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Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

The Composite Lightweight Automotive Suspension System is a composite rear suspension knuckle/tieblade consisting of UD prepreg (epoxy resin), SMC (vinylester resin) carbon fibre and a steel insert to reduce the weight of the component by 35% and reduce Co2. The compression moulding manufacturing process and CAE optimisation are unique and ground-breaking for this product and are designed to allow high volume manufacture of approx. 30,000 vehicles per year. The manufacturing techniques employed allow for multi-material construction within a five minute cycle time to make the process viable for volume manufacture. The complexities of the design lie in the areas of manufacturing, CAE prediction and highly specialised design methods. It is a well-known fact that the performance of a composite part is primarily determined by the way it is manufactured.

In this study, we present an intelligent and wireless subsystem for powering and communicating with three sets of seat belt buckle sensors that are each installed on removable and interchangeable automobile seating. As automobile intelligence systems advance, a logical step is for the driver’s dashboard to display seat belt buckle indicators for rear seating in addition to the front seating. The problem encountered is that removable and interchangeable automobile seating outfitted with wired power and data links are inherently less reliable than rigidly fixed seating, as there is a risk of damage to the detachable power and data connectors throughout end-user seating removal/re-installation cycles.

Although autonomous driving technology has become an emerging research focus, safety is still the most crucial concern when autonomous vehicles leave research laboratory and enter public traffic. Direct yaw moment control (DYC), which differentially brakes the wheels to produce a yaw moment, is an important system to ensure the driving stability of vehicle under extreme conditions. Traditional DYC system must need to take into account driver’s intention and vehicle dynamics. However, for autonomous vehicle, no human is involved in driving process, and enforcing traditional DYC system may conflict with the demands of the desired path. Therefore, in this paper, a novel DYC system for autonomous vehicle is proposed to simultaneously suppress lateral path tracking deviation while maintaining autonomous vehicle stability at or close to the driving limits. In the hardware aspect, an integrated-electro-hydraulic brake (IEHB) actuator scheme is adopted.

Brake squeal is a complex dynamics instability issue for automobile industry. Closed-loop coupling model deals with brake squeal from a perspective of structural instability. Friction characteristics between pads and disc rotor play important roles. In this paper, a closed-loop coupling model which incorporates negative friction-velocity slope is presented. Different from other existing models where the interface nodes are coupled through assumed springs, they are connected directly in the presented model. Negative friction slope is taken into account. Relationship between nodes’ frictional forces, relative speeds and brake pressure under equilibrant sliding and vibrating states is analysed. Then repeated nodal coordinate elimination and substructures’ modal coordinate space transformation of system dynamic equation are performed. It shows that the negative friction slope leads to negative damping items in dynamic equation of system.

The dynamic properties of disc rotor play important role in the NVH performance of a disc brake system. Disc rotor in general is a centrosymmetric structure. It has many repeated-root modes within the interested frequency range and they may have significant influence on squeal occurrence. A pair of repeated-root modes is in nature one vibration mode. However, in current complex eigenvalue analysis model and relevant analysis methods, repeated-root modes are processed separately. This may lead to contradictory result. This paper presents methods to deal with repeated-root modes in substructure modal composition (SMC) analysis to avoid the contradiction. Through curve-fitting technique, the modal shape coefficients of repeated-root modes are expressed in an identical formula. This formula is used in SMC analysis to obtain an integrated SMC value to represent the total influence of two repeated-root modes.

In this paper, analysis methods for brake squeal including substructure modal composition analysis and substructure modal parameters sensitivity analysis are presented. These methods are based on a new closed-loop coupling disc brake model, where the coupled nodal pairs in each coupling interface are connected tightly. This assumption is different from other existing models in literatures, where the interface nodes are coupled through assumed springs. Based on this new model, two analysis methods are derived: Substructure modal composition analysis indicates the contribution of modes of each substructure to the noise mode; Substructure modal parameters sensitivity analysis indicates the sensitivity of the real part of system’s eigenvalue to component’s modal frequency and shape. Finally, the presented analysis methods are applied to analyse a high frequency squeal problem of a squealing disc brake.

As a typical parameter of the road-vehicle interface, the road friction potential acts an important factor that governs the vehicle motion states under certain maneuvering input, which makes the prior knowledge of maximum road friction capacity crucial to the vehicle stability control systems. Since the direct measure of the road friction potential is expensive for vehicle active safety system, the evaluation of this variable by cost effective method is becoming a hot issue all these years. A ‘wheel slip based’ maximum road friction coefficient estimation method based on a modified Dugoff tire model for distributed drive electric vehicles is proposed in this paper. It aims to evaluate the road friction potential with vehicle and wheel dynamics analyzing by using standard sensors equipped on production vehicle, and fully take the advantage of distributed EV that the wheel drive torque and rolling speed can be obtained accurately.

Particle Number (PN) have already been a big issue for developing high efficiency internal combustion engines (ICEs). In this study, controlled spark-assisted stratified compression ignition (SSCI) with moderate end-gas auto-ignition was used for reducing PN in a high compression ratio gasoline direct injection (GDI) engine. Under wide open throttle (WOT) and Maximum Brake Torque timing (MBT) condition, high external cooled exhaust gas recirculation (EGR) was filled in the cylinder, while two-stage direct injection was used to form desired stoichiometric but stratified mixture. SSCI combustion mode exhibits two-stage heat release, where the first stage is associated with flame propagation induced by spark ignition and the second stage is the result of moderate end-gas auto-ignition without pressure oscillation at the middle or late stage of the combustion process.

In this paper, a soft computing approach to a model-free vehicle stability control (VSC) algorithm is presented. The objective is to create a fuzzy inference system (FIS) that is robust enough to operate in a multitude of vehicle conditions (load, tire wear, alignment), and road conditions while at the same time providing optimal vehicle stability by detecting and minimizing loss of traction. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm. This paper outlines the FIS detection algorithm and its benefits over a model-based approach. The performance of the FIS-based VSC is evaluated via a co-simulation of MATLAB/Simulink and CarSim model of the vehicle under various road and load conditions. The results showed that the proposed algorithm is capable of accurately indicating unstable vehicle behavior for two different types of vehicles (SUV and Sedan).

Closed-loop coupling model, based on complex eigenvalue analysis, is one of the most popular and effective methods for brake squeal analysis. In the model, imaginary coupling springs are used to represent the normal contacting force between coupled nodes. Unfortunately, the physical meaning of these coupling springs was seldom discussed and there’s no systematic method to determine the value of spring stiffness. Realizing this problem, this paper, based on finite element model and modal synthesis technique, develops a new closed-loop coupling disc brake squeal model without introducing imaginary coupling springs. Different from the traditional model where two nodes at coupling interface are connected through a spring, these node-pairs in the new model are assumed to remain in tight contact during vibration. Details of the model, including force analysis, coordinate reduction and transformation and complex eigenvalue decomposition are given in this paper.

This paper presents the application of a proposed fuzzy inference system as part of a stability control design scheme implemented with active steering actuator sets. The fuzzy inference system is used to detect the level of overseer/understeer at the high level and a speed-adaptive activation module determines whether an active front steering, active rear steering, or active 4 wheel steering is suited to improve vehicle handling stability. The resulting model-free system is capable of minimizing the amount of model calibration during the vehicle stability control development process as well as improving vehicle performance and stability over a wide range of vehicle and road conditions. A simulation study will be presented that evaluates the proposed scheme and compares the effectiveness of active front steer (AFS) and active rear steer (ARS) in enhancing the vehicle performance. Both time and frequency domain results are presented.

Drivelines used in modern pickup trucks commonly employ universal joints. This type of joint is responsible for second driveshaft order vibrations in the vehicle. Large displacements of the joint connecting the driveline and the rear axle have a detrimental effect on vehicle NVH. As leaf springs are critical energy absorbing elements that connect to the powertrain, they are used to restrain large axle windup angles. One of the most common types of leaf springs in use today is the multi-stage parabolic leaf spring. A simple SAE 3-link approximation is adequate for preliminary studies but it has been found to be inadequate to study axle windup. A vast body of literature exists on modeling leaf springs using nonlinear FEA and multibody simulations. However, these methods require significant amount of component level detail and measured data. As such, these techniques are not applicable for quick sensitivity studies at design conception stage.

Electric vehicles are receiving considerable attention because they offer a more efficient and sustainable transportation alternative compared to conventional fossil-fuel powered vehicles. Since the battery pack represents the primary energy storage component in an electric vehicle powertrain, it requires accurate monitoring and control. In order to effectively estimate the battery pack critical parameters such as the battery state of charge (SOC), state of health (SOH), and remaining capacity, a high-fidelity battery model is needed as part of a robust SOC estimation strategy. As the battery degrades, model parameters significantly change, and this model needs to account for all operating conditions throughout the battery's lifespan. For effective battery management system design, it is critical that the physical model adapts to parameter changes due to aging.

This paper proposes an approach that characterizes a driver's driving behavior and style in real-time during car-following drives. It uses an online learning of the evolving Takagi-Sugeno fuzzy model combined with the Markov model. The inputs fed into the proposed algorithm are from the measured signals of on-board sensors equipped with current vehicles, including the relative distance sensors for Adaptive Cruise Control feature and the accelerometer for Electronic Stability Control feature. The approach is verified using data collected using a test vehicle from several car-following test trips. The effectiveness of the proposed approach has been shown in the paper.

The reference path played a very important role in the parking schemes. In this paper, an arc tangent liked polynomial trajectory model is proposed, and an optimal trajectory is obtained for automatic parallel parking based on genetic algorithm, which ensures that the vehicle does not collide with obstacles or other vehicles during parking. The proposed algorithm has strong robustness because of that all the parameters of the vehicle and the parallel parking spaces are parameterized. Using the trajectory model with the vehicle and parking space parameters, a cost function with multi-constraints, were established for path planning. The start and end points of the planning trajectory are the actual starting point and the desired final parking point of the vehicle by choosing three parameters of the trajectory model appropriately. Simulation results illustrate the effectiveness of the proposed algorithm.

This paper proposes an approach to determine driver's driving behavior, style or habit during vehicle handling maneuvers and heavy traction and braking events in real-time. It utilizes intelligence inferred from driver's control inputs, vehicle dynamics states, measured signals, and variables processed inside existing control modules such as those of anti-lock braking, traction control, and electronic stability control systems. The algorithm developed for the proposed approach has been experimentally validated and shows the effectiveness in characterizing driver's handling behavior. Such driver behavior can be used for personalizing vehicle electronic controls, driver assistant and active safety systems, and the other vehicle control features.

A deeper understanding of the complex phenomenology associated with the multiphase flow-induced noise and vibration in a dynamic valve is of critical importance to the automotive industry. To this purpose, a two-dimensional axisymmetric numerical model has been developed to simulate the complex processes that are responsible for the noise and vibration in a poppet valve. More specifically, an Eulerian multiphase flow model, a dynamic mesh and a user-defined function are utilized to facilitate the modeling of this complicated two-phase fluid-structure interaction problem. For a two-phase flow through the valve, our simulations showed that the deformation and breakup of gas bubbles in the gap between the poppet and the valve seat generates a vibration that arises primarily from the force imbalance between the spring and the two-phase fluid flow induced forces on the poppet.

Side swipe accidents occur primarily when drivers attempt an improper lane change, drift out of lane, or the vehicle loses lateral traction. Past studies of lane change detection have relied on vehicular data, such as steering angle, velocity, and acceleration. In this paper, we use three physiological signals from the driver to detect lane changes before the event actually occurs. These are the electrocardiogram (ECG), galvanic skin response (GSR), and respiration rate (RR) and were determined, in prior studies, to best reflect a driver's response to the driving environment. A novel system is proposed which uses a Granger causality test for feature selection and a neural network for classification. Test results showed that for 30 lane change events and 60 non lane change events in on-the-road driving, a true positive rate of 70% and a false positive rate of 10% was obtained.

Aftertreatment system design involves multiple tradeoffs between engine performance, fuel economy, regulatory emission levels, packaging, and cost. Selection of the best design solution (or “architecture”) is often based on an assumption that inherent catalyst activity is unaffected by location within the system. However, this study acknowledges that catalyst activity can be significantly impacted by location in the system as a result of varying thermal exposure, and this in turn can impact the selection of an optimum system architecture. Vehicle experiments with catalysts aged over a range of mild to moderate to severe thermal conditions that accurately reflect select locations on a vehicle were conducted on a chassis dynamometer. The vehicle test data indicated CO and NOx could be minimized with a catalyst placed in an intermediate location.