Browse Topic: Control systems

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Multi-steering Mode and Path Following Control of Vehicle with Dual Modular Chassis2026-01-0625To be published on 04/07/2026
The cooperative formation of four-wheel-steering and four-wheel-drive (4WS-4WD) modular chassis offers a flexible and scalable solution for oversized cargo transportation. To enable coordinated control between the dual chassis modules, a distributed model predictive control (DMPC) framework is introduced. This framework facilitates inter-module communication between the front and rear chassis controllers, thereby achieving consistent and cooperative behavior. Each chassis module is equipped with an independent model predictive control (MPC) path tracking controller, which is designed based on a single-module dynamic model. The MPC cost function jointly optimizes path tracking accuracy and roll stability. In terms of path planning, a hierarchical framework is proposed: the upper layer generates a global path, while the lower layer derives local reference trajectories for the front and rear modules based on their respective poses. Moreover, multiple steering modes are analyzed under
Liu, Zuyang, Shen, Yanhua, Wang, Kaidi, Lv, Xin
Multiple-Parameter Joint Estimation for Unmanned Mining Trucks Based on Real-Time Cornering Stiffness Identification2026-01-0618To be published on 04/07/2026
High-precision estimation of key vehicle–road state parameters is essential for accurate and safe vehicle control, as well as for reliable trajectory tracking and path planning. However, the strong nonlinearity of tire forces makes real-time estimation of tire cornering stiffness challenging, and the uncertainty of this parameter significantly limits the performance of conventional estimation methods. To overcome this issue, this study proposes a novel joint estimation framework that integrates the Square-Root Cubature Kalman Filter (SCKF) with Moving Horizon Estimation (MHE). In the proposed approach, the SCKF provides high-accuracy estimation of the vehicle sideslip angle, while the MHE enables real-time identification and updating of tire cornering stiffness using vehicle state information over a short horizon. This establishes a closed-loop joint estimation mechanism between sideslip angle and cornering stiffness. Furthermore, the real-time updated cornering stiffness is
Xia, Xue, Shen, Peihong, Jiao, Leqi, Li, Tao, Chen, Huiyong, Zhao, Kun, Jiao, Leqi, Zhao, Zhiguo
Research on MPC-based Control of Electric Active Anti-roll Bar2026-01-0632To be published on 04/07/2026
To enhance vehicle lateral stability and anti-roll performance, a model predictive controller (MPC) is designed for an electrically driven active anti-roll bar. First, a vehicle roll dynamics model and an active anti-roll bar model based on motor drive are established. Then, model predictive controllers for the front and rear suspension anti-roll bars are designed to calculate the anti-roll moments for the front and rear suspensions. Finally, co-simulation using Carsim and Simulink is conducted to simulate typical vehicle driving scenarios. The simulation results of passive suspension, PID and MPC control are compared to validate the improvement in vehicle stability achieved by MPC control, and the sensitive parameters of the controller affecting anti-roll performance are analyzed. The research results show that, compared to passive suspension and traditional PID control, the maximum roll angle with model predictive control is reduced by 80% and 40%, respectively. The MPC controller
Shen, Leiming
Efficient Embedded Control of Nonlinear Systems for Software-Defined Vehicles: A Case Study Using the Inverted Pendulum2026-01-0075To be published on 04/07/2026
Software-defined vehicles (SDVs) are reshaping automotive control architectures by shifting intelligence to embedded systems, where computational efficiency is paramount. This paper presents the implementation of multiple control strategies such as PID, LQR, and MPC for the inverted pendulum on a cart problem, a canonical example of nonlinear and unstable system control. The objective is to demonstrate how sophisticated control algorithms can be optimized for execution on microcontrollers with limited processing power and memory, reflecting the constraints typical of embedded platforms in SDVs. Each control strategy is implemented with careful consideration of algorithmic complexity, real-time responsiveness, and resource utilization. Performance is evaluated across key metrics including stability, convergence time, and computational load, enabling a comparative analysis that highlights trade-offs between control fidelity and hardware efficiency. The results provide actionable insights
Vupparige, Varun, Pandya, Vidit
Research on Intelligent Vehicle Fleet Longitudinal Control Based on Fuzzy Model Predictive Control2026-01-0040To be published on 04/07/2026
Addressing the challenge of balancing tracking efficiency and stability in longitudinal cooperative control of intelligent vehicle platoons, this paper proposes a hierarchical control strategy integrating fuzzy logic with Model Predictive Control (MPC). The upper-layer controller, based on the platoon's longitudinal dynamics model, formulates an optimization objective function that considers both tracking performance and ride comfort. A fuzzy logic system is introduced to adaptively adjust the weighting coefficients within the MPC objective function in real-time based on the prevailing tracking state, outputting an optimal desired acceleration. The lower-layer controller, utilizing an inverse longitudinal dynamics model incorporating the engine's universal characteristic map and a brake system model, accurately translates the desired acceleration into throttle opening or brake pressure via a Proportional Integral Derivative (PID) control algorithm. To validate the control strategy, a
Li, Ye
Development of the Honda S+ Shift Utilizing e:HEV System2026-01-0418To be published on 04/07/2026
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, UKAI, YOHEI, Kurachi, Shinobu, SHIKI, KAZUKI, Nagakura, Akari, Maeda, Sadaharu, Mitogawa, Terumasa
A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)2026-01-0147To be published on 04/07/2026
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.
Adaptive Economic Model Predictive Control for Engine Emissions Management with Compensation for Performance Drift2026-01-0287To be published on 04/07/2026
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
Path Following Strategy for Multi-Axle Vehicles via Nash Game-Based Coordinated Control of AFS and DYC2026-01-0629To be published on 04/07/2026
Due to their complex structure and large inertia, multi-axle hub electric drive vehicles often suffer from response delays and insufficient control sensitivity. To improve path tracking accuracy and handling stability under high-speed and low-adhesion conditions, this paper proposes a coordinated control strategy that integrates a three-dimensional stability assessment with a Nash game-based approach for active front steering (AFS) and direct yaw moment control (DYC) systems. A path tracking control model tailored for multi-axle vehicles is developed, and a robust Tubular Model Predictive Control (Tube-MPC) method is employed to enhance trajectory tracking performance under model uncertainties and external disturbances. Within the prediction horizon of Tube-MPC, the system continuously evaluates whether the vehicle state remains within the stable region. If the state exceeds this region, a Nash game-based coordination mechanism is activated to formulate a multi-objective control game
Lv, Xin, Liu, Zuyang, Shen, Yanhua, Wang, Kaidi
Backward Fuzzy Driving Control for 6×4 Off-Road Vehicles2026-01-0148To be published on 04/07/2026
Offroad 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 6x4 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 6x4 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 6x4 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, Sam, Patterson, Albert, Sreenivas PhD, Ramavarapu, Norris, William, Soylemezoglu PhD, Ahmet, Nottage PhD, Dustin
Dynamics and Control Research of a Momentum-Wheel-Based Two-Wheeled Self-Balancing Vehicle Based on Cascade PID2026-01-0216To be published on 04/07/2026
Momentum wheel-based two-wheeled self-balancing vehicle represent typical underactuated, nonlinear systems, posing significant challenges for stability control under varying operating conditions. To achieve self-stabilizing control during uniform horizontal motion, a cascaded PID control method is proposed. This approach stabilizes the frame tilt angle via an outer-loop PID controller while simultaneously controlling the handlebar angle and momentum wheel speed through an inner-loop PID controller, thereby enabling constant-speed self-balancing vehicle operation. First, based on the Lagrange method, the five rigid-body Lagrangian functions of the self-balancing vehicle are derived. The nonlinear dynamic equations of the self-balancing vehicle system, incorporating Lagrange multipliers, are established. Using Taylor series expansion, these equations are approximated linearly near the system's equilibrium point, leading to the linearized dynamic equations of the self-balancing vehicle
wang, nannan, lu, jiahao, zhao, yanlei, fan, yigang
A Sub Optimal Fuzzy Control Strategy for Balancing Driver Demand and Actuator Acceleration in Electrified Powertrains2026-01-0451To be published on 04/07/2026
Electrified powertrains—such as Power Splits, Series Hybrids, and EVs with Disconnect Actuators—enable flexible management of actuator acceleration and torque from shared power sources. In power-limited or high-demand conditions, the Hybrid Supervisor must balance available power to sustain performance and drivability; poor coordination can cause control imbalance, reduced actuator performance, and unintended motion. Conventional methods often favour a single control objective, compromising overall system efficiency. This paper introduces FLAIR (Fuzzy Learning Adaptive Integral Response) Control, a supervisory strategy for actuator speed profiling and driver demand tracking in SIMO systems. FLAIR integrates a integral of tracking error with fuzzy inferencing to dynamically weigh multiple control goals, adapting acceleration limits in real time while preserving driver power demand tracking. Unlike rigid approaches, it enables bi-directional power-flow decisions—allocating system power
Banuso, Abdulquadri, Sha, Hangxing, Shenoy, Aayush, Madireddy, Krishna Chaitanya
Neural Surrogates and Reinforcement Learning for Heavy-Duty Diesel Torque Control: A Data-Driven Alternative to PID2026-01-0199To be published on 04/07/2026
Accurately reproducing the torque trace of regulatory drive cycles is central to heavy-duty diesel emissions certification and repeatable development testing. Conventional controllers—PI/PID with gain scheduling and feedforward mapping—can meet requirements but demand extensive hand-tuning and struggle with strong nonlinearities and time-varying delay. Building on prior work that quantified the limits and benefits of gain-scheduled and feedforward strategies under the FTP transient cycle, this work investigates a fully data-driven control framework. First, a recurrent neural network (LSTM) torque surrogate is trained on dynamometer data to capture engine dynamics from readily available signals (e.g., engine speed, commanded pedal/torque, recent torque history) and actuator constraints. Second, this learned “world model” is used to train a reinforcement learning (RL) policy that outputs pedal commands to minimize torque-tracking error while regularizing control effort and respecting
Cook, James, Puzinauskas, Paulius, Bittle, Joshua, Hall, Spencer
Vehicle Test Platform with Experimentally Correlated Tire Model for Torque Vectoring Control2026-01-0622To be published on 04/07/2026
This paper presents a testing platform for torque vectoring controls utilizing a 10 degree of freedom (DOF) vehicle simulation and a ⅕ scale radio control test vehicle. These vehicle simulations or “Digital Twins” have been widely adopted throughout the automotive industry for 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 Advanced Driver Assist Systems (ADAS). As a result, a simulation environment working in conjunction with a test vehicle represents an optimal hybrid approach. In this work, a 1/5 scale radio controlled vehicle with torque vectoring capability is designed and assembled. A high fidelity vehicle model is then constructed in the Matlab/Simulink environment to model test vehicle behavior. The test vehicle features independent suspension for
Petersen, Nicholas Conner, Robinette, Darrell
Study of Approaches for Enhanced Traction Control Response for Hybrid Powertrain Architectures2026-01-0415To be published on 04/07/2026
Traction Control functionality requires fast and accurate response by powertrain system for wheel slip control. Close loop control effort of wheel slip shall be greatly reduced with more accurate feedforward torque. This paper explores the method of achieving requested traction control torque with better accuracy for torque converter-based hybrid electric powertrain architectures using improved dynamic modeling of torque converter. This method uses input inertia compensation involving feedforward and closed loop control modeling of torque converter and includes conversion of traction control intervention request through appropriate domains. Commanded torques by hybrid controller at wheel with and without these approaches are compared in Model in the loop simulation environment to identify optimum approach.
Karogal, Indrasen, Ober, Mitchell, Banuso, Abdulquadri, Sharma, Ashay
Development of Tilt Test Facility2026-01-0265To be published on 04/07/2026
Mobility, fire power and protection are the three most important characteristics of an Armoured Fighting Vehicle (AFV). Transmission system facilitates mobility to the fighting vehicle. Combat Vehicles Research and Development Establishment (CVRDE) is developing Automatic Transmission for Armoured Fighting vehicles which includes gearbox, steering system, braking system and control system. In the battle field, AFVs have to operate at different gradients & side slopes. The Automatic Transmission system being developed by CVRDE is a hydro-mechanical automatic transmission in which hydraulic oil is used for lubrication & control of various systems of it. A Tilt Test Facility (TTF) has been designed, developed, and installed at CVRDE to simulate different angles of gradients from +35° to - 35° & side slopes from +20° to -20°. The TTF enables to evaluate the functional aspects of the transmission system at different angles. The TTF consists of two tiltable platforms, Rotary actuators
V, Uma, Naik, Sheshu
Model-Based Design and dSPACE/MathWorks Environment Enable Innovative Engineering Education2026-01-0073To be published on 04/07/2026
The exponentially growing complexity of engineering systems, such as a robotic system, an autonomous vehicle, and an unmanned aerial vehicle, requires sophisticated control strategies that can efficiently coordinate the system’s 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
Repaka, Sindhura, Chen, Bo
Intersection Assist For Vehicle With Dual-Trailers Using Nonlinear Model Predictive Control And Unscented Kalman Filter2026-01-0074To be published on 04/07/2026
This paper presents a novel approach utilizing Nonlinear Model Predictive Control (NMPC) and Unscented Kalman Filter (UKF) to predict system state and control the trajectory of the vehicle with double 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
Navigation Integrated End-to-end Path Planning Development and Vehicle Testing2026-01-0032To be published on 04/07/2026
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 and lighting conditions, and it shows improved route adherence in vehicle testing. The proposed model is validated in a comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test
Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin
Sensor Fusion Method for In-Cabin Humidity Prediction2026-01-0131To be published on 04/07/2026
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 without the need for a 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 (open/closed), 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 HVAC system. The paper details the development and
Ghannam, Mahmoud, Schroeter, Robert, Shaik, Faizan
A Comparison of Road Grade Preview Signals from Lidar and Maps2026-01-0026To be published on 04/07/2026
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
Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning2026-01-0041To be published on 04/07/2026
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
BEV drivetrain parameters sensitivity for efficient and effective active damping controls modeling2026-01-0004To be published on 04/07/2026
Effective active damping control is critical for Battery Electric Vehicle drive quality. Central to this control is 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 as 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 system. This approach provides engineers with a practical framework to focus on critical variables of driveline torsional system, reduce model complexity, and implement effective active damping controls strategies.
Sharma, Pranjal, Kolluri, Murali Mohan, Lin, Chihang
A Latent Space Framework for Modeling Transient Engine Emissions Using Joint Embedding Predictive Architectures2026-01-0423To be published on 04/07/2026
Accurately modeling exhaust emissions during transient events, such as rapid acceleration and deceleration, remains a long-standing challenge in the automotive field and is a prerequisite for real-time control and optimization in modern powertrain systems. The nonlinear emission behavior is difficult to capture, and early mathematical models frequently relied on approximations that limited their accuracy. With the rise of data-driven methods, especially deep neural networks, including multilayer perceptrons and long short-term memory (LSTM) networks, modeling capabilities have improved significantly. However, to achieve meaningful accuracy, these models often require deep architectures, which increase computational load and inference time. This makes their application in real-time control systems challenging, as controllers must run at high sampling rates while maintaining accuracy, which is often a contradictory requirement. This paper addresses the trade-off with a new framework
Sundaram, Ganesh, Gehra, Tobias, Ulmen, Jonas, Heubaum, Mirjan, Görges, Daniel, Günthner, Michael
Embedded Real-Time Control of a Distributed Trailer Propulsion System with Regenerative Braking for Net Neutral Load Towing2026-01-0065To be published on 04/07/2026
Towing significantly degrades vehicle efficiency, reducing battery-electric vehicle (BEV) range by up to 50% and increasing internal-combustion engine (ICE) fuel consumption by ~30%. To address this challenge, this paper presents a real-time embedded control architecture for a distributed trailer propulsion system that actively regulates drawbar force to achieve net neutral load towing while enabling regenerative braking. Unlike prior e-trailer concepts, this platform demonstrates a cost-effective, fully networked, model-based control implementation using commercial off-the-shelf components. The control system integrates dual Raspberry Pi controllers running ROS 2: one samples a hitch-mounted load cell at 100 Hz and the other processes OBD-II/CAN messages. Controllers designed in MATLAB/Simulink are deployed to a 5 kWh LiFePO₄ pack and a 5 kW traction motor, utilising embedded Linux for real-time scheduling and onboard data logging. Two operating modes are implemented: (i) PI
Joshi, Gaurav, Adelman, Ian, Liu, Jun, Donnaway, Ruthie
Method of Estimating Electric Vehicle Charge Time Based on Neural Networks2026-01-0382To be published on 04/07/2026
Accurate prediction of electric vehicle charging time is critically hindered by dynamic, non-linear factors including battery aging which is indicated by the State of Health (SOH), substantial power diversion to thermal management systems in extreme temperatures, fluctuating user-defined accessory loads, and hardware limitations of the charging infrastructure. Traditional estimation methods, reliant on static models or predefined calibrations, fail to adapt to these real-world variables, leading to inaccurate predictions and user dissatisfaction. This paper presents a novel data-driven estimation framework utilizing a tailored feedforward neural network architecture specifically designed for this complex task. The model processes a sensitive set of inputs—including initial State of Charge (SOC), SOH, battery temperature, charging station power level and user-selected target SOC—to effectively capture the intricate, non-linear interdependencies governing the charging process. The
Xie, Zhentao, shojaei, Sina, Weslati, Feisel
AI model creation and System Verification of “Telemotion” Infrastructure-Cooperative Autonomous Driving System using Digital Twin2026-01-0255To be published on 04/07/2026
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. Telemotion 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
Multi-Objective Reinforcement Learning Framework for Transmission Shift Schedule Optimization2026-01-0162To be published on 04/07/2026
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 realworld 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
An Integrated Model Approach for Predicting Vehicle Dynamics Performance during HiL Validation2026-28-0044To be published on 02/12/2026
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 noise levels inside and outside the vehicle under various conditions before real-world testing is performed. This study addresses the conversion of a physical vehicle model in the AMESim environment into FMU-RT (Linux) format, and the translation of a powertrain control system from Simulink to C++ code. Key challenges in the FMU-RT conversion include the integration of appropriate submodels for each component and enabling the interfaces necessary for SCANeR and virtual acoustic testing. In the C++ code conversion, challenges involved configuring the correct parameters, ensuring the model runs at a fixed time step and sampling time of 1 ms, and resolving issues related to the
Visuvamithiran, Rishikesan, Chougule, Sourabh, Srinivasan, Rangarajan, Laurent, Nicolas
Development of Multiple level Auto Mode for Climate control in electric vehicles2026-28-0008To be published on 02/12/2026
Electric vehicles, or EVs, are significantly transforming the automotive industry as a cleaner and more environmentally friendly which finds an alternative solution for the vehicles propelled using internal combustion engines. As EV utilize the energy directly from the battery which causes customer or a user to be in panic state as charging infra is not developed well in most of the countries, thus improving vehicle efficiency and driving range requires optimizing energy consumption from battery of EVs. Apart from traction motor load for driving, major supplemental loads in electric car are climate control system, sometimes referred to as HVAC (Heating, Ventilation, and Air Conditioning). HVAC systems can consume a lot of battery power, especially during severe temperatures, which can drastically reduce the vehicle's range. Range anxiety continues to be an important barrier to the general adoption of EVs, the intelligent climate management systems which helps to reduce HVAC power usage
Mulamalla, Sarveshwar Reddy, SV, Master Eniyan, M, Nisshok, Anugu, Anil, E A, Muhammed, Guturu, Sravankumar
MINER’S SAFETY BOT2026-28-0116To be published on 02/12/2026
Mining remains one of the most hazardous industries, with workers constantly exposed to life-threatening conditions such as toxic gases, high temperatures, and confined spaces. To address these concerns, we propose the design and development of an autonomous Miner’s Safety Bot aimed at enhancing underground safety through real-time environmental monitoring and intelligent navigation. The system is built around the ESP32 microcontroller, integrating critical sensors including the MQ7 gas sensor for detecting carbon monoxide and methane, the DHT11 sensor for measuring ambient temperature, and an ultrasonic sensor for obstacle detection. These components work to monitor hazardous parameters and assist the bot in navigating through narrow mine shafts. A servo motor connected to a remote-controlled chassis adjusts the bot's direction when obstacles are detected. The bot employs the ESP32-CAM module to transmit live visuals from the mining site to external observers. Furthermore, sensor data
D, Suchitra, D, Anitha, Muthukumaran, Balasubramaniam, Mohanraj, Siddharth, Subash Chandra Bose, Rohan
Development of an Optimized XCP Software Stack for Real-Time Validation of Vehicle Control Units Using Next-Generation Communication Protocols2026-26-01901/16/2026
With the rapid adoption of electric vehicles (EVs), ensuring the reliability, safety, and cost-effectiveness of power electronic subsystems such as onboard chargers, DC-DC converters, and vehicle control units (VCUs) has become a critical engineering focus. These components require thorough validation using precise calibration and communication protocols. This paper presents the development and implementation of an optimized software stack for the Universal Measurement and Calibration Protocol (XCP), aimed at real-time validation of VCUs using next-generation communication methods such as CAN, CAN-FD, and Ethernet. The stack facilitates read/write access to the ECU’s internal memory in runtime, enabling efficient diagnostics, calibration, and parameter tuning without hardware modifications. It is designed to be modular, platform-independent, and compatible with microcontrollers across different EV platforms. By utilizing the ASAM-compliant protocol architecture, the proposed system
Uthaman, Sreekumar
Advanced Driver Assistance for Sunroof Misuse: Alarm Trigger and Speed Limiting System2026-26-00411/16/2026
Sunroof-equipped vehicles are gaining rapid popularity in India, especially among young and urban users. However, unsafe practices like occupants protruding through the sunroof during driving have led to increasing injuries and fatalities, particularly in sudden braking or collisions. This behavior, prohibited under the Motor Vehicles Act, remains an overlooked safety risk in today’s vehicles. This paper presents an industry-first innovation: an Automated Safety Alarm and Speed Control System designed to detect and prevent sunroof misuse. Using integrated photoelectric and infrared beam sensors, the system detects human extension beyond the sunroof boundary while the vehicle is in motion. Upon detection, it triggers a tiered safety response: an immediate dashboard warning, an audible alert if vehicle speed exceeds 15 km/h and an active speed limiter that restricts vehicle speed to 20 km/h until safe conditions are restored. This marks a shift from passive warnings to active vehicle
Padmanapan, Gopi, Yadav, Sanjeev
Conceptual Framework for Real-Time Battery Health Estimation in Automotive Digital Twins Using Reinforcement Learning2026-26-06581/16/2026
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
Mitigating Air Flow Sensor Signal Variability to Optimize Engine Operation in Passenger Cars2026-26-05121/16/2026
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 Charging2026-26-01671/16/2026
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
Optimization of Tactile Vibrations in Agricultural Tractor Using Design of Experiments2026-26-03441/16/2026
One of agricultural tractors most important aspects is operator comfort. In addition to working long hours, tractor operators may be at risk for health problems due to vibrations and mechanical shocks. The tactile vibrations of a tractor are a major consideration when choosing one for agricultural use. This project's mandate includes a study of tractor vibration control problems. It is essential to investigate the governing system in order to determine the cause of the problem. Evaluating the vibrations transmitted via the tractor and using the design of experiments (DOE) approach to lessen vibrations on particular tactile regions were the study's goals. There are several measures currently under investigation which can be used to reduce the vibrations caused by resonance in this paper, these include reducing the natural frequency so as to be able to avoid resonance with the second order engine frequency and the damping coefficient; this will ensure the amplitude of vibration at
Baviskar, Shreyas, dhobale, Vishwajeet, Bhangare, Amit, Kunde, Sagar, Wagh, Sachin
Driving Thermal Intelligence – Pioneering Digital Twin-Driven Hardware-In-Loop for Smart Climate Control Modules2026-26-03801/16/2026
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
More, Shweta, Shinde, Vivek, Turankar, Darshana, Patel, Dafiya, Gosavi, Santosh, Ghanwat, Hemant
Simplified Simulation of Electric Vehicle Battery Packs Using Deep Learning Based Reduced Order Models2026-26-04031/16/2026
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
HIL Simulation of Power Electronics Switching Circuit for Automotive Software Application2026-26-05161/16/2026
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 Vehicles2026-26-05931/16/2026
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
Analysis of Energy Consumption for Heating City Electric Buses and Ways to Reduce It2026-26-01561/16/2026
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
Accelerating Sunroof System Development with Modular Hardware-in-the-Loop Testing2026-26-05191/16/2026
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
Performance Enhancement and Safety Discharge Control Strategies for SPMSM in Automotive Applications2026-26-01791/16/2026
Surface Permanent Magnet Synchronous Motors (SPMSMs) have gained significant attention in modern industrial, automotive, and aerospace applications due to their high efficiency, power density, and superior dynamic performance. This paper explores the fundamental principles, control strategies, and optimization techniques for SPMSMs. The study focuses on advanced vector control methods, i.e., Field-Oriented Control (FOC), to achieve precise torque and speed regulation. Additionally, to ensure the safety and reliability of EV motors. Active discharge strategies used in EV motor drives focus on circuit topologies, control techniques, and implementation challenges. The paper also discusses a comparison of Sinusoidal Pulse Width Modulation (SPWM) and Space Vector Pulse Width Modulation (SVPWM) techniques, where the maximum speed of the motor is achieved. The findings highlight the potential of SPMSMs in high-performance applications, emphasizing future research directions in energy
Munnur, Swathi, Gandhi, Nikita, Tendulkar, Swati, Masand, Deepika, Murty, V. Shirish, Peruka, Mahesh
Comparative Analysis of Pothole Depth Estimation Using LIDAR and Stereo Imaging with Polarized Lenses: A DOE Approach2026-26-02691/16/2026
Potholes are a common road hazard that significantly compromise road safety. Water filled potholes can be particularly dangerous. These hidden hazards may cause vehicles to hydroplane [1], leading to a loss of control and potential collisions. At night or in low visibility conditions, such potholes can appear deceptively shallow, increasing the risk of severe suspension damage or tire blowouts. Additionally, deep water intrusion can affect critical components such as the exhaust system, air intake, or electrical wiring, potentially leading to engine stalling or short circuits. This research proposes a novel approach for identifying and determining the depth of potholes, especially those that are filled with water. By integrating YOLO, cutting edge computer vision methods like stereo imaging and Lidar. We hope to create a system that can precisely detect and evaluate potholes' severity, reducing the risks connected to these road hazards. A structured 2k factorial Design of Experiment
Ashok, Deeksha, Kumar, Pradeep, Singh, Amandeep
Virtual Validation of ADAS Algorithms Using Driver-in-the-Loop Simulation2026-26-00501/16/2026
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
Robust Validation of Rotary Switch with TFT Display in Hardware in Loop Simulation2026-26-05401/16/2026
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
Optimizing Diesel Emission Controls through Advanced Software Solutions for HC Desorb Fault in Diesel Oxidation Catalysts2026-26-04241/16/2026
After the implementation of BS-VI emission standards, effective exhaust after-treatment has become critical in minimizing harmful emissions from diesel engines. One significant challenge is the accumulation of hydrocarbons (HC) in the Diesel Oxidation Catalyst (DOC). Certain hydrocarbons may adsorb onto the catalyst surface yet remain unreactive, leading to potential operational inefficiencies. This phenomenon necessitates the desorption of unreactive hydrocarbons to allow space for more reactive species, thereby enhancing oxidation efficiency and overall catalyst performance. The process of desorption (DeSorb) is vital to maintaining the balance of reactive hydrocarbons within the DOC. When a vehicle is idling, unburnt fuel produces hydrocarbons that accumulate in the DOC. Upon acceleration, these hydrocarbons can lead to an uncontrolled rise in temperature, resulting in DOC push-out, catalyst damage, and downstream impacts on the Diesel Particulate Filter (DPF). To mitigate these
K, Sabareeswaran, K K, Uthira Ramya Bala, Raju, Manikandan, K J, Ramkumar, YS, Ananthkumar
Introducing a Novel System Engineering Framework for the Safe Deployment of Software Defined-Vehicles2026-26-06901/16/2026
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
Feasibility and Performance of Post-Quantum Cryptography on Embedded Systems for Two-Wheeler Security2026-26-06221/16/2026
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
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