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As we move towards the world of autonomous vehicles it becomes increasingly important to integrate several chassis control systems to provide the desired vehicle stability without mutual interference. The principles for integration proposed in existing technical literature are majorly centralized which are not only computationally expensive but does not fit the current supplier based OEM business model. An Automotive OEM brings multiple suppliers on-board for developing the Active Safety systems considering several factors such as cost, quality, time, ease of business etc. When these systems are put together in the vehicle they may interfere with each other’s function. Decoupling their function results in a need of heavy calibration causing performance trade-offs and loss in development time.

A coupled magnetic-thermal model is established to study the reason for the damage of the starter motor, which belongs to the idling start-stop system of a city bus. A finite element model of the real starter motor is built, and the internal magnetic flux density nephogram and magnetic line distribution chart of the motor are attained by simulation. Then a model in module Transient Thermal of ANSYS is established to calculate the stator and rotor loss, the winding loss and the mechanical loss. Three kinds of losses are coupled to the thermal field as heat sources in two different conditions. The thermal field and the components’ temperature distribution in the starting process are obtained, which are finally compared with the already-burned motor of the city bus in reality to predict the damage. The analysis method proposed is verified to be accurate and reliable through comparing the actual structure with the simulation results.

The unmanned roller is one of the most popular construction vehicles, for which the accurate path-following is one of the most important control task. The dual-antenna Global Positioning System (GPS), usually mounted on the top of the cabin and the front drum separately, is used to approximately measure the position and heading direction at the contact patch between the wheel and the road. However, in the presence of large variation in the attitude of the roller, caused by the uneven construction site, there is bias in position and heading measurement due to the wobble of the roller. Obviously, this introduces several disturbances to the path-following control. In this paper, the Attitude Heading Reference System (AHRS) is used to measure the attitude information thereby corrects the position and heading of the roller measured by GPS only.

Formula SAE (FSAE) is a student design competition organized by SAE International. The competition requires student teams to design and manufacture a formula style race car, to compete against other teams. Testing and validation of the vehicle is an integral part of the design and performance during the competition. Drivers for the collegiate competition are typically at an amateur level. As a result, the human factor plays a significant role in the outcome of dynamic events. In order to reduce uncertainty and improve the general performance, emphasis on driver training is necessary. Instead of overall performance of the driver based on an individual lap, the current research focuses on detailed components of the driver’s actions throughout different sections of the lap.

Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed. The system is implemented on an Nvidia Jetson TX2 embedded computing platform, the classification and location of the objects are determined by the neural network. Coordinates and other properties of the object are published on to various ROS topics which are then serviced by visualization and data handling routines.

How to improve the measurement accuracy of road gradient is the key content of the research on the speed warning of commercial vehicles in mountainous roads. The large error of the measurement causes a significant effect of the vehicle speed threshold, which causes a risk to the vehicle's safety. Conventional measuring instruments such as accelerometers and gyroscopes generally have noise fluctuation interference or time accumulation error, resulting in large measurement errors. To solve this problem, the Kalman filter method is used to reduce the interference of unwanted signals, thereby improving the accuracy of the slope measurement. However, the Kalman filtering method is limited by the estimation error of various parameters, and the filtering effect is difficult to meet the project research requirements.

This SAE Recommended Practice covers the design and application of a 120 VAC single phase engine based auxiliary power unit or GENSET. This document is intended to provide design direction for the single phase nominal 120 VAC as it interfaces within the truck 12 VDC battery and electrical architecture providing power to truck sleeper cab hotel loads so that they may operate with the main propulsion engine turned off.

This SAE Recommended Practice covers the design and application of a 120 VAC single phase engine based auxiliary power unit or GENSET. This document is intended to provide design direction for the single phase nominal 120 VAC as it interfaces within the truck architecture providing power to truck sleeper cab hotel loads so that they may operate with the main propulsion engine turned off.

This SAE Recommended Practice covers the design and application of a 120 VAC single phase engine based auxiliary power unit or GENSET. This document is intended to provide design direction for the single phase nominal 120 VAC as it interfaces within the truck architecture providing power to truck sleeper cab hotel loads so that they may operate with the main propulsion engine turned off.

This document is written to address acceleration and deceleration control issues related to Heavy Duty Trucks and Buses greater than 10 000 GVW.

Fuel consumption and exhaust gas emission regulations are being tightened around the world year by year. Electric vehicles are needed to reduce carbon dioxide emissions. Especially, Plug-in hybrid heavy-duty vehicles (PHEVs) are expected to become widespread. PHEVs enable all-electric modes, as well as hybrid modes, using both engines and electric motors, but the control system significantly affects the characteristics of fuel consumption and gas emission. In this study, we used new testing machine (we call extended HILS) to analyze the fuel consumption and gas emission for different plug-in hybrid control systems and investigated the optimal control method for PHEVs.

Due to the importance of the fast transportation under every circumstance, the transportation process may require a high speed heavy vehicle from time to time, which may turn the transportation process more unsafe. Due to that fact the truck safety during braking and the ride comfort during long distance travelling with high speeds should be improved. Therefore, the aim of this work is to develop a control system which combines the suspension and braking systems. The control system consists of three controllers; the first one for the active suspension system of the truck body and cab, the second one for the ABS and, the third for the integrated control system between the active suspension system and the ABS. The control strategy is also separated into two strategies.

The work presents the design of a traction electric equipment set of an electromechanical drivetrain (EMD) to be used with 300-hp-class agricultural wheeled tractors. Comparative characteristics of various drivetrain types used in agricultural tractors are presented; and the advantages of using EMDs in tractors are discussed. The EDM advantages are as follows: improved technical and economic parameters of the tractor; reduced dynamic loads on the tractor and diesel units; reduces wheel slippage; reduced fuel consumption (by up to 30%); continuous variation of speed of the tractor and aggregated implements; reduced expenses on maintenance, repair and spare parts; increased total reliability, controllability and comfort; possibility to use the tractor as a power source (optionally). Considered in combination, all these advantages clearly speak in favor of using electromechanical drivetrains.

This paper describes the preliminary results of a study focused on the semi empirical modeling of an excavator's hydraulic pump. From the viewpoint of designing and tuning an efficient control system, the excavator is a very complex nonlinear plant. To design and tune such a complex control system an extremely good nonlinear model of the plant is necessary. The problem of modeling an excavator is considered in this paper; a nonlinear mathematical model of an excavator has been developed using the bond graph methodology realized in the AMESim® simulation software to replicate actual operating conditions. The excavator model is described by two models: a hydraulic model and a kinematic model. At this stage of research the hydraulic model deals solely with the model of the main hydraulic pump, which has been conceived as a semi empirical model.

Modern day commercial vehicles use Electronic Control Units that are complex. Integration testing of these Control Units as one control system has many challenges. Many of these challenges are met by driving Integration test activities towards automation, based on Hardware In the Loop (HIL) simulation. The simulation scenarios consist of functional tests designed with the aim of replicating real world tests that are performed in an actual vehicle. However, most of the HIL simulators used these days in industry have functional tests which are assumed to be independent and are executed in a sequence pre-determined by a Test engineer. We demonstrate that the assumption of test independence doesn't hold well in many simulation scenarios. Integration test process efficiency can be shown to be improved by dynamically scheduling functional tests while simulation is running instead of a pre-determined sequence. We propose two solutions to for the dynamic test scheduling problem.

The main trend in designs of modern automobiles is a widespread use of mechatronic modules and systems. These modules are built as a symbiosis of electric, electronic and hydraulic components, united by means of the control system and intended to fulfill particular targeted functions. In the article results of a comprehensive theoretical and experimental study aimed at creating a drive and a control system of a wheel module intended to be used with the steering and spring systems of an automobile and a mobile robot have been considered. Also the basic principles of the a complex of mechatronic drives of the wheel module (CMD WM) and technical requirements for the components of the complex as a mechatronic module of the system of active safety of the automobile and mobile robot have been formulated.

Improving driving safety and freeway capacity is an indispensable research issue for road vehicles, especially for tractor semi-trailers, which on the one hand exhibit unstable motion modes at high speeds due to their articulated configurations and undertake the largest part of freight transportation on freeways. Automatic driving is rated as the ultimate solution of vehicle safety since it can significantly reduce accidents resulting from human driver errors. Proposed in this paper is a gain-scheduled PID controller for automatic path-following of a tractor semi-trailer. The PID controller minimizes the vehicle's predicted lateral deviation and heading error with respect to the desired path at a preview point, and gains of the controller are scheduled with respect to vehicle speed.

Abstract An energy management strategy based on model predictive control (MPC) was proposed for the hybrid bus. For the series configuration, MPC was used for power distribution among transmission components. Real-time optimization of the control strategy was achieved, which improved the fuel economy. First, a rule-based energy management strategy was proposed, and the logical thresholds of the stage of charge (SOC) and the demand power were formulated to underlie the subsequent study of the control strategy. Second, an energy management strategy based on global optimization was established where the dynamic programming algorithm was used to determine the SOC optimal reference curve and the limitation of fuel economy. In this way, the target and reference can be provided for the subsequent control strategy. Third, a radial basis neural network speed prediction model based on wavelet transform was formulated.

Abstract Hybrid Electric Vehicles (HEVs) achieve better fuel economy than conventional vehicles by utilizing two different power sources: an internal combustion engine and an electrical motor. The power distribution between these two components must be controlled using some algorithm, be it rule based, optimization based, or reinforcement learning based. In the design of such control algorithms, it is important to evaluate the impact that variations of certain design parameters will have on the system performance, in this case, fuel economy. Traditional methods of sensitivity analysis have been applied to various power flow control algorithms to determine their robustness to the variations of HEV design parameters. This article presents a sensitivity analysis of three power flow control algorithms: twin delayed deep deterministic policy gradient (TD3), deep deterministic policy gradient (DDPG), and adaptive equivalent consumption minimization strategy (A-ECMS).

Abstract A new method that allows data-enabled (empirical) models, commonly used for automotive engine calibration, to extrapolate beyond the range of training data has been developed. This method used a physics-based system-level one-dimensional model to improve interpolation and allow extrapolation for three data-based algorithms, by modifying the model input (feature) space. Neural network, regression, and k-nearest neighbor predictions of engine emissions and volumetric efficiency were greatly improved by generating 736,281 artificial feature spaces and then performing feature selection to choose feature spaces (feature selection) so that extrapolations in the original feature space were interpolations in the new feature space. A novel feature selection method was developed that used a two-stage search process to uniquely select the best feature spaces for every prediction.