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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 The linear engine as compared with the traditional internal combustion engine has high efficiency and low emissions, so as a new type of hybrid power unit, it is very suitable for a hybrid electric vehicle to improve energy efficiency and environmental protection performances. In this article, a novel linear engine-based hybrid power system that is primarily selected for hybrid electric urban light commercial vehicles is introduced. Furthermore, the working efficiency of the proposed hybrid power system is briefly analyzed through a validation study example, and various inherent factors affecting the working efficiency of the hybrid power system are analyzed and discussed in detail. This work can provide a reference implementation for the research on the power unit for the hybrid electric urban light commercial vehicles.

Abstract In this work, a three-phase integrated onboard battery charger is investigated and implemented for electric vehicle (EV) applications. A three-switch add-on interface is introduced to connect with the inverter and the motor windings, such that a two-channel interleaved boost converter is formed for the battery charging. The detailed system analysis, design methodology, and control strategy are discussed. Moreover, a simulation study is carried out to validate the effectiveness of the proposed integrated charger. As verification, a 5 kW liquid-cooled prototype is built and tested. The proposed integrated charging system achieves a power factor of 0.99, and total harmonic distortion (THD) of 4.82% at 5 kW with an efficiency of 93.2%.

Abstract To design better power converters with enhancement-mode Gallium Nitride high-electron-mobility transistor (eGaN HEMT) for emerging applications such as Electric Vehicles (EV), it is essential to model their switching transients and loss accurately. Analytical modeling has proved to be an effective approach to study the transistor’s dynamic behaviors and analyze the switching energy loss during the turn-on and turn-off transients. Furthermore, it helps to understand the essential factors that influence the switching transients and loss calculation. The accuracy of the analytical model mainly depends on the equivalent circuits and the parasitic parameters inside the transistor packaging and external circuits under different switching stages. It is always challenging to extract the parasitic parameters accurately due to its natural character of nonlinearity and complex correlation during the switching transients.

Abstract Present-day vehicles come with a variety of new features like the pre-crash warning, the vehicle-to-vehicle communication, semi-autonomous driving systems, telematics, drive by wire. They demand very high bandwidth from in-vehicle networks. Various ECUs present inside the automotive transmits useful information via automotive multiplexing. Transmission of data in real-time achieves optimum functionality. The high bandwidth and high-speed requirement can be achieved either by using multiple buses or by implementing higher bandwidth. But, by doing so, the cost of the network as well as the complexity of the wiring increases. Another option is to implement higher layer protocol which can reduce the amount of data transferred by using data reduction (DR) techniques, thus reducing the bandwidth usage. The implementation cost is minimal as the changes are required in the software only and not in hardware.

Abstract The electric-field coupled power transfer (ECPT) system with a coupling capacitor double-resonance circuit is proposed for electric vehicle (EV) charging. The article analyzes the plate capacitors between the EV and ground copperplate and introduces the coupling capacitor double-resonance circuit. The two-port network impedance matching of two topologies coupling capacitor double resonance is simulated, and then double side L impedance matching network and coupling capacitor double resonance with Series-Series (S-S) topology are proposed to solve the transmission efficiency decrease led by plate capacitances’ fluctuation. A prototype of the ECPT system is designed and built to prove the validity of the proposed methods. It is shown that the ECPT system realized higher than 60 W of electrical power, which is dynamic wireless transferred through the tire steel belt and the ground copperplate with at least 88% efficiency when the tires are rolling.

Abstract An energy management strategy (EMS) has an essential role in ameliorating the efficiency and lifetime of the powertrain components in a hybrid fuel cell vehicle (HFCV). The EMS of intelligent HFCVs is equipped with advanced data-driven techniques to efficiently distribute the power flow among the power sources, which have heterogeneous energetic characteristics. Decentralized EMSs provide higher modularity (plug and play) and reliability compared to the centralized data-driven strategies. Modularity is the specification that promotes the discovery of new components in a powertrain system without the need for reconfiguration. Hence, this article puts forward a decentralized reinforcement learning (Dec-RL) framework for designing an EMS in a heavy-duty HFCV. The studied powertrain is composed of two parallel fuel cell systems (FCSs) and a battery pack.

Abstract The cold crank-start stage, including the first three engine cycles, is responsible for a significant amount of the cold-start phase emissions in a Gasoline Direct Injection (GDI) engine. The engine crank-start is highly transient due to substantial engine speed changes, Manifold Absolute Pressure (MAP) dynamics, and in-cylinder temperatures. Combustion characteristics change depending on control inputs variations, including throttle angle and spark timing. Fuel injection strategy, timing, and vaporization dynamics are other parameters causing cold-start first cycles analysis to be more complex. Hybrid Electric Vehicles (HEVs) provide elevated cranking speed, enabling technologies such as cam phasing to adjust the valve timing and throttling, and increased fuel injection pressure from the first firings.

Abstract This article proposes a novel approach of a hybrid system of physics and data-driven modeling for accurately estimating the state of charge (SOC) of a battery. State of Charge (SOC) is a measure of the remaining battery capacity and plays a significant role in various vehicle applications like charger control and driving range predictions. Hence the accuracy of the SOC is a major area of interest in the automotive sector. The method proposed in this work takes the state-of-the-art practice of Kalman filter (KF) and merges it with intelligent capabilities of machine learning using neural networks (NNs). The proposed hybrid system comprises a physics-based battery model and a plurality of NNs eliminating the need for the conventional KF while retaining its features of the predictor-corrector mechanism of the variables to reduce the errors in estimation.

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 Turbocharging can provide a low cost means for increasing the power output and fuel economy of an internal combustion engine. Currently, turbocharging is common in multi-cylinder engines, but due to the inconsistent nature of intake air flow, it is not commonly used in single-cylinder engines. In this article, we propose a novel method for turbocharging single-cylinder, four-stroke engines. Our method adds an air capacitor-an additional volume in series with the intake manifold, between the turbocharger compressor and the engine intake-to buffer the output from the turbocharger compressor and deliver pressurized air during the intake stroke. We analyzed the theoretical feasibility of air capacitor-based turbocharging for a single-cylinder engine, focusing on fill time, optimal volume, density gain, and thermal effects due to adiabatic compression of the intake air.

Abstract Quantifying exhaust gas composition and temperature in vehicles with internal combustion engines (ICEs) is crucial to understanding and reducing emissions during transient engine operation. This is particularly important before the catalytic converter system lights off (i.e., during cold start). Most commercially available gas analyzers and temperature sensors are far too slow to measure these quantities on the timescale of individual cylinder-firing events, thus faster sensors are needed. A two-color mid-infrared (MIR) laser absorption spectroscopy (LAS) sensor for gas temperature and carbon monoxide (CO) mole fraction was developed and applied to address this technology gap. Two quantum cascade lasers (QCLs) were fiber coupled into one single-mode fiber to facilitate optical access in the test vehicle exhaust. The QCLs were time-multiplexed in order to scan across two CO absorption transitions near 2013 and 2060 cm–1 at 15 kHz.

Abstract Technological advancements and growth in electric motors and battery packs enable vehicle propulsion electrifications, which minimize the need for fossil fuel consumption. The mobility shift to electric motors creates a demand for an efficient electric motor thermal management system that can accommodate heat dissipation needs with minimum power requirements and noise generation. This study proposes an intelligent hybrid cooling system that includes a gravity-aided passive cooling solution coupled with a smart supplementary liquid cooling system. The active cooling system contains a radiator, heat sink, variable frequency drive, alternating current (AC) fan, direct current (DC) pump, and real-time controller. A complete nonlinear mathematical model is developed using a lumped parameter approach to estimate the optimum fan and pump operations at each control interval.

Abstract Fast charging is significant for the driving convenience of an electric vehicle (EV). However, this technology causes lithium (Li)-ion batteries’ massive heat generation under such severe current rates. To ensure the thermal performance and lifespan of a Li-ion battery module under fast charging, an artificial neural network (ANN) regression method is proposed for a hybrid phase change material (PCM)—liquid coolant-based battery thermal management system (BTMS) design. Two ANN regression models are trained based on experimental data considering two targets: maximum temperature (Tmax ) and temperature standard deviation (TSD) of the hybrid cooling-based battery module. The regression accuracy reaches 99.942% and 99.507%, respectively. Four sets of experimental data are employed to validate the reliability of this method, and the cooling effect (Tmax and TSD) of the hybrid BTMS are predicted using the trained ANN regression models.

Abstract The principal issue in choosing an energy management strategy (EMS) for hybrid electric vehicles (HEVs) has been the way of determining the optimal share of electric energy in hybrid drive. In this article, a novel EMS is proposed that, along with maximum engine efficiency in the hybrid drive, can optimize the share of battery energy for the maximum efficiency of vehicle power train expanded with an imaginary power plant that, by delivering the electric energy to a grid, feeds the vehicle battery. It is proved that the expanded power train efficiency has the local maximum for a wide range of wheel power demand. The relation between the wheel power demand in hybrid drive, the share of battery energy, and the maximum efficiency of the expanded power train is conducted offline. Downloaded to the onboard control system, it enables the operation with the instantaneously optimal share of battery energy and the control system to operate with the low computational load.

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 Many consider hydrogen to be the automobile fuel of the future. Indeed, it has numerous characteristics that makes it very attractive. Hydrogen has a much higher energy density than gasoline, can be produced from water, and its only emission is water. However, there are numerous challenges associated with hydrogen. In particular, the production of hydrogen is a key issue. Currently, most hydrogen is developed from methane, resulting in hydrogen having a carbon footprint. New investments into electrolysis from renewable energy sources is showing promise as an alternative for generating hydrogen. Further, the distribution of hydrogen poses many problems, requiring substantial infrastructure to support a hydrogen economy. Additionally, hydrogen storage is a key issue since most conventional storage mechanisms are overly bulky. If these three issues can be addressed, hydrogen is posed for being a key fuel as the world tries to move away from fossil fuels.

Abstract The automotive industry is facing a multifaceted problem of supervisory energy management, computational power, and digitalization. In response, this article proposes the use of agents utilizing the belief, desire, and intent (BDI) framework as a means to flexibly create online vehicle management systems (VMSs). Under such proposal, a community of agents form a vehicle configuration. Each agent represents a vehicle subsystem and contains knowledge specific to its respective hardware. With this knowledge and partial observation over its operating environment, each agent uses the BDI framework to deliberate over its actions. An interaction protocol, which implements a distributed constraint satisfaction problem (DCSP) algorithm, is used between the agents to create sensible emergent behavior of the vehicle. This interaction protocol allows independently reasoning components to produce emergent behavior that is flexible, robust, verifiable, and explainable.

Abstract This article reports a reduced-order modeling framework of bladed disks on a rotating shaft to simulate the vibration signature of faults in different components, aiming toward simulated data-driven machine learning. We have employed lumped and one-dimensional analytical models of the subcomponents for better insight into the complex dynamic response. The framework addresses some of the challenges encountered in analyzing and optimizing fault detection and identification schemes for health monitoring of aeroengines and other rotating machinery. We model the bladed disks and shafts by combining lumped elements and one-dimensional finite elements, leading to a coupled system. The simulation results are in good agreement with previously published data. We model and analyze the cracks in a blade with their effective reduced stiffness approximation.