, we consider an adaptive economic Model Predictive Control (eMPC) strategy to mitigate the effects of performance drift on Nitrogen Oxides (NOx) and Soot emissions from compression ignition (diesel) engines. A performance drift block, which applies a multiplier and offset to nominal emissions, is integrated with a high-fidelity Neural Network (NN) plant model to simulate these characteristic changes. To counteract variability, two online adaptation methods are integrated within the eMPC framework: One is based on Recursive Least Squares (RLS) and another on a continuously updated online NN. The proposed control architecture is validated through simulations over standard transient cycles. Results demonstrate that while the rate-based eMPC possesses inherent robustness to performance drift

Zhang, Jiadi, Li, Xiao, Kolmanovsky, Ilya, Tsutsumi, Munecika, Nakada, Hayato

Sensor Fusion Method for In-Cabin Humidity Prediction

2026-01-0131

4/7/2026

Maintaining optimal in-cabin humidity levels is part of occupant comfort, air quality, and the effective operation of climate control systems, particularly for functions like windshield defogging. This paper introduces a novel sensor fusion methodology for predicting in-cabin humidity distribution without dedicated humidity sensor. The proposed approach leverages readily available vehicle data, integrating information from ambient temperature sensors, in-cabin temperature sensors, occupant detection systems, window status, and climate control settings. By intelligently fusing these diverse data streams, a predictive model is developed to infer the dynamic humidity conditions within the vehicle cabin. We discuss the complex interactions between these parameters, such as the moisture contribution from occupants, the influence of external air ingress through open windows, and the dehumidifying or humidifying effects of the Heating, Ventilation, and Air Conditioning system. The paper

Ghannam, Mahmoud, Schroeter, Robert, Shaik, Faizan

BEV Drivetrain Parameters Sensitivity for Efficient and Effective Active Damping Controls Modeling

2026-01-0004

4/7/2026

Effective active damping control is critical for battery electric vehicle (BEV) drive quality. Central to this control is the open loop frequency response function between actuator (motor torque) and response (motor speed). While many driveline parameters influence the system dynamics, sensitivity analysis reveals that only a few of them significantly shape the resonance frequency and amplitude of crucial low frequency driveline modes. In this study, an orthogonal array-based sensitivity analysis is conducted and validated using 1D simulation tool Amesim. The results show that identifying the most influential parameters enables meaningful model simplification without loss of accuracy, guiding quick and efficient modelling to help design control systems. This approach provides engineers with a practical framework to focus on critical variables of driveline torsional system, reduce model complexity and implement effective control strategies.

Sharma, Pranjal, Kolluri, Murali Mohan, Lin, Chihang

Development and Testing of a Concentric Tube Camshaft for Continuous Adjustment of Intake Valve Duration and Timing in Otto and Miller Cycle Operations

03-19-02-0010

4/1/2026

The development of technologies capable of expanding the operational flexibility of internal combustion engines—particularly through advanced valve actuation strategies—has become essential for improving energy efficiency and reducing exhaust emissions. This work presents the design, manufacturing, and experimental evaluation of a novel, mechanically simple, and low-cost valve control system intended for spark-ignition engines originally designed to operate under the Otto cycle. The proposed innovation, designated VVT-D (Variable Valve Timing—Duration), introduces continuous and independent control of intake valve opening duration using a concentric tube camshaft architecture. Unlike conventional variable valve timing systems limited to phase control, the VVT-D concept enables continuous transition between Otto- and Miller-equivalent operating conditions by modulating intake valve duration as a function of engine load. This approach allows engine load control via Late Intake Valve

Alvares, Gabriel Coelho Rodrigues, Woiski, Emanuel Rocha, dos Santos, Paulo Sergio Barbosa, Kashani, Masoud Ghanbari, Gasche, José Luiz

Analysis and Determination the Time to Rollover of Tractor Semi-Trailer Vehicle Based on Multi-body System Analysis and Newton–Euler Equations

02-19-03-0013

3/27/2026

This article aims to determine the time to rollover (TTR) of a tractor semi-trailer vehicle (TSTV). It uses a full dynamics model for assessment, specifically applying multi-body system analysis and Newton–Euler Equations with a nonlinear tire model. The model is applied to investigate velocities ranging from 40 km/h to 80 km/h and magnitude of steering angles ranging from 12.5° to 300°. The times at which the Load Transfer Ratio (LTR), Roll Safety Factor (RSF), and lateral acceleration reach their maximum values are evaluated. The survey results demonstrate the impact of velocity and steering wheel angle on the time it takes for the LTR, RSF, and lateral acceleration to reach their maximum values. The time interval between the RSF reaching 1 and the LTR reaching 1 range from 0.144 s to 0.655 s. Similarly, the time it takes for the tractor body’s lateral acceleration to peak and the LTR to reach 1 varies between 0.228 s and 1.555 s. Additionally, the time interval from when the semi

Hung, Ta Tuan, Khanh, Duong Ngoc

Accuracy Requirements for the Road Friction Coefficient Estimation of a Friction-Adaptive Automatic Emergency Braking System as a Software Solution for Integrated Chassis Control Systems

10-10-02-0016

3/10/2026

As part of this work, the accuracy requirements for the road friction coefficient estimation of a friction-adaptive automatic emergency braking (AEB) system are determined using a complex, nonlinear vehicle model. The AEB system varies its trigger distance depending on an estimated value of the road friction coefficient. The accuracy requirements are determined at a driving speed of 40 km/h depending on the severity classification of ISO 26262 in the statistically relevant Euro NCAP test scenario with a stationary target vehicle. MATLAB/Simulink is used as simulation software. The permissible estimation error (difference between estimated value and road friction coefficient) is determined by the severity classification S1 (light and moderate injuries). The results show that the positive permissible estimation error (road friction coefficient is overestimated) must not exceed about 30% of the road friction coefficient to comply with the severity classification S1 of ISO 26262.

Ahrenhold, Tim, Wielitzka, Mark, Binnewies, Tomas, Henze, Roman

Research on the Working Characteristics of Thermal Management System of Refrigerant Direct Cooling for Battery

2026-01-7024

2/27/2026

This paper focus on the direct cooling plate with serpentine flow channels, the effects of heat load power, compressor speed, fan speed, and types of heating plates on the temperature field of the cold plate were investigated respectively based on the direct cooling thermal management system.The experimental results show that as the heating power decreases, both the overall temperature and temperature difference of the cold plate decrease synchronously. The temperature distribution along the flow channel is non-monotonic, with the highest temperature at the first elbow (T2/T3) and the lowest temperature at the outlet (T12), which is lower than the inlet temperature.A study on the T4-T11 region reveals that when the fan speed is low, with the increase of compressor speed, both Tmax and Tmin first decrease and then increase, while ΔT decreases. When the fan speed is constant at medium or high levels, as the compressor speed increases from low to medium, Tmax and Tmin decrease and ΔT also

Chen, Sijian, Huo, Guojun, Chen, Jiyong, Wei, Shaoliang, Zhang, Guihao, Zhang, Jinglong, Ju, Xinze, Yang, Xiaoxia

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

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

AI Smart Timer-Based Power Control and Energy Monitoring System for Home Appliances

2026-28-0060

2/12/2026

The paper presents the design and implementation of an AI-enabled smart timer-based power control and energy monitoring solution for household appliances. The proposed system integrates real-time sensing of electrical device parameters with cloud artificial intelligence for predictive analytics and automatic control. Continuous measurement of voltage, current and power consumption of the connected appliances are performed for analysis of the usage patterns. The appliance operation is completely automated by choosing between the best option which is the user-defined schedule or the load shifted schedule recommended by AI. The AI recommendation depends on peak demand of the day and the current load requirement thereby aiding approximate smoothening of daily load curve and improving load factor. The data collected is transmitted to the cloud for real-time and historical data collection, for prediction of consumption patterns, anomaly detection, and clustering appliances according to their

D, Anitha, D, Suchitra, Jain, Utsav, Maity, Souvik, Dinda, Atish

IoT-Enabled Cloud-Integrated Smart Parking System with Real-Time Monitoring and AI-Based Space Optimization for Next-Gen Mobility

2026-28-0113

2/12/2026

This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless

Deepan Kumar, Sadhasivam, S, Balakrishnan, Dhayaneethi, Sivaji, Boobalan, Saravanan, Abdul Rahim, Mohamed Arshad, S, Manikandan, R, Jamuna, L, Rishi Kannan

Miner’s Safety Bot: An ESP32-Based Autonomous Mobile Mine Safety Monitoring System

2026-28-0116

2/12/2026

Mining operations are important to industrial growth, but they expose the mining workers to risk including hazardous gases, elevated ambient temperatures, and dynamic structural instabilities within underground environments. Safety systems in the past, typically based on fixed sensor networks or manual patrols, fall short in accurate hazard detection amidst shifting mine conditions. The proposed project Miner's Safety Bot advanced this paradigm by leveraging an ESP 32 microcontroller as a mobile platform that integrates gas sensing, thermal monitoring, visual inspection and autonomous obstacle avoidance. The system incorporates MQ7 semiconductor gas sensor to monitor real time carbon monoxide (CO), offering detection range from 5 to 2000 ppm with accuracy of 5 ppm. Temperature and humidity are monitored through DHT11 digital sensor, calibrated to ensure reliability across the harsh microclimates in mines. Navigation and autonomous movement are enabled by Ultrasonic Sensor (HC-SR04

D, Suchitra, D, Anitha, Muthukumaran, Balasubramaniam, Mohanraj, Siddharth, Subash Chandra Bose, Rohan

Development of Smart Climate Control with Multiple Level Auto Calibrations

2026-28-0008

2/12/2026

Electric Vehicles (EVs) are rapidly transforming the automotive landscape, offering a cleaner and more sustainable alternative to internal combustion engine vehicles. As EV adoption grows, optimizing energy consumption becomes critical to enhancing vehicle efficiency and extending driving range. One of the most significant auxiliary loads in EVs is the climate control system, commonly referred to as HVAC (Heating, Ventilation, and Air Conditioning). HVAC systems can consume a substantial portion of the battery's energy—especially under extreme weather conditions—leading to a noticeable reduction in vehicle range. This energy demand poses a challenge for EV manufacturers and users alike, as range anxiety remains a key barrier to widespread EV acceptance. Consequently, developing intelligent climate control strategies is essential to minimize HVAC power consumption without compromising passenger comfort. These strategies may include predictive thermal management, cabin pre-conditioning

Mulamalla, Sarveshwar Reddy, SV, Master Eniyan, M, Nisshok, Anugu, Anil, E A, Muhammed, Guturu, Sravankumar

Research on Active Predictive Speed Control of Dual-Fuel Engine for Hardware Real-Time Applications

03-19-01-0004

2/9/2026

The present article proposes an active observation speed prediction control algorithm architecture for embedded applications, with the aim of addressing the problems of complex operating conditions, strong perturbations, and high control real-time requirements of high-pressure direct injection (HPDI) dual-fuel engines. A nonlinear speed prediction model with diesel and natural gas injection mass as inputs has been established, and the nonlinear model predictive control (NMPC) method is used to realize the optimized control of engine speed. In order to enhance the operational efficiency of the algorithm on the embedded platform, a system has been developed that includes an event triggering mechanism and a warm-start strategy. These mechanisms work in tandem to dynamically adjust the computation cycle. Additionally, a torque reduced-order expansion state observer (RESO) has been integrated to improve the accuracy of perturbation estimation and computational efficiency. The model-level

Yang, Xinda, Li, Yunhua, Chen, Dongdong, Li, Yao, Zhang, Shutao, Zhao, Feiyang, Yu, Wenbin

Feasibility and Performance of Post-Quantum Cryptography on Embedded Systems for Two-Wheeler Security

2026-26-0622

1/16/2026

The proliferation of connectivity features (V2X, OTA updates, diagnostics) in modern two-wheelers significantly expands the attack surface, demanding robust security measures. However, the anticipated arrival of quantum computers threatens to break widely deployed publickey cryptography (RSA, ECC), rendering current security protocols obsolete. This paper addresses the critical need for quantum-resistant security in the automotive domain, specifically focusing on the unique challenges of two-wheeler embedded systems. This work presents an original analytical and experimental evaluation of implementing selected Post-Quantum Cryptography (PQC) algorithms, primarily focusing on NIST PQC standardization candidates (e.g., lattice-based KEMs/signatures like Kyber/Dilithium), on microcontroller platforms representative of those used in two-wheeler Electronic Control Units (ECUs) - typically ARM Cortex-M series devices characterized by limited computational power, memory (RAM/ROM), and strict

Mishra, Abhigyan

Mitigating Urea Deposits in Selective Catalytic Reduction Systems through Software Control for Improved Performance in Low Duty Cycle Operations

2026-26-0424

1/16/2026

In the pursuit of achieving stringent BS VI emission standards, maintaining the efficiency of Selective Catalytic Reduction (SCR) systems is paramount, especially in vehicles operating under low duty cycles. A significant concern in such scenarios is the accumulation of urea deposits within the SCR, which can lead to detrimental push-out effects and compromised catalyst performance. This issue is particularly prevalent during low-temperature operations, where the conditions are less favorable for the effective conversion of nitrogen oxides (NOx). To address this challenge, an innovative software control system has been developed to monitor operating conditions and detect potential urea deposit faults. The software continuously evaluates parameters such as temperature and vehicle duty cycle, identifying conditions that may lead to urea crystallization within the SCR system. When unfavorable conditions are detected, the software triggers a fault alert that activates a regeneration

K, Sabareeswaran, K K, Uthira Ramya Bala, Raju, Manikandan, K J, Ramkumar, YS, Ananthkumar

Mitigating Air Flow Sensor Signal Variability to Optimize Engine Operation in Passenger Cars

2026-26-0512

1/16/2026

In order to control the engine performance which is driven by the strict emission regulations and customer request for the improved fuel economy, precise air intake measurement and fuel control system are essential. In the modern engines, the mass air flow sensor (MAF) acts an important role which provides a precise estimation of air flow from the clean side ducting of air intake system to engine control unit module (ECU). The hot wire mass air flow sensor are mounted on the clean side of the air intake system in order to protect the sensing element from the contamination and to extend their lifespan as well as maintain its accuracy. It is essential to maintain a steady and a uniform airflow at the sensing element of the MAF sensor for reliable sensor reading at different engine speeds and varying engine load. However, the physical limitations of engine packaging inside the engine bay, limits the sensor placement. Incorrect sensor mounting can lead to errors in the airflow estimation

Sonone, Sagar Dinesh, Zope, Mahesh, Kale, Vishal, Padmawar, Harshad, Sridhar, S, Kolhe, Vivek M, Panwar, Anupam

Innovative Preconditioning Solutions for Efficient High-Current Battery Charging

2026-26-0167

1/16/2026

In high-performance charging systems, managing higher currents is crucial for efficient battery charging. Elevated battery temperature is the main challenge for limiting the duration and effectiveness of high-current charging. Our proposal of control system addresses these barriers by optimizing charging time by maintaining optimal temperature ranges for the battery. This is achieved through innovative preconditioning solutions that are incorporated with active Battery cooling configurations. Our system features a unique preconditioning approach with dedicated active cooling circuit for the battery which will provide cooling to battery even though cabin HVAC (Heat Ventilation & Air-conditioning unit) is switched off. The active liquid cooling system ensures effective temperature management without additional energy consumption, while the dedicated Battery active liquid cooling system provides enhanced cooling capabilities for more demanding scenarios and preconditioning. By integrating

Badgujar, Pankaj Ravindra, Bhosale, Subhash, Dave, Rajeev

High-Fidelity Modeling of Vehicle Dynamics for Control-Oriented Stability Enhancement

2026-26-0093

1/16/2026

This study presents an integrated vehicle dynamics framework combining a 12-degree-of-freedom full vehicle model with advanced control strategies to enhance both ride comfort and handling stability. Unlike simplified models, it incorporates linear and nonlinear tire characteristics to simulate real-world dynamic behavior with higher accuracy. An active roll control system using rear suspension actuators is developed to mitigate excessive body roll and yaw instability during cornering and maneuvers. A co-simulation environment is established by coupling MATLAB/Simulink-based control algorithms with high-fidelity multibody dynamics modeled in ADAMS Car, enabling precise, real-time interaction between control logic and vehicle response. The model is calibrated and validated against data from an instrumented test vehicle, ensuring practical relevance. Simulation results show significant reductions in roll angle, yaw rate deviation, and lateral acceleration, highlighting the effectiveness

Duraikannu, Dinesh, Dumpala, Gangi Reddi

Introducing a Novel System Engineering Framework for the Safe Deployment of Software Defined-Vehicles

2026-26-0690

1/16/2026

The past decade has seen a systemic shift in the automotive landscape and the constituent parts of a vehicle. The automotive industry has shifted from a primarily hardware components industry to a software heavy industry, with software controlling majority of the vehicle functions. Coupled with the ability to fully update or evolve a vehicle’s capabilities or functionalities, post point of sale through software updates, the technical, commercial and service landscape of the automotive industry is rapidly changing. This has brought increasing focus to the concept of Software Defined Vehicle, where the vehicle is not only constantly evolving, but is also becoming more personalised by leveraging data collected through the life of the vehicle. This requires a rethink of the current development and deployment approaches for vehicles, which are software-intensive. In this paper, we introduce a novel four-step system engineering framework for the safe development and deployment of Software

El Badaoui, Halima, Jame-Elizebeth, Mariat, Khastgir, Siddartha, Jennings, Paul

Model Based Design of Gear Shift Control for Twin Clutch AMT

2026-26-0054

1/16/2026

Model Based Design (MBD) uses mathematical modelling to create, test and refine systems in simulated environment, primarily applied in control system development. This paper discusses an approach to control gear shifting using shift logic on vehicle level for twin clutch transmission using prototype controller. Twin clutch transmission is a concept with two clutches, one at input end of the transmission called primary clutch and the other at output end of the transmission called secondary clutch. This concept is proposed to counter the challenges with conventional transmission which include increased gear shift time and effort in lower gears, potential rollback of vehicle in uphill condition and chance of missed shifts. The advantages of this concept include reduced gear shift effort and improved synchronizer life with potential for reducing the size of the synchro pack. This paper proposes a methodology to develop shift logic, integrate hardware with software, flashing and calibration

Patel, Hiral, Thambala, Prashanth, Tongaonkar, Yogesh, Mosthaf, Joerg, Malpure, Khushal

Conceptual Framework for Real-Time Battery Health Estimation in Automotive Digital Twins Using Reinforcement Learning

2026-26-0658

1/16/2026

The explosive growth of electric vehicles (EVs) calls forth the need for smart battery management systems that can perform health monitoring and predictive diagnostics in real-time. The conventional battery modelling methods mostly do not cover the complicated, dynamic behaviors coming from different usage patterns. The study outlines a structure that would use Reinforcement Learning (RL)-based AI agent as a part of the Battery Electrical Analogy (BEA) simulation platform. With the help of the AI agent, different health parameters such as State of Health (SOH), State of Charge (SOC), and the signs of early thermal runaway can be predicted in real-time. The suggested design takes advantage of the simulation-based approach to have the agent learn and utilizes a decentralized cloud architecture suitable for scaling and reducing the response time. The RL agent performs an essential role in the process by tagging along with the continuous learning and the adjustment of the battery

Pardeshi, Rutuja Rahul, Kondhare, Manish, Sasi Kiran, Talabhaktula

HIL Simulation of Power Electronics Switching Circuit for Automotive Software Application

2026-26-0516

1/16/2026

Power electronics switching applications are essential for energy management and conversion in automotive electric vehicles (EVs). This paper focuses on DC-DC converters, particularly the integration of 48V DC-DC converters in modern automotive systems. These converters are crucial for efficient power delivery to auxiliary systems such as infotainment, lighting, safety electronics, and thermal management units. In mild hybrid electric vehicles (MHEVs), 48V systems support advanced features like regenerative braking, electric turbocharging, and start-stop functionality. To ensure the reliability, safety, and performance of these converters, Hardware-in-the-Loop (HIL) testing has emerged as a powerful validation technique. HIL enables real-time simulation of the converter’s electrical environment and load conditions, allowing comprehensive testing of the control system without high-level voltage, significantly reducing development time, cost, and risk. The methodology involves utilizing

Yadav, Vikaskumar, Wakure, Vinod

Smart Charge Port Housing with Integrated Single Shaft Self-Locking Mechanism for Electric Vehicles

2026-26-0593

1/16/2026

The invention tackles the main drawback of traditional electric vehicle charge ports which use Vehicle Control Unit (VCU) communication intensively and tend to have separate actuators to fulfill the locking function and requirements. These existing systems do not only limit autonomous operation of the charging lid in ignition-off condition but they also add mechanical complexity and packaging space, as well. To overcome these limitations, this research work introduces a Smart Charge Port Housing (CPH), which combines a rotary actuator with an onboard microcontroller and single shaft self-locking device, which allows intelligent and autonomous control of the flaps without relying on vehicle wide control networks. The actuator can remember the last position that the charging lid was in so it can be operated even while the VCU is in the inactive state. The integrated self-locking functionality is achieved by using a specially designed hinge shaft that allows a certain free play for

Mohunta, Sanjay, Khadake, Sagar

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

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

Simplified Simulation of Electric Vehicle Battery Packs Using Deep Learning Based Reduced Order Models

2026-26-0403

1/16/2026

The increasing adoption of electric vehicles (EVs), efficient and accurate battery modeling has become crucial for reliable performance evaluation and control system design. However, maintaining high accuracy in simulations generally requires complex computations, which can limit real-time applicability and scalability. High-fidelity battery models often require significant computational time, making them unsuitable for real-time simulations and large-scale system integration. This paper presents the application of Simulink Reduced Order Models (ROM) to simplify the simulation of EV batteries while maintaining acceptable levels of accuracy. The EV simulation environment has been developed in MATLAB/Simulink to analyze Battery Management System (BMS) control system design and assess EV system level performance. This simulation platform consists of BMS and other important EV controller models and high-fidelity plant models for battery and powertrain systems. While these high-fidelity

Vernekar, Kiran

Virtual Validation of ADAS Algorithms Using Driver-in-the-Loop Simulation

2026-26-0050

1/16/2026

Driver-in-the-Loop (DIL) simulators have become crucial tools across automotive, aerospace, and maritime industries in enabling the evaluation of design concepts, testing of critical scenarios and provision of effective training in virtual environments. With the diverse applications of DIL simulators highlighting their significance in vehicle dynamics assessment, Advanced Driver Assistance Systems (ADAS) and autonomous vehicle development, testing of complex control systems is crucial for vehicle safety. By examining the current landscape of DIL simulator use cases, this paper critically focuses on Virtual Validation of ADAS algorithms by testing of repeatable scenarios and effect on driver response time through virtual stimuli of acoustic and optical warnings generated during simulation. To receive appropriate feedback from the driver, industrial grade actuators were integrated with a real-time controller, a high-performance workstation and simulation software called Virtual Test

Sharma, Chinmaya, Bhagat, Ajinkya, Kale, Jyoti Ganesh, Karle, Ujjwala

Analysis of Energy Consumption for Heating City Electric Buses and Ways to Reduce It

2026-26-0156

1/16/2026

For regions with cold climate, the range of an electric bus becomes a serious restriction to expanding the use of this type of transport. Increased energy consumption affects not only the autonomous driving range, but also the service life of the batteries, the schedule delays and the load on the charging infrastructure. The aim of the presented research is to experimentally and computationally determine the energy consumption for heating the driver's cabin and passenger compartment of an electric bus during the autumn-winter operation period, as well as to identify and analyze ways to reduce this energy consumption. To determine the air temperature in the passenger compartment, a mathematical model based on heat balance equations was used. This model was validated using data from real-world tests. The research was conducted at a proving ground under two conditions: driving at a constant speed and simulating urban bus operation with stops and door openings. The causes of heat loss in

Kozlov, Andrey, Terenchenko, Alexey, Stryapunin, Alexander

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