Integrated Longitudinal Vehicle Dynamics Control with Tire/Road Friction Estimation

<div class="section abstract"><div class="htmlview paragraph">For modern vehicle development, on-board vehicle Mass and road Gradient Estimation (MGE) can offer great benefit to many sub-systems on the vehicle, such as vehicle control system, transmission control system, and active safety system etc. However, there are still several challenges that need to be solved. Firstly, thanks to good accuracy, reliability, and robustness, regression analysis-based approaches: Recursive Least Squares (RLS) and Kalman Filter (KF) are very popular for MGE, but the trade-off between estimator’s accuracy and converge time is challenging. Furthermore, depending on vehicle and powertrain types, the implementation of MGE function could be very different. It is desired to have a structured approach for various vehicle applications’ MGE development. Lastly, good reliability of MGE does not always satisfy for complicated real-world driving maneuvers and road conditions.</div><div class="htmlview paragraph">This paper introduced a mature MGE development approach which utilizes an innovative Series Kalman Filter (S-KF) structure with a 3-axis accelerometer to address the challenges above, leading to the demonstration on 3 different types of vehicles with different powertrain applications: a truck with Internal Combustion Engine (ICE) and Automated Manual Transmission (AMT) gearbox, a mid-size Plug-in Hybrid Electric Vehicle (PHEV) SUV, and an A-segment Battery Electric Vehicles (BEV) mini car. They are tested under wide range real-world driving maneuvers and road conditions. Moreover, to improve the converge time whilst keeping the same accuracy of MGE, several dedicated designed engineering features are implemented and introduced in this paper. The vehicle-based experimental result and structured development approach showed good performance and maturity of the production oriented MGE, and good efficiency and flexibility to adapt it to various applications.</div></div>

<div class="htmlview paragraph">This article describes an application of <i>sliding mode</i> techniques to the design of an air/fuel ratio control system for a 4-stroke engine, to minimize exhaust gas and emissions.</div> <div class="htmlview paragraph">This technique allows to achieve good control performance in terms of precision, robustness, and fast transient response.</div> <div class="htmlview paragraph">To support <i>sliding mode control</i> a second PI stage was added, based on the signal of a second oxygen sensor installed after the catalytic converter.</div> <div class="htmlview paragraph">Experimental results were better than those obtained with a conventional PI control, currently used on production applications.</div> <div class="htmlview paragraph">The new control algorithm (<i>sliding mode</i> based on the first oxygen sensor, and PI on the second) is very versatile because the approach chosen allows to calculate the parameters values for the ECU using computer simulation.</div>

<div class="section abstract"><div class="htmlview paragraph">Small commercial vehicles (SCV) with Diesel engines require efficient exhaust aftertreatment systems to reduce the emissions while keeping the fuel consumption and total operating cost as low as possible. To meet current emission legislations in all cases, a DOC and DPF and some NOx treatment device (e,g. lean NOx trap or SCR) are required.</div><div class="htmlview paragraph">Creating a cost-effective SCV also requires keeping the cost for the exhaust aftertreatment system as low as possible because the contribution to total vehicle cost is high. By using more sophisticated and more robust operating strategies and control algorithms, the hardware cost can be reduced. To keep the calibration effort at a low level, it is necessary to apply only algorithms which have a time-efficient calibration procedure.</div><div class="htmlview paragraph">This paper will focus on the active regeneration of the DPF. For safe and efficient DPF regeneration, a very reliable and stable DOC out temperature control is required. DOC characteristics and design are often limited by cost and available space but also strongly influence the control requirements and thus the performance. This leads to more sophisticated and more robust control algorithms.</div><div class="htmlview paragraph">In this paper an advanced control algorithm for DOC outlet temperature control for a SCV is presented. The control algorithm applies model-based control and gain-scheduling techniques. An overview over the control algorithm is given and its performance is evaluated on engine test bench, chassis dynamometer and on the public road and compared to the traditional concept which was used before. The results and experiences are presented and analyzed.</div></div>

<div class="section abstract"> <div class="htmlview paragraph">This Recommended Practice provides a taxonomy for motor vehicle <i>driving automation systems</i> that perform part or all of the <i>dynamic driving task</i> (<i>DDT</i>) on a <i>sustained</i> basis and that range in level from <i>no driving automation</i> (level 0) to <i>full driving automation</i> (level 5). It provides detailed definitions for these six levels of <i>driving automation</i> in the context of <i>motor vehicles</i> (hereafter also referred to as “<i>vehicle</i>” or “<i>vehicles</i>”) and their <i>operation</i> on roadways. These level definitions, along with additional supporting terms and definitions provided herein, can be used to describe the full range of <i>driving automation features</i> equipped on <i>motor vehicles</i> in a functionally consistent and coherent manner. “On-road” refers to publicly accessible roadways (including parking areas and private campuses that permit public access) that collectively serve users of <i>vehicles</i> of all classes and <i>driving automation</i> levels (including no <i>driving automation</i>), as well as motorcyclists, pedal cyclists, and pedestrians.</div> <div class="htmlview paragraph">The levels apply to the <i>driving automation feature(s)</i> that are engaged in any given instance of on-road <i>operation</i> of an equipped <i>vehicle</i>. As such, although a given <i>vehicle</i> may be equipped with a <i>driving automation system</i> that is capable of delivering multiple <i>driving automation features</i> that perform at different levels, the level of <i>driving automation</i> exhibited in any given instance is determined by the <i>feature(s)</i> that are engaged.</div> <div class="htmlview paragraph">This document also refers to three primary actors in <i>driving</i>: the (human) <i>driver</i>, the <i>driving automation system</i>, and other <i>vehicle</i> systems and components. These other <i>vehicle</i> systems (or the <i>vehicle</i> in general terms) do not include the <i>driving automation system</i> in this model, even though as a practical matter a <i>driving automation system</i> may actually share hardware and software components with other <i>vehicle</i> systems, such as a processing module(s) or operating code.</div> <div class="htmlview paragraph">The levels of <i>driving automation</i> are defined by reference to the specific role played by each of the three primary actors in performance of the <i>DDT</i>. “Role” in this context refers to the expected role of a given primary actor, based on the design of the <i>driving automation system</i> in question and not necessarily to the actual performance of a given primary actor. For example, a <i>driver</i> who fails to <i>monitor</i> the roadway during engagement of a level 1 adaptive cruise control (ACC) system still has the role of <i>driver</i>, even while s/he is neglecting it.</div> <div class="htmlview paragraph"><i>Active safety systems</i>, such as electronic stability control and automated emergency braking, and certain types of driver assistance systems, such as lane keeping assistance, are excluded from the scope of this <i>driving automation</i> taxonomy because they do not perform part or all of the <i>DDT</i> on a <i>sustained</i> basis and, rather, merely provide momentary intervention during potentially hazardous situations. Due to the momentary nature of the actions of <i>active safety systems</i>, their intervention does not change or eliminate the role of the <i>driver</i> in performing part or all of the <i>DDT,</i> and thus are not considered to be <i>driving automation</i>.</div> <div class="htmlview paragraph">It should, however, be noted that crash avoidance features, including intervention-type <i>active safety systems</i>, may be included in <i>vehicles</i> equipped with <i>driving automation systems</i> at any level. For <i>ADS</i>-equipped <i>vehicles</i> (i.e., levels 3-5) that perform the complete <i>DDT</i>, crash avoidance capability is part of <i>ADS</i> functionality.</div></div>

<div class="htmlview paragraph">Electronic throttle control is increasingly being considered as a viable alternative to conventional air management systems in modern spark-ignition engines. In such a scheme, driver throttle commands are interpreted by the powertrain control module together with many other inputs; rather than directly commanding throttle position, the driver is now simply requesting torque - a request that needs to be appropriately interpreted by the control module.</div> <div class="htmlview paragraph">Engine management under these conditions will require optimal control of the engine torque required by the various vehicle subsystems, ranging from HVAC, to electrical and hydraulic accessories, to the vehicle itself. In this context, the real-time estimation of engine and load torque can play a very important role, especially if this estimation can be performed using the same signals already available to the powertrain control module.</div> <div class="htmlview paragraph">In this paper we present two methods to estimate the instantaneous indicated torque, combustion by combustion, and the vehicle load torque; we also present some preliminary results. The first method is based on the reconstruction of the in-cylinder pressure based on the measured engine block vibration. The second one exploits a nonlinear estimation technique, in which the difference between the instantaneous measurement of crankshaft angular velocity and an estimate of the same velocity, based on a model of the engine, forms the input to a sliding-mode observer.</div> <div class="htmlview paragraph">Experimental results of both methods have been obtained in an engine test cell on a two-cylinder compression-ignition.</div>

<div class="section abstract"><div class="htmlview paragraph">As a new brake-by-wire solution, the electro-booster (Ebooster) brake system can work with the electronic stability program (ESP) equipped in the real vehicle to realize various excellent functions such as basic force boosting (BFB), active braking and energy recovery, which is promoting the development of smart vehicles. Among them, the BFB is the function of Ebooster's servo force to assist the driver's brake pedal force establishing high-intensity braking pressure. After the BFB function failure of the Ebooster, it was not possible to provide sufficient brake pressure for the driver's normal braking, and eventually led to traffic accidents. In this paper, a compensation redundancy control strategy based on ESP is proposed for the BFB failure of the self-designed Ebooster. Firstly, introduced the working principle of Ebooster and ESP, and a suitable pressure-building circuit was selected for the dual brake actuator system; Secondly, after the BFB failure of Ebooster, the rule-based strategy of braking awareness recognition was designed. Thirdly, a layered closed-loop compensation control strategy is designed based on the ESP to restore the pressure building capacity of the hydraulic system. Finally, based on dSPACE products, a hardware-in-the-loop (HiL) experimental bench with dual brake actuators including ESP and Ebooster was built for algorithm verification. The HiL experiment results show that after the BFB failure of Ebooster, the designed compensation redundancy control algorithm can restore the Pressure-Volume (P-V) characteristics of the brake system just like Ebooster's conventional BFB mode, and improve vehicle driving safety.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Conventional cruise control systems in automotive applications are usually designed to maintain the constant speed of the vehicle based on the desired set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods namely adopting the Model Predictive Control (MPC) technology utilizing the road grade preview information and allowance of the vehicle speed variation. This paper is focused on the extension of the Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier by application to the trailer tow use-case. As the connected trailer changes the aerodynamic drag and the overall vehicle mass, it may lead to the undesired downshifts for the conventional cruise controller introducing the fuel economy losses. In this work, the ANLMPC concept is extended to avoid downshifts by translating the downshift conditions to the constraints of the underlying optimization problem to be solved. To deal with significant noise factors, e.g., overall vehicle mass, change of aerodynamic drag, actual weather conditions, fuel type, the on-line adaptation of the parameters is performed by the constrained Recursive Least Squares (RLS) method. The issue of the lack of the excitation due to the limited changes in torque and engine speed in cruise is solved by the directional forgetting and dead-zone approach together with limits on the parameters. The implementation details of ANLMPC are given describing a technique leading the significant reduction of the RAM memory. The ANLMPC has been validated in real world driving conditions running in production PCM module of a Sport Utility Vehicle (SUV) towing a trailer with 1600 kg of load, showing 6.3-8.8% fuel economy improvement compared to production cruise controller with the same time of arrival.</div></div>

<div class="section abstract"><div class="htmlview paragraph">In this paper, the behavior of a downsized spark-ignition engine firing with alcohol/gasoline blends has been analyzed. In particular, different butanol-gasoline and ethanol-gasoline blends have been examined. All the alcohol fuels here considered are derived from biomasses. In the paper, a numerical approach has been followed.</div><div class="htmlview paragraph">A one dimensional model has been tuned in order to simulate the engine operation when it is fueled by alcohol/gasoline mixtures. Numerous operating points, characterized by two different engine speeds and several low-medium load values, have been analyzed. The objective of the numerical analysis is determining the optimum spark advance for different alcohol percentages in the mixtures at the different engine operating points. Once the best spark timing has been selected, the differences, in terms of both indicated torque and efficiency, arising in the different kinds of fueling have been evaluated.</div><div class="htmlview paragraph">Butanol-gasoline blends have led to torque values comparable to those obtainable with pure gasoline; while ethanol-gasoline blends have shown slightly higher torque values. The indicated efficiency increases by adding alcohol to gasoline, both using butanol and ethanol. Both engine torque and efficiency result almost linearly depending on the biofuel percentage in the fuel mixture. The spark advance results linearly decreasing with the biofuel amount in the mixture.</div><div class="htmlview paragraph">This result allowed the authors writing an equation aimed to redefine the spark advance values to be implemented in the engine ECU in order to restore the best performances when fueling is switched from pure gasoline to alcohol-gasoline blends.</div></div>

<div class="section abstract"><div class="htmlview paragraph">This study presents a novel approach for predicting fuel consumption in heavy-duty vehicles using a Machine Learning-based model, which is based on feedforward neural network (FFNN). The model is designed to enhance real-time vehicle monitoring, optimize route planning, and reduce both operational costs and environmental impact, making it particularly suitable for fleet management applications. Unlike traditional physics-based approaches, the FFNN relies solely on a refined selection of input variables, including vehicle speed, acceleration, altitude, road slope, ambient temperature, and engine power. Additionally, vehicle mass is estimated using a methodology presented elsewhere and is included as an input for a better generalization of the consumption model. This parameter significantly impacts fuel consumption and is particularly challenging to obtain for heavy-duty vehicles. Engine power is derived from both engine torque and speed (RPM), ensuring a direct relationship with fuel consumption while keeping computational complexity low.</div><div class="htmlview paragraph">Experimental data were collected from a fleet of heavy-duty trucks under real-world operating conditions. In particular, the study focuses on a turbocharged diesel truck with a maximum power output of 353 kW. The acquisition system is based on an On-Board Unit (OBU) featuring CANbus connectivity, GPS tracking and 4G/LTE-5G communication. The OBU enables continuous logging of vehicle and engine parameters over each mission. Additional road data, such as altitude and slope, were obtained from external mapping services.</div><div class="htmlview paragraph">For the purpose of FFNN development, the training dataset was compiled from approximately 10 different routes, capturing diverse driving conditions, while validation was conducted on 10 independent routes to assess model generalization. Before training, input data were pre-processed using normalization and standardization techniques to ensure stable convergence and mitigate the impact of scale differences among input variables. A correlation analysis was performed to evaluate the relationships among available parameters and fuel consumption, guiding the selection of the most informative inputs. This step reduced redundancy in the dataset and improved network efficiency. Furthermore, hyperparameter optimization was conducted using a Randomized Grid Search algorithm, enabling the identification of an optimal network architecture - specifically in terms of the number of layers and hidden neurons - and training parameters while minimizing overfitting.</div><div class="htmlview paragraph">The final model demonstrated high predictive accuracy across various validation routes, confirming the effectiveness of the FFNN in estimating fuel consumption with a reduced input set. Accuracy was tested on up to 50 routes, whose data where not considered for both training and validation; it was assessed that fuel consumption percentage error per route never exceeded 2%. This approach provides a practical and computationally efficient solution for fleet operators, facilitating advanced route planning and enabling more sustainable transportation strategies through cost-effective fuel management and emissions reduction.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Aiming at the problems of ineffective collision avoidance and vehicle instability in the process of vehicle emergency braking in road conditions with low adhesion and sudden change in adhesion coefficient, a stability-coordinated emergency braking and collision avoidance control system SEBCACS) is proposed. First, according to the motion of the ego vehicle and the target vehicle as well as the road adhesion conditions, a collision time model is proposed for evaluating the vehicle collision risk, and the expected deceleration required to avoid the collision is calculated. Then, the MPC method is used to calculate the yaw moment generated by the four-wheel braking force required to maintain vehicle stability according to the actual and reference yaw rate and side slip angle deviation. Then it is decided whether to implement additional yaw moment control according to the body stability evaluation results. Finally, the required four-wheel braking force is calculated according to the expected deceleration and the expected additional yaw moment (produced by differential braking). The simulation results show that the control system designed in this paper can effectively ensure the vehicle’s longitudinal collision avoidance safety and vehicle stability in the face of harsh environments, compared with the traditional AEB method.</div></div>

<div class="section abstract"> <div class="htmlview paragraph">This document specifies the procedure to be used for a manufacturer to certify the net power and torque rating of a production engine according to SAE J1349 (Rev. 8/04) or the gross engine power of a production engine according to SAE J1995.</div> <div class="htmlview paragraph">Manufacturers who advertise their engine power and torque ratings as Certified to SAE J1349 or SAE J1995 shall follow this procedure. Certification of engine power and torque to SAE J1349 or SAE J1995 is voluntary, however, this power certification process is mandatory for those advertising power ratings as “Certified to SAE J1349” or “Certified to SAE J1995.”</div> <div class="htmlview paragraph">In the event that an engine made by one manufacturer is sold to a consumer in a vehicle produced by a second manufacturer, engine certification may be completed by either manufacturer or by both manufacturers working together. An example of the latter would be the completion of witness testing by the engine manufacturer with the submission of certification documents by the vehicle manufacturer.</div> </div>

Vanadium redox flow battery (VRB) energy storage system has been widely utilized in renewable energy applications such as wind power integration and green buildings. An online electrical model of VRB is needed to monitor and estimate battery states by using real time data since the internal parameters of battery are varying during operation. In this paper, an improved second-order Thevenin model is proposed to describe the operating characteristics of VRB. To overcome the drawback of recursive least squares (RLS) algorithm in tracking parameter identification, this paper proposes a method based on time-varying forgetting factor recursive least Squares (TFF-RLS) algorithm to identify model parameters. According to the data collected by the pulse charging experiment, the RLS algorithm and the TFF-RLS algorithm are used to identify the model parameters. Experiments are carried out on a 5kWh/3kWh VRB experimental platform in Wuhan University of Technology. Two online identification methods are compared. The results show that the TFF-RLS method demonstrates a better performance on tracking the variation of parameters dynamically, and improving estimation accuracy compared with the RLS method.

<div class="section abstract"><div class="htmlview paragraph">Now-a-days, Advanced driver-assistance systems (ADAS) is equipping cars and drivers with advance information and technology to make them become aware of the environment and handle potential situations in better way semi-autonomously. High-quality training and test data is essential in the development and validation of ADAS systems which lay the foundation for autonomous driving technology.</div><div class="htmlview paragraph">ADAS uses the technology like radar, vision and combinations of various sensors including LIDAR to automatize dynamic driving tasks like steering, braking, and acceleration of vehicle for controlled and safe driving. And to integrate these advance technologies, the ADAS needs labeled data to train the algorithm to detect the various objects and moments of driver. Image annotation is one the well-known service to create such training data for computer vision.</div><div class="htmlview paragraph">There are number of open source annotated datasets available viz. COCO, KITTI etc. But these datasets are limited to either US or European road environment scenarios. There is hardly any dataset available for Indian Specific road conditions. In order to capture all the India Specific objects on road, we are capturing data through various environmental conditions, road conditions and annotating objects on the collected camera images. 2-D bounding boxes are being used to annotate objects in an image.</div><div class="htmlview paragraph">These annotated images will then be used to train a machine learning model. Finally this trained model will be then used to detect Indian specific objects with real time data acquired through camera sensors mounted on the vehicle. Object detection is one of the prime aspects for ADAS functionality. The complete process will demonstrate end to end solution for object detection techniques from data generation to real-time object identification and recognition.</div></div>

<div class="htmlview paragraph">Additional future requirements for automobiles such as improved vehicle dynamics control, enhanced comfort, increased safety and compact packaging are met by modern electrical steering systems.</div> <div class="htmlview paragraph">Based on these requirements the new functionality is realized by various additional electrical components for measuring, signal processing and actuator control. However, the reliability of these new systems has to meet the standard of today's automotive steering products. To achieve the demands of the respective components (e.g. sensors, bus systems, electronic control units, power units, actuators) the systems have to be fault-tolerant and/or fail-silent. The realization of the derived safety structures requires both expertise and experience in design and mass production of safety relevant electrical systems. Beside system safety and system availability the redundant electrical systems also have to meet economic and market requirements.</div> <div class="htmlview paragraph">Within this scope the paper discusses three different realizations of electrical steering systems</div> <div class="htmlview paragraph"> <ol class="list nostyle"> <li class="list-item"><span class="li-label">1</span><div class="htmlview paragraph">Electrical power steering system (mechanical system with electrical boosting)</div></li> <li class="list-item"><span class="li-label">2</span><div class="htmlview paragraph">Steer-by-wire system with hydraulic back-up and</div></li> <li class="list-item"><span class="li-label">3</span><div class="htmlview paragraph">Full steer-by-wire system</div></li> </ol> </div> <div class="htmlview paragraph">The paper presents solutions for these systems and discusses the various advantages and disadvantages, respectively. Furthermore strategies for failure detection, failure localization and failure treatment are presented. Finally the various specifications for the components used are discussed.</div>

<div class="section abstract"><div class="htmlview paragraph">Heavy truck rollover remains a primary factor in truck driver fatalities and injury. Roll stability control (RSC) and electronic stability control (ESC) are technologies that have been introduced to reduce the incidence of rollover in heavy truck crashes. This report provides an analysis of the real-world experience of a large for-hire company that introduced RSC into its fleet starting in 2004. The carrier provided a well-documented set of data on the operations of its truck-tractors, including both those equipped with RSC and those that did not have RSC installed. The purpose of the analysis is to determine the effect of RSC on the probability of rollover, as well as to identify other factors that either contribute to rollover or help reduce its incidence.</div><div class="htmlview paragraph">This study presents results on the incidence of rollover both in terms of rollovers per 100 million miles traveled and the percentage of crashes that resulted in rollover. The effect on rollover of cargo loading, driver tenure at the company, driver age, type of operation (bulk, truckload, LTL, etc.), and other factors are examined.</div><div class="htmlview paragraph">Recent hires had a 50% higher rollover rate than more experienced drivers; younger drivers had higher rollover rates than older drivers. Cargo loading and curved roads were both strongly associated with rollover. A statistical model was developed to estimate the separate effect of RSC, controlling for driver age, tenure, cargo loading, roadway curvature, and type of operation. The results showed that RSC reduced the probability of rollover by about 25%.</div></div>