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With the introduction of automated driving, new requirements for the design and control of a vehicle powernet and powertrain arise, since the increasing level of driving automation shifts the responsibility for passengers’ safety from a driver towards automation system. Therefore, the functionality of the subsystems required for the automated transition to a standstill in case of a failure must be guaranteed. The work presented in this paper proposes a novel and generic system architecture for the energy management of a fail-operational powernet and powertrain in fault-free and failure case operation. The underlying control strategy is based on a concept enabling safety based range extension with the main goal to complete a driving mission at the safest possible location for the passengers. The proposed Energy Management System (EMS) approximates a driving trajectory to the destination based on route preview, which is then used for the dynamic optimization of the torque split in a powertrain with multiple motors. In this way, the total energy required for propulsion, which is also used as an input for the energy distribution, can be accurately predicted. The optimal distribution of the energy for the supply of safety-critical subsystems and powernet auxiliaries is then estimated by solving a mixed-integer optimization problem. If the desired driving mission cannot be completed, a three-level-degradation concept adapting the driving mission is applied. The individual modules of the EMS are presented in this paper and a system architecture enabling model predictive and adaptive energy distribution in a vehicle powertrain and powernet with automated fault reactions is proposed and exemplified with simulation results. [ABSTRACT FROM AUTHOR]

This paper proposes a traction unit that combines an interior permanent magnet motor and a PM-assisted synchronous reluctance motor for electric vehicles to improve traction performance, enhance energy efficiency, and reduce cost. The characteristics of three traction units are analyzed and compared, including (a) rear-wheel-drive configuration using one IPM motor; (b) all-wheel-drive configuration using two interior permanent magnet motors; and (c) all-wheel-drive with a combination of one interior permanent magnet motor and one PM-assisted synchronous reluctance as proposed in this paper. The operational characteristics of each motor to achieve a wide constant power speed range are first investigated. Then, the Urban Dynamometer Driving Schedule is utilized to compare and evaluate the operational characteristics, energy consumption, and most importantly, the efficiency of the traction units. Simulation results reveal that the overall efficiency of the proposed traction unit can be improved compared to the other configurations. The advantages and drawbacks of the proposed traction unit are also investigated. Finally, experiments are conducted to validate the analysis. [ABSTRACT FROM AUTHOR]

This paper presents a decomposed co-design optimization framework for an electric vehicle powertrain equipped with a Continuously Variable Transmission (CVT). We jointly design the sizes of the powertrain components and the energy management strategy, considering the design and the dynamics of the transmission. Specifically, we divide the resulting nonlinear optimization problem into separate hierarchical optimization subproblems leveraging the concept of Analytical Target Cascading (ATC). Furthermore, we compare the proposed decomposition-based strategy with a centralized approach, in which we solve the same problem, and show that the method presented in this paper can be a potential alternative to a centralized optimization formulation for solving the large-scale, complex integrated plant and control system design. Finally, we compare the resulting performance of using a CVT on the vehicle total-cost-of-ownership with that equipped with a Fixed-Gear Transmission (FGT), which is what is commonly implemented in current electric vehicles. Our results show that by jointly optimizing the design of the transmission along with the control strategy, the CVT yields a lower energy consumption and total-cost-of-ownership compared to that of the FGT. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on electric power trains, with particular emphasis on state-of-the-art research and development and new design concepts. These papers were presented at the IEEE Vehicle Power and Propulsion Conference (VPPC) that was held in Hangzhou, China, in October 2016. [ABSTRACT FROM PUBLISHER]

Electric vehicles (EVs) have attracted enormous attention in recent years due to their potentials in mitigating energy crisis and air pollutions. However, the long battery charging time and lack of sufficient charging infrastructure highly restrict the popularization of EVs. In this context, it is promising to establish battery charging and swapping systems (BCSSs) based on the concept of battery swapping services. To optimally achieve the combined operation of BCSSs, this paper proposes a hybrid swapped battery charging and logistics dispatch model in continuous time domain. Identifying the special structure of the mathematical models of the two problems, this paper innovatively formulated the swapped battery charging strategy as the rectangle packing problem and the battery logistics model as the vehicle routing problem. The two models are closely linked by the delivery time of transporting the well-charged batteries from battery charging stations to battery swapping stations. A hybrid optimal operation model of BCSSs is further formulated as a mixed-integer linear programming model by incorporating the interaction between the battery charging and battery logistics. Finally, case studies are conducted on several BCSSs and numerical results validate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This paper proposes a novel emulation system for emulating the power system in the fuel cell electric vehicle. The major purpose of the proposed emulation system is to test the dc–dc converter between the fuel cell and high-voltage powertrain. The bidirectional buck–boost type dc–dc converter is applied in the emulation system as the basic elements. A modularized approach is presented for easily scaling up and down the power level of the emulation system. Meanwhile, an energy loop structure is implemented in the proposed emulation system for power capability boosting purpose. Because of the proposed energy loop configuration, the emulation system offers long-term running capability with high power rating without the grid connection. A neutral current control mechanism is applied in this paper to further enhance the long-term testing capability as well as extending the lifespan for the battery packages. The simulation studies are proposed in this paper to validate the control mechanisms of the emulation system. A 10 kW hardware prototype has been built and the experimental tests are presented for validating the functionalities of the emulation system. [ABSTRACT FROM AUTHOR]

The fuel efficiencies of Hybrid Electric Vehicles (HEVs) depend heavily on the control concepts, especially on energy management strategies, because the system efficiency of the vehicle is determined by the exploitation of the electric energy. Control concepts based on optimal control theories like Dynamic Programming (DP) or Pontryagin's Minimum Principle (PMP) have been widely studied over the last two decades, and it has been proven that these control concepts provide near-optimal solutions if future driving conditions are given prior to actual driving. However, it is difficult to apply to real vehicles unless the future driving conditions are known. In this paper, a well-known control strategy based on PMP has been developed, which needs only current driving conditions. The significance of the study is that the control concept introduced herein was evaluated in the IEEE VTS Challenge 2018 competition. It produced the best results in terms of the total energy consumption, where 52 controllers had been competing to minimize the energy consumption in unknown driving cycles. Although the control system was designed from traditional PMP-based control, it becomes a practicable and effective solution by using an adaptive concept for balancing State Of Charge (SOC) of a battery. This study is an extended version of the study presented during IEEE VPPC 2018 and provides the development process of the control concept used in the controller during the IEEE VTS Challenge 2018. [ABSTRACT FROM AUTHOR]

The work presented in this paper describes a multi-issue negotiation protocol between active consumers and a management platform in order to establish load coordination in a highly congested network. The multi-issue negotiation protocol considers simultaneously the consumption interval, the price, and the size of the energy packages, which is the main contribution of this work. Regarding the implementation methodology, the proposed algorithms have been implemented using MATLAB classes that allow emulating the behaviour of a multi-agent system in which each vehicle is an agent that interacts with the platform, which is another agent. In the present work, and without loss of generality, the algorithm is applied to coordinate the charging of electric vehicles (EVs) in a distribution network in which building loads represent critical loads. The algorithm is tested in a realistic environment, and its stability and performance are evaluated. Furthermore, the description of the algorithm is provided in a generic form, and it could be applied to any other scenario. [ABSTRACT FROM AUTHOR]

Autonomous electric vehicles (AEVs) are gaining ground around the world. They are equipped with intelligent decision-making capabilities that allow them to optimize on their battery usage and engage in energy trading for profit maximization. This paper envisions a market model, where energy aggregators considers buying and selling electricity from AEVs. In this system, the aggregators reside on cloudlets at the edge of a cloud computing system for prompt communication and negotiation with AEVs. Whether they are parked or en-route, AEVs can be crowdsourced to provide energy to consumers at needed times. This study proposes double auction models to incentivize AEV agents to participate in the market based on their time preferences and energy pricing. The proposed system implements dynamic pricing structures, which are applicable to various scenarios of energy trading. To evaluate the proposed system, this paper provides extensive theoretical analysis and simulation experiments to demonstrate that the proposed auctioning models are computationally efficient, truthful, individually rational, and budget balanced. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to review the recent literature regarding the effects of low temperatures on Lithium ion (Li-ion) batteries for electric vehicle, plug-in hybrid electric vehicle, and hybrid electric vehicle applications. Special consideration is given to nine major effects that directly or indirectly impact the battery use at low temperatures and it is presented that they are correlated to each other. The main discussions in this paper are capacity loss, power loss, life degradation, safety hazard, unbalanced capacity, charging difficulty, thermal management system complexity, battery model and state estimation method complexity, and incremental cost. The effects correlations and possible solutions are explained to provide a detailed, yet broader understanding of the Li-ion batteries low-temperature operating scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a novel piecewise-linear robust traction controller for electric vehicles with in-wheel motors. Considering the nonlinear tire force and the time-varying wheel and vehicle speeds, a piecewise-linear parameter-varying (PWLPV) slip-ratio model with disturbances is first established. A novel traction controller is then designed. The key of the traction controller proposed in this paper is to separate the traction torque into a feedforward control torque and a PWLPV feedback control torque to track the desired slip ratio. The feedforward controller is calculated through the desired working point of the PWLPV slip-ratio model. The PWLPV controller can guarantee the closed-loop stability and $H_{\infty }$ performance for disturbance rejection. To verify the effectiveness of the proposed traction controller, both simulations and experiments are performed. A high-fidelity vehicle model in veDYNA is employed for simulation studies. Experiments were carried out on an electric vehicle with in-wheel motors. The experimental tests were performed on a brake-measurement track at Nong'an Automotive Proving Ground of the First Automobile Works. [ABSTRACT FROM AUTHOR]

This paper presents a novel 12/15 radial field C-core switched reluctance motor (SRM) design, which offers combined advantages of axial field and conventional radial field SRM. In addition, the in-wheel implementation of motor with removable rotor without disturbing stator is one of the unique features. It makes this design more beneficial for electrical vehicle application. This arrangement offers good torque with low excitation current. In this paper, step-by-step design procedure of the proposed radial field C-core SRM is discussed. Parameters are calculated using computer-aided design. These parameters are justified with finite-element analysis and prototype model results. [ABSTRACT FROM AUTHOR]

In the rapidly growing field of electrified transportation technology, hybrid electric and electric vehicle (HEV/EV) manufacturers have been placing increasing demands on the designers to improve the efficiency and reduce the costs of vehicle components to sustain a niche in the EV market. To understand the significance and challenges in an efficient and economical drivetrain design, this paper presents a part of an ongoing project that aims at developing a comprehensive study on dc/ac drive motor technology in EV applications. In this paper, a new three-level three-phase matrix converter topology, along with two conventional frequently used three-level three-phase dc/ac converter topologies, namely, diode-clamped and H-bridge converters, are implemented. Furthermore, comprehensive analysis and comparative studies are conducted to examine the performance and efficiency of these converter topologies according to EV design criteria. In addition, experimental investigations are carried out on the aforementioned converter configurations to help EV designers choose the most suitable converter topology. [ABSTRACT FROM PUBLISHER]

The large-scale application of electric taxis (ETs) in various regions has aroused great interest in developing high-efficiency charging solutions. ETs and charging stations (CSs) can send information to the information management center for real-time information sharing with the technical support of the vehicle-to-grid network. This paper studies the charging problem of intelligent transportation system composed of CSs and networked ETs. Each taxi selects the CS with the lowest charging cost based on the charging price and regional hotspot information. The CS obtains the strategy of taxi and adjusts the charging price to maximize its revenues. We use a multi-leader and multi-follower Stackelberg game to model the interaction between the CSs and ETs. Finally, the hotspot thematic map is generated by using the taxi information of the global positioning system in one day. Simulation results show that the model can effectively reduce the operating cost of taxis and guarantee the revenue of CSs considering the regional hotspot information and reliability coefficient. [ABSTRACT FROM AUTHOR]

High reliability and fault-tolerant operation capability are the application requirements of electric vehicles(EVs). In case of the drive system suffering from inter-turn short-circuit (ITSC) fault, it is necessary to avoid the aggravation of the fault and ensure the normal output performance, this paper proposed a torque ripple minimization control strategy for ITSC fault based on the open-winding (OW) five phase fault tolerant fractional slot concentrated winding interior permanent magnet (FTFSCW-IPM) motor drive system with common DC bus. Once the ITSC fault is detected, the fault phase is active open-circuit (OC) (null the current of the normal part in the fault phase) and connected to a voltage sensor. In this way, the torque ripple minimization control strategy can deal with the OC fault and ITSC fault. For the OC fault, the equal amplitude phase current modulation strategy is adopted based on the carrier-based pulse width modulation (CPWM). For the ITSC fault, this paper proposed a feedforward compensation approach to eliminate the influence of the short-circuit loop current. The novelty of the proposed method is that the compensation can be real-time estimated using the zero-sequence voltage (ZSV) and zero-sequence current (ZSC), even the ITSC level changes. Simulation and experimental results demonstrate the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The operation of electric vehicles in cold weather is a concern, but there is not a lot of literature available regarding the precise nature of impacts on travel range. Two types of commercial battery electric vehicles, namely, the Nissan Leaf and the Mitsubishi i-MiEV, were driven to depletion across a broad range of temperatures that occur naturally in Winnipeg, MB, Canada, due to its climate. Analysis of data showed that the travel range can be reasonably interpreted as a function of ambient temperature using a series of simple linear segments: an upper plateau above about +20 °C, a lower plateau below about −15 °C, and a linearly varying segment in the middle. Both the Leaf and i-MiEV appeared to follow this model, with a good correlation of data for the middle (linearly varying) segment. Impacts of air conditioning on the travel range were also separately tested. This paper provides guidance for more rigorous assessments of electric-vehicle range performance into the future. [ABSTRACT FROM PUBLISHER]

This paper presents an online diagnosis method for the supercapacitors' aging problem. State-of-health (SoH) estimation is an important feature since aging introduces degradation in the supercapacitors' performance, which might eventually lead to their failure. SoH is usually measured by electrochemical impedance spectroscopy. However, this has to be performed offline and requires interruption of the system's operation. Unlike this method, this paper presents an online diagnosis technique for supercapacitors using an extended Kalman observer as a well-known tool for its particularities and performances to study nonlinear parameter estimation. The main objective of this paper is the online SoH diagnosis based on the supercapacitors' aging indicator estimation. Moreover, the proposed online estimation strategy requires only voltage and current measurements, which reduces the number of sensors with respect to other methods. The effectiveness of the proposed online observer is shown through experimental results, and robustness to noise is also studied. [ABSTRACT FROM PUBLISHER]

This paper presents a novel method, advisory dynamic programming (AD-DP), for power management of a fuel cell hybrid vehicle (FCHEV). The presented method embraces a new understanding of vehicle states as driver-dependent and -independent states in time domain to define a suitable state space to be used for dynamic programming. Driver-dependent states are defined in terms of multiple characteristic parameters for vehicle speed and power demand, corporately. Driver-independent states are defined in terms of discrete values for supercapacitor's state of charge $SoC_{sc}$. Transition costs between all states in the state space are calculated offline and tabulated in look-up tables for online implementation. A state predictive model is developed based on the transition statistics of driver-dependent states for a suitable number of driving cycles. Backward calculation of the total transition cost for the predicted horizon in state space is used to define optimal power split strategy for the powertrain. The formulation of optimal control problem, in terms of situation-based solutions related to vehicle states, enables a significant reduction of computational steps and hence addresses the main challenge of real-time applications. The algorithm is adapted in terms of optimization horizon and number of discrete states for DIS to suit the real-time application. Experimental application of AD-DP, using an emulation test-rig, is conducted over different driving cycles. The obtained results reveal an improvement in energy efficiency up to 29% compared to the adaptive rule-based method. The contribution of this paper can be identified as: first, development of a corporate definition for vehicle states, that can be further implemented in optimization-based power management methods. Second, the formulation of an adaptive DP that requires lower computational steps and hence suits real-time applications in hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

How to reasonably coordinate and control the torque of four wheels to enhance vehicle performance is an important research problem of the four in-wheel-motor independent-drive electric vehicle (4MIDEV). In this paper, a coordinated hierarchical control strategy for the dynamic wheel torque of 4MIDEV steering is proposed, which considers both safety and energy-saving performance. The innovation of this paper lies in two new valuable algorithms in the top and bottom level controller, respectively. Besides the optimal control method on the basis of the 2-degree of freedom (DoF) ideal model in the top level controller, a novel model predictive control method based on the 7-DoF dynamic model is put forward to obtain two virtual generalized forces required during vehicle driving. In the bottom level controller, a novel optimal allocation (OA) algorithm is brought forward to improve the safety and energy-saving performance. The tire stability margin and the electric power of driving system balancing the driving energy consumption and regenerative braking energy recovery are considered in the quadratic objective function of OA algorithm at the same time. Carsim-Simulink joint simulation results under open-loop and closed-loop steering conditions illustrate that, compared with the 2-DoF LQR control method, the 7-DoF MPC method could effectively improve vehicle handling stability and safety. The bottom optimal allocation algorithm can not only improve the vehicle handling stability, but also make the driving system more energy-saving. [ABSTRACT FROM AUTHOR]

To improve the structure layout flexibility and performance of the X-by-wire (XBW) ground vehicle, the control configured vehicle (CCV) principle is implemented on an XBW electric vehicle in this paper. By including the electric control system design together with the mechanical component design for the final layout, the CCV enhances the structure layout flexibility of the XBW ground vehicle considerably. Under the CCV principle, the closed-loop stability of the vehicle lateral dynamics system is improved by the involved yaw control system. In this paper, a robust pole assignment yaw controller with the consideration of parametric uncertainties is used. A full XBW electric vehicle testbed is shown as an example to demonstrate the benefit of the CCV of improving the hardware layout flexibility. Under the CCV, all the XBW components can be located flexibly to any position to enhance the configuration of the electric wires, saving of the space, and convenience of the assembling. The discussion of the yaw moment demand in different cases shows how the mechanical components and electric control systems influence and benefit each other in the CCV. The experiments based on the testbed show the desirable efficiency of the proposed yaw controller. [ABSTRACT FROM PUBLISHER]

Using a special type of sliding mode controller, a new type of traction control system (TCS) for hybrid four-wheel drive vehicles is developed. This paper makes two major contributions. First, a new electric powertrain architecture with an in-wheel motor at the front wheels and a clutch on the rear of the transmission is proposed for maximum traction force. The in-wheel motors are controlled to cycle near the optimal slip point. Based on the cycling patterns of the front wheels, the desired wheel speed for rear wheel is defined. The rear wheels are controlled to track this defined speed by controlling the clutch torque. Unlike conventional TCS algorithms, the proposed method exploits clutch control instead of brake control. Second, a special type of sliding mode controller which uses a nonlinear characteristic of the tire is proposed. An important distinction between the proposed sliding mode control method and other conventional feedback controllers is that the former does not depend on feedback error but provides the same functionality. Therefore, the practical aspects are emphasized in this paper. The developed method is confirmed in simulations, and the results reveal that the proposed method opens up opportunities for new types of TCS. [ABSTRACT FROM PUBLISHER]

The electric vehicle technology is one of the most promising candidates to reduce fuel consumption and $\rm CO_2$ emission. Although electric vehicles have been widely promoted by governments around the world, their development is seriously hampered due to charger unavailability and range anxiety. Based on this, this paper designs an energy interaction converter between two electric vehicles, which is controlled through disturbance observer based sliding mode control algorithm. For this converter, three main demands should be satisfied, i.e., high power density, weak source and constant power load. Therein, weak source whose minimum short circuit ratio (SCR) belongs to Jia et al., 2020 and Wang et al., 2020, is always called weak grid. Firstly, the equivalent impedance switching process is introduced to eliminate the impact of weak source. Meanwhile, the equivalent six channel interleaved floating dual boost converter is chosen to satisfy the high power density demand, whose generalized state-space function is further built to provide an indispensable preprocessing for following controller design. Moreover, in order to solve the problem regarding low frequency/sub-synchronous oscillation caused through constant power load feature regarding the energy consumption vehicle and weak source feature regarding the energy supply vehicle, a disturbance observer based sliding mode control algorithm is proposed through using generalized state-space function to provide standard DC power with both constant voltage and power. Furthermore, the proportional-resonant controller is proposed to solve the current sharing problem among six parallel channels, which reduces the heat loss and improves the service life of the device. Finally, simulation and experimental results verify the high performance of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic method for developing a model of a dc distribution system, based on the configuration of the system. The dc distribution system is assumed to host electric vehicles and photovoltaic modules, using dc–dc converters, and to integrate them with an ac power grid. The developed model is of the matrix form and, therefore, can be readily expanded to represent a dc distribution system of any desired number of dc–dc converters. The model captures both the steady-state and dynamic characteristics of the system and includes port capacitors of the converters, as well as the interconnection cables. Thus, it can be used for identifying the condition for existence of a steady state, as well as for stability analysis. This paper further proposes an alternative set of characteristic equations that are less computationally intensive than the original matrix representation, for example, for online stability assessment tasks. The adequacy of the proposed model has been demonstrated through a number of case studies conducted and compared in the PSCAD/EMTDC and MATLAB software environments. [ABSTRACT FROM PUBLISHER]

This paper aims to optimize real-time control for the degraded mode of a fault-tolerant power architecture but not the fault detection and isolation procedure itself. Such power architecture is dedicated to electric vehicles in which it performs the three following essential functions: traction, battery charging, and electric-grid assistance. For safety reasons, in the degraded mode, power control is limited to the traction mode. Thus, for a given torque, the proposed innovative strategy uses a novel current/voltage transform that leads to efficient real-time control of the torque. The key idea is to drive the current without a priori restrictions on its waveform, while minimizing Joule losses, i.e., the effective value of the current. It has been validated on a laboratory test bench. The studied system is based on a three-phase open-end-winding synchronous machine powered by an inverter with three full H-bridges. The last section of the paper analyzes the comparison between the classic sinusoidal current waveforms and the proposed sinusoidal current waveforms while operating on the two remaining motor phases. It results in a 14% increase of the torque produced by the permanent-magnet machine under test and a 14% decrease of the global system losses in traction mode. As a result, the new control strategy enhances traction performance in degraded mode and increases electric-vehicle autonomy in a postfailure condition. [ABSTRACT FROM PUBLISHER]

Long queues of vehicles are often found at signalized intersections, which increases the energy consumption of all the vehicles involved. This paper proposes an enhanced eco-approach control (EEAC) strategy with consideration of the queue ahead for connected electric vehicles (EVs) at a signalized intersection. The discharge movement of the vehicle queue is predicted by an improved queue discharge prediction method (IQDP), which takes both vehicle and driver dynamics into account. Based on the prediction of the queue, the EEAC strategy is designed with a hierarchical framework: the upper-stage uses dynamic programming to find the general trend of the energy-efficient speed profile, which is followed by the lower-stage model predictive controller to computes the explicit solution for a short horizon with guaranteed safe inter-vehicular distance. Finally, numerical simulations are conducted to demonstrate the energy efficiency improvement of the EEAC strategy. Besides, the effects of the queue prediction accuracy on the performance of the EEAC strategy are also investigated. [ABSTRACT FROM AUTHOR]

Inductive power transfer technology has gained massive attention for electric vehicle charging due to the associated reliability, convenience, and safety. Nevertheless, the loose coupling between the transmitter and receiver generates intensive electromagnetic fields (EMFs) around the system, which may influence living objects’ safety if the typical limits are exceeded. Moreover, these fields induce voltages in the vehicle body, which may lead to touch current passing through the human body when it gets in contact with two metal parts or one part and the ground. Following the international standards and recommendations, this paper presents comprehensive test guidelines for the near-EMFs and touch currents from a commercial 25-kW wireless charger from Momentum Dynamics, which was installed in an electric shuttle operating at the National Renewable Energy Laboratory (NREL) campus. The paper investigates the measuring devices, test setup, test points, and standard limits. The impact of the misalignments on the measurements is considered during testing. EMFs are measured in areas around and inside the shuttle and near the power supply and compared with the reference levels defined by the international standards, including ICNIRP 2010, IEEE C95.1, and ACGIH TLV 2017. Touch currents are evaluated among different metal parts and ground in comparison with the standard limits. The results show that both EMFs and touch currents comply with the standard limits. Moreover, thermal inspection for the system after installation was provided. Finally, five days of operation data for the wireless charger are collected and presented. [ABSTRACT FROM AUTHOR]

This paper presents a control method of an antilock brake system (ABS) for electric vehicles. For decades, serious efforts have been dedicated to designing a wheel slip-based ABS controller, but there are some inherent flaws. To realize a robust control system, the wheel slip and road friction information are generally required. Unfortunately, however, these parameters cannot be accurately measured in production vehicles. The method suggested in this paper is aimed at solving these problems by exploiting the nonlinear characteristics of tire force. The optimal wheel slip can thereby be found without wheel slip and road friction information. We employ the motor as an actuator instead of a conventional hydraulic brake system at the front wheels. However, the rear wheels are still hydraulically controlled. That is, the front and rear wheels have different roles in the proposed method. This hardware configuration can be changed for control purposes, so the proposed approach is not designed for a specific hardware configuration. The performance of the proposed method is confirmed by simulations and real vehicle-based experiments. [ABSTRACT FROM AUTHOR]

Inductive power transfer (IPT) is becoming increasingly popular in stationary electric vehicle (EV) charging systems. In this paper, the influence of the different IPT coupler geometries on the performance factors such as efficiency, power density, misalignment tolerance, and stray field is studied. Four different coupler topologies, namely the circular, rectangular, double-D (DD-DD), and the double-D transmitter with double-D-quadrature receiver (DD-DDQ) are considered in this study. The electromagnetic behavior of the couplers is modeled using three-dimensional finite-element method, which is validated by experiments on a laboratory prototype. A multi-objective optimization (MOO) framework is developed to analyze the Pareto tradeoffs between conflicting performance metrics for the couplers. Optimization results depict that the circular topology performs best among the selected topologies regarding higher coupling coefficient, and efficiency for similar active mass and coupler area. Circular and rectangular couplers perform better than the polarized couplers like DD-DD and DD-DDQ regarding stray field exposure in both vertical and lateral direction of the coupler position in the EV. However, polarized couplers show more tolerance toward misalignment compared to circular and rectangular couplers. Thus, this study provides information regarding the specific strengths and weaknesses of different coupler topologies, which can be used during the initial design phase. [ABSTRACT FROM AUTHOR]

Electric motorcycles use a battery storage system and a controlled inverter (motor drive) to drive the wheels. Given that the sale of electric motorcycles is expected to grow significantly over the next decade, the aim of this research is to add a supercapacitor storage system to this simple network in order to extend the autonomy of the motorcycle. This paper analyzes and demonstrates the benefits of two different structures for adding a supercapacitor bank to a lithium-ion battery pack: a passive filter and a buffer for the battery. The advantages of each structure are presented and design guidelines are provided. In addition, we propose a synthetic inductor filter typology based on the two analyzed structures in order to increase the autonomy of the motorcycle. Simulations and experimental results using real data validate the analysis. This paper then uses real data extracted from an electric motorcycle to compare the four topologies: batteries only, the passive filter, the buffer, and the synthetic inductor semi-active hybrid storage system. The comparison shows that battery capacity is extended up to 7.8% with a smoother current profile. However, due to the losses from the added converter in cascade, the net capacity of the whole system is extended only up to 3.7%. Therefore, improvements in the dc/dc converter can further increase the entire system capacity. [ABSTRACT FROM AUTHOR]

This paper proposes a new topology-reconfigurable capacitive compensation network, aiming to achieve the energy encryption of the contactless charging for multiple electric vehicles (EVs). The proposed variable topology of compensation network consists of an M × N capacitor matrix, where capacitors can be connected either in series or in parallel by controlling switches. Then, the proposed capacitor matrix can be formed in the series-parallel and the series-parallel–series-parallel topologies according to the adjustment of the resonant frequency. In such ways, the proposed topology-reconfigurable capacitor matrix can significantly extend the capacitive compensating range while reducing the number of capacitors, thus effectively enhancing the security performance of multiple-objective contactless charging systems. Finally, this paper gives the simulated and experimental results to verify the theoretical analysis, which can further illustrate the feasibility of the proposed topology-reconfigurable capacitor matrix for the energy encryption of EV contactless charging systems. [ABSTRACT FROM AUTHOR]

The contemporary problem of scheduling the recharge operations of electric vehicles (EVs) has gained a lot of research attention. This is particularly true given the governmental and industrial confidence in a bright future for EVs accompanied with the widespread installation of an enormous number of charging stations across the world. As such, this paper addresses the delay-optimal scheduling of charging EVs at several charging stations (CSs) each with multiple charging outlets. At first, a centralized optimization framework is formulated using an integer linear problem (ILP) that accounts for the delayed arrival of EVs to CSs and the randomness in the requested recharge time interval. Simulation results showed the efficacy of the ILP model when compared to naive as well as sophisticated scheduling heuristics. Next, motivated by the scalability issues of the ILP model, this paper then proposes a distributed game-theoretical approach where each EV communicates with its selected CS and iterates on modifying its strategy until all EVs converge to selecting an appropriate CS that minimizes their waiting times for receiving services. The distributed game-theoretical approach recorded promising results especially when compared to the well-known shortest job first scheduling algorithm. Further, unlike the other approaches, which normally are centralized and suited for offline scheduling, the game-based method is suited for online scheduling since it played at anytime a batch of EVs requests charging services. The running time of the game is remarkably small and outperforms all other heuristics and its convergence to Nash equilibrium is guaranteed after only small number of iterations. [ABSTRACT FROM AUTHOR]

In this paper we propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The CNN is trained using two publicly available battery datasets. The influence of different types of noises on the estimation capabilities of the CNN model has been studied. Moreover, a transfer learning mechanism is proposed in order to make the developed algorithm generalize better and estimate with an acceptable accuracy when a battery with different chemical characteristics than the one used for training the model, is used. It has been observed that using transfer learning, the model can learn sufficiently well with significantly less amount of battery data. The proposed method fares well in terms of estimation accuracy, learning speed and generalization capability. [ABSTRACT FROM AUTHOR]

In this paper, a model fusion method (MFM) is proposed for online state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs). Firstly, a particle swarm optimization-genetic algorithm (PSO-GA) method is cooperated with a 2-RCCPE fractional-order model (FOM) to construct battery open-circuit voltage (OCV)-SOC curve, which only relies on a part of dynamic load profile without the prior knowledge of an initial SOC. Secondly, a dual extended Kalman filter (DEKF) algorithm based on a 1-RC model is employed to identify the model parameters and estimate battery SOC with the extracted OCV-SOC curve. Furthermore, battery polarization dynamics in a SOP prediction window is analyzed from two aspects: (1) self-recovery; and (2) current excitation. They are separately simulated using 2-RCCPE FOM and 1-RC model, and then integrated through a model fusion for online SOP estimation, which enables an analytical expression of battery peak charge/discharge current in a prediction window without weakening the nonlinear characteristic of FOM. Experimental results demonstrate the improved performance of the proposed MFM for online discharge SOP estimation, where the mean absolute error and root mean square error are only 0.288 W and 0.35 W, respectively, under the urban dynamometer driving schedule profile. [ABSTRACT FROM AUTHOR]

Electric vehicles will become a popular mode of travel when petroleum prices increase and global warming intensifies. However, the short driving range of electric vehicles remains a challenge. To avoid battery depletion, navigation systems route electric vehicles to surrounding charging stations for battery exchange or recharge. Nonetheless, zero inventories and occupied sockets may result in variable charging time and longer travel time. Conventional navigation approaches have been unable to respond to variable charging time because these approaches do not consider the charging times and energy consumption of electric vehicles. Navigation systems can manage shared information, traffic, and battery life, and can improve the mileage per kilowatthour of electric vehicles. In this paper, an electric-vehicle navigation system (EVNS) based on an autonomic computing architecture and a hierarchical architecture over vehicular ad-hoc networks (VANETs) is proposed. In addition to moving energy, the proposed EVNS considers air conditioner energy to predict the state of charge (SOC) because air conditioning is the most demanding auxiliary load in electric vehicles. Moreover, this paper proposes a time-dependent routing algorithm that considers shared information. The results show that the proposed EVNS method results in 31.15% and 9.52% improvements in mileage compared with the shortest path first (SPF) method and the distributed method in Manhattan, New York City, NY, USA, respectively. Furthermore, the proposed EVNS method results in 32.39% and 20.67% improvements in mileage compared with the SPF method and the distributed method in Taipei, Taiwan, respectively. [ABSTRACT FROM AUTHOR]

An energy internet is a system that enables energy sharing in a distribution system such as the Internet. It has been attracting a lot attention from both academia and industry. The main purpose of this paper is to develop a model of an electric vehicle (EV) energy network to transmit, distribute, and store energy by EVs from renewable energy sources to places that need the energy. We describe the EV energy internet in detail and then formulate an optimization problem to place charging stations in an EV energy internet. We develop two solutions: one using a greedy heuristic and another based on diffusion. Simulation results using real-world data show that the greedy heuristic requires less charging stations, whereas the diffusion-based algorithm incurs less energy transmission loss. [ABSTRACT FROM PUBLISHER]

This paper presents the development of an electric-vehicle (EV) interior permanent-magnet synchronous motor (IPMSM) drive and its operation control. The power circuit of the established EV IPMSM drive consists of a two-leg interleaved bidirectional front-end dc/dc buck–boost converter and a three-phase inverter. The front-end converter can establish adjustable and boostable dc-link voltage for the motor drive to enhance its high-speed driving performance. It can also allow the battery bank to be charged from the motor during regenerative braking. Good current sharing control between converter modules and dc-link boosted voltage regulation response are preserved via the developed robust control scheme. Moreover, fast disturbance rejection response against the module fault and recovery is achieved. The comparative driving performance enhancements via dc-link voltage boosting and commutation instant tuning are conducted. Then, through proper setting and control, the established EV IPMSM drive possesses good driving performances, including starting, acceleration/deceleration, regenerative braking, and reversible operation characteristics. [ABSTRACT FROM PUBLISHER]

This paper presents a novel function of energy-optimal adaptive cruise control (EACC) for electric vehicles based on model predictive control (MPC), which plans the host car's speed trajectory in real time for higher energy efficiency by taking look-ahead traffic information and road conditions into consideration. After linear MPC (LMPC) is formulated in time domain, a nonlinear MPC (NMPC) formulation in space domain is proposed in this paper to overcome the drawbacks of LMPC in time domain. To cope with computational complexity of NMPC in space domain, the nonlinear equality constraints are relaxed to inequality constraints to yield a convex optimization problem. Moreover, it is proven that the relaxed optimization problem can be recast as a second-order cone programming (SOCP) problem, for which the efficient numerical optimizers exist. The performance of NMPC is evaluated in simulation, which is compared with the time domain LMPC and a theoretically optimal receding horizon dynamic programming (RH-DP) solution. Results indicate that the proposed space domain NMPC outperforms LMPC in time domain and the optimal solution of NMPC is very close to the result of RH-DP. [ABSTRACT FROM AUTHOR]

This paper proposes a novel direct-drive dual-PM motor for electric automatic guided vehicles (AGV). The proposed motor has split teeth on stator and dual PMs in stator and rotor sides, which contributes to reduced torque ripple, and enhanced torque. The airgap field of the proposed motor is analyzed theoretically, and the slot/pole selection guide line is concluded. The simulation results of the airgap flux density are shown and Fourier Transformation (FFT) analysis results agree well with the theoretically analyses results. Besides, the effects of design parameters of the proposed motor to the performances of the proposed motor are investigated. And it is found that reduced slot open could produce improved efficiency and decreased torque ripple. The efficiency of the proposed motor is relatively high within the operation range. A corresponding prototype is fabricated and the experiment validates the design. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are considered to relieve energy crisis, and environmental problems due to their high efficiency, and low emissions, and energy management strategies (EMSs) have been extensively studied to improve the performance of hybrid energy storage systems (HESSs) for EVs. To effectively reduce HESS energy loss, and extend battery life, this paper proposes a predictive EMS (PEMS) for the battery/supercapacitor HESSs. First, the pattern sequence-based velocity predictor is presented to accurately predict the future short-term velocity profile. Second, the PEMS is proposed by formulating an HESS power split optimization problem, where the HESS energy loss, and the battery capacity loss are considered. Third, an improved chaotic particle swarm optimization algorithm is presented to solve the formulated optimization problem. Simulation results demonstrate that, compared with the benchmark, the proposed PEMS can effectively reduce the HESS energy loss, and extend the battery lifetime at the same time. [ABSTRACT FROM AUTHOR]

Under the unbalanced condition of grid voltages, the second-order harmonic voltage ripple will appear on the DC bus of the three-phase PWM rectifier due to the existence of the negative sequence voltages. In addition, the total harmonic distortion (THD) of the grid currents will increase correspondingly. In this paper, the coupling mechanism between the DC bus voltage ripple and the grid harmonic currents is revealed. Based on the situation above, a grid harmonic current suppression method is proposed. Compared with existing methods, the proposed method has the advantages in the following three aspects. Firstly, the proposed method does not require either the extraction of positive/negative sequence or the positive/negative sequence current control, which can highly reduce the complexity of the system. Secondly, the grid current harmonics can be reduced by an improved proportional quasi resonant (IPQR) controller. Thirdly, the IPQR controller has little effect on the parameter design of the control system, which facilitates the control design. Finally, the experiments show the accuracy of the theoretical analysis and the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Most of the commercially available range indicator systems for electric vehicles (EVs) do not provide a sufficiently accurate range to destination, as the environmental factors and driver's behavior are generally not taken into account. In this paper, a real-time range indicator system is developed and implemented using online environmental data from various internet resources to estimate accurately the real-time state of charge and the remaining range for the EV while it is on the road. The estimation considers 1) the dynamic wind speed and wind direction with respect to vehicle position, 2) the probability of rain and ambient temperature, 3) the dynamic effective rolling resistance and terrain adhesion coefficient (based on the condition of the road surface), 4) the time-domain efficiency analysis of the propulsion system, 5) the online traffic conditions and auxiliary loads, and 6) the braking force distribution used in commercially available EVs. The real-time range indicator system is validated using measured data from a 2012 Nissan Leaf driven along a selected route in Australia with the maximum error of 8% for the entire route and less than 1% error at the destination. [ABSTRACT FROM AUTHOR]

Permanent magnet (PM) temperature is critical to ensure high-performance and reliable control of permanent magnet synchronous machines (PMSMs) for electric vehicle (EV) applications. High-frequency (HF) voltage injection based approach has been shown to be capable of PM temperature estimation under all-speed range with simple implementation. This paper improves existing HF voltage injection based PM temperature estimation approach by considering the cross-coupling effect. The key to PM temperature estimation is the temperature-dependent HF resistance estimated from the injected HF voltage and the current response. It is found that the cross-coupling effect has a great influence on the HF resistance estimation. This paper firstly improves the HF voltage injection model by considering the cross-coupling effect. Then, a comparative numerical investigation is conducted to analyze the estimation errors induced by the cross-coupling effect. A novel HF resistance estimation approach is derived from the proposed improved model and the PM temperature is calculated from the HF resistance with a linear thermal model. The influence of inverter nonlinearity is also analyzed. Experimental investigations demonstrate that the proposed approach is able to improve the performance of PM temperature estimation. [ABSTRACT FROM PUBLISHER]

Wireless charging vehicle to grid (V2G) system is not-so-futuristic. It can maintain the power supply balance and stabilize the grid through a wireless link between vehicles using batteries based on energy supply-demand statistics. Most of the existing privacy preservation methods are based on data tampering approaches (e.g., encryption, adding noise, aggregation), which are not practicable over V2G due to complexity in billing and inaccurate state estimation. In this paper, we develop a novel adaptive demand-side energy management framework by employing federated learning-based privacy preservation for the wireless charging V2G systems. Our framework learns the temporal evolution of energy consumption of dynamic charging electric vehicles in a distributed fashion and exploits the reinforcement learning model for cost-saving and reward maximization. The convergence and the privacy preservation properties of the framework are demonstrated with extensive evaluations. [ABSTRACT FROM AUTHOR]

In vehicular edge computing networks, electric vehicles can get charging services from edge servers through some road-side charging stations. Several recent studies have investigated how to deal with security requirements in these communications. Nevertheless, simultaneous provision of security and lightness, which is crucial for resource-constrained vehicles, is still an open issue. More critically, the anonymity from the perspective of honest-but-curious edge nodes has not been yet addressed. Thus, this paper proposes a novel ultra-lightweight framework for the secure and anonymous communications of vehicles during their charging reception by means of blockchain. Thanks to the blockchain technology, the accountability of electric vehicle possessors guaranteed. The proposed scheme has been validated in terms of security metrics and also implemented on two ARM-based platforms, one 32-bit ARM microcontroller and one 64-bit ARM processor. Further, its blockchain part has been deployed on a Hyperledger Fabric network. The obtained results besides the comparison with well-respected similar schemes acknowledge the usefulness and practicability of the presented scheme. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic stable control protocol for heterogeneous vehicle platoon subject to communication topologies change, external disturbance, and information delay. First, a random vehicle platoon system composed entirely of several pure electric vehicles is built. The random variation of data transmission link among the platoon in a natural traffic environment is considered and molded by the Markov chain combined with the directed graph method. The influence of delays and discrete data in wireless communication, road slope, and air resistance on the vehicle platoon is also considered by introducing the external interferences and equivalent information delays. Additionally, to ensure the vehicle platoon’s inner-vehicle stability, the variable-gain distributed controller is proposed based on the Markovian jumping system stability theory and $ H_\infty$ control. Finally, the $ \mathcal {L}_{2}$ stochastic string stability is defined to attenuate perturbations as they propagate through the platoon. Simulation studies about a vehicle platoon under four communication topologies random switching with two different control methods are provided to verify the theoretical result. It is shown that, compared to traditional platoon robust control, it is possible to achieve the vehicle platoon’s stability even if in the continuous mutation of unstable topologies by using the proposed control approach. [ABSTRACT FROM AUTHOR]

The performance and safety of batteries of electric vehicles deteriorate when the battery temperature is too low or too high. The thermal management system regulating the battery temperature consumes considerable electric energy, particularly, for cooling the battery. To maximize the vehicle driving range, the means of controlling the battery temperature should minimize the energy consumption. In this paper, stochastic model predictive control is applied to the battery-cooling controller. Effective model predictive control requires a good but simple system model with proper estimation of near-future disturbances. The components of the battery cooling system are modeled to represent the energy-consumption and heat exchange mechanism with some assumptions for simplicity. The future information is estimated using historical driving data in a stochastic sense. For real-time implementation, an unequally spaced probability distribution is introduced when designing a stochastic model of future heat generation. The proposed control method shows significantly lower energy consumption while maintaining an acceptable temperature regulation performance compared to other temperature controllers, such as a thermostat-type controller and a model predictive controller with simply assumed future information. The proposed predictive controller shows robust performance compared to typical controllers by utilizing the stochastic estimation of future information. [ABSTRACT FROM AUTHOR]

This paper proposes a battery charger (BC) for electric vehicles based on sinusoidal-ripple-current (SRC) method. The SRC method is used as an advanced charging-discharging method. On the one hand, the sinusoidal current variations alternately change output active power of the BC, negatively affect AC-side terminal current of the BC, and therefore, cause a power quality problem. On the other hand, the output active and reactive powers must be regulated at constant values without any variations for participation in vehicle-to-grid technology. Thus, a particular topology is suggested and then a control system is precisely designed in order to implement SRC charging-discharging method and adjust the output active and reactive powers for vehicle-to-grid technology without the power quality problem. SRC method can also be used in online electrochemical impedance spectroscopy (EIS) for state of charge and state of health estimation in battery management. The proposed control system can also generate the SRC signals, suitable for online EIS, in a controlled way unlike the recently proposed BCs. The simulation and experimental results demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

The electro-mechanical brake booster (EMBB) is a kind of mechatronic actuator, which is developed to suit the brake assist requirement of electric vehicles. In this paper, we report on the design of an EMBB system consisting of a dc motor, a two-state reduction of a gear and ball screw, a servo body, and a reaction disk. Considering the inconvenience of installation and high price of the pedal force sensor, we translate the control problem of brake power assist control to position tracking control. Meanwhile, a nonlinear control method for position tracking is presented to solve the problem of power assist braking, which is formalized as three parts: the steady-state control, feed-forward control based on reference dynamics, and state-dependent feedback control. The benefit of the nonlinear control method is that it offers a concise control law and performs well in engineering implementations. In addition, a second-order filter was designed to do the signal processing and obtain a higher-order derivative. Finally, the bench tests based on rapid control prototyping environment were designed and implemented to verify the performance of the controller. Test results show that both the position tracking performance and response time of the EMBB system performed well. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time nonlinear model predictive control (NMPC) strategy for direct yaw moment control (DYC) of distributed drive electric vehicles (DDEVs). The NMPC strategy is based on a control-oriented model built by integrating a single track vehicle model with the Magic Formula (MF) tire model. To mitigate the NMPC computational cost, the continuation/generalized minimal residual (C/GMRES) algorithm is employed and modified for real-time optimization. Since the traditional C/GMRES algorithm cannot directly solve the inequality constraint problem, the external penalty method is introduced to transform inequality constraints into an equivalently unconstrained optimization problem. Based on the Pontryagin's minimum principle (PMP), the existence and uniqueness for solution of the proposed C/GMRES algorithm are proven. Additionally, to achieve fast initialization in C/GMRES algorithm, the varying predictive duration is adopted so that the analytic expressions of optimally initial solutions in C/GMRES algorithm can be derived and gained. A Karush-Kuhn-Tucker (KKT) condition based control allocation method distributes the desired traction and yaw moment among four independent motors. Numerical simulations are carried out by combining CarSim and Matlab/Simulink to evaluate the effectiveness of the proposed strategy. Results demonstrate that the real-time NMPC strategy can achieve superior vehicle stability performance, guarantee the given safety constraints, and significantly reduce the computational efforts. [ABSTRACT FROM AUTHOR]

This paper proposes cloudlet-based intelligent agents for energy crowdsourcing from autonomous electric vehicles (AEVs). Existing energy crowdsourcing mechanisms focus on load shedding and cost savings, but lack incentive models for strategically behaving agents. In the proposed model, crowdsourcing agents residing on the edge network communicate with AEVs to stimulate them to participate in providing energy to the grid during peak time periods. The focus of this paper is on the development of incentive mechanisms that allow the agents to engage in an auctioning process according to their energy contribution. Through theoretical analysis and simulations, we evaluate the performance of the proposed model with respect to individual rationality, computational efficiency, and truthfulness. [ABSTRACT FROM AUTHOR]