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Reviewer Acknowledgment

Abstract This work puts presents an all-inclusive state of the art bibliographical review of all categories of electrified transportation (xEVs) standards, issued by the most important standardization organizations. Firstly, the current status for the standards by major organizations is presented followed by the graphical representation of the number of standards issued. The review then takes into consideration the interpretation of the xEVs standards developed by all the major standardization organizations across the globe. The standards are differentiated categorically to deliver a coherent view of the current status followed by the explanation of the core of these standards. The ISO, IEC, SAE, IEEE, UL, ESO, NTCAS, JARI, JIS and ARAI electrified transportation vehicles xEV Standards from USA, Europe, Japan, China and India were evaluated. A total approximated of 283 standards in the area have been issued.

Abstract One of the most important factors affecting the performance of vehicle active chassis control systems is the tire-road friction coefficient. Accurate estimation of the friction coefficient can lead to better performance of these controllers. In this study, a new three-step friction estimation algorithm, based on intelligent tire concept, is proposed, which is a combination of experiment-based and vehicle dynamic based approaches. In the first step of the proposed algorithm, the normal load is estimated using a trained Artificial Neural Network (ANN). The network was trained using the experimental data collected using a portable tire testing trailer. In the second step of the algorithm, the tire forces and the wheel longitudinal velocity are estimated through a two-step Kalman filter. Then, in the last step, using the estimated tire normal load and longitudinal and lateral forces, the friction coefficient can be estimated.

Abstract A Direct Yaw-Moment Control (DYC) logic for a rear-wheel-drive electric-powered vehicle is proposed. The vehicle is a Formula SAE (FSAE) type race car, with two electric motors powering each rear wheel. Vehicle baseline balance is neutral at low speeds, for increased maneuverability, and increases understeering at high speeds (due to the aerodynamic configuration) for stability. A controller that can deal with these yaw response variations, modelling uncertainties, and vehicle nonlinear behavior at limit handling is proposed. A two-level control strategy is considered. For the upper level, yaw rate and sideslip angle are considered as feedback control variables and a cubic-error Proportional Derivative (PD) controller is proposed for the feedback control. For the lower level, a traction control algorithm is used, together with the yaw moment requirement, for torque allocation.

Abstract Aiming to improve the handling performance of heavy tractor semi-trailer during turning or changing lanes at high speed, a hierarchical structure controller is proposed and a hardware-in-the-loop (HIL) test bench of the electronic pneumatic braking system is developed to validate the proposed controller. In the upper controller, a Kalman filter observer based on the heavy tractor semi-trailer dynamic model is used to estimate the yaw rates and sideslip angles of the tractor and trailer. Simultaneously, a sliding mode direct yaw moment controller is developed, which takes the estimated yaw rates and sideslip angles and the reference values calculated by the three-degrees-of-freedom dynamic model of the heavy tractor semi-trailer as the control inputs. In the lower controller, the additional yaw moments of tractor and trailer are transformed into corresponding wheel braking forces according to the current steering characteristics.

Abstract Autonomous vehicles (AVs) are expected to have a great impact on mobility by decreasing commute time and vehicle fuel consumption and increasing safety significantly. However, there are still issues that can jeopardize their wide impact and their acceptance by the public. One of the main limitations is motion sickness (MS). Hence, the last year’s research is focusing on improving motion comfort within AVs. On one hand, users are expected to perceive AVs driving style as more aggressive, as it might result in excessive head and body motion. Therefore, speed reduction should be considered as a countermeasure of MS mitigation. On the other hand, the excessive reduction of speed can have a negative impact on traffic. At the same time, the user’s dissatisfaction, i.e., acceptance and subjective comfort, will increase due to a longer journey time.

Abstract A kinematic modeling framework was established to predict status (position, displacement, velocity, acceleration, and shape) of a towing vehicle system with different driver inputs. This framework consists of three components: (1) a state space model to decide position and velocity for the vehicle system based on Newton’s second law; (2) an angular acceleration transferring model, which leads to a hypothesis that the each towed unit follows the same path as the towing vehicle; and (3) a polygon model to draw instantaneous polygons to envelop the entire system at any time point.

Abstract This article explores the value of simulation for autonomous-vehicle research and development. There is ample research that details the effectiveness of simulation for training humans to fly and drive. Unfortunately, the same is not true for simulations used to train and test artificial intelligence (AI) that enables autonomous vehicles to fly and drive without humans. Research has shown that simulation “fidelity” is the most influential factor affecting training yield, but psychological fidelity is a widely accepted definition that does not apply to AI because it describes how well simulations engage various cognitive functions of human operators. Therefore, this investigation reviewed the literature that was published between January 2010 and May 2022 on the topic of simulation fidelity to understand how researchers are defining and measuring simulation fidelity as applied to training AI.

Abstract The tailpipe zero-emission legislation has pushed the automotive industry toward more electrification. Regenerative braking is the capability of electric machines to provide brake torque. So far, the regenerative braking feature is primarily considered due to its effect on energy efficiency. However, using individual e-machines for each wheel makes it possible to apply the antilock braking function due to the fast torque-tracking characteristics of permanent magnet synchronous motors (PMSM). Due to its considerable cost reduction, in this article, a feasibility study is carried out to investigate if the ABS function can be done purely through regenerative braking using a mid-fidelity model-based approach. An uni-tire model of the vehicle with a surface-mount PMSM (SPMSM) model is used to verify the idea. The proposed ABS control system has a hierarchical structure containing a high-level longitudinal slip controller and a low-level SPMSM torque controller.

Abstract Integrated control for automated vehicles in platoons with nonlinear coupled dynamics is developed in this article. A nonlinear MPC approach is used to address the multi-input multi-output (MIMO) nature of the problem, the nonlinear vehicle dynamics, and the platoon constraints. The control actions are determined by using model-based prediction in conjunction with constrained optimization. Two distinct scenarios are then simulated. The first scenario consists of the multivehicle merging into an existing platoon in a controlled environment in the absence of noise, whereas the effects of external disturbances, modeling errors, and measurement noise are simulated in the second scenario. An extended Kalman filter (EKF) is utilized to estimate the system states under the sensor and process noise effectively.

Abstract Nowadays, with the great advancement of automobile intellectualization, vehicle integrated dynamic control is increasingly becoming a hot research field. For vehicle stability, this article focuses on the coordinated control of Direct Yaw-moment Control (DYC) and Active Front Steering (AFS). First of all, the nominal control variables (yaw rate and sideslip angle) are designed based on the linear two Degrees of Freedom (2 DOF) vehicle model, in which the phase difference between the actual and nominal variables has been pointed out due to the approximate substitution with first-order time-delay transfer function. Secondly, considering the uncertainty of cornering stiffness per axle, and increasing robustness of the system, the Optimal Guaranteed Cost Control (OGCC) theory is adopted to design the coordinated controller.

Abstract Electrochemical impedance spectroscopy (EIS) is widely used to diagnose the state of health (SOH) of lithium-ion batteries. One of the essential steps for the diagnosis is to analyze EIS with an equivalent circuit model (ECM) to understand the changes of the internal physical and chemical processes. Due to numerous equivalent circuit elements in the ECM, existing parameter identification methods often fail to meet the requirements in terms of identification accuracy or convergence speed. Therefore, this article proposes a novel impedance model parameter identification method based on the random mutation differential evolution (RMDE) algorithm. Compared with methods such as nonlinear least squares, it does not depend on the initial values of the parameters. The method is compared with chaos particle swarm optimization (CPSO) algorithm and genetic algorithm (GA), showing advantages in many aspects.

Abstract An accurate Unmanned Aerial System (UAS) Flight Dynamics Model (FDM) allows us to design its efficient controller in early development phases and to increase safety while reducing costs. Flight tests are normally conducted for a pre-established number of flight conditions, and then mathematical methods are used to obtain the FDM for the entire flight envelope. For our UAS-S4 Ehecatl, 216 local FDMs corresponding to different flight conditions were utilized to create its Local Linear Scheduled Flight Dynamics Model (LLS-FDM). The initial flight envelope data containing 216 local FDMs was further augmented using interpolation and extrapolation methodologies, thus increasing the number of trimmed local FDMs of up to 3,642. Relying on this augmented dataset, the Support Vector Machine (SVM) methodology was used as a benchmarking regression algorithm due to its excellent performance when training samples could not be separated linearly.

Abstract A major contributor to fatal vehicle crashes is hydroplaning, which has traditionally been reported at a specific vehicle speed for a given operating condition. However, hydroplaning is a complex phenomenon requiring a holistic, probabilistic, and multidisciplinary approach. The objective of this article is to develop a probabilistic approach to predict Hydroplaning Potential and Risk that integrates fundamental understanding of the interdependent factors: hydrology, fluid-solid interactions, tire mechanics, and vehicle dynamics. A novel theoretical treatment of Hydroplaning Potential and Risk is developed, and simulation results for the prediction of water film thickness and Hydroplaning Potential are presented. The results show the advantages of the current approach which could enable the improvement of road, vehicle, and tire design, resulting in greater safety of the traveling public.

Abstract This article describes the real-time simulation of a tire model for the off-highway sector. The off-highway area is characterized by soft surfaces. The additional deformation of the ground can result in more complex interactions between the tires and ground than in the on-highway area. The basics for these relationships are explained using normal and shear stress models. Aspects such as elastic tires, sinking due to slip, and multipass are also described. It is explained how soft soil modeling is used by a height field model to calculate the deformations of the soil and the resulting tire forces. Particular emphasis is placed on the calculation time and the numerical stability. The implementation in an existing real-time-capable vehicle model is described, which is important to provide a comprehensive simulation solution. During the validation it could be shown that the implemented height field can correctly map the soft soil properties.

Abstract The Noise, Vibration, and Harshness (NVH) performance of automotive powertrain (PT) mounts involves the PT source vibration, PT mount stiffness, road input, and overall transfer path design. Like safety, performance, and durability driving dynamics, vehicle-level NVH also plays a major contributing factor for electric vehicle (EV) refinement. This article highlights the recent research on PT mounting-related NVH controls on electric cars. This work’s main contribution lies in the comparative study of the internal combustion engine (ICE)-based PT mounting and EV-based PT mounting system (PMS) with specific EV challenges. Various literature on PT mounts from the passive, semi-active, and active mounting systems are studied. The parameter optimization technique for mount stiffness and location by various research papers is summarized to understand the existing methodologies and research gap in EV application.

Abstract Precise vehicle state and the surrounding traffic information are essential for decision-making and dynamic control of intelligent connected vehicles. Tremendous research efforts have been devoted to developing state estimation techniques. This work investigates the research progress in this field over recent years. To be able to describe the state of multiple traffic elements uniformly, the concept of a vehicle neighborhood system is proposed to describe the system composed of vehicles and their surrounding traffic elements and to distinguish it from the traditional macroscopic traffic research field. In this work, the vehicle neighborhood system consists of three main traffic elements: the host vehicle, the preceding vehicle, and the road. Therefore, a review of state estimation methods for the vehicle neighborhood system is presented around the three traffic objects mentioned earlier.

Abstract Numerous researchers are committed to finding solutions to the path planning problem of intelligence-based vehicles. How to select the appropriate algorithm for path planning has always been the topic of scholars. To analyze the advantages of existing path planning algorithms, the intelligence-based vehicle path planning algorithms are classified into conventional path planning methods, intelligent path planning methods, and reinforcement learning (RL) path planning methods. The currently popular RL path planning techniques are classified into two categories: model based and model free, which are more suitable for complex unknown environments. Model-based learning contains a policy iterative method and value iterative method. Model-free learning contains a time-difference algorithm, Q-learning algorithm, state-action-reward-state-action (SARSA) algorithm, and Monte Carlo (MC) algorithm.

Abstract This article is a review of automobile sensor technologies that have the potential to enhance fuel economy. Based on an in-depth review of the literature and demonstration projects, the following sensor technologies were selected for evaluation: vehicular radar systems (VRS), camera systems (CS), and vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) systems. V2V and V2I systems were found to have the highest merit in improving fuel economy over a wide range of integration strategies, with fuel economy improvements ranging from 5 to 20% with V2V and 10 to 25% for V2I. However, V2V and V2I systems require significant adoption for practical application which is not expected in this decade. Numerous academic studies and contemporary vehicular safety systems attest VRS as more technologically mature and robust relative to other sensors. However, VRS offers less fuel economy enhancement (~14%).

Abstract Electromagnetic forming (EMF) is a high-speed impulse forming process developed during the 1950s and 1960s to acquire shapes from sheet metal that could not be obtained using conventional forming techniques. In order to attain required deformation, EMF process applies high Lorentz force for a very short duration of time. Due to the ability to form aluminum and other low-formability materials, the use of EMF of sheet metal for automobile parts has been rising in recent years. This review gives an inclusive survey of historical progress in EMF of continuum sheet metals. Also, the EMF is reviewed based on analytical approach, finite element method (FEM) simulation-based approach and experimental approach, on formability of the metals.