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This paper uses the brake control allocation method for Electric Vehicles (EVs) based on system-level vehicle Reference Point (RP) motion feedback. The RP motion control is an alternative to the standard brake torque allocation methods and results in improved vehicle stability in both longitudinal and lateral directions without requiring additional measurements beyond what is available in EVs with ABS and ESP. The proposed control law simplifies the brake torque allocation algorithm, reduces overall development time and effort, and merges most of the braking systems into one. Additionally, the measured or estimated signals required are reduced compared to the standard approach. The system-level RP measurements and references are transformed into individual wheel coordinate systems, where tracking is ensured by actuating both friction torques and electric motor regenerative torques using a proposed brake torque blending mechanism.

Brake squeal is a common phenomenon across all types of vehicles. It becomes prominent in the absence of other noise sources, as in the case of electric vehicles. Earlier simulation attempts date back to late nineties and early 2000s. Identification of unstable modes of the coupled system of brake rotor and pads, and occasionally some caliper components, was the primary goal. Simulating the rotation of the rotor along with squeezing of the pads was attempted in a multi-body dynamics tools with flexible representation of rotor and pads. Though this gave some insights into the dynamics of stopping mechanism, squeal required capturing the nonlinearities of the contact in a more rigorous sense. Also, efforts were made to capture noise from vibrations using boundary- and finite- element methods [1].

The prediction of natural frequencies is a crucial aspect of engineering design and analysis. Traditional methods involve finite element analysis (FEA) which is a standard method for calculating natural frequencies of dynamic systems. For each design variant, FEA calculation can be time-consuming and computationally expensive. In this study, we propose a novel method for predicting the natural frequencies of design variants using transfer learning and artificial neural networks (ANN). The proposed method involves the use of FEA to generate the stiffness and mass matrices of the brake disc, which are then used as inputs to the neural network. However, the prediction can become tedious when there is a change in the design. To address this, we employ transfer learning followed by linear regression using a design variant of the previous structure as test data. The neural network learns through transfer learning and fine-tunes its outputs using regression for final frequency prediction.

In recent years, brakes emission tests have become increasingly standardized to meet progressively stricter intra and inter laboratory reproducibility requirements. In particular, following the recent EURO 7 regulation proposal, WLTP-Brake cycle has surged as EU standard braking sequence to determine emission factors of investigated brake systems. Furthermore, the UN GTR (United Nations Global Technical Regulation) on Laboratory Measurement of Brake Emissions for Light-Duty Vehicles collects all the information needed to perform emission tests in laboratory. This includes design specifications for the testing platforms as well as the typology and configuration of measuring instruments.

During validation of a new brake lining on a light duty truck application, the brake rotor exhibited high lateral runout on the friction surfaces. As the engineering team investigated the issue more carefully, they noticed the rotor lateral runout was also changing from revolution to revolution. The team ran testing on multiple light pickup vehicles and found differences in the amount of rotor runout variation. The rotor lateral runout and runout variation can cause vibration and pulsation of the passenger seat and the steering wheel. To identify the root cause of the high level of rotor lateral runout and runout variation, measurement data was collected and analyzed from the vehicle level test. During further analysis, some of the runout variation corresponded to a wheel bearing internal frequency. The bearing internal geometry was studied to confirm what factors affected the runout variation.

As the regulations aiming to limit air pollution become stricter, the battle against non-exhaust emissions known to be harmful to human health and the environment is attracting more focus and extending worldwide. EVs are equipped with a hybrid braking system combining regenerative and hydraulic braking to provide the same performance as traditional vehicles. Whenever the regenerative braking torque is insufficient to give the necessary deceleration rate, the hydraulic and electromechanical braking torque is applied. Thus, the recuperative braking of EVs reduces the need for brakes. As the brakes are not used as often, dust and rust will accumulate and impede their performance, so brake problems can arise from not using them enough. Due to the extra weight of EVs compared to ICEVs, more particulates are released through increased corrosion and friction on the braking system.

The use of personal light electric vehicles (PLEVs), such as electric scooters, has rapidly increased in recent years. However, their widespread use has raised concerns about rider safety due to their vulnerability in shared traffic spaces. To address this issue, this paper presents a radar-based rider assistance system aimed at enhancing the safety of PLEV riders. The system consists of an adaptive feedback system and a single-channel anti-lock braking system (ABS). The adaptive feedback system uses multiple-input multiple-output (MIMO) radar sensors to detect nearby objects and provide real-time warnings to the rider through haptic, visual, and acoustic signals. The system takes into account traffic density and uses online data to warn about obscured objects, thereby improving the rider’s situational awareness. Results from testing the feedback system show that it effectively detects potential collisions and provides warning signals, reducing the risk of accidents.

Liquefied petroleum gas (LPG), like many other alternative fuels, has witnessed increased adoption in the last decade, and its use is projected to rise as stricter emissions regulations continue to be applied. However, much of its use is limited to dual fuel applications, gaseous phase injection, light-duty passenger vehicle applications, or scenarios that require conversion from gasoline engines. Therefore, to address these limitations and discover the most efficient means of harnessing its full potential, more research is required in the development of optimized fuel injection equipment for liquid port and direct injection, along with the implementation of advanced combustion strategies that will improve its thermal efficiency to the levels of conventional fuels.

Aiming at the problem of braking shock caused by the inconsistent response time of the inner motor (IM), the outer motor (OM) and the hydraulic brake when the regenerative braking mode of dual-rotor in-wheel motor (DRIWM) is switched, this paper proposes a U-shaped transition coordinated control strategy for the DRIWM. Ensure that the total braking torque can be smoothly transitioned when any one or more of the hydraulic braking torque, the braking torque of the IM and the braking torque of the OM enter/exit braking. The dynamic model of electric vehicle (EV) with DRIWMs is established, the division of braking mode is based on the principle of optimal DRIWM system efficiency, and the U-shaped transition coordinated controller of DRIWM is designed. Finally, two cases of switching the IM single braking mode to hydraulic braking mode and OM and hydraulic coordinated braking mode switching to compound braking mode are taken as examples to verify.

This paper describes a Hardware-In-the-Loop (HIL) platform based on the dual-axis dynamometer for development and validation of ABS/TCS controllers. Antilock Braking System (ABS) and Traction Control System (TCS) are standard equipment for passenger vehicles. The ABS, an anti-skid braking assistance system, promotes safety by preventing the locking of wheels during braking. TCS is a control system that prevents the wheels from slipping by moderating driving power to the one that is losing its grip on the road. The real-time platform is based on a dSPACE vehicle model and the simulation environment, and it consists of an actual drive motor, hydraulic braking system and Chroma dual-axis dynamometer test bench, which provide more realistic and complicated conditions than the one-axis platform. With dual-axis architecture, it could effectively perform simulation results of model on two axes.

Brake-by-wire systems are an innovative and important component of modern high-performance and also electrified vehicles. Due to their decoupled architecture, they enable driver-independent vehicle dynamics control (e.g., brake torque blending) and easy integration of assistance functionalities (e.g. Emergency Brake Assist (EBA)). On the other hand, the development of these functions can cause high costs and development effort, and testing can be critical in case of improper gain tuning. Therefore, already in the concept phase, a large part of the testing is shifted to virtual environments and simulations that allow safe and reproducible experiments without damage. Therefore, suitable and reliable models are needed to represent reality as accurately as possible. This paper deals with the modelling of a purely electrohydraulic brake-by-wire system and a hybrid system with electrohydraulic brakes on the front axle and electromechanical brakes on the rear axle.

The interaction between driveline control and anti-lock braking system (ABS) control in electric vehicles (EV) was investigated based on multi-body dynamics (MBD) model and control model co-simulation. Two primary driveline control algorithms, active damping control and wheel flare control, were integrated with ABS control in Simulink model and the influence on ABS control was studied. The event for high mu to low mu transition was simulated. When ABS control is active on low mu surface, the vehicle shows large wheel slip and long duration time before wheel speed returns to stable control. This performance could be improved with activating driveline control. Deceleration uniformity metric shows that active damping control has very small effect when ABS control becomes stable after passing through the high mu to low mu transition period. Driveline damping control can help to reduce vibration, but it is difficult to find satisfied tuning for wheel speed performance.

The Anti-Lock Braking System (ABS) is a safety critical feature primarily used to control slipping of wheels, to maximize available traction and minimize stopping distance. Regulatory authorities of many countries have mandated implementation of an ABS as a compulsory safety feature to be present in all road legal automobiles. Hence, apart from avoiding wheel lock-up, an ABS must also ensure that the vehicle maintains its handling stability and steerability while braking. Thus, it is important that the ABS controller modulate and apply adequate amount of brake cylinder pressure. This paper suggests the use of a Tire Force based algorithm to analyze vehicle behavior and accordingly a control law is employed to modulate the wheel brake pressure.

The anti-lock brake system (ABS) is a vital system in modern vehicles that prevents automotive wheels from locking during an emergency brake. This paper aims to introduce an efficient, optimized proportional integral derivative (PID) controller tuned using a genetic algorithm (GA) to enhance the performance of ABS. The PID control method is a very famous control algorithm employed in numerous engineering applications. The GA is used to solve the nonlinear optimization problem and search for the optimum PID controller gains by identifying the solution to the problem. A mathematical model of ABS is derived and simulated using Matlab and Simulink software. The proposed optimized PID-controlled ABS is compared to the conventional ABS controlled using a Bang-Bang controller. System performance criteria are evaluated and assessed under different road adhesion coefficient values to judge the success of the proposed PID controller tuned using GA.

Regenerative braking is an effective way to increase the cruising range of vehicles. In commercial vehicles with large vehicle mass, regenerative braking can be maintained in a high-power working state for a long time theoretically because of the large braking torque and long braking time. But in fact, it is often impossible to run at full power because of battery safety problems. In this paper, a control strategy is designed to maintain the maximum power operation of regenerative braking as much as possible. The maximum charging power of the battery is obtained through the battery model, and it is set as the battery limiting parameter. The regenerative braking torque and power are obtained by using the motor model. The eddy current retarder is used to absorb the excess power that the battery can't bear, and the braking torque of the eddy current retarder is calculated. Finally, mechanical braking is used to make up the insufficient braking torque.

The Brake judder is a low-level vibration caused due to Disc Thickness Variation (DTV), Temperature, Brake Torque Variation (BTV), thermal degradation, hotspot etc. which is a major concern for the past decades in automobile manufacturers. To predict the judder performance, the modelling methods are proposed in terms of frequency and BTV respectively. In this study, a mathematical model is constructed by considering full brake assembly, tie rod, coupling rod, steering column, and steering wheel as a spring mass system for identifying judder frequency. Simulation is also performed to predict the occurrence of brake judder and those results are validated with theoretical results. Similarly, for calculating BTV a separate methodology is proposed in CAE and validated with experimental and theoretical results.

This paper presents an innovative combined control using Model Predictive Control (MPC) to enhance the stability of automated vehicles. It integrates path tracking and vehicle stability control into a single controller to satisfy both objectives. The stability enhancement is achieved by computing two expected yaw rates based on the steering wheel angle and on lateral acceleration into the MPC model. The vehicle's stability is determined by comparing the two reference yaw rates to the actual one. Thus, the MPC controller prioritises path tracking or vehicle stability by actively varying the cost function weights depending on the vehicle states. Using two industrial standard manoeuvres, i.e. moose test and double lane change, we demonstrate a significant improvement in path tracking and vehicle stability of the proposed MPC over eight benchmark controllers in the high-fidelity simulation environment.

Dihydrogen, as a zero CO2 fuel, is a strong candidate for internal combustion engine to limit global warming. This study shows the impact of standard tuning parameters on mixture homogeneity and combustion characteristics. A 2.2L Diesel engine on which the head was reworked to allow side mounted direct injector and central mounted spark plug was selected. The discussed tests were made at low engine speed and partial load. A spark advance sweep at different air-fuel ratios (λ) was conducted. The exponential relation between λ and NOx emissions is highly marked and extremely low NOx emissions up to 1.7 g/kWh at minimum spark advance for maximum brake torque can be measured. A λ sweep was performed at different starts of injection (SOI). The results show that, depending on the engine speed, a later SOI might lead to lower NOx emissions. For a λ setpoint of 1.8, at 1500 rpm, late SOI leads to 30% higher NOx emissions where at 2500 rpm these emissions are 26% lower.

High-efficient simulations are mandatory to manage the ever-increasing complexity of automotive powertrain system and reduce development time and costs. Integrating AI methods into the development process provides an ideal solution thanks to massive increase in computational power. Based on an 1D physical engine model of a turbo-charged direct injection gasoline engine with variable valve timing (VVT), a high-performance hybrid simulation model has been developed for increasing computing performance. The newly developed model is made of a physics-based low-pressure part including intake and exhaust peripheries and a neural-network-based high-pressure part for combustion chamber calculations. For the training and validation of the combustion chamber neural networks, a data set with 10.5 million operating points was generated in a short time thanks to the parallelizable combustion chamber simulations in stand-alone mode.

Anti-lock brake systems (ABS) produce high levels of vehicle deceleration under emergency braking conditions by modulating tire slip. Currently there are limited data available to quantify the mean, variance, and distribution of vehicle deceleration levels for modern ABS-equipped vehicles. We conducted braking tests using twenty (20) late-model vehicles on contiguous dry asphalt and concrete road surfaces. All vehicles were equipped with a 5th wheel sampled at 200 Hz, from which vehicle speed and deceleration as a function of time were calculated. Eighteen (18) tests were conducted for each vehicle and all tests were conducted from a targeted initial speed of 65 km/h (40 mph). Overall, we found that late-model ABS-equipped vehicles can decelerate at average levels that vary from about 0.871g to 1.081g across both surfaces, and that deceleration levels were on average about 0.042g higher on asphalt than on concrete.