Search Results

Abstract A time domain analysis method of ride comfort and energy dissipation characteristics is proposed for automotive vibration proportional–integral–derivative (PID) control. A two-degrees-of-freedom single wheel model for automotive vibration control is established, and the conventional vibration response variables for ride comfort evaluation and the energy consumption vibration response variables for energy dissipation characteristics evaluation are determined, and the Routh stability criterion method was introduced to assess the impact of PID control on vehicle stability. The PID control parameters are tuned using the differential evolution algorithm, and to improve the algorithm’s adaptive ability, an adaptive operator is introduced, so that the mutation factor of differential evolution algorithm can change with the number of iterations.

Abstract Letter from the Focus Issue Editors

Abstract This article presents surrogate mixtures that simulate the physical and chemical properties in the auto-ignition of hydrotreated vegetable oil (HVO). Experimental investigation was conducted in the Ignition Quality Tester (IQT) to validate the auto-ignition properties with respect to those of the target fuel. The surrogate development approach is assisted by artificial neural network (ANN) embedded in MATLAB optimization function. Aspen HYSYS is used to calculate the key physical and chemical properties of hundreds of mixtures of representative components, mainly alkanes—the dominant components of HVO, to train the learning algorithm. Binary and ternary mixtures are developed and validated in the IQT. The target properties include the derived cetane number (DCN), density, viscosity, surface tension, molecular weight, and volatility represented by the distillation curve. The developed surrogates match the target fuel in terms of ignition delay and DCN within 6% error range.

Abstract This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms.

Abstract During mining material hauling, the chassis frame structure of rear dump trucks is subjected to fatigue loading due to uneven road conditions. This loading often leads to crack propagation in the frame rails, necessitating the determination of stresses in the critical zone during the design stage to ensure structural integrity. In this study, a computer-aided engineering (CAE) methodology is employed to size and select the rectangular profile cross section of the chassis frame rail. A detailed design investigation of the chassis frame is conducted to assess its load resistance, structural flexibility, and weld joint fatigue life under critical stresses arising from combined bending and torsion loads. The optimization process aims to determine the optimal rail size and material thickness, striking a balance between minimizing mass and maximizing structural reliability.

Abstract In the process of injection molding, the vacuum pump rear housing is prone to warping deformation and volume shrinkage, which affects its sealing performance. The main reason is the improper control of the injection process and the large flat structure of the vacuum pump rear housing, which does not meet its production and assembly requirements (the warpage deformation should be controlled within 1.1 mm and the volume shrinkage within 10%). To address this issue, this study initially utilized orthogonal experiments to obtain training samples and conducted a preliminary analysis using gray relational analysis. Subsequently, a predictive model was established based on a one-dimensional convolutional neural network (1D CNN).

Abstract Lithium-ion (LI) batteries are widely used to power electric vehicles (EVs), owing to their high charge density, to minimize the environmental pollution caused by fossil fuel-based engines. It experiences an enormous amount of heat generation during charging and discharging cycles, which results in higher operating temperatures and thermal nonuniformity. This affects performance, useful battery life, and operating costs. This can be mitigated by an effective battery thermal management system (BTMS) to dissipate the heat there by safeguarding the battery from adverse thermal effects and ensuring high performance, safety, and longevity of the battery.

Abstract This research was initiated with the goal of developing a significantly stronger aircraft transparency design that would reduce transparency failures from bird strikes. The objective of this research is to demonstrate the fact that incorporating high-strength tempered glass into cockpit window constructions for commercial aircraft can produce enhanced safety protection from bird strikes and weight savings. Thermal glass tempering technology was developed that advances the state of the art for high-strength tempered glass, producing 28 to 36% higher tempered strength. As part of this research, glass probability of failure prediction methodology was introduced for determining the performance of transparencies from simulated bird impact loading. Data used in the failure calculation include the total performance strength of highly tempered glass derived from the basic strength of the glass, the temper level, the time duration of the load, and the area under load.

Abstract The global automotive industry’s shift toward electrification hinges on battery electric vehicles (BEV) having a reduced total cost of ownership compared to traditional vehicles. Although BEVs exhibit lower operational costs than internal combustion engine (ICE) vehicles, their initial acquisition expense is higher due to expensive battery packs. This study evaluates total ownership costs for four vehicle types: traditional ICE-based car, BEV, split-power hybrid, and plug-in hybrid. Unlike previous analyses comparing production vehicles, this study employs a hypothetical sedan with different powertrains for a more equitable assessment. The study uses a drive-cycle model grounded in fundamental vehicle dynamics to determine the fuel and electricity consumption for each vehicle in highway and urban conditions. These figures serve a Monte Carlo simulation, projecting a vehicle’s operating cost over a decade based on average daily distance and highway driving percentage.

Abstract An automatic collision avoidance control method integrating optimal four-wheel steering (4WS) and direct yaw-moment control (DYC) for autonomous vehicles on curved road is proposed in this study. Optimal four-wheel steering is used to track a predetermined trajectory, and DYC is adopted for vehicle stability. Two single lane change collision avoidance scenarios, i.e., a stationary obstacle in front and a moving obstacle at a lower speed in the same lane, are constructed to verify the proposed control method. The main contributions of this article include (1) a quintic polynomial lane change trajectory for collision avoidance on curved road is proposed and (2) four different kinds of control method for autonomous collision avoidance, namely 2WS, 2WS+DYC, 4WS, and 4WS+DYC, are compared. In the design of DYC controller, two different feedback control methods are adopted for comparison, i.e., sideslip angle feedback and yaw rate feedback.

Abstract In order to enhance the path-tracking accuracy and adaptability of the electric flatbed vehicle (EFV) in the soft-target vehicle test system, an improved controller is designed based on the linear quadratic regulator (LQR) algorithm. First, the LQR feedback controller is designed based on the EFV dynamics tracking error model, and the genetic algorithm is utilized to obtain the optimal weight coefficient matrix for different speeds. Second, a weight coefficient matrix compensation strategy is proposed to address the changes in the relationship between the vehicle–road position and attitude caused by external disturbances and the state of EFV. An offline parameter table is created to reduce the computational load on the microcontroller of EFV and enhance real-time path-tracking performance. Furthermore, an adaptive preview time control strategy is added to reduce the overshooting caused by control delay. This strategy is based on road curvature and traveling speed.

Abstract Reactivity-controlled compression ignition (RCCI) engine is an innovative dual-fuel strategy, which uses two fuels with different reactivity and physical properties to achieve low-temperature combustion, resulting in reduced emissions of oxides of nitrogen (NOx), particulate matter, and improved fuel efficiency at part-load engine operating conditions compared to conventional diesel engines. However, RCCI operation at high loads poses challenges due to the premixed nature of RCCI combustion. Furthermore, precise controls of indicated mean effective pressure (IMEP) and CA50 combustion phasing (crank angle corresponding to 50% of cumulative heat release) are crucial for drivability, fuel conversion efficiency, and combustion stability of an RCCI engine.

Abstract Real-world driving in diverse traffic must cope with dynamic environments including a multitude of agents with uncertain behaviors. This poses a challenging motion planning and decision-making problem, as suitable algorithms should manage to obtain optimal solutions considering nearby vehicles. The state-of-the-art way of environment and action generalization is built on mathematical modeling and probabilistic programming of idealistic incidents. In this article we present dynamic anytime decision making, a decision-making algorithm that takes advantage of natural evolutionary and developmental processes to make decisions for an autonomous vehicle navigating in traffic. The methodology to achieve multidimensional judgment under unforeseen circumstances is to enable stochastic Bayesian game theory when modeling interactive properties and scenario estimation.

Abstract This scientific publication presents the application of artificial intelligence (AI) techniques as a virtual sensor for tailpipe emissions of CO, NOx, and HC in a high-performance vehicle. The study aims to address critical challenges faced in real industrial applications, including signal alignment and signal dynamics management. A comprehensive pre-processing pipeline is proposed to tackle these issues, and a light gradient-boosting machine (LightGBM) model is employed to estimate emissions during real driving cycles. The research compares two modeling approaches: one involving a unique “direct model” and another using a “two-stage model” which leverages distinct models for the engine and the aftertreatment. The findings suggest that the direct model strikes the best balance between simplicity and accuracy.

Abstract Composite ceramic brake discs are made of ceramic material reinforced with carbon fibers and offer exceptional advantages that translate directly into higher vehicle performance. In the case of an electric vehicle, it could increase the range of the vehicle, and in the case of conventional internal combustion engine vehicles, it means lower fuel consumption (and consequently lower CO2 emissions). These discs are typically characterized by complex internal geometries, further complicated by the presence of drilling holes on both friction surfaces. To estimate the aerothermal performance of these discs, and for the thermal management of the vehicle, a reliable model for predicting the air flowing across the disc channels is needed. In this study, a real carbon-ceramic brake disc with drilling holes was investigated in a dedicated test rig simulating the wheel corner flow conditions experimentally using the particle image velocimetry technique and numerically.

Abstract Accurate estimation of traction force is essential for the development of advanced control systems, particularly in the domain of autonomous driving. This study presents an innovative approach to enhance the estimation of tire–road interaction forces under combined slip conditions, employing a combination of empirical models and neural networks. Initially, the well-known Pacejka formula, or magic formula, was adopted to estimate tire–road interaction forces under pure longitudinal slip conditions. However, it was observed that this formula yielded unsatisfactory results under non-pure slip conditions, such as during curves. To address this challenge, a neural network architecture was developed to predict the estimation error associated with the Pacejka formula. Two distinct neural networks were developed. The first neural network employed, as inputs, both longitudinal slip ratios of the driving wheels and the slip angles of the driving wheels.

Abstract The swift progress of electric vehicles (EVs) and hybrid electric vehicles (HEVs) has driven advancements in battery management systems (BMS). However, optimizing the algorithms that drive these systems remains a challenge. Recent breakthroughs in data science, particularly in deep learning networks, have introduced the long–short-term memory (LSTM) network as a solution for sequence problems. While graphics processing units (GPUs) and application-specific integrated circuits (ASICs) have been used to improve performance in AI-based applications, field-programmable gate arrays (FPGAs) have gained popularity due to their low power consumption and high-speed acceleration, making them ideal for artificial intelligence (AI) implementation. One of the critical components of EVs and HEVs is the BMS, which performs operations to optimize the use of energy stored in lithium-ion batteries (LiBs).

Abstract To address the challenge of directly measuring essential dynamic parameters of vehicles, this article introduces a multi-source information fusion estimation method. Using the intelligent front camera (IFC) sensor to analyze lane line polynomial information and a kinematic model, the vehicle’s lateral velocity and sideslip angle can be determined without extra sensor expenses. After evaluating the strengths and weaknesses of the two aforementioned lateral velocity estimation techniques, a fusion estimation approach for lateral velocity is proposed. This approach extracts the vehicle’s lateral dynamic characteristics to calculate the fusion allocation coefficient. Subsequently, the outcomes from the two lateral velocity estimation techniques are merged, ensuring rapid convergence under steady-state conditions and precise tracking in dynamic scenarios.

Abstract This article presents a merge-aware cruise control method that incorporates vehicle-to-vehicle (V2V) information and aims at improving the energy efficiency of vehicles and reducing speed disruptions of merging traffic during highway merges. During the events of highway merges, the gap between the ego and the preceding vehicle reduces drastically, which can result in sudden braking of the ego vehicle and thus reduction of its energy efficiency. We propose a rather simple cruise control algorithm to eliminate such sudden variations in the gap and velocity with respect to the preceding vehicle during highway merges, thus reducing the large accelerations and braking during such events and thereby improving energy efficiency. The proposed algorithm incorporates future traffic information and has computational requirements similar to adaptive cruise control methods, hence it is real-time applicable.

Abstract This study proposes a machine learning tabulation (MLT) method that employs deep neural networks (DNNs) to predict ignition delay and knock propensity in spark ignition (SI) engines. The commonly used Arrhenius model and Livengood–Wu integral for fast knock prediction are not accurate enough to account for residual gas species and may require adjustments or modifications to account for specific engine characteristics. Detailed kinetics modeling is computationally expensive, so the MLT approach is introduced to solve these issues. The MLT method uses precalculated thermochemical states of the mixture that are clustered based on a combustion progress variable. Hundreds of DNNs are trained with the stochastic Levenberg–Marquardt (SLM) optimization algorithm, reducing training time and memory requirements for large-scale problems. MLT has high interpolation accuracy, eliminates the need for table storage, and reduces memory requirements by three orders of magnitude.