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We show that the two equations of interior permanent magnet synchronous motors always satisfy four (speed, position) solution pairs, predetermining where sensorless algorithms will end up. With such finiteness, we see a silver lining in establishing theoretical basics for the open problem of startup with an unknown initial position. We reveal normal-reversal duality that demystifies the familiar binary behavior of sensorless field-oriented control (FOC) scheme with the genuine/specious pair, and devise a detection-quit mechanism to help FOC systems flee the specious pair using the knowledge of motor/generator mode. These two ingredients enable a tailored sensorless algorithm that can obviate the need for initial position detection.

Isolated three-port dc–dc converters (TPCs) facilitate integration of three voltage sources/loads in electric vehicle applications. Three-port resonant converter (TPRC) is an attractive TPC topology as it inherits the advantages of resonant converters. Phase-shift (PS) control applied to TPRCs enables independent power flow control among all ports. Phase-shift and duty-ratio (PSDR) control introduces three additional degrees of freedom providing the potential for improving the converter efficiency compared to PS control. This article presents a generalized harmonic approximation-based steady-state mathematical model for a TPRC with five-variable PSDR control. Mathematical solutions to the steady-state converter bridge voltages and the ac currents under PSDR control are provided. The proposed mathematical model is integrated with a TPRC power loss model and together are used to formulate a control optimization problem for evaluating the optimal control variables at maximum converter efficiency. The optimized five-variable PSDR control is compared against PS control using a 6 kW/100 kHz rated hardware demonstrator, with efficiency improvements as high as 12.4%.

We show that the pair (speed, rotor position) of interior permanent magnet synchronous motors is not globally observable, and the number of all indistinguishable pairs is at most four, an invariant independent of any parameter. This result follows as a corollary by virtue of characterizing the observability condition as a special restricted form of the injectivity of a family of polynomials via immersion. The field-oriented control (FOC), which causes the “collapse” of at most two indistinguishable points easy to be told apart, joins forces with the knowledge of motor/generator mode to make IPMSMs “observable,” leading to a simple algorithm.

This paper investigates off-the shelf performance traction machine, a yokeless and segmented armature (YASA) axial flux surface permanent magnet machine, model P400HC from YASA Motors. A series of manual measurements and automated dynamometer tests were performed at various conditions. From these tests parameters are determined including friction and windage torque, phase resistance, permanent magnet flux linkage, and inductance. The efficiency, phase current, phase voltage, and power factor of the machine was measured over a wide torque, speed, and dc bus voltage range up to around the maximum ratings given by the manufacturer. A range of d-q current values were tested, showing that the machine is slightly salient since maximum torque is achieved when including a small amount of d-axis current. A Chevrolet Bolt electric vehicle (EV) was modeled with the YASA Motors machine and the stock Bolt EV machine. Over four different vehicle drive cycles, the YASA Motors machine was shown to be considerably more lossy than the Bolt EV machine, thereby achieving 4% to 6% less range. The higher loss of the YASA Motors machine likely has several causes, including higher phase resistance, significant friction, windage, and no-load iron losses, and the fact that Bolt EV machine was heavily optimized for an EV application while the YASA Motors machine was optimized to be a highly power dense more general-purpose machine.

A unified unsaturated-reluctance-based wide speed range sensorless control method for switched reluctance machines (SRMs) is proposed in this article. First, the sensitivity analysis of the estimated position to the possible reluctance calculation error is analyzed. This analysis reveals the feasible regions with low sensitivity and low risk of entering saturation. Then, the unsaturated reluctance in these feasible regions is obtained and modeled. A simple second-order rational expression is proposed to fit the highly nonlinear unsaturated reluctance curve. With this simple model, the rotor position can be analytically calculated only by solving a simple quadratic problem. Based on the abovepresented analysis, a wide-speed range sensorless control is developed. The rotor position is universally estimated from the real-time reluctance value and the proposed analytical model. The high-frequency voltage injection and normal conduction phase are combined to calculate the phase reluctance over the wide-speed range. The concerns of calculating reluctance at the low-speed range are also discussed. A 5.1 kW four-phase 8/6 SRM is used to verify the proposed method. Various steady state and transient state experimental results prove the effectiveness of the proposed sensorless control method.

This article proposes a methodology of analytical modeling and optimization of power modules, especially compatible with modules with silver-sintered molybdenum (SSM) packaging or other insulated metal substrate types of packaging schemes. First, a decoupled Fourier-based thermal model is presented, which considers the barrier effect between substrate segments. Compared with the original Fourier-based model, it reduces the average error from 93.8% to 10.9%, when estimating the difference of junction temperatures ($T_{j}$) for power modules with asymmetric substrates. Then, a stray inductance ($L_{s}$) model is developed based on the partial inductance method and the actual current distribution, whose error is less than 12.1% when tested with example half-bridge SSM modules. Next, analytical models are combined with the particle swarm optimization algorithm to design a half-bridge power module with SSM packaging. Numerical simulations prove that the analytical estimations of $T_{j}$ and $L_{s}$ of the optimized module are accurate, with errors of 4.6% and 8.3%, respectively. The fabrication process of the designed SSM module is then elaborated. Finally, the accuracy of $L_{s}$ estimation is validated by the double-pulse test, where the error is 0.4%. The junction-to-case thermal resistance is characterized by the structural function analysis, in which the error is 3.4%

Multiphase drives (MPD) in general and six-phase in particular have been gaining popularity in many industries, which calls for a proper multiphase inverter (MPI) design. For voltage source inverter (VSI)-fed drives, the knowledge of voltage and current stresses on the dc-link are imperative for input capacitor sizing. To this end, the voltage and current stresses on the dc-link capacitor in two-level six-phase VSIs are examined thoroughly in this article for two configurations of load/winding spatial distribution: symmetric and asymmetric. First, the harmonic spectrum of the input dc current of each inverter is analyzed in detail by benchmarking them against the conventional three-phase VSI, to precisely establish the dc-capacitor requirement reduction in six-phase counterparts. Second, analytical formulae for the dc-link capacitor voltage ripples are derived for both configurations. Third, simple formulae for dc-capacitor sizing for six-phase VSIs with different load configurations are provided. The accuracy of the derived formulae is verified by simulation and experimental testing at various power factors and modulation depth. It is found that six-phase VSI supplying symmetric and asymmetric loads reaps 10% and 7% lower dc-link current ripples, respectively. Hence, six-phase symmetric loads yield the smallest capacitor size.

An adaptive voltage controller scheme for flux-weakening control under the six-step operation of the Permanent Magnet Synchronous Machine (PMSM) drives is propose in this paper. In the presented technique, the voltage feedback control is applied to fully utilize the DC link voltage, consequently extending the maximum torque range in the flux-weakening region. The behavior of a voltage controller is analyzed under the effect of modulation delay and nonlinear-gain characteristics of six-step operation. Subsequently, a compensatory control action is generated with varying voltage feedback gains based on the magnitude difference between the voltage vectors. This action maximizes the gain of the voltage controller as a function of current vector magnitude and the voltage angle to achieve a greater torque range and higher efficiency in the flux-weakening zone. Features and effectiveness of the proposed technique were verified by simulation and experimental results on a PMSM drive platform.

•3D printed jet impingement housing is used for cooling SiC power modules.•Thermal and hydraulic characteristics were experimentally investigated.•71% weight reduction achieved in comparison with metallic pin finned heat sinks.•Numerical study was performed to minimize temperature difference between SiC switches.

Powertrain electrification has been proven as an effective solution to public concerns about fossil fuel usage and carbon emissions. As a midterm technology from conventional internal combustion engine vehicles to electric vehicles, hybrid electric vehicles have received wide research attention from industry and academics alike and are sharing an increasing percentage of vehicles in the market. As a crucial component in hybrid powertrains, the internal combustion engine has important impacts on vehicle performance. Recent years have witnessed tremendous effort towards hybrid electric vehicle-specific engine technologies. Low temperature combustion concept, alternative fuels, over-expansion Atkinson cycle, and waste heat recovery technique provide the most promising solutions to clean and efficient hybrid-specific engines. In this paper, recent progress in these four technologies on hybrid powertrain platforms is reviewed comprehensively. Working principles, influencing factors, benefit potentials, advantages, and disadvantages of each technology are discussed in detail. Although these technologies present improvement in fuel economy and emissions, there are several issues as well. Challenges and concerns associated with system complexity, restricted operating conditions, controls, cost, safety, etc., are specified, and corresponding recommendations are provided from technology level to powertrain level. Future research opportunities are suggested at the end to encourage more research activities in this area.

High-speed position-sensorless control of switched reluctance motor (SRM) drives is often realized by searching a priori flux linkage lookup table. However, obtaining the nonlinear flux linkage characteristic requires time-consuming offline measurement and occupies large memory storage. To deal with this problem, this article proposes a new position estimation scheme for SRM drives without using any magnetic characteristics. This feature is achieved by using a quadrature flux estimator (QFE) that can extract the fundamental flux and its quadrature signal from the real-time calculated flux linkage. The flux bias and flux harmonics can also be suppressed by the QFE’s adaptive bandpass capability. Therefore, the proposed method can reduce the nonlinearity in SRM flux linkage and derive simple sine–cos position signals. Afterward, the rotor position and speed can be estimated from the orthogonal flux signals using a three-phase phase-locked loop. To baseline the advantages, comparative experimental validation with the conventional method is conducted on a three-phase 12/8 SRM test bench. The results show that the proposed scheme can achieve the same estimation accuracy as the conventional method even though no magnetic characteristics are used.

In this article, a novel online optimal tracking method is proposed for permanent magnet synchronous machine drives for maximum torque per ampere (MTPA) and flux-weakening (FW) operations. The conventional model-based optimal tracking methods impose heavy computational burden, because the optimality criteria are usually used along with related boundaries to develop different optimization problems for different operation regions. Compared with the conventional methods, the proposed method reduces the mathematical complexity of the optimization problem and improves the computational efficiency in real-time implementation. Only one optimization problem is solved in both MTPA and FW regions, while the enhanced projection operations are developed to ensure the current references are within the voltage and current constraints. Magnetic saturation, phase winding resistance, nonlinearity of the inverter, and dc-link voltage variation are all taken into consideration. The feasibility of the proposed online optimal tracking method has been validated on an interior permanent magnet synchronous machine (IPMSM) test bench with an off-the-shelf motor control unit. The execution time and convergence rate of the proposed method have also been evaluated with benchmark conventional methods and presented in this article.

This article proposes a computationally efficient position-sensorless control scheme for medium- and high-speed permanent magnet synchronous motor (PMSM) drives under distorted conditions. An adaptive comb observer (ACO) is designed for accurate position estimation by eliminating primary distortions simultaneously, including dc offsets from current measurement, odd harmonics caused by inverter nonlinearity, and even harmonics due to the modulation. A frequency-locked loop based on the Lyapunov stability theory is embedded in the ACO to ensure the observer’s stability and fast convergence of the estimated variables. Compared with conventional adaptive position observers that usually require high-order filtering networks, the proposed scheme only consists of four delay operators and does not require complicated discretization. Therefore, it can simplify digital implementation and reduce the computational burden of the sensorless algorithm. To validate the effectiveness of the proposed observer, comparative experiment tests of the proposed ACO and conventional methods are performed on a 15-kW PMSM setup.

Worldwide, more than 1 billion vehicles are used regularly, with the majority being gasoline powered. These transport methods are known emitters of carbon dioxide, particulate matter (PM), and other pollutants (i.e., nitric oxides). Such compounds pose a great environmental risk, but recent research has also suggested health consequences. PM, a microscopic carcinogenic substance, affects many biological systems and has been associated with medical concerns in clinical and laboratory settings. In clinical settings, research on the effects of PM of 2.5 μm or lower in diameter (PM2.5) have focused on interactions with the cardiovascular, respiratory, and nervous systems. Vulnerable populations (i.e., the elderly and hospitalized patients) disproportionately experience an increase in cardiovascular and respiratory deaths along with hospital admissions for heart disease and asthma. Also, studies have found an increase in tumorigenesis.

This work aims at presenting a design methodology capable of modeling, generating, and testing a large number of multimode power split hybrid electric vehicle transmission designs in a relatively short period of time. Design parameters include the planetary gear ratios, the final drive ratio, the configuration of hookups to link the hybrid powertrain components to the planetary gear sets and the locations of clutch connections between different nodes of the planetary gear sets. The system modeling approach is first presented, including formulations for each component (the vehicle and road load, the engine, the motor/generators and the battery). A rapid and automated modeling procedure is proposed for hybrid electric vehicle transmissions including multiple planetary gear sets and clutch connections. Two algorithms are subsequently presented that enable fast evaluation of fuel economy and acceleration performance of hybrid electric vehicle transmission designs, namely the enhanced Power-Weighted Efficiency Analysis for Rapid Sizing and the Rapid Efficiency-based Launching Performance Analysis algorithms. The developed design methodology is tested by first modeling and evaluating three hybrid electric vehicle designs from the state-of-art. Later, an investigation for optimal designs that can ameliorate the examined benchmarks is performed. Several millions of design options are rapidly generated and tested using the proposed procedure. The methodology is proved effective by quickly coming up with two sub-optimal designs. Fuel economy and acceleration performance are improved by 5.56% and 40.56%, respectively, compared to the corresponding best benchmarks.

On-line optimal control represents a crucial issue in the development of multimode power split hybrid electric vehicles (HEVs). Finding a control strategy that guarantees fuel economy optimality and ease of implementation still reveals an open research question. This paper aims at developing an on-line control approach for multimode HEVs based on previously implemented off-line control. The two control levels for multimode HEVs are presented: the operating mode selection and the torque split determination. The former is addressed adopting a machine learning approach where artificial neural networks (NNs) are trained in supervised learning using off-line control data. The torque split is resolved on-line according to efficiency-based maps extracted off-line. Simulation results for a specific multimode HEV design demonstrate the effectiveness of the developed control strategy in minimizing the value of predicted fuel consumption. Furthermore, a sensitivity study is conducted for the NN sizing parameters. The ease of implementation and adaptability suggests the potential application of the developed online control approach in a design methodology for multimode HEVs.

With the emerging trend of transportation electrification, energy management strategies have become of significant importance in automotive engineering. Control systems are developed with the objective to increase vehicle efficiency, thus lowering fuel consumption and harmful gas emissions. Both the methodology and tools for powertrain systems design as well as vehicle performance assessment are challenging with a constant necessity for improvement. For both aspects, the importance of developing realistic driving scenarios accounting for real and sometimes random driving conditions is irrefutable. This paper provides an overview of the current state-of-the-art technologies in energy management strategies (EMSs), highlighting how driving conditions and journey mapping can influence the performance of these systems. In addition, a review of vehicle simulators is performed emphasising how such tools could support the system level vehicle development.

Plug-in hybrid electric vehicles (PHEV) have become vital for oil consumption reduction. They have not achieved their maximum potential due to control strategy limitations. Existing controllers are often tuned to achieve the best fuel economy for specific conditions. It is impractical to optimise a controller for every scenario. A control strategy for PHEVs using artificial neural networks (ANN) is presented. The advantages of implementing a controller using ANN include independence from drive cycle or user, precision and robustness, and updatable training set. Existing PHEV control strategies are used to model a base for city and highway driving. Simulation data was extracted to form an ANN training set, which was used to develop a new strategy that was better than existing ones. The controller was validated using different drive cycles. Furthermore, the sensitivity of ANN controllers is presented. The controller is also used to validate the charge depleting mode of PHEVs.

The automotive industry is seen to be making a monumental paradigm shift from manual to semi-autonomous to fully autonomous vehicles. As such, this paper attempts to offer an overview of some of the major aspects associated with an autonomous vehicle. The demand for autonomous vehicles has grown tremendously with the increasing number of road accidents caused due to driver distraction. Based on the level of autonomy within vehicles, it is possible to either have autonomous systems that will just assist the driver for safe operation of the vehicle or could take over the complete control of the vehicle. Autonomous vehicles seem to have numerous advantages for road safety, traffic optimisation, military or medical applications where only minimal human intervention is desirable and so on. This paper attempts to highlight the vast majority of vital and most relevant topics emerging in the field of autonomous vehicle technology.

Intuition would assume that the number of people without energy access would decrease over time. However, it has been estimated that the number of people without access to electricity will continue to increase over the coming years, as shown in Table 1. In Africa, for example, there are almost 600 million people currently living without access, with most living in rural areas.

Under research is a switched reluctance machine for the powertrain of an electric vehicle. The research focuses on accurate estimation and optimisation of winding losses in switched reluctance machines. These losses result from both AC and DC resistance. An FEA-based procedure is described which computes both components of loss. Round and rectangular conductors are both analysed for a number of cases. Ultimately, rectangular conductors are chosen for more thorough analysis. Here, a number of winding geometries are simulated. Plotting conductor geometry versus conductor loss confirms a loss trend with a global minimum. For the motor design under consideration, this represents up to 4 kW reduction in loss over the initially simulated round conductors.

Today's complex aerospace applic a tions demand embedded computing systems that are capable of reliable data transfer and retrieval in optimized power, size, and mass formats. The computing systems are required to be low in cost and ready for deployment across a range of aircraft platforms. Many aircraft controllers employ embedded computing frameworks to perform and execute commands for various systems and subsystems.

With the increasing popularity of hybrid electric vehicles (HEVs), updating their test procedures to have more accurate vehicle performance prediction has become essential. Traditionally, vehicles are tested using standardised drive cycles, which are not sufficient to represent real-life driving scenarios for different conditions that a vehicle might encounter during its life-cycle across all users. This results in high discrepancies between the predicted and the actual vehicle performance. As such, this study highlights the application of a novel concept called Journey Mapping (JM), which re-defines drive cycles to provide more realistic and accurate vehicle performance prediction, for studying a test HEV's performance. JM incorporates real-life conditions that might influence a vehicle during its journey from an origin to a destination. The JM model was able to predict the test HEV's performance with only about 2% error, on average, between the predicted and the actual performance, for the scope of this study.

Despite the demonstrated benefits of transcatheter aortic valve replacement (TAVR), subclinical leaflet thrombosis and hypoattenuated leaflet thickening are commonly seen as initial indications of decreased valve durability and augmented risk of transient ischemic attack.

Recently, increased emissions regulations and a push for less dependence on fossil fuels are factors that have enticed a growth in the market share of alternative energy vehicles. Readily available energy storage systems (ESSs) pose a challenge for the mass market penetration of hybrid electric vehicles (HEVs), plug-in HEVs, and EVs. This is mainly due to the high cost of ESS available today. However, tremendous research efforts are going into reducing the cost of these storage devices, increasing their lifespan, and improving their energy density. This paper aims to give an overview of the current state of readily available battery and ultracapacitor (UC) technologies as well as a look ahead toward promising advanced battery chemistries and next generation ESS. Energy management systems and various battery balancing configurations are discussed in addition to battery state/parameter estimation and protection mechanisms. Finally, hybrid ESSs (HESS) are reviewed as a mitigation strategy to the shortcomings of traditional battery and UC technologies. Consideration is given to the combination of advanced battery chemistries with UCs to portray HESS performance, which can meet and exceed the performance of current ESS technologies.

Automotive electrification has become the centre of current transportation technology especially in the age of surging petroleum fuel prices and rising social awareness of vehicle emissions. A range of electrified powertrains from micro hybrid to full electric have been implemented in a wide variety of vehicle platforms from subcompacts to heavy-duty trucks and buses. The main focus of this paper is to comprehensively review the state-of-the-art electrified powertrains that have been developed and commercialised in the North American automotive industry. Vehicles are categorised based on different powertrain configurations and electrification levels. The powertrain structure and operating modes are also analysed in detail. In addition, a comprehensive database of electrified powertrains and their components is created. Electrified powertrains and the corresponding conventional powertrains are compared in terms of vehicle efficiency, powertrain complexity, and pump-to-wheel CO2emissions.

A fixed-switching-frequency model-based sliding mode current controller with an integral switching surface for switched reluctance motor (SRM) drives is presented in this paper. Based on the equivalent circuit model of SRM, including the magnetic saturation and mutual coupling, an integral sliding mode current controller is designed. The stability of sliding mode controller is analyzed in two scenarios: 1) one with known motor parameters and 2) the other with bounded modeling error. In order to analyze the robustness of the sliding mode controller, the motor controller parameter constraints are derived and the stability analysis is demonstrated by considering motor parameter modeling errors. The sliding mode controller is validated by both simulation and experimental results with a 2.3-kW, 6000-r/min, three-phase 12/8 SRM over the wide speed range in both linear and magnetic saturation regions. Compared to the hysteresis current controller, the sliding mode controller demonstrates comparable transient response and steady-state response in terms of torque ripples, current ripples, root-mean-square error of current and torque. Moreover, the sliding mode controller has some advantages over the hysteresis controller, including constant switching frequency and much lower sampling rate.

Hybrid electric/electric vehicles are gaining momentum in the automotive industry. Achieving fuel energy efficiency without compromising performance targets are key challenges in this research and development area. This topic includes multidisciplinary subjects and the primary research and development areas can be categorised into the following systems: electrical propulsion, electrical power, energy storage, mechanical propulsion, mechanical transmission, thermal management, power electronics, and control. This paper focuses on addressing different electromechanical propulsion system integration methods. This review is divided into seven different sections, the first is an introduction to understand the reasons why new technologies should emerge in the automotive industry. Second, the primary differences and the advantages of hybrid powertrains are analysed. Vital issues on designing different hybrid powertrain systems are addressed in the third section. Following that, different hybrid powertrain topologies are analysed for various application requirements. Issues related to regenerative braking are highlighted in Section 5. Section 6 describes different electro mechanical propulsion system integration methods and compares hybrid powertrain topologies available in the market. The last section addresses directions for improvement in developing advanced hybrid powertrain systems for automotive applications.

The transportation sector is one of the largest energy users, and the main source of energy in our transportation system is still fossil fuels. As an example, in the United States, 98% of transportation energy comes from oil, but most of it is wasted due to the low efficiency of con-ventional internal combustion engine (ICE) vehicles. To-day's low fuel efficiencies make the automotive industry one of largest sources of greenhouse gas emissions.

The average internal combustion engine (ICE)-propelled automo?bile is roughly 10?20% efficient on average at converting the energy in gasoline into forward motion. The remainder of the energy is dissipated into heat or ejected and not fully burned. This means that 80?90% of the fuel is wasted. If you consider an analogy where a person filling the gas tank pumped 1?2 gal into the tank, then pumped 8?9 gal onto the ground, you begin to understand just how much fuel your automobile can waste. This may startle those who might think of their vehicle as clean burning and energy efficient; but, as we will see, the numbers actually get much worse.

Existing controllers of hybrid electric vehicles (HEV) are mostly tuned to achieve higher fuel economy for specific driving conditions. In this paper, a brief review of control strategies is presented followed by new approaches to implementing an optimisation based controller. First, a control strategy based on stochastic dynamic programming that can be used for real time applications is presented. This strategy achieves about 10% increase in fuel efficiency in most drive cycles over a rule-based control strategy. The second analysis uses an artificial neural network (ANN) to develop an optimised control strategy. Several existing hybrid control strategies are used to model a base for city and highway driving. Elite simulation data was extracted to form a training set for the ANN. The training set was then used to develop a new ANN control strategy that was superior to all the existing strategies used to develop the training set.

Research has been done on developing advanced control strategies for industrial DC motor drives. However, there is little advancement in the controllers designed for small appliance motors, which still use old and inefficient methods of control. The old methods of appliance motor control are the triac control and the PWM control. These methods are cheap and easy to implement, but they have a drawback – not regulating the controlled quantity properly and fast enough. The only reason they still persist in the appliance market is because of their cost of implementation. Appliance markets are very sensitive to cost changes; therefore, everything for an appliance motor is optimised for low cost. In this paper, we present a new control strategy for appliance motors, which is inherently digital in nature. 'The pulse adjustment method' can be used to control the speed of appliance motors very effectively. The implementation is easy and the cost of implementation is also low; yet, this method gives better results as compared to the PWM and triac control. The said performance of the pulse adjustment method is proven through simulations.

The increasing use of power electronic and motor drives in land vehicles, marine vessels and aircraft, for lots of applications such as propulsion, braking, steering, and actuation is creating particular problems when the power distribution system is in the form of multi-converter dc electrical systems. This is because of many power electronic loads and associated controllers which have a 'constant power' characteristic resulting in negative incremental input impedance. The effect of such loads in a dc system is to cause instability. The purpose of this paper is first to identify the most appropriate theoretical techniques for analysis of this nonlinear instability, and then to devise a novel technique for damping the instabilities and ensuring stable operation of system. We call this new control technique, Pulse Adjustment. In spite of its simplicity it offers a very fast and smooth response. Simulation and experimental results are presented to verify the proposed technique.

•Developed feedforward & recurrent neural network state of charge estimators•Memory effectively added to feedforward network through filtered inputs•Performance evaluated from -10°C to 25°C for automotive drive cycles•Microprocessor execution time measured for both network types•Datasets and neural network training code freely available to download

(Bloomberg View) -- End the Unwarranted Blockade Against Qatar: Ali Shareef Al Emadi For two months, the United Arab Emirates and Saudi Arabia have led an illegal and unwarranted blockade on Qatar. Saudi Arabia, the UAE and other Gulf countries gain nothing from cutting off trade and diplomacy. [Extracted from the article]

The twenty-two articles in this special section focus on components, subsystems, systems, and grid interface technologies related to power and energy conversion for different types of electrified vehicles, including electric vehicles (EVs), range-extended EVs, hybrid electric vehicles, and plug-in hybrid electric vehicles as well as heavy-duty, rail, and off-road vehicles and airplanes and ships.