Browse Topic: Control systems

Items (5,581)
This paper presents a comparative study between many control techniques to investigate the efficiency of the path tracking in various driving scenarios. In this study the Model predictive control (MPC), the adaptive model predictive control (AMPC) and the Stanley controller are employed to ensure that the vehicle follows reference paths accurately and robustly under varying environmental and vehicular conditions. Two driving scenarios are utilized S-road and Curved-road with MATLAB/Simulink under three different vehicle speeds to investigate vehicle performance employing the root mean square error (RMSE) as the performance evaluation function. Particle swarm optimization (PSO) utilized for optimizing the six parameters of the MPC prediction horizon (P), Control horizon(m), manipulated variable rates, manipulated variables weights and two output variables weights. Four objective functions are employed with PSO and compared to each other in terms of the time domain regarding the RMSE of
Eldesouky, Dina M.MustafaAbdelaziz, Taha HelmyMohamed, Amr.E
Deadbeat Predictive Current Control (DPCC) has emerged as a highly effective control strategy, owing to its outstanding dynamic performance. However, the control effectiveness of traditional methods is limited by the machine parameters set in advance, which inevitably reduces the parameter robustness of the method. When machine parameters change due to factors like temperature, the discrepancy between the actual values and the parameters configured in the controller leads to a decline in DPCC performance, and cause system instability. To tackle the challenge of parameter dependence, this paper proposes an adaptive parameter-free model-free deadbeat predictive current control (PF-MFDPCC) method suitable for interior permanent magnet synchronous motors (IPMSM). The method estimates the actual gain parameters based on the sampled current values and reference values, and determines the required harmonic current injection by minimizing torque ripple. First, the relationship of the
Guo, RongGu, hongyang
Model-based developers are turning to DevOps principles and toolchains to increase engineering efficiency, improve model quality and to facilitate collaboration between large teams. Mature DevOps processes achieve these through automation. This paper demonstrates how integrating modern version control (Git) with collaborative development practices and automated quality enforcement can streamline workflows for large teams using Simulink. The focus is on enhancing model consistency, enabling team collaboration, and development speed.
Mathews, JonTamrawi, AhmedFerrero, SergioSauceda, Jeremias
The electric vehicle market, vehicle ECU computing power, and connected electronic vehicle control systems continue to grow in the automotive industry. The results of these advanced and expanded vehicle technologies will provide customers with increased cost savings, safety, and ride quality benefits. One of these beneficial technologies is the tire wearing prediction. The improved prediction of tire wear will advise a customer the best time to change tires. It is expected that this prediction algorithms will be essential part for both the optimization of the chassis control systems and ADAS systems to respond to changed tire performance that varies with a tire’s wear condition. This trend is growing, with many automakers interested in developing advanced technologies to improve product quality and safety. This study is aimed at analyzing the handling and ride comfort characteristics of the tire according to the depth of tire pattern wear change. The handing and ride comfort
Kim, ChangsuKwon, SeungminSung, Dae-UnRyu, YonghyunKo, Younghee
In order to improve the safety and reliability of the inverter used in hybrid vehicles and reduce the risk of inverter failure, based on the functional safety ISO26262 development process and software architecture, a safe shutdown path scheme is designed in this paper. Firstly, after entering the initialization mode, on the basis of adding the inverter control signal feedback mechanism on the inverter control system, this scheme designs the control methods and specific processes of the shutdown path test and insulation detection. The shutdown path test and insulation detection designed in this scheme are implemented during the control initialization process, including designing the hardware diagnostic safety mechanism and the unique output shutdown path test method. If the shutdown path test or insulation detection fails, the risk of IGBT out of control can be avoided; the detection mechanism of this system can effectively reduce the failure rate and potential failure rate of faults
Jing, JunchaoLiu, YiqiangZuo, BotaoHuang, WeishanDai, Zhengxing
In order to effectively improve the chassis handling stability and driving safety of intelligent electric vehicles (IEVs), especially in combing nonlinear observer and chassis control for improving road handling. Simultaneously, uncertainty with system input, are always existing, e.g., variable control boundary, varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis prescribed performance control strategy of IEVs become a hot topic in both academia and industry. To issue the above mentioned, a fuzzy sliding mode control method based on phase plane stability domain is proposed to enhance the vehicle’s chassis performance during complex driving scenarios. Firstly, a two-degree-of-freedom vehicle dynamics model, accounting for tire non-linearity, was established. Secondly, combing with phase plane theory, the stability domain boundary of vehicle yaw rate and side-slip phase plane based
Liao, YinshengWang, ZhenfengGuo, FenghuanDeng, WeiliZhang, ZhijieZhao, BinggenZhao, Gaoming
Magnetorheological (MR) dampers, known for their remarkable dependability and cost-effectiveness, have established themselves as prime semi-active vibration control devices in engineering systems. MR dampers are categorized as adaptive devices because their features may be readily adjusted by applying a regulated voltage signal. Their ability to offer superior performance while mitigating the drawbacks of fully active actuators underscores their practical significance. This research is to investigate some system hybrid controllers using a combination state derivative feedback and a linear-quadratic regulator for use in conjunction with the damper controller of a semi-active suspension of a Quarter vehicle model to improve ride comfort and vehicle stability. The mathematical model of 3 degrees of freedom for semi-active suspension using MR dampers will be derived and simulated using MATLAB and SIMULINK software. In order to quantify the effectiveness of the suggested control strategies
M.Faragallah, MohamedMetered, HassanEssam, Mahmoud A.
The pollutant emission regulation for Non-Road Mobile Machinery (NRMM) is currently under consideration, both in the European Union (EU) and the United States (US). In Europe a Stage V review is expected within 2025 and in the US, the California Air Resource Board (CARB) has released their Tier 5 proposal in late 2024. It is expected that there will be further focus on covering a wide variety of operation conditions in actual use cases, including continuous low load scenarios. In addition, CO2-neutral fuels are being investigated to reduce the carbon footprint of NRMM Internal Combustion Engines (ICE), which remains an important powertrain for the sector. The objective of the work presented is to assess the potential for emissions reductions in the future, both NOx and CO2. A simulation study is conducted, modelling a 9l class engine with 8-10 g/kWh engine-out NOx emission level. Three different emission control systems are investigated: an enhanced stage V system with single SCR, a
Demuynck, JoachimBosteels, DirkMichelitsch, PhilippNoll, Hannes
This paper presents an advanced control system design for an engine cooling system in an internal combustion engine (ICE) vehicle. Building upon our previous work, we have derived models for crucial temperatures within the engine, including combustion wall temperature, coolant-out temperature, block temperature, as well as temperatures in external components such as heat exchangers and radiator. To accurately predict these temperatures in a rapid manner, we have utilized a lumped parameter concept with a mean-value approach. This approach allows for precise temperature estimation while maintaining computational efficiency. Given the complexity of the cooling system, we have proposed a linear time-varying (LTV) model predictive control (MPC) system to regulate the temperatures. This control system linearizes the model at each time step and applies linear MPC over the control and prediction horizons. By doing so, we effectively control the highly nonlinear and time-delayed system
Chang, InsuSun, MinEdwards, David
The linear region of the side-slip mechanical properties of tires is often used in the simulation of linear monorail models for vehicles, especially in the design of active control systems. Side-slip stiffness is a key parameter in tire side-slip, and is significantly influenced by camber and load. In response to the tire industry's need for efficient acquisition of tire mechanical properties and the development of virtual prototyping technology, this paper proposes a method to address the influence mechanism of camber on side-slip in the study of tire camber side-slip prediction models. This paper analyzes the impact of camber on the linear region of tire side-slip mechanical properties at the microscopic level. It then examines the effect of camber on the side-slip condition from the perspective of tire external characteristics, combined with the tire theoretical model, to map the local characteristics of camber onto the external characteristics of tire side-slip. First, a finite
Yin, HengfengSuo, YanruWu, HaidongMin, HaitaoLiu, Dekuan
As the complexity of electrified powertrains and their architectures continue to grow and thrive, it becomes increasingly important and challenging for the supervisory torque controller to optimize the torque commands of the electric machines. The hybrid architecture considered in this paper consists of an internal combustion engine paired with at least one electric motor and a DC-DC switching converter that steps-up the input voltage, in this case the high voltage battery, to a higher output voltage level allowing the electric machines to operate at a greater torque range and increased torque responsiveness for efficient power delivery. This paper describes a strategy for computing and applying the losses of the converter during voltage transformation to determine the optimal engine and electric motor torque commands. The control method uses a quadratic fit of the losses at the power limits of the torque control system and on optimal motor torque commands, within the constraints of
Venkataramu, AchyutWalsh, McKenzieTischendorf, ChristophSullivan, MaryPatel, NadirshHuo, ShichaoSharma, Ashay
With the advancement of intelligent transportation and smart logistics systems, tractor semi-trailers have gradually become one of the primary modes of transport due to their substantial cargo capacity. However, the growing number of tractor semi-trailers has raised significant traffic safety concerns. Due to their significant spring mass and strong body strength, accidents involving tractor semitrailers often result in severe consequences. Active collision avoidance control strategies provide assurance for vehicle safety. However, existing research predominantly focuses on passenger cars and small commercial vehicles. Research specifically addressing tractor semi-trailers, which have longer bodies and more complex dynamic characteristics, is relatively sparse. Therefore, this paper proposes a collision risk assessment-based longitudinal collision avoidance control strategy for tractor semi-trailers with slip ratio control. Firstly, the paper introduces the braking characteristics and
Yan, YangZheng, HongyuZhang, Yuzhou
This paper initially delineates the control process of driver-initiated gear changes. The gear-shifting point control module computes the new target gear based on the current updated driving state, and the gear-shifting point decision module assesses the rationality of the new target gear and conveys it to the gear-shifting timing control module. The gear-shifting timing control module selects the reasonable new stage in accordance with the current execution status and outputs the new target gear, coordinating the clutch control module and the brake control module to regulate the clutch engagement/disengagement and the switches of the two clutches. Altering the intention regarding gear changes encompasses gear replacement and variations in power type, which involve the necessary recalculation of the target speed based on the new target gear. Secondly, the conditions for the “change of mind” request in the speed stage are stipulated, which is the stage where the input shaft speed is
Jing, JunchaoHuang, WeishanLi, DongfeiZuo, BotaoLiu, Yiqiang
In future planetary exploration missions, the Eight-Wheeled Planetary Laboratory (EWPL) will have sufficient capacity for tasks but will experience significant lateral slips during high-speed turns due to its large inertia. Modern technology allows for independent steering of all eight wheels, but controlling each wheel's steering angle is key to improving stability during turns. This paper introduces a novel rear-axle steering feed-forward controller to reduce sideslip. First, a mathematical model for the vehicle's steering is established, including kinematic equations based on Ackermann steering. Feed-forward zero side-slip control is applied to the third and fourth axles to counteract the side-slip angle of the center of mass. A multi-body dynamics model of the EWPL is then built in Chrono to evaluate the turning radius and optimize steering angle ratios for the rear axles. Finally, a steady-state cornering simulation on loose terrain compares the performance of the proposed
Liu, JunZhang, KaidiShi, JunweiYang, WenmiaoZhang, YunqingWu, Jinglai
Motor drive control is crucial for achieving the performance, reliability, and comfort of electric vehicles. Multi-phase motors, represented by dual-winding permanent magnet synchronous motors (PMSMs), have significant research value in the electric vehicle field due to their high-power drive capabilities and strong fault tolerance. A simple and easily analyzable motor model is essential for achieving high precision in control. This paper employs VSD coordinate transformation (vector space decomposition) based on electromagnetic principles and the positional relationships between windings, treating the multi-phase motor as a whole and mapping various physical quantities to multiple subspaces for simplified analysis. Consequently, a mathematical model for the dual-winding PMSM is established. The vector control system based on VSD coordinate transformation adopts a dual closed-loop structure for speed and current. It focuses on a comparative analysis between traditional two-vector
Gao, ChaoFanZheng, HongyuKaku, Chuyo
Adverse weather conditions such as rain and snow, as well as heavy load transportation, can cause varying degrees of damage to road surfaces, and untimely road maintenance often results in potholes. Perception sensors equipped on intelligent vehicles can identify road surface conditions in advance, allowing each wheel’s suspension to actively adjust based on the road information. This paper presents an active suspension control strategy based on road preview information, utilizing a newly designed dual-chamber active air suspension system. It addresses the issue of point cloud stratification caused by vehicle body vibrations in onboard LiDAR data. The point cloud is processed through segmentation, filtering, and registration to extract real-time road roughness information, which serves as preview information for the suspension control system. The MPC algorithm is applied to actively adjust the nonlinear stiffness and damping of the suspension’s dual-chamber air springs, enhancing
Dong, FuxinShen, YanhuaWang, KaidiLiu, ZuyangQian, Shuo
Distributed electric vehicles, equipped with independent motors at each wheel, offer significant advantages in flexibility, torque distribution, and precise dynamic control. These features contribute to notable improvements in vehicle maneuverability and stability. To further elevate the overall performance of vehicles, particularly in terms of handling, stability, and comfort, this paper introduces an coordinated control strategies for longitudinal, lateral, and vertical motion of distributed electric vehicles. Firstly, a full-vehicle dynamics model is developed, encompassing interactions between longitudinal, lateral, and vertical forces, providing a robust framework for analyzing and understanding the intricate dynamic behaviors of the vehicle under various operating conditions. Secondly, a vehicle motion controller based on Model Predictive Control is designed. This controller employs a sophisticated multi-objective optimization algorithm to manage and coordinate several critical
Jia, JinchaoYue, YangSun, AoboLiu, Xiao-ang
Mass estimation in light-duty vehicles (LDVs) is a crucial aspect of vehicle dynamics, control systems, energy optimization, range prediction, and overall performance. Accurate mass estimation is essential for precise energy predictions, which are used by energy optimization algorithms. It also enhances vehicle safety and the effectiveness of advanced driver assistance systems (ADAS). The mass of a vehicle can vary depending on occupancy and load. This paper presents a comprehensive study on in-situ mass learning in light-duty vehicles under real-world driving conditions. Utilizing simple longitudinal dynamics, road grade calculated from GPS with RTK correction, and the vehicle’s torque model, we developed a robust framework for vehicle mass estimation. A detailed sensitivity analysis was performed to evaluate the impact of uncertainties or errors in various inputs and parameters, identifying optimal regions for learning the mass and ensuring the model's reliability. This method was
Poovalappil, AmanRobare, AndrewApostol, PeterBahramgiri, MojtabaChen, BoNaber, JeffreyRobinette, Darrell
Advanced driver assistance systems (ADASs) and driving automation system technologies have significantly increased the demand for research on vehicle-state recognition. However, despite its critical importance in ensuring accurate vehicle-state recognition, research on road-surface classification remains underdeveloped. Accurate road-surface classification and recognition would enable control systems to enhance decision-making robustness by cross-validating data from various sensors. Therefore, road-surface classification is an essential component of autonomous driving technologies. This paper proposes the use of tire–pavement interaction noise (TPIN) as a data source for road-surface classification. Traditional approaches predominantly rely on accelerometers and visual sensors. However, accelerometer signals have inherent limitations because they capture only surface profile properties and are often distorted by the resonant characteristics of the vehicle structure. Similarly, image
Yoon, YoungsamKim, HyungjooLee, Sang KwonLee, JaekilHwang, SungukKu, Sehwan
The performance of a second-generation Toyota Mirai fuel cell was characterized as part of the SwRI internal research program. This data was used to develop a supervisory controller scheme designed to balance the plant for the fuel cell system during steady-state and transient vehicle conditions. This was accomplished using a Supervisory Integrated Controller (SIC) implemented on a Real-time Power Electronics Control System (RPECS) with a Simulink-based control algorithm. The actuators of interest are the three hydrogen injectors at anode inlet, air compressor and three air side valves on at the cathode inlet. The FC power measurement and pressure sensor readings at the anode and cathode were utilized as real-time feedback for the controller operation. The aim of the controller was to achieve and maintain the power target set by the hybrid powertrain ECU present on the vehicle, which is responsible for balancing power on the fuel cell and battery over the high-voltage bus. These
Chundru, Venkata RajeshKubesh, MatthewLegala, Adithya
As longitudinal Automated Driving System (ADS) technologies, such as Adaptive Cruise Control (ACC), become more prevalent, robust testing frameworks that encompass both simulation and vehicle-in-the-loop (VIL) methodologies are essential to ensure system reliability, safety, and performance refinement. Although significant research has focused on ACC algorithm development and simulation testing, existing VIL dynamometer testing frameworks are typically tailored to specific vehicle models and sensor simulation tools. These highly customized approaches often fail to account for broader interoperability while overlooking energy consumption as a key performance metric. This paper presents a novel modular framework for ACC dynamometer testing, designed to enhance interoperability across a diverse range of vehicle platforms, simulation tools, and dynamometer facilities with a focus on evaluating impacts of automated longitudinal control on the overall energy consumption of the vehicle. The
Goberville, NicholasHamilton, KaylaDi Russo, MiriamJeong, JongryeolDas, DebashisOrd, DavidMisra, PriyashrabaCrain, Trevor
The electric motor is a significant source of noise in electric vehicles (EVs). Traditional hardware-based NVH optimization techniques can prove insufficient, often resulting in trade-offs between motor torque or efficiency performance. The implementation of motor control-based torque ripple cancellation (TRC) technology provides an effective and flexible solution to reduce the targeted orders. This paper presents an explanation of the mathematical theory underlying the TRC method, with a particular focus on the various current injection methods, including those that allow up to 4DOFs (degrees-of-freedom). In the case study, the injection of controlled fifth or seventh order current harmonics into a three-phase AC motor is shown to be an effective method for cancelling the most dominant sixth order torque ripple. A dedicated feedforward harmonic current generation module is developed the allows the application of harmonic current commands to a motor control system with adjustable
He, SongGong, ChengChang, LePeddi, VinodZhang, PengGSJ, Gautam
Automatic transmission (AT) is a complex hydroelectromechanical system, since it consists of hydraulic, electrical and mechanical parts. On cars produced by “UzAuto Motors” mainly AT of the 6T family, 6-speed AT family GM: 6T30, 6T40, 6T45, 6T50 “Hydra-Matic” are used. Production of parts of these transmissions is carried out both at American GM plants and at GM plants in China and Korea. The differences between AT 6T30/6T40/6T45/6T50 are in the thickness of the output chain and in the planetary gear, in the sizes of some other units and the housing itself. But the differences are much more influenced by the settings of the transmission unit, which select operating modes with different engines so as not to overload the thinnest places of the automatic transmission. Automatic transmissions 6T30, 6T40, 6T45, 6T50 have the ability to manually shift gears and activate the “kickdown” mode (for quick acceleration of the car, when you sharply press the gas “all the way” it makes the automatic
Turakulov, Bakhtiyor
Trajectory tracking control is a critical component of the autopilot system, essential for achieving high-performance autonomous driving. This paper presents the design of a stable, reliable, accurate, fast, and robust trajectory tracking controller. Specifically, a lateral and longitudinal trajectory tracking controller based on a linear parameter time-varying model predictive control (LPV-MPC) framework is designed. Firstly, a three-degree-of-freedom vehicle dynamics model and a tracking error model are established. Secondly, a multi-objective function and constraints considering tracking accuracy and lateral stability are formulated, and the quadratic programming (QP) method is employed to solve the optimization problem. Finally, PID speed tracking control is introduced in the longitudinal control scheme for comparison with the proposed MPC longitudinal speed control. A step velocity tracking test validates the effectiveness of the MPC speed tracking controller. In the lateral
Pan, ShicongLu, JunYu, YinquanZeng, DequanYang, JinwenHu, YimingJiang, ZhiqiangLiu, Dengcheng
In a conventional cam-based valve actuation system, the valve events are tied up with the rotation of the crankshaft. In contrast, the electronic variable valve actuation (VVA) system enables flexible control of valve events independent of the crankshaft rotation. The present article discusses the development and control system design of a single-acting electro-pneumatic variable valve actuation (EPVVA) system that can be retrofitted to a conventional SI engine. The EPVVA system utilizes fast switching solenoid valves which modulate the flow of pressurized air in and out of a pneumatic chamber. The control system design is conducted in MATLAB Simulink platform using model-based approach. The valve actuator model is formulated such that it simulates the trajectory of the motion of the engine valve by numerically integrating a set of coupled differential equations that govern the thermo-fluid-dynamics and applied mechanics aspects of the valve actuation of the EPVVA system. The timings
Satalagaon, Ajay KumarGuha, AbhijitSrivastava, Dhananjay Kumar
Online road profiling capability is required for automotive active suspension systems to be realized in a consumer and commercial landscape. One challenge that impedes the realization of these systems is the need for the online road profiler to maintain an optimal spatial resolution of the oncoming road profile. Shifting of the road profiling sensor measurement frame of reference due to body motion experienced by the vehicle can negatively impact profiling accuracy. Prior work proposed a corrective look-ahead road profiling system (CLARPS) and demonstrated the CLARPS architecture and initial MATLAB/Simulink simulation environment. First, this work further develops the robust simulation environment. The simulation allows the look-ahead viewing angles to be optimized for the best road profile spatial resolution and facilitates a study on the impact of road profiler sensor location on the accuracy of the generated road profile. Second, this work introduces a lab-scale physical CLARPS
Morison, DaneMynderse, James
Accurate estimation of the state of charge (SoC) of battery cells is crucial for the efficient management and longevity of battery systems, particularly in electric vehicles and renewable energy storage. This paper presents an approach utilizing a nonlinear autoregressive exogenous (NARX) model to estimate the SoC of battery cells. The proposed method leverages hyperparameter optimization to determine the optimal configuration of the neural network, including the number of neurons, the number of hidden layers, the number of feedback loops, the best activation function, and the most effective learning rate. The primary objective of this research is to minimize the estimation error of the SOC to within 2%, thereby enhancing the reliability and performance of battery management systems. The hyperparameter optimization process involves a systematic search and evaluation of various configurations to identify the most effective neural network architecture. This process is critical as it
Saini, SandeepAdmane, Chinmay
A rule-based unilateral fault-tolerant control strategy is proposed to improve the vehicle's yaw stability and dynamics in case of drive system failure for multi-axle differential steering vehicles due to various unpredictable factors during driving. Meanwhile, based on the conditional integration algorithm, a multi-axis differential steering vehicle dynamics control is designed to resist integration saturation, which ensures that the vehicle tracks the reference signal accurately. In order to test the designed fault-tolerant control strategy, the first wheel drive motor in the left is set to be in a state of complete failure after the 10th second on the basis of the vehicle's forward-steering driving condition in order to construct a failure test condition; deviation angle error without fault-tolerant control is 0.22182rad and the extreme value of the yaw velocity error is 0.03687 rad/s. After fault-tolerant control, the extreme value of the center-of-mass lateral deviation angle
Zou, MingyuLu, JunZeng, DequanYu, YinquanYang, JinwenHu, YimingYu, QinWang, Xiaoliang
Clamping force control in Electromechanical Brake (EMB) systems must overcome various nonlinear characteristics, such as motor distorted voltage, Back Electromotive Force (EMF), and actuator friction disturbances. Therefore, modeling and parameter identification of these nonlinearities are necessary. This paper first proposes a motor parameter identification method based on the mathematical model of a Permanent Magnet Synchronous Motor (PMSM). A combination of the Least Square Method and Particle Swarm Optimization (PSO) is used to stepwise identify both the electrical and mechanical parameters of the motor. The accuracy of the identified parameters is validated by comparing simulation results with test bench responses. The identified parameters are applied to design the motor Back EMF compensation module, the distorted voltage compensation module, and to tune the current loop parameters. Next, a lumped parameter friction model suitable for closed-loop clamping force control in EMB
Qiao, LeXiong, LuZhuo, GuirongShu, Qiang
Because the steer-by-wire (SBW) system cancels the mechanical connection between the steering wheel and the steering wheel in the traditional mechanical steering system (MSS), various road information on the road cannot be directly transmitted to the driver through the steering wheel in the form of road sense. Consequently, drivers are unable to genuinely perceive this road information, which adversely affects their control of the vehicle. This paper investigates the road perception simulation method for SBW systems. Initially, a dynamic model of the SBW system is developed, and its validity is confirmed under conditions of step changes in steering wheel angle and dual-shifting scenarios. A state estimation approach is employed to simulate road perception torque, and a corresponding torque calculation formula is derived based on the dynamic model of traditional steering systems. A two-degree-of-freedom vehicle model is constructed to independently compute the lateral force experienced
Li, XuesongLi, ZhichengZheng, HongyuKaku, Chuyo
Abstract Real-world driving data is an invaluable asset for several types of transportation research, including emissions estimation, vehicle control development, and public infrastructure planning. Traditional methods of real-world driving data collection use expensive GPS-based data logging equipment which provide advanced capabilities but may increase complexity, cost, and setup time. This paper focuses on using the Google Maps application available for smartphones due to the potential to scale-up real-world driving data logging. Samples of the potential data processing and information that can be gathered by such a logging methodology is presented. Specifically, two months of Google Maps driving data logged by a rural Michigan resident on their smartphone may provide insights on their driving range, duration, and geographic area of coverage (AOC) to guide them on future vehicle purchase decisions. Aggregating such statistics from crowd-sourcing real-world driving data via Google
Manoj, AshwinYin, SallyAhmed, OmarVaishnav, ParthStefanopoulou, AnnaTomkins, Sabina
The motor controller, as one of the important controllers in the electric drive system, may cause unexpected acceleration or deceleration of the vehicle by the driver due to systematic failure and random hardware failure. Conducting research on the functional safety of drive motors for new energy vehicles is of great significance for reducing the systematic failure and random hardware failure of the electric drive. This paper has carried out designs including the allowable motor torque design for safety monitoring, the motor torque prediction design for safety monitoring, the rationality judgment design of the motor torque for safety monitoring, the rationality judgment design of the motor direction for safety monitoring, the functional safety motor degradation design, and the active discharge state monitoring of the motor, so that the system can transition to a safe state when an error occurs. Among them, the motor torque prediction design for safety monitoring includes predicting the
Jing, JunchaoZuo, BotaoLiu, YiqiangHuang, WeishanDai, Zhengxing
Toyota Motor Corporation pursuing an omnidirectional strategy that includes battery electric vehicle (BEV), plug-in hybrid electric vehicle (PHEV), and fuel cell electric vehicle (FCEV) to accelerate electrification. One of the technical challenges with our xEV batteries which feature good degradation resistance and long battery life, is that regenerative braking cannot be fully effective due to the decrease in regenerative power in some situations, such as low battery temperature. For the electrified vehicles with an internal combustion engine such as PHEVs, the solution has been running the engine to increase deceleration through engine braking during coasting. PHEVs are expected to extend their cruising range and enhance EV driving experience as "Practical BEVs". While increasing battery capacity and enhancing convenience, the restrictions on EV driving opportunity due to low battery temperature may negatively affect PHEV’s appealing. As an alternative, introducing a battery heater
Hoshino, Yu
With the development and maturity of new generation digital technologies such as artificial intelligence, Internet of Things, and 5G mobile communication, their integration with physical products is becoming increasingly seamless. Automobiles serve as a prime example in this regard. In recent years, automated vehicle (AV) technologies have emerged as a prominent focal point, witnessing an escalating acceptance in the market and a growing number of self-driving vehicles on the roads, existing roads are primarily designed for traditional human-driven vehicles (HVs). Due to the differences in perception between automated systems and human drivers, it is essential to assess AVs' feasibility to current road infrastructure. This paper analyzes the safety and comfort of automated vehicles equipped with adaptive cruise control systems (ACC-AVs) on longitudinal road profiles from the perspective of vehicle dynamics. Firstly, a co-simulation platform integrating PreScan, CarSim, and Simulink
Li, ZezhouCai, MingmaoGu, TianqiYu, Bin
In response to the complex shore slope road conditions and the switching of water–land environments during the amphibious vehicle’s landing process, a landing drive force control strategy for amphibious vehicles is proposed. First, based on the shore slope gradient, buoyancy effect, and amphibious vehicle acceleration, the drive force of the front and rear wheels of the amphibious vehicle is pre-allocated. Then, referring to the road parameters of common road types, the road adhesion coefficient and optimal slip ratio of the current road surface where the amphibious vehicle is located are identified based on the principle of fuzzy control. Subsequently, with the slip ratio difference as the control target, the drive motor is controlled based on the sliding mode control algorithm to achieve tracking of the optimal slip ratio. A joint simulation is carried out using CarSim and Simulink, and the results are compared with those without control. The simulation results show that the drive
Huang, BinYuan, ZinengYu, Wenbin
Secondary crashes, including struck-by incidents are a leading cause of line-of-duty deaths among emergency responders, such as firefighters, law enforcement officers, and emergency medical service providers. The introduction of light-emitting diode (LED) sources and advanced lighting control systems provides a wide range of options for emergency lighting configurations. This study investigated the impact of lighting color, intensity, modulation, and flash rate on driver behavior while traversing a traffic incident scene at night. The impact of retroreflective chevron markings in combination with lighting configurations, as well as the measurement of “moth-to-flame” effects of emergency lighting on drivers was also investigated. This human factors study recruited volunteers to drive a closed course traffic incident scene, at night under various experimental conditions. The simulated traffic incident was designed to replicate a fire apparatus in the center-block position. The incident
D. Bullough, JohnParr, ScottHiebner, EmilySblendorio, Alec
With the rapid development of smart transport and green emission concepts, accurate monitoring and management of vehicle emissions have become the key to achieving low-carbon transport. This study focuses on NOx emissions from transport trucks, which have a significant impact on the environment, and establishes a predictive model for NOx emissions based on the random forest model using actual operational data collected by the remote monitoring platform.The results show that the NOx prediction using the random forest model has excellent performance, with an average R2 of 0.928 and an average MAE of 43.3, demonstrating high accuracy. According to China's National Pollutant Emission Standard, NOx emissions greater than 500 ppm are defined as high emissions. Based on this standard, this paper introduces logistic regression, k-nearest neighbor, support vector machine and random forest model to predict the accuracy of high-emission classification, and the random forest model has the best
Lin, YingxinLi, Tiezhu
Path-tracking control occupies a critical role within autonomous driving systems, directly reflecting vehicle motion and impacting both safety and user experience. However, the ever-changing vehicle states, road conditions, and delay characteristics of control systems present new challenges to the path tracking of autonomous vehicles, thereby limiting further enhancements in performance. This article introduces a path-tracking controller, time-varying gain-scheduled path-tracking controller with delay compensation (TGDC), which utilizes a linear parameter-varying system and optimal control theory to account for time-varying vehicle states, road conditions, and steering control system delays. Subsequently, a polytopic-based path-tracking model is applied to design the control law, reducing the computational complexity of TGDC. To evaluate the effectiveness and real-time capability of TGDC, it was tested under a series of complex conditions using a hardware-in-the-loop platform. The
Hu, XuePengZhang, YuHu, YuxuanWang, ZhenfengQin, Yechen
As wire control systems advance, they have given rise to a diverse suite of advanced driver assistance services and sophisticated fusion control capabilities. This article presents an innovative strategy for achieving comfortable braking in electric vehicles, propelled by the unwavering goal of enhancing driving experience. By integrating active suspension systems with brake-by-wire technology, the approach ensures that drivers retain their confidence throughout the braking process. The brake-by-wire system adeptly discerns the driver’s braking intent through the pedal’s displacement sensor. Utilizing this technology, we have developed a pioneering function aimed at delivering comfort braking control (CBC). This function not only refines the braking experience but also solidifies the driver’s trust in the braking system. Designed to counteract the head nodding effect during vehicle deceleration, the CBC system minimizes or even eradicates the jarring sensation of pitching for both the
Tian, BoshiLi, LiangLiao, YinshengLv, HaijunQu, WenyingHu, ZhimingSun, Yue
Autonomous vehicles utilise sensors, control systems and machine learning to independently navigate and operate through their surroundings, offering improved road safety, traffic management and enhanced mobility. This paper details the development, software architecture and simulation of control algorithms for key functionalities in a model that approaches Level 2 autonomy, utilising MATLAB Simulink and IPG CarMaker. The focus is on four critical areas: Autonomous Emergency Braking (AEB), Adaptive Cruise Control (ACC), Lane Detection (LD) and Traffic Object Detection. Also, the integration of low-level PID controllers for precise steering, braking and throttle actuation, ensures smooth and responsive vehicle behaviour. The hardware architecture is built around the Nvidia Jetson Nano and multiple Arduino Nano microcontrollers, each responsible for controlling specific actuators within the drive-by-wire system, which includes the steering, brake and throttle actuators. Communication
Ann Josy, TessaSadique, AnwarThomas, MerlinManaf T M, AshikVr, Sreeraj
The Tractor is essential in both agriculture and construction, equipped with a variety of implements for different operational conditions. Its hydraulic system is crucial for controlling these implements during fieldwork and transport. The quadrant assembly is a key part of the tractor’s hydraulic control system, allowing the operator to manage important functions. This includes hydraulic control and draft control, enabling the farmer or operator to use the PC and DC levers to adjust the movement of implements during various tasks. Tractors are commonly used in fields and farms where the soil can be loose and muddy, particularly during wet puddling operations. In these muddy conditions, tractors can accumulate mud in critical components, such as the quadrant assembly. This can lead to functional issues, increased friction, and problems within the hydraulic system, especially affecting the controls for hydraulics and lever shifting for implement handling. As a result, operators may need
K, BheshmaPhadtare, YogeshGomes, MaxsonV, Ashok KumarPerumal, SolairajMagendran, G
Intelligent vehicles can utilize a variety of sensors, computing, and control technologies to autonomously perceive the environment and make decisions to achieve safe, efficient, and automated driving. If the speed planning of intelligent vehicles ignores the vehicle dynamics state, it leads to unreasonable planning speed and is not conducive to improving the accuracy of trajectory tracking control. Meanwhile, trajectory tracking usually does not consider the road and speed information beyond the prediction horizon, resulting in poor tracking precision that is not conducive to improving driving comfort. To solve these problems, this study proposes a new longitudinal speed planning method based on variable universe fuzzy rules and designs the piecewise preview model predictive control (PPMPC) to realize the vehicle trajectory tracking. First, the three-degrees-of-freedom vehicle dynamics model and trajectory tracking model are established and verified. Then, the variable universe fuzzy
Zhang, JieTeng, ShipengGao, JianjieZhou, XingxingZhou, Junchao
PEM electrolysis system has characteristic of excellent performance such as fast response, high electrolysis efficiency, compact design and wide adjustable power range. It provides a sustainable solution for the production of hydrogen, and is well suited to couple with renewable energy sources. In the development process of PEM electrolysis controller, this article originally applied the V-mode development process, including simulation modeling, RCP testing, and HIL testing, which can provide guidance in the practical application of electrolytic hydrogen production. In this paper, we present modeling and simulation study of PEM water electrolysis system. Model of electrolytic cell, hydrogen production subsystem and thermal management subsystem are constructed in Matlab/Simulink. Controller model was designed based on PI control strategy. A rapid prototyping controller with MPC5744 chip was used to develop the control system of electrolytic hydrogen production system. Hardware in the
Hua, YuweiJin, ZhenhuaTian, YingTao, Yuepeng
The dynamic behavior of the water and thermal management are critical to stabilize the performance of the proton exchange membrane fuel cell (PEMFC) during severe load changes. In this paper, a fuel cell hybrid electric vehicle (FCHEV) dynamic simulation model is established to evaluate the changes in liquid water and temperature distribution inside the fuel cell stack under a vehicle driving cycle conditions. This paper focuses on analyzing the power generation performance of the stack and the dynamic behavior of internal water and heat transfer following the demand of the vehicle. According to the simulation results, the temperature of MEA and cooling water fluctuates greatly, but the temperature of MEA is always higher than the cooling water temperature by about 1.57 degrees Celsius (average value). Compared to the experimental measurements of temperature, the simulation error for the maximum temperature is 3.4% and the simulation error for the average temperature is 4.4%. The
Zhao, XiaojunShen, XuesongWang, YanboShi, WangyingYang, TaoShan, FengxiangMa, XiaoWang, XinZhang, YonghengPan, Fengwen
This paper addresses a series of issues in the thermal management system of proton exchange membrane fuel cells (PEMFC) during power fluctuations, such as slow system response, insufficient stability, significant temperature fluctuations, and the complexity of coupled control between coolant flow and air flow. A solution is proposed by designing separate Linear Active Disturbance Rejection Controllers (LADRC) for the coolant flow and air flow control loops. A one-dimensional model of the PEMFC thermal management system was established on the LMS AMESIM simulation platform, combined with a hydrogen fuel cell vehicle model and a driver model, fully considering various influencing factors such as vehicle power fluctuations and driver demands. Subsequently, the LADRC control algorithm was developed on the Matlab-Simulink platform, and a co-simulation analysis was performed to compare the control effects of PID control and LADRC under both custom operating conditions and the New European
Zhu, ShaopengMei, JingYang, LangZong, YajingLiu, YunmeiZhang, BoChen, Huipeng
The advancement of clean energy technology has resulted in the emergence of fuel cells as an efficient and environmentally friendly energy conversion device with a diverse range of potential applications, including those in the fields of transportation and power generation. Among the challenges facing fuel cell technology, thermal management represents a significant technical hurdle. The advancement of innovative thermal management methods and system design is imperative to address issues such as high waste heat. In light of the above, this paper presents a methodology for the application of fuel cell thermal management predictive control algorithms in engineering, with a particular focus on fuel cell engine systems that have been implemented in fuel cell cars. This paper proposes a thermal management control method based on a model predictive control algorithm for proton exchange membrane fuel cell systems. The objective of the methodology is to predict and adjust the thermal
Yu, ZhiyangDing, TianweiHuang, XingWang, YupengChen, Guodong
Energy management strategy (EMS) based on vehicle speed prediction has been widely used in fuel cell vehicles (FCVs). Actually, not only the actual power demand but also other factors affect the optimal power allocation between fuel cell system (FCS) and battery. However, this relationship is difficult to express in formulas especially under urban conditions because the power demand fluctuates greatly under the above conditions. To address the issue, a novel EMS for FCV based on short-term power demand and FCS output power is proposed. In the offline part, the short-term SOC change rate is used to characterize short-term power allocation. Besides, the average of short-term power demand and the FCS output power are selected as input factors. The feedforward neural network is used to learn the relationship of the above three state variables based on historical driving cycles. In the online part, a long short-term memory (LSTM) network is used to predict the short-term speed based on the
Wu, HuiduoMin, HaitaoZhao, HonghuiSun, Weiyi
This paper proposes a method that speeds up the Model Predictive Control (MPC) algorithm in the thermal management system of air-cooled Proton Exchange Membrane Fuel Cell (PEMFC), with an integration of machine learning and Active Set Method (ASM) of quadratic programming. Firstly, the parameters of the electrochemical model and mass transfer model of PEMFC are identified by swarm intelligence algorithms such as particle swarm algorithm and bat algorithm, and a semi-empirical model that can simulate actual dynamics is established. Based on this, a model predictive controller based on Active Set Method (ASM) is designed, and the optimization solution algorithm is optimized to solve the problem of slow and poor real-time performance. Combined with machine learning methods such as K-nearest neighbor algorithm and support vector machine, the warm start of the optimization solution algorithm is realized to improve the solution efficiency. The results show that using the warm-start MPC
Lv, HangChen, FengxiangPei, Yaowang
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