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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]

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]

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]

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]

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]

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]

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]

In this paper, a detailed design methodology of a Resonant Dual Active Bridge (R-DAB) converter is proposed for battery charging applications. The converter design methodology is tailored to the charging profile of a Li-ion battery and is based on unique operating points (UOPs) that derive from a dual control method analysis. Specifically, both Pulse Frequency Modulation (PFM) and Phase-Shift Modulation (PSM) are adopted, to minimize the switching losses and the reactive power at the resonant tank. A detailed flowchart is presented, considering multiple design parameters and the UOPs, so that the resonant tank parameters are not overestimated. Simulation results of a conventional charger are presented, that validate the control method and design procedure. Finally, a 1kW|30A laboratory prototype charger is designed and evaluated, using a closed loop control system. The analysis of the experimental results proves the significance of the dual control method and verifies the proposed design methodology. [ABSTRACT FROM AUTHOR]

In this paper, a battery charging and swapping optimization model is established for electric vehicles (EVs) and battery charging/swapping stations (BCSS). The EVs are categorized into private electric vehicles (PrEVs) and electric taxis (ETs), where the charging behaviors of PrEVs are modeled based on the Monte Carlo (MC) method, and the battery swapping (BS) strategies of ETs are optimized by bi-level dynamic game. Moreover, the voltage deviation of the power grid is considered in the load regulation process of ETs. A “path-location” model is established combining with the Floyd algorithm in the simulation and the IEEE 14-Bus system is used to derive the node voltage. Numerical results show that the proposed strategy can simultaneously increase the revenue of BCSS and ETs and reduce the voltage deviation. [ABSTRACT FROM AUTHOR]

A novel longitudinal, lateral, and vertical integrated stability control system is proposed for distributed electric vehicle in this paper. The integrated stability control system is based on model predictive control and takes advantage of its multi-objective optimization feature to takes manipulability, stability and comfort requirements into consideration at the same time, and realize the optimal longitudinal $/$ lateral $/$ vertical forces distribution with constraints overall considered. The advantage of the proposed integrated control system is that it can solve the control objective conflict of the cooperative motion control subsystems and the interference of the actuators compared to distributed control system. Finally, the simulation and comparison analysis are given in several different driving conditions under Carsim and Matlab $/$ Simulink environment. Results show that an improvement of over 80 $\%$ in the pitch angle and roll angle is realized compared to the simple control strategy. [ABSTRACT FROM AUTHOR]

Lateral misalignment along the driving direction may achieve half the length of a transmission coil in a dynamic wireless charging system for electric vehicles, which is called limit misalignment. This limit misalignment may cause the dynamic wireless charging system to malfunction because of the large fluctuation in mutual inductance. In this paper, an asymmetric and reverse series coil (ARSC) structure is proposed. In the ARSC structure, the transmission coil is smaller than the receiving coil in size to smoothen the fluctuation in mutual inductance. The receiving coil consists of a large coil ($\boldsymbol{R}_{\mathrm {x1}}$) and a small coil ($\boldsymbol{R}_{\mathrm {x2}}$) and $\boldsymbol{R}_{\mathrm {x1}}$ is reversely connected to $\boldsymbol{R}_{\mathrm {x2}}$ in series. The purpose of this design is to further reduce the fluctuation in mutual inductance. A calculation method of mutual inductance is then proposed based on the vector potential to calculate the mutual inductance of the ARSC structure. The law of mutual inductance is analyzed by the proposed calculation method for the ARSC structure. Moreover, an optimization method of coil parameters is proposed to obtain quasi-constant mutual inductance. The ARSC structure and optimization method are not only simple but can also make the mutual inductance nearly constant. Calculation, simulation, and experimental results validating the proposed method and structure are shown. [ABSTRACT FROM AUTHOR]

To address the limited driving range of electric vehicles (EVs) and promote EVs’ penetration, vehicular energy networks (VENs) have emerged and opened possibility to charge EVs in motion via dynamic wireless power transfer (DWPT) technology. However, security and efficiency concerns arise due to the untrusted operating environment and EVs’ selfish charging/discharging behaviors. Existing trust models rely on the personal recommendations from neighboring EVs to identify malicious entities in VENs, which may cause potential privacy breaches and data misuse for recommenders. Besides, it is challenging to optimally schedule EVs’ energy behaviors by considering complex interactions among three energy entities (i.e., energy nodes, charging EVs, and discharging EVs). To this end, by leveraging blockchain technology and game theory, this paper proposes a secure and efficient wireless charging scheme to address these issues in VENs. Firstly, a blockchain-based fine-grained access control mechanism with traceability and auditability is presented to enable EV users to fully control and audit their personal rating data usage during trust management by logging data activities and issuing access tokens into decentralized ledgers. In this manner, the privacy of recommenders can be preserved by fully controlling the access and usage of personal rating data. Furthermore, by introducing cooperative wireless energy transfer mode, a hierarchical game-based energy scheduling algorithm is developed to optimize the strategies of three energy parties tier by tier, while considering their cooperation and competition. Finally, extensive simulations are conducted, which demonstrate that the proposed scheme can effectively improve users’ utility and security of energy transmission for EVs, compared with existing representative approaches. [ABSTRACT FROM AUTHOR]

Accurate state of energy (SOE) estimation of the battery pack is very important for the electric vehicle's driving range estimation, which is still a very challenging problem under actual vehicle application conditions at present. This paper proposes a method for estimating the SOE of the battery pack based on prediction, taking into account the future voltage and temperature changes of the battery pack. Firstly, a novel definition of the battery pack SOE is proposed, which considers the inconsistency of the battery pack. Secondly, the thermo-electric lumped parameter equivalent circuit model of a single cell in the battery pack is applied. The electrical parameters are identified using the forgetting factor recursive least-squares algorithm, and the extended Kalman filter is used to estimate the SOC of each cell. Finally, the future current, voltage and temperature response of the cell in the battery pack are accurately predicted and iterative calculations are performed to get SOE under the condition that the future output power of the battery pack is predicted by the Markov chain model method. The effectiveness of the proposed method is verified under dynamic conditions at different temperatures. The results show that the above method has high precision and good robustness, and can meet the needs of actual vehicle applications. [ABSTRACT FROM AUTHOR]

Lap time is the most relevant performance indicator in electric racing. In order to minimize it, it is important to carefully design the powertrain of the race car in terms of sizing and technology, and meticulously administer the energy available on-board. To facilitate efficient design, this paper presents a convex optimization framework to rapidly compute the minimum-lap-time control strategies for a battery electric race car. Specifically, we first identify a convex model of the electric powertrain, including the battery, the electric motor, and two transmission technologies: a fixed-gear transmission (FGT) and a continuously variable transmission (CVT). Second, assuming an expert driver, we formulate the minimum-lap-time control problem in a convex fashion for a given driving path, and compute its globally optimal solution with second-order conic programming algorithms. Third, we showcase our framework on the Le Mans track by comparing the performance achievable with an FGT and a CVT, and validate it with nonlinear simulations. Our results show that for the given setup a CVT can balance its lower efficiency and higher weight with a higher-efficiency and more aggressive motor operation, significantly outperforming the lap time achievable with an FGT. Finally, we leverage the computational efficiency of our framework to carry out parameter studies on the components, revealing that optimizing the size of the battery and the motor for the specific scenario can considerably improve the achievable lap time, and that the best transmission strongly depends on the sizing decisions. [ABSTRACT FROM AUTHOR]

Accelerated battery degradation, resulting in a reduced capacity, is the main concern when discussing vehicle-to-grid (V2G) services. This paper gives a unique empirical insight into the long-term effects on batteries performing an energy demanding V2G service. To this end, an extensive method for measuring the battery capacity of series produced electric vehicles (EVs) via the DC charge port was developed. The reproducibility of the method is demonstrated with repeated tests on new and aged vehicles and the results are compared with the EVs internal estimates, read via the on-board diagnostics port. The method is also validated with a state-of-the-art battery degradation model. The capacity test method is used to evaluate the battery degradation of a fleet of commercially owned EVs which, as a part of the world's first commercial operation, have been used for V2G services for five years. The EVs have, together with ± 10 kW V2G chargers, been delivering primary frequency regulation for 15 hours per day with a daily energy throughput of 50.6 kWh. The usable battery capacity, initially found to be 23 kWh, after two years is reduced on average to 20.7 kWh and after five years is reduced to 18.9 kWh. [ABSTRACT FROM AUTHOR]

The benefits of intermediate couplers (IC) in inductive power transfer (IPT) systems are well documented in the literature. However, the lack of guideline rules makes the selection process of the passive components and operation mode difficult. This paper presents an iterative design methodology based on the equivalent mathematical model of the system to determine the optimal magnetic coupler (MC) dimensions and resonant tanks parameters that operates the system in a quasi constant current mode. It was found that the transconductance function of a series-series (SS) with an intermediate coupler exhibits a quasi-load independent characteristic when the reflected reactance from the receiver side is null. The design methodology then combines the self and mutual coupling profiles of the MC, obtained from a finite element analysis (FEA) tool, with the system specifications and constraints to find all possible solutions. A set of selection metrics are proposed to identify the optimal solution. This work also presents an identification method of the load and mutual inductances based only in the information available from the transmitter side. The proposed method uses the steady-state equations and does not require any form of communication to the receiver side. An experimental prototype was built and assembled in a converted combustion vehicle with efficiency of 89 $\%$. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are being actively adopted as a solution to sustainable transportation. However, a bottleneck remains with charging, where two of the main problems are the long charging time and the range anxiety of EV drivers. In this research, we investigate the deployment of dynamic charging systems, i.e., electrified roads that wirelessly charge EVs on the go, with a view to accelerating EV adoption rate. We propose a traffic-based deployment strategy, statistically quantify its impact, and apply the strategy to two case studies of real traffic in New York City (USA) and Xi’an (China). We find that our analytical estimates not only closely match the real data, but they also suggest that dynamic charging considerably extends the driving range of popular EV models in urban mobility. For example, when only 5% of the existing roads in New York City are equipped with this technology, an EV model such as the Nissan Leaf will approximately maintain its battery level without stopping to recharge. If the percentage of charging roads is increased to 10%, then the Leaf will gain nearly 10% of its battery after every 40 kilometers of driving. Our framework provides a solution to public and private organizations that support and facilitate vehicle electrification through charging infrastructure. [ABSTRACT FROM AUTHOR]

The multiple fluctuations, such as grid DC input voltage, mutual inductance and the plug-in and plug-out of battery, may lead to instability and deterioration of dynamic performance of electric vehicle (EV) wireless power transfer (WPT) system. The overshoot issue can bring great current stress to EV WPT system, and may induce potential disastrous malfunction of the lithium-ion (Li-ion) battery. This study theoretically reveals the slow dynamic response and large overshoot of a traditional WPT system. For solving these issues, a reliable adaptive feedforward control scheme is proposed and applied in the user-side DC-DC converter, where the communication unit is eliminated. The feedforward loop is proved theoretically to suppress overshoot and system dynamic response. Besides, the feedforward control increases the selective range of control parameters, which can improve the system dynamics. Experiment results verifies that the proposed adaptive feedforward gain allows faster dynamics, and the aforementioned disturbances in EV WPT system can be solved while the system performance is not affected. [ABSTRACT FROM AUTHOR]

Featuring the fast response and flexibility in control allocation, an electric vehicle with in-wheel motors is a good platform for implementing advanced vehicle dynamics control. Among many active safety functions of an in-wheel motor driven vehicle (IMDV), lateral stability control is a key technology, which can be realized through torque vectoring. To further advance the lateral stabilization performance of the IMDV, in this article a novel data-driven nonlinear model predictive control (NMPC) is proposed based the recurrent high-order neural network (RHONN) modelling method. First, the new RHONN model is developed to represent vehicle’s nonlinear dynamic behaviors. Different from the conventional physics-based modelling method, the RHONN model forms high-order polynomials by neuron states to feature nonlinear dynamics. Based on the RHONN model, the steady-state responses of vehicle’s yaw rate and sideslip angle are iteratively optimized and set as the control objectives for low-level controller, aiming to improve the system robustness. Besides, a nonlinear model predictive controller is designed based on the RHONN, which is expected to improve the prediction accuracy during the receding horizon control. Further, a constrained optimization problem is formulated to derive the required yaw moment for vehicle lateral dynamics stabilization. Finally, the performance of the developed RHONN-based nonlinear MPC is validated on an IMDV in the CarSim/Simulink simulation environment. The validation results show that the developed approach outperforms the conventional method, and further improves the stable margin of the system. It is able to enhance the lateral stabilization performance of the IMDV under various driving scenarios, demonstrating the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This article deals with the control chassis dynamics of a light electric vehicle. In the frame of Global Chassis Control, it proposes a hierarchical control approach focuses on compensating the chassis dynamics against driver disturbances. It is based on a supervisory structure consists of two main levels, a global controller for regulating chassis variables and a local controller to each suspension system. Both controllers are designed using a fractional-order robust controller, namely CRONE controller, to provide an optimized solution for the performance-robustness tradeoff. The main objective is to improve ride comfort for passengers while respecting road holding and handling criteria. In contrast to other studies, random road disturbances are considered to investigate the limits of the proposed approach. Analysis in frequency and time domain has been done to evaluate the performance and robustness of the designed controller. Simulation results based on a full nonlinear 14 degree of freedom vehicle model show that the proposed strategy can effectively improve the ride quality in the field of interest where a significant performance in driver disturbance rejection is reported. [ABSTRACT FROM AUTHOR]

A new equivalent consumption minimization strategy (ECMS) is proposed for a hybrid electric vehicle in which the equivalent factor varies dynamically with the current speed and the minimum engine operation time that leads to improvement in vehicle fuel economy and battery performance. To construct the strategy, a self-organizing map neural-network was utilized to recognize and group the speed patterns. The driving cycles corresponding to these speed patterns were reconstructed for solving the corresponding optimal equivalent factors. When applying the strategy on the road, the current vehicle speed pattern is recognized by using the historical vehicle speed data, and the corresponding optimal equivalent factor is adopted based on that pattern. Furthermore, to constrain the battery state of charge (SOC) to avoid exceeding the allowable values and to consider the hysteresis of engine starting, a fuzzy logic controller was used to dynamically adjust the minimum engine operation time. The simulated results indicated that the proposed ECMS improved the vehicle fuel economy by 7.87% and 6.23% over the conventional ECMS and the rule-based single-point strategy, respectively. Furthermore, the peak battery charging-current was reduced by 42.94% and 27.73%, and the peak discharging-current by 62.19% and 56.97%, respectively. In addition, the proposed strategy could lead to better SOC evolution of the battery. [ABSTRACT FROM AUTHOR]

Vehicle-to-grid (V2G) technology enables bidirectional energy flow between the electric vehicle and power grid thereby making the electric vehicle as a virtual power plant. This seamless exchange of energy flow is possible only when the entities involved communicate wirelessly in a secured manner. In order to ensure privacy-preserving in the V2G communication, development of a novel key establishment protocol is an active area of research. This article presents a lightweight mutual authentication and key establishment protocol for secure V2G communication among the electric vehicle user, charging station, and utility service provider. The correctness of the proposed protocol is verified using the formal method BAN logic, and many of its security threats are analyzed informally. Further, the protocol is compared for its security and efficiency with the related protocols in the literature under various aspects, and the comparison results show that our protocol outshines the others. The functional correctness of the protocol is also verified in Xilinx Zynq-7000 series FPGA board so that it can be deployed in any real-time applications. [ABSTRACT FROM AUTHOR]

To ensure driving safety, autonomous electric vehicles need to follow the planned path accurately, which depends on the vehicle path following control strategy. It is widely acknowledged that the model-based control strategies have excellent potential on path following, but their effect is seriously affected by the parametric uncertainty. To solve this problem, an adaptive constrained path following control scheme considering the influence of parametric uncertainty is proposed for autonomous electric vehicles. Firstly, an adaptive feedback control law and its update law are proposed to deal with the variation of tire cornering stiffness during vehicle path following process. Secondly, a constraint function for lateral displacement error during path following process is designed to further improve driving safety. And then, the closed-loop stability of the proposed control scheme is proved. Finally, the validation is implemented by simulation and experiment, the results show that the proposed control scheme is effective under different working conditions. The maximum lateral displacement error is reduced to 0.0297m by using the proposed scheme in the experiment. The proposed scheme might provide a theoretical reference for control practice of autonomous vehicles. [ABSTRACT FROM AUTHOR]

A multi-actuation model predictive controller is designed to improve the stability of the performance vehicles during high-speed maneuvers. The actuators included in this study are four electric motors for the wheels and two active aerodynamic wings at the front and rear of the vehicle. The designed controller integrates optimal corner torque allocation with an optimal active aerodynamics control system. A model predictive control scheme is used to adjust the air wings angle of attack and optimize the corner torques. A high-level constraint adjustment module is added to the controller to observe nonlinear tire behavior and optimize aerodynamic wings activation as required. Nonlinear tire behavior and actuator dynamics are considered and included in the prediction model. The controller performance is verified in simulation with MATLAB/Simulink and CarSim. [ABSTRACT FROM AUTHOR]

Aiming at the problem of trajectory tracking and handling stability of intelligent four hub-motor independent-drive electric vehicle at high speed, an integrated control strategy based on model predictive control (MPC) is proposed. Firstly, a kinematics preview model is established considering the adaptive change of preview distance with longitudinal vehicle speed. Secondly, combined with the proposed preview model and considering the vehicle dynamic characteristics, an upper level controller of MPC is created to track the lateral deviation, heading angle deviation, sideslip angle and yaw rate to obtain the optimal solution of front wheel angle and additional yaw moment, which realize the control of handling stability while ensuring the trajectory tracking accuracy at same time. Then considering the varying road adhesion and the influence of longitudinal slip at the four tires, a lower level controller composed of torque optimal control and sliding mode controller is designed to realize the optimal torque distribution of four hub motors. Finally, through the co-simulation of MATLAB / Simulink and CarSim and the hardware-in-the-loop experiment, it is verified that the proposed integrated control strategy can effectively improve the path tracking accuracy and ensure the vehicle handling stability at high speed. Compared with the general MPC strategy, who does not control the sideslip angle and yaw rate with no preview model, the cumulative lateral tracking error of the integrated control is reduced by 51.9% and 87.7% respectively. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) have emerged as one of the most promising solutions for reducing carbon emissions in smart cities. However, due to the limited battery life of EVs and the scarcity of charging stations, EV drivers are not willing to travel long distances. Thus, blockchain-enabled energy trading (BET) has lately been used to securely share energy among EVs via wireless power transfer (WPT) technology. Blockchain is used to ensure the security and privacy of transactions between untrustworthy EVs in the WPT process. Nevertheless, previous works on BET have relied on existing consensus mechanisms built on the requirements of the cryptocurrency systems. These consensus mechanisms have faced significant challenges in maintaining high reliability, throughput, low latency, and network scalability in V2V energy trading that requires real-time services. To address these issues, we propose a new consensus mechanism that leverages the benefits of Practical Byzantine Fault Tolerance (PBFT) and Proof of Reputation (PoR) called PBFT-based PoR (PPoR). The energy trading process runs in a clustered vehicular network, where validator selection, block generation, and consensus processes are performed in each cluster. We adopt an incentive mechanism based on a Stackelberg game model to optimize the utility of sellers, buyers, and validator nodes, which motivates honest and cooperative nodes. The simulation results show that the proposed scheme reduces buyers’ costs by 21.1% while increasing the utility of sellers by 18%. Moreover, compared to benchmarks, the proposed scheme reduces the transaction processing delay and increases the throughput by more than 47.1% and 15.7%, respectively. [ABSTRACT FROM AUTHOR]

For a broad range of industrial, service, and household electric vehicles, dynamic charging is a viable option for energy replenishing that could reduce the size of the onboard energy storage, remove range limitations, and increase operating time. For these systems, timely detection of an approaching vehicle and the detection of strong coupling conditions between transmitter and receiver coils are of prime interest. A novel method is proposed in this article, which utilizes the existing embedded transmitter coil and power electronics modules to establish a precise detection system based on the power footprint of a traveling receiver. Next, a control algorithm is proposed to control charger activation and deactivation time accurately and deliver the required energy during the maximum coupling of the coils. The proposed sigmoid-curve-based control algorithm achieves a robust, speed-independent, smooth transition between two states while providing fast dynamics for aligned and misaligned conditions. Finally, a novel approach to test high-speed dynamic charging in laboratory conditions based on the receiver emulator is developed and used for experimental validation. The vehicle detection and control techniques have been validated through simulations and experimentally on a speed-controlled dynamic charging test station for a wide speed range. The proposed method does not require any additional hardware, sensing devices, or high-speed communication between the vehicle and the charging infrastructure. [ABSTRACT FROM AUTHOR]