Rollover Index for Rollover Mitigation Function of Intelligent Commercial Vehicle’s Electronic Stability Control

This paper describes a rollover index for detection or prediction of impending rollover in different driving situations using minimum sensor signals which can be easily obtained from an electronic stability control (ESC) system. The estimated lateral load transfer ratio (LTR) was used as a rollover index with only limited information such as the roll state of the vehicle and some constant parameters. A commercial vehicle has parameter uncertainties because of its load variation. This is likely to affect the driving performance and the estimation of the dynamic state of the vehicle. The main purpose of this paper is to determine the rollover index based on reliable measurements and the parameters of the vehicle. For this purpose, a simplified lateral and vertical vehicle dynamic model was used with some assumptions. The index is appropriate for various situations although the vehicle parameters may change. As part of the index, the road bank angle was investigated in this study, using limited information. Since the vehicle roll dynamics are affected by the road bank angle, the road bank angle should be incorporated, although previous studies ignore this factor in order to simplify the problem. Because it increases or reduces the chances of rollover, consideration of the road bank angle is indispensable in the rollover detection and mitigation function of the ESC system. The performance of the proposed algorithm was investigated via computer simulation studies. The simulation studies showed that the proposed estimation method of the LTR and road bank angle with limited sensor information followed the actual LTR value, reducing the parameter uncertainties. The simulation model was constructed based on a heavy bus (12 tons).

A Study on the Manufacture of Permanent Magnet Traction Control Valve for Electronic Stability Control in Electric Vehicles

Most solenoid valves in use today require a magnetic coil to be continuously energized to maintain the magnetization of the magnetic body in order to operate. The problem is that if the power is still supplied, the power consumption will continue. In addition, problems such as shortening the lifespan of solenoid valve internal parts due to the increase in the internal temperature of the electronic stability control (ESC) due to the continuous heating of the magnetic coil, and malfunction due to instantaneous power failure may occur. In this study, we conducted a study on the permanent magnet traction control valve (TCV) for ESC that can minimize the unnecessary power consumption of electric vehicle batteries. For optimal permanent magnet design, polarity direction setting and permanent magnet specifications were studied through FE simulation. A permanent magnet TCV was fabricated and an electromagnetic force test was conducted to compare and evaluate it with the FE simulation result. By using a permanent magnet, it was possible to lower the initial current value for the TCV to drive, therefore, it was possible to develop a permanent magnet TCV that can minimize the unnecessary power consumption of electric vehicle batteries.

A Comparative Study of Energy Consumption and Recovery of Autonomous Fuel-Cell Hydrogen–Electric Vehicles Using Different Powertrains Based on Regenerative Braking and Electronic Stability Control System

Today, with the increasing transition to electric vehicles (EVs), the design of highly energy-efficient vehicle architectures has taken precedence for many car manufacturers. To this end, the energy consumption and recovery rates of different powertrain vehicle architectures need to be investigated comprehensively. In this study, six different powertrain architectures—four independent in-wheel motors with regenerative electronic stability control (RESC) and without an RESC, one-stage gear (1G) transmission, two-stage gear (2G) transmission, continuously variable transmission (CVT) and downsized electric motor with CVT—were mathematically modeled and analyzed under real road conditions using nonlinear models of an autonomous hydrogen fuel-cell electric vehicle (HFCEV). The aims of this paper were twofold: first, to compare the energy consumption performance of powertrain architectures by analyzing the effects of the regenerative electronic stability control (RESC) system, and secondly, to investigate the usability of a downsized electrical motor for an HFCEV. For this purpose, all the numerical simulations were conducted for the well-known FTP75 and NEDC urban drive cycles. The obtained results demonstrate that the minimum energy consumption can be achieved by a 2G-based powertrain using the same motor; however, when an RESC system is used, the energy recovery/consumption rate can be increased. Moreover, the results of the article show that it is possible to use a downsized electric motor due to the CVT, and this powertrain significantly reduces the energy consumption of the HFCEV as compared to all the other systems. The results of this paper present highly significant implications for automotive manufacturers for designing and developing a cleaner electrical vehicle energy consumption and recovery system.

Development and Validation of Electronic Stability Control System Algorithm Based on Tire Force Observation

In view of the higher and higher assembly rate of the electronic stability control system (ESC in short), the control accuracy still needs to be improved. In order to make up for the insufficient accuracy of the tire model in the nonlinear area of the tire, in this paper, an algorithm for the electronic stability control system based on the control of tire force feedforward used in conjunction with tire force sensors is proposed. The algorithm takes into consideration the lateral stability of the tire under extreme conditions affected by the braking force. We use linear optimal control to determine the optimal yaw moment, and obtain the brake wheel cylinder pressure through an algorithm combining feedforward compensation based on measured tire force and feedback correction. The controller structure is divided into two layers, the upper layer is controlled by a linear quadratic regulator (LQR in short) and the lower layer is controlled by PID (Proportional-integral-derivative) and feedforward. After that, verification of the controller’s algorithms using software cosimulation and hardware-in-the-loop (HIL in short) testing in the double lane change (DLC in short) and sine with dwell (SWD in short) conditions. From the test results it can be concluded that the controller based on tire force observation has partially control advantages.

Diagnosing Automotive Damper Defects Using Convolutional Neural Networks and Electronic Stability Control Sensor Signals

Chassis system components such as dampers have a significant impact on vehicle stability, driving safety, and driving comfort. Therefore, monitoring and diagnosing the defects of these components is necessary. Currently, this task is based on the driver’s perception of component defects in series production vehicles, even though model-based approaches in the literature exist. As we observe an increased availability of data in modern vehicles and advances in the field of deep learning, this paper deals with the analysis of the performance of Convolutional Neural Networks (CNN) for the diagnosis of automotive damper defects. To ensure a broad applicability of the generated diagnosis system, only signals of a classic Electronic Stability Control (ESC) system, such as wheel speeds, longitudinal and lateral vehicle acceleration, and yaw rate, were used. A structured analysis of data pre-processing and CNN configuration parameters were investigated in terms of the defect detection result. The results show that simple Fast Fourier Transformation (FFT) pre-processing and configuration parameters resulting in small networks are sufficient for a high defect detection rate.

Electronic Stability Control of Vehicles

The paper presents the Electronic Control Unit of vehicles with the main focus on Anti-lock braking system. ABS is used in traction control and to maintain vehicle stability. The paper describes the detail study of CarSim model and it gives us the most accurate and efficient method to simulate the performance of vehicle and how ABS can be used in different scenarios. The result of vehicular model without and with ABS for different test cases is presented in the paper. The vehicular model is implemented on MATLAB Simulink environment and the corresponding results are presented.