Browse Topic: Control systems

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Trajectory Tracking Control and State Estimation for Highly Automated Vehicles: A System Framework Implemented on UShift II2026-01-0779To be published on 07/01/2026
Trajectory tracking control and state estimation are core functionalities of highly automated vehicles and must operate reliably under strict real-time constraints as well as in the presence of model uncertainties and limited sensor availability. In particular, low-speed maneuvers pose additional challenges due to weakly excited vehicle dynamics and system delays. This paper presents an integrated, real-time capable framework for trajectory tracking control and state estimation, developed within the UShift II research project and implemented on the highly automated vehicle platform. The framework combines nonlinear model predictive control (NMPC) for trajectory tracking with extended Kalman filter based state estimation within a modular system architecture. The NMPC is based on a vehicle model designed for low-speed automated driving maneuvers and explicitly accounts for actuator constraints. Trajectories are tracked based on local planned reference trajectories while ensuring smooth
Fuchs, Sören, Neubeck, Jens, Wagner, Andreas
Hybrid Physics-Informed Neural Network and PID Control for Fault-Tolerant Autonomous Multi-copters2026-26-0770To be published on 06/01/2026
This paper addresses the critical challenge of fault-tolerant control in autonomous multi-copters, particularly under conditions of one or two rotor failures a scenario that often leads to severe instability and a complete loss of directional control due to unbalanced torque and resultant autorotation. Existing advanced control strategies, including optimal approaches such as LQR, typically require precise system modeling and state estimation, which are difficult to achieve in real-world, dynamic failure scenarios. Alternative methods like fuzzy logic, sliding mode control, and gain-scheduling either lack robust generalization or are impractical for enumerating all possible failure cases. In this work, a hybrid control framework integrating Physics Informed Neural Networks (PINN) with a standard PID controller is proposed for fault-tolerant operation of autonomous multi-copters subject to multiple actuator failures. PINNs incorporate governing physical laws as regularization in their
Charapalle, Samruddhi, Venugopalan, Nandagopalan, Nerkundram Muralidharan, Arun, Sundararaj, Laveen
Electrical Harnessing in Satellite Launch Vehicles of ISRO – Methods for Sustainable, Safe, and Reliable systems2026-26-0779To be published on 06/01/2026
The electrical harness serves as the "neural network" for satellite launch vehicles, providing the interfaces for all guidance, health monitoring, and control systems during a mission. This system is composed of cables, and connectors. The wires are made of metallic conductors, typically copper plated with silver or gold to enhance conductivity and prevent oxidation. These conductors are insulated with polymeric materials such as PTFE, Kapton, ETFE, or TKT. Connectors, which are the interface points for electrical circuits, consist of gold or nickel-plated copper contacts housed within a shell. A typical launch vehicle contains an extensive network of electrical harnesses, with approximately 4,200 connectors and 110,000 meters of cable, which contributes to about 30% of the vehicle's total weight. The weight is mostly due to the polymeric insulation rather than the conductors. Challenges and Solutions for Sustainable Systems The paper highlights several key issues with current
K S, Nithish, TR, Binny, D S, Praveen Kumar
Electric Health-Monitoring Wiring (E-Wiring) for Aircraft2026-26-0785To be published on 06/01/2026
Modern aircraft depend on extensive electrical wiring networks for power distribution, avionics, and control systems; however, these wiring systems are vulnerable to wear, insulation degradation, and arcing over time, leading to safety risks and costly unscheduled maintenance. The proposed Electric Health-Monitoring Wiring (E-Wiring) system integrates temperature, current, insulation, vibration, and environmental sensors directly into aircraft wiring harnesses to enable continuous monitoring and intelligent fault detection. Data from these embedded sensors are processed through a distributed edge AI network, forming an Electrical Health Monitoring System (EHMS) capable of real-time diagnostics, predictive maintenance, and fault localization. The architecture comprises smart cable segments with sensor nodes, local harness gateways for edge processing, aircraft-level EHMS integration via AFDX/Ethernet, and cockpit or maintenance displays linked to ground-based cloud analytics for fleet
Tammana, Bala Sai Sri Rohit, Murthy, Harsha, Mendu, HarikaSivaniSunandha
AI Based Energy Sustained Series Hybrid Aircraft2026-26-0776To be published on 06/01/2026
This paper investigates the energy consumption characteristics of series hybrid aircraft with a focus on comparing conventional energy management approaches against an AI-powered optimization framework. The study comprehensively models the energy demands of a series hybrid aircraft across all major flight phases, including Idle & Ground Operations, Taxi, Takeoff, Climb, Cruise, Descent, Approach, Landing, and Rollout & Taxi. For each phase, detailed mathematical formulations are developed to capture power requirements and energy flow, incorporating real-time operational parameters to enhance the accuracy of the energy consumption estimations measured in kilowatt-hours (kWh). The AI-based optimization leverages advanced control strategies, specifically Model Predictive Control (MPC) and Reinforcement Learning (RL) algorithms, to dynamically manage the aircraft's energy systems. MPC is employed to predict and optimize future energy usage by solving constrained optimization problems over
Kanchagar, Amogha
Energy Saving and Personalized Thermal Comfort Control Based on Reinforcement Learning and Decision Tree2026-99-1714To be published on 05/22/2026
Indoor thermal comfort is closely related to people’s health and work efficiency. Control systems typically consume a large amount of energy to maintain a comfortable thermal environment. Currently, reinforcement learning is widely applied to optimize thermal comfort control systems. However, existing research mainly adopts universal thermal comfort evaluation models that aim to satisfy the majority of people, which makes it difficult to quickly and accurately reflect the specific thermal comfort needs of individuals. As a result, the hot environment is neither comfortable nor energy-efficient in practical use. Therefore, this paper proposes an energy-saving personalized thermal comfort control method based on decision trees and reinforcement learning. First, decision tree learning is used to obtain an individual thermal comfort evaluation model from a small amount of historical data. Then, this individual comfort model is combined with energy consumption to form a reward function
Li, Xianying
Adaptive SMPC for Multi-Objective Optimal Scheduling of Offshore Wind Farm Cluster Energy Island System2026-99-1739To be published on 05/22/2026
Based on the multi-objective hierarchical optimization solution method, this paper takes both system balance and scheduling economy into account, and constructs a hierarchical collaborative optimization model for the multi-energy complementary system of offshore energy islands. To address the impact of the volatility and randomness of offshore wind farm clusters on the scheduling of energy island systems, the Stochastic Model Predictive Control (SMPC) method is adopted to optimize and solve the scheduling of offshore energy islands. This paper innovatively proposes a scheduling method based on adaptive variable-step stochastic model predictive control. In the rolling optimization process of SMPC, this method tracks the real-time scheduling deviation degree through the deviation reference coefficient and changes the rolling optimization step size. It solves the problems of insufficient scheduling accuracy and being trapped in local optimization in the rolling optimization process of the
Huang, Haocheng, Zhang, Jinqi, Zhou, Fengfeng, Yan, Qihui, Xu, Chang, Yin, Gaojun
Optimal Power Sharing and Voltage Restoration in DC Microgrids Based on Adaptive Droop Control2026-99-07075/15/2026
The stable operation of islanded DC microgrids is conditioned by two essential objectives. One is to maintain the bus voltage at its nominal value, and this can ensure system stability. The other is to achieve cost-effective power allocation among distributed generation units, which guarantees economic efficiency. These two objectives are often conflicting. Adding droop control to the voltage and current dual closed-loop control can achieve primary current sharing. However, it inevitably introduces steady-state voltage deviations on the DC bus and results in inflexible or not optimal power sharing. To resolve these inherent limitations, this paper proposes a innovative distributed secondary control strategy. The method is designed to meet both requirements within a unified framework. In the primary control layer, it uses adaptive droop gains to optimize power distribution in real time based on changing load requirements which enables distributed generation units to achieve cost
Sun, Wei, She, Dunjun, Yu, Jinzhu, Yuan, Weibo, Peng, Bo, Zheng, Yingchun
Wavelet-Based Spatio-Temporal Fusion Network for Electromyographic Decoding in Bionic Hand Control Systems2026-99-07445/15/2026
Surface electromyography (EMG) signals are essential for facilitating intuitive interactions between humans and bionic hands. However, their inherent non-stationarity, low signal-to-noise ratio, and significant inter-individual variability present considerable obstacles to precise decoding. To overcome these challenges, this study proposes a novel recognition framework combining wavelet packet decomposition and a dynamic graph convolutional-Transformer model. The process starts with multi-layer wavelet packet decomposition and adaptive threshold denoising, effectively removing noise while retaining critical signal features. Subsequently, a dynamic graph convolutional network is employed to capture spatial interactions among multi-channel electrodes, and a Transformer encoder models long-term temporal dependencies within the signals. By integrating these methods, the model generates a fused feature representation that incorporates both spatial and temporal correlations. Experimental
Huang, Rui, Zhao, Yue, Yang, Penghua, Zhu, Jintao, Xiong, Xibei
A Reinforcement Learning Trajectory Planning Model with Kalman Filter-Based Dynamic Tracking for Six-Axis Robotic Manipulators2026-99-07385/15/2026
Robot Arm Tracking Control refers to the control of robot end effectors following a prescribed trajectory as their movement in robotic systems. The work presents a combination of Kalman Filter Based Dynamic System Tracking with Reinforcement Learning Based Trajectory Planning. These two aspects of tracking and planning help the robotic manipulator dynamically track a target that is located on an arbitrary moving path. In particular, by using Kalman filtering to estimate the position of a moving target and to compensate for sensor noise and sparse sampling, we take high-precision estimation values of each point’s coordinates along the target trajectory as a reliable basis to build a policy network using reinforcement learning. Based on it, the robot manipulator could produce effective motion planning under its own dynamic capabilities and physical constraint limit. Comprehensive simulation results illustrate advantages of the new algorithm against the classical control method, confirm
Yu, Jingze, Wang, Yujia, Li, Junshen, Chen, Cong, Xu, Peng
Torque Coordination Control Strategy for Dual-Motor Electric Vehicles Considering Load Transfer and Slip Energy Dissipation2026-99-07095/15/2026
To address the issues of significant slip energy dissipation induced by severe tire slip, degradation of vehicle control stability, and insufficient accuracy of vehicle speed tracking under low-adhesion road conditions, a torque coordination control strategy for dual-motor electric vehicles (DM-EVs) considering load transfer and slip energy dissipation is proposed. First, a vehicle dynamics model integrating suspension system dynamics and tire slip characteristics is developed, fully accounting for the influence of front-rear axle load transfer on the tire slip ratio. Next, founded on the energy dissipation mechanism of tire slip, a quantitative model for energy dissipation during tire slip is developed. Finally, a longitudinal coordinated control system for vehicles according to nonlinear model predictive control (NMPC) is introduced. By comprehensively considering the tire slip ratio and vehicle load distribution, multi-objective coordinated optimization of wheel torque is achieved
Hou, Yingming, Li, Jie, Bai, Xianxu
Design and Research of an Elderly Mobility Assist System Based on Intelligent Power-Assistance Device2026-99-07375/15/2026
The aging of the population has been a key issue worldwide, with mobility and fall of the elderly an important problem to be solved. In this paper, we propose an elderly mobility assist system based on the intelligent power-assisted device consisting of an assistive cane and an intelligent companion. It has the functions of standing support after falling, daily support and on-site rest. The assistive cane adopts a two-stage expansion mechanism of crank and slider structure, which forms a stable triangular support after unfolding, so that the patient can stand safely. The intelligent companion platform is driven by drive wheels, equipped with pushrod motors and vacuum suction devices, it can automatically approach the user and form an stable support column when the cane is in the out-of reach range; the control system is designed by combining microcontroller, camera object recognition, wristband remote control, to realize automatic steering and autonomous navigation at differential
Yu, Chenxi, Wang, Longyi, Zhu, Huayun, Dong, Yan, Mi, Ruixue, Zhu, Lihong
Study on the Hierarchical Control Algorithm of SMES-BESS Hybrid Energy Storage Based on VMD and Fuzzy Control2026-99-07105/15/2026
In China, the installed capacity of renewable energy sources such as wind and photovoltaic power has ranked first in the world for consecutive years, and new energy has become a core driver of energy structure transition. However, the strong volatility and intermittency of new energy output seriously affect the safe and stable operation of the power system, and high-efficiency energy storage technology is the key to solving this problem. Focusing on the short-term high-power charging and discharging characteristics of high-temperature superconducting magnets (SMES), this study proposes a Hybrid Energy Storage System (HESS) that combines SMES with Battery Energy Storage Systems (BESS) to enhance the short-term power support capability of electrochemical energy storage. Variational Mode Decomposition (VMD) is introduced to establish a multi-level power allocation method, which addressing issues such as mode mixing, end effects, and low decomposition efficiency that are prone to occur in
Liu, Haiyang, Wang, Pengfei, Zhou, Wen, Lu, Jing, Wu, Yanan, Yin, Yunkuo, Jiang, Liping
Robust Curvature Preview Drift Control Using STA-ISMC under Parameter Uncertainty10-10-03-00215/6/2026
Autonomous vehicles exhibit extremely strong nonlinearity during drift. However, existing autonomous drift algorithms often neglect previewed path curvature and offer only limited consideration of road surface uncertainty because of the influence of vehicle nonlinear dynamics, which can affect tracking accuracy and robustness of drift control. To solve these problems, this study proposes a robust optimal drift control framework based on curvature preview. First, a preview vehicle kinematic model is constructed, and a preview model predictive control path-tracking controller that considers the forthcoming curvature is designed. Through the analysis of equilibrium points with additional yaw moment, a robust optimal drift controller is developed, which employs a three-degrees-of-freedom vehicle model with an additional yaw moment. This controller adopts integral sliding mode control with a super-twisting algorithm (STA) and exhibits good stability, which is verified through Lyapunov
Gan, Yurun, Song, Ziyu, Gu, Tongtong, Ding, Haitao, Xu, Nan, Zhang, Jianwei
Development of a Model-Aware Data-Driven Control Algorithm for a Drive-by-Wire System for a Level 4 Automated Vehicle2026-01-00434/7/2026
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
Embedded Real-Time Control of a Distributed Trailer Propulsion System with Regenerative Braking for Net Neutral Load Towing2026-01-00654/7/2026
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
Intersection Assist for Vehicle with Dual-Trailers using Nonlinear Model Predictive Control and Unscented Kalman Filter2026-01-00744/7/2026
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 Twin2026-01-02554/7/2026
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
A Latent Space Framework for Modeling Transient Engine Emissions Using Joint Embedding Predictive Architectures2026-01-04234/7/2026
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 Maps2026-01-00264/7/2026
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 Vehicles2026-01-00754/7/2026
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 Control2026-01-06224/7/2026
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 Vehicles2026-01-01484/7/2026
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)2026-01-01474/7/2026
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 Optimization2026-01-01624/7/2026
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 Testing2026-01-00324/7/2026
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 Learning2026-01-00414/7/2026
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
Model-Based Design and dSPACE/MathWorks Environment Enable Innovative Engineering Education2026-01-00734/7/2026
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 Control2026-01-01994/7/2026
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 System2026-01-04184/7/2026
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 Data2026-01-01694/7/2026
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 Identification2026-01-06184/7/2026
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 Drift2026-01-02874/7/2026
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 Prediction2026-01-01314/7/2026
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 Modeling2026-01-00044/7/2026
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 Operations03-19-02-00104/1/2026
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 Equations02-19-03-00133/27/2026
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 Systems10-10-02-00163/10/2026
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 Battery2026-01-70242/27/2026
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
IoT-Enabled Cloud-Integrated Smart Parking System with Real-Time Monitoring and AI-Based Space Optimization for Next-Gen Mobility2026-28-01132/12/2026
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
An Integrated Model Approach for Predicting Vehicle Dynamics Performance in HiL Validation2026-28-00442/12/2026
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 Appliances2026-28-00602/12/2026
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
Miner’s Safety Bot: An ESP32-Based Autonomous Mobile Mine Safety Monitoring System2026-28-01162/12/2026
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 Calibrations2026-28-00082/12/2026
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 Applications03-19-01-00042/9/2026
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
RDAS: A Shared Control Strategy Considering Real-Time Driving Ability in Driver Takeover Tasks10-10-02-00141/24/2026
Autonomous vehicles require drivers to assume control of the vehicle in situations where the vehicle control system cannot perform its intended task. A shared control-based approach to driving authority transfer can effectively mitigate the driving risks associated with diminished driver capability due to prolonged disengagement, but it may readily precipitate human–machine conflicts—oscillatory steering behavior, excessive driver workload, and unstable control during weight transitions. Addressing the characteristics of driver capability variations during takeover tasks, a shared control strategy incorporating real-time driving ability, termed the real-time driving ability strategy (RDAS), is proposed. Initially, a real-time capability assessment strategy based on an expected steering angle model is developed. By collecting driving data under conditions of adequate driver capability to train an adaptive neuro-fuzzy inference system (ANFIS) neural network, the expected steering angle
Qi, Zhenliang, Liu, Ping, Duan, Haotian, Zhou, Zilong, Huang, Haibo
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
Mitigating Urea Deposits in Selective Catalytic Reduction Systems through Software Control for Improved Performance in Low Duty Cycle Operations2026-26-04241/16/2026
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 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
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