Showing papers in "IEEE Transactions on Vehicular Technology in 2013"
TL;DR: The results indicate that the proposed online SoC estimation with the AEKF algorithm performs optimally, and for different error initial values, the maximum soC estimation error is less than 2% with close-loop state estimation characteristics.
Abstract: An accurate State-of-Charge (SoC) estimation plays a significant role in battery systems used in electric vehicles due to the arduous operation environments and the requirement of ensuring safe and reliable operations of batteries. Among the conventional methods to estimate SoC, the Coulomb counting method is widely used, but its accuracy is limited due to the accumulated error. Another commonly used method is model-based online iterative estimation with the Kalman filters, which improves the estimation accuracy in some extent. To improve the performance of Kalman filters in SoC estimation, the adaptive extended Kalman filter (AEKF), which employs the covariance matching approach, is applied in this paper. First, we built an implementation flowchart of the AEKF for a general system. Second, we built an online open-circuit voltage (OCV) estimation approach with the AEKF algorithm so that we can then get the SoC estimate by looking up the OCV-SoC table. Third, we proposed a robust online model-based SoC estimation approach with the AEKF algorithm. Finally, an evaluation on the SoC estimation approaches is performed by the experiment approach from the aspects of SoC estimation accuracy and robustness. The results indicate that the proposed online SoC estimation with the AEKF algorithm performs optimally, and for different error initial values, the maximum SoC estimation error is less than 2% with close-loop state estimation characteristics.
345 citations
TL;DR: A more accurate battery state of charge (SOC) estimation method for electric drive vehicles is developed based on a nonlinear battery model and an extended Kalman filter supported by experimental data.
Abstract: In this paper, a more accurate battery state of charge (SOC) estimation method for electric drive vehicles is developed based on a nonlinear battery model and an extended Kalman filter (EKF) supported by experimental data. A nonlinear battery model is constructed by separating the model into a nonlinear open circuit voltage and a two-order resistance-capacitance model. EKF is used to eliminate the measurement and process noise and remove the need of prior knowledge of initial SOC. A hardware-in-the-loop test bench was built to validate the method. The experimental results show that the proposed method can estimate the battery SOC with high accuracy.
315 citations
TL;DR: Results show that variable damping coefficients and the asymmetric feature in jounce and rebound motions are achieved by controlling the electrical load of the shock absorber.
Abstract: This paper presents the design, modeling, bench experiments, and road tests for a retrofit regenerative shock absorber based on a permanent magnetic generator and a rack-pinion mechanism for the purposes of energy harvesting and vibration damping. Results show that variable damping coefficients and the asymmetric feature in jounce and rebound motions are achieved by controlling the electrical load of the shock absorber. Improved efficiency and reliability are achieved by utilizing a roller to guide the rack and preload on the gear transmission to reduce backlash and friction. A peak power of 68 W and average power of 19 W are attained from one prototype shock absorber when the vehicle is driven at 48 km/h (30 mi/h) on a fairly smooth campus road.
304 citations
TL;DR: An adaptive algorithm is introduced to increase system reliability in terms of the probability of successful reception of the packet and the delay of emergency messages in a harsh vehicular environment to reduce performance degradation in dense and high-mobility conditions.
Abstract: An analytical model for the reliability of a dedicated short-range communication (DSRC) control channel (CCH) to handle safety applications in vehicular ad hoc networks (VANETs) is proposed. Specifically, the model enables the determination of the probability of receiving status and safety messages from all vehicles within a transmitter's range and vehicles up to a certain distance, respectively. The proposed model is built based on a new mobility model that takes into account the vehicle's follow-on safety rule to derive accurately the relationship between the average vehicle speed and density. Moreover, the model takes into consideration 1) the impact of mobility on the density of vehicles around the transmitter, 2) the impact of the transmitter's and receiver's speeds on the system reliability, 3) the impact of channel fading by modeling the communication range as a random variable, and 4) the hidden terminal problem and transmission collisions from neighboring vehicles. It is shown that the current specifications of the DSRC may lead to severe performance degradation in dense and high-mobility conditions. Therefore, an adaptive algorithm is introduced to increase system reliability in terms of the probability of successful reception of the packet and the delay of emergency messages in a harsh vehicular environment. The proposed model and the enhancement algorithm are validated by simulation using realistic vehicular traces.
278 citations
TL;DR: Experiments show that the proposed solution outperforms many previous solutions, and LPR can be better solved by solutions with settings oriented for different applications.
Abstract: We split the applications of vehicle license plate recognition (LPR) into three major categories and propose a solution with parameter settings that are adjustable for different applications. The three categories are access control (AC), law enforcement (LE), and road patrol (RP). Each application is characterized by variables of different variation scopes and thus requires different settings on the solution with which to deal. The proposed solution consists of three modules for plate detection, character segmentation, and recognition. Edge clustering is formulated for solving plate detection for the first time. It is also a novel application of the maximally stable extreme region (MSER) detector to character segmentation. A bilayer classifier, which is improved with an additional null class, is experimentally proven to be better than previous methods for character recognition. To assess the performance of the proposed solution, the application-oriented license plate (AOLP) database is composed and made available to the research community. Experiments show that the proposed solution outperforms many previous solutions, and LPR can be better solved by solutions with settings oriented for different applications.
253 citations
TL;DR: An intracluster D2D retransmission scheme with optimized resource utilization is proposed, which can adaptively select the number of cooperative relays performing multicast retransmissions and give an iterative subcluster partition algorithm to enhanceretransmission throughput.
Abstract: Device-to-device (D2D) communications help improve the performance of wireless multicast services in cellular networks via cooperative retransmissions among multicast recipients within a cluster. Resource utilization efficiency should be taken into account in the design of D2D communication systems. To maximize resource efficiency of D2D retransmissions, there is a tradeoff between multichannel diversity and multicast gain. In this paper, by analyzing the relationship between the number of relays and minimal time-frequency resource cost on retransmissions, we derive a closed-form probability density function (pdf) for an optimal number of D2D relays. Motivated by the analysis, we then propose an intracluster D2D retransmission scheme with optimized resource utilization, which can adaptively select the number of cooperative relays performing multicast retransmissions and give an iterative subcluster partition algorithm to enhance retransmission throughput. Exploiting both multichannel diversity and multicast gain, the proposed scheme achieves a significant gain in terms of resource utilization if compared with its counterparts with a fixed number of relays.
252 citations
TL;DR: The root cause of performance bottlenecks in current full-duplex systems is investigated and signal models for wideband and multiple-input-multiple-output (MIMO) full- DUplex systems are proposed, capturing all the salient design parameters, thus allowing future analytical development of advanced coding and signal design for full- duplex systems.
Abstract: Recent experimental results have shown that full-duplex communication is possible for short-range communications. However, extending full-duplex to long-range communication remains a challenge, primarily due to residual self-interference, even with a combination of passive suppression and active cancelation methods. In this paper, we investigate the root cause of performance bottlenecks in current full-duplex systems. We first classify all known full-duplex architectures based on how they compute their canceling signal and where the canceling signal is injected to cancel self-interference. Based on the classification, we analytically explain several published experimental results. The key bottleneck in current systems turns out to be the phase noise in the local oscillators in the transmit-and-receive chain of the full-duplex node. As a key by-product of our analysis, we propose signal models for wideband and multiple-input-multiple-output (MIMO) full-duplex systems, capturing all the salient design parameters, thus allowing future analytical development of advanced coding and signal design for full-duplex systems.
251 citations
TL;DR: This paper studied EV charging scheduling problems from a customer's perspective by jointly considering the aggregator's revenue and customers' demands and costs and presents linear programming (LP)-based optimal schemes for the static problems and effective heuristic algorithms for the dynamic problems.
Abstract: Electric vehicles (EVs) are considered to be a promising solution for current gas shortage and emission problems. To maximize the benefits of using EVs, regulated and optimized charging control needs to be provided by load aggregators for connected vehicles. An EV charging network is a typical cyber-physical system, which includes a power grid and a large number of EVs and aggregators that collect information and control the charging procedure. In this paper, we studied EV charging scheduling problems from a customer's perspective by jointly considering the aggregator's revenue and customers' demands and costs. We considered two charging scenarios: static and dynamic. In the static charging scenario, customers' charging demands are provided to the aggregator in advance; however, in the dynamic charging scenario, an EV may come and leave at any time, which is not known to the aggregator in advance. We present linear programming (LP)-based optimal schemes for the static problems and effective heuristic algorithms for the dynamic problems. The dynamic scenario is more realistic; however, the solutions to the static problems can be used to show potential revenue gains and cost savings that can be brought by regulated charging and, thus, can serve as a benchmark for performance evaluation. It has been shown by extensive simulation results based on real electricity price and load data that significant revenue gains and cost savings can be achieved by optimal charging scheduling compared with an unregulated baseline approach, and moreover, the proposed dynamic charging scheduling schemes provide close-to-optimal solutions.
240 citations
TL;DR: This paper defines a new adaptive congestion control algorithm that can be applied to the message rate of devices in this vehicular environment and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high-density scenarios.
Abstract: Wireless vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (eg, vehicle speed and location or traffic signal state and timing) Unless controlled, the aggregate of these broadcasts will congest the channel under dense traffic scenarios, reducing the effectiveness of collision avoidance applications that use transmitted information Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology This paper defines a new adaptive congestion control algorithm that can be applied to the message rate of devices in this vehicular environment While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full-precision control inputs that are available on the wireless channel The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters This “perfect” convergence avoids the limit cycle behavior that is inherent to binary control We also discuss several practical aspects associated with implementing LIMERIC, including guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust convergence under all conditions, convergence with asynchronous updates, and using channel load to determine the aggregate message rate that is observable at a receiver This paper also extends the convergence analysis for two important cases, ie, measurement noise in the input signal and delay in the update process This paper illustrates key analytical results using MATLAB numerical results and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high-density scenarios
239 citations
TL;DR: This paper derives accurate approximations for the maximal throughput in both scenarios in the high signal-to-noise ratio region, and gives new insights into the additional power cost for achieving a higher security level while maintaining a specified target throughput.
Abstract: In this paper, we investigate the design of artificial-noise-aided secure multi-antenna transmission in slow fading channels. The primary design concerns include the transmit power allocation and the rate parameters of the wiretap code. We consider two scenarios with different complexity levels: 1) the design parameters are chosen to be fixed for all transmissions; and 2) they are adaptively adjusted based on the instantaneous channel feedback from the intended receiver. In both scenarios, we provide explicit design solutions for achieving the maximal throughput subject to a secrecy constraint, given by a maximum allowable secrecy outage probability. We then derive accurate approximations for the maximal throughput in both scenarios in the high signal-to-noise ratio region, and give new insights into the additional power cost for achieving a higher security level while maintaining a specified target throughput. In the end, the throughput gain of adaptive transmission over non-adaptive transmission is also quantified and analyzed.
232 citations
TL;DR: Simulation results demonstrate that the proposed data-gathering algorithm can greatly shorten the moving distance of the collectors compared with the covering line approximation algorithm and is close to the optimal algorithm for small networks.
Abstract: In this paper, we propose a new data-gathering mechanism for large-scale wireless sensor networks by introducing mobility into the network. A mobile data collector, for convenience called an M-collector in this paper, could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, working like a mobile base station and gathering data while moving through the field. An M-collector starts the data-gathering tour periodically from the static data sink, polls each sensor while traversing its transmission range, then directly collects data from the sensor in single-hop communications, and finally transports the data to the static sink. Since data packets are directly gathered without relays and collisions, the lifetime of sensors is expected to be prolonged. In this paper, we mainly focus on the problem of minimizing the length of each data-gathering tour and refer to this as the single-hop data-gathering problem (SHDGP). We first formalize the SHDGP into a mixed-integer program and then present a heuristic tour-planning algorithm for the case where a single M-collector is employed. For the applications with strict distance/time constraints, we consider utilizing multiple M-collectors and propose a data-gathering algorithm where multiple M-collectors traverse through several shorter subtours concurrently to satisfy the distance/time constraints. Our single-hop mobile data-gathering scheme can improve the scalability and balance the energy consumption among sensors. It can be used in both connected and disconnected networks. Simulation results demonstrate that the proposed data-gathering algorithm can greatly shorten the moving distance of the collectors compared with the covering line approximation algorithm and is close to the optimal algorithm for small networks. In addition, the proposed data-gathering scheme can significantly prolong the network lifetime compared with a network with static data sink or a network in which the mobile collector can only move along straight lines.
TL;DR: This paper proposes to use vehicular ad hoc networks (VANETs) to collect and aggregate real-time speed and position information on individual vehicles to optimize signal control at traffic intersections and proves that the OJF algorithm is 2-competitive, implying that the delay is less than or equal to twice the delay of an optimal offline schedule.
Abstract: In this paper, we propose to use vehicular ad hoc networks (VANETs) to collect and aggregate real-time speed and position information on individual vehicles to optimize signal control at traffic intersections. We first formulate the vehicular traffic signal control problem as a job scheduling problem on processors, with jobs corresponding to platoons of vehicles. Under the assumption that all jobs are of equal size, we give an online algorithm, referred to as the oldest job first (OJF) algorithm, to minimize the delay across the intersection. We prove that the OJF algorithm is 2-competitive, implying that the delay is less than or equal to twice the delay of an optimal offline schedule with perfect knowledge of the arrivals. We then show how a VANET can be used to group vehicles into approximately equal-sized platoons, which can then be scheduled using OJF. We call this the two-phase approach, where we first group the vehicular traffic into platoons and then apply the OJF algorithm, i.e., the oldest arrival first (OAF) algorithm. Our simulation results show that, under light and medium traffic loads, the OAF algorithm reduces the delays experienced by vehicles as they pass through the intersection, as compared with vehicle-actuated methods, Webster's method, and pretimed signal control methods. Under heavy vehicular traffic load, the OAF algorithm performs the same as the vehicle-actuated traffic method but still produces lower delays, as when compared with Webster's method and the pretimed signal control method.
TL;DR: From numerical results, it is observed that the proposed robust compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information, and the proposed BS selection algorithm is seen to perform close to the more complex exhaustive search solution.
Abstract: This paper studies distributed compression for the uplink of a cloud radio access network where multiple multiantenna base stations (BSs) are connected to a central unit, which is also referred to as a cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, distributed source coding strategies are potentially beneficial. However, they require each BS to have information about the joint statistics of the received signals across the BSs, and they are generally sensitive to uncertainties regarding such information. Motivated by this observation, a robust compression method is proposed to cope with uncertainties on the correlation of the received signals. The problem is formulated using a deterministic worst case approach, and an algorithm is proposed that achieves a stationary point for the problem. Then, BS selection is addressed with the aim of reducing the number of active BSs, thus enhancing the energy efficiency of the network. An optimization problem is formulated in which compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed that is shown to converge to a locally optimal point. From numerical results, it is observed that the proposed robust compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information. Moreover, the proposed BS selection algorithm is seen to perform close to the more complex exhaustive search solution.
TL;DR: The problem of controlling a string of vehicles moving in one dimension is considered so that they all follow a lead vehicle with constant spacing between successive vehicles and the negative effect of the tracking lag parameter is taken into account.
Abstract: The problem of controlling a string of vehicles moving in one dimension is considered so that they all follow a lead vehicle with constant spacing between successive vehicles. Due to practical design and implementation, the negative effect of the tracking lag parameter is taken into account. A hierarchical platoon controller design framework is established comprising a feedback linearization controller at the first layer and a decentralized bidirectional control controller at the second layer. The stability criterion is examined by using a partial differential equation approximation in the limit of the number of vehicles subjected to unequal asymmetry in position and velocity feedback. For disturbance attenuation, string stability analysis is also examined. At the end of the paper, simulations are given to show the efficiency of the proposed results.
TL;DR: This paper focuses on the analysis of the 802.11p safety-critical broadcast on the CCH in a VANET environment and improves the existing work by taking several aspects into design consideration and extensive performance evaluations based on the NS-2 simulator help to validate the accuracy of the proposed model.
Abstract: Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are gaining increasing importance in vehicular applications. Dedicated short-range communication (DSRC) is a fundamental set of short-to-medium-range communication channels and a set of protocols and standards that are specifically designed for V2V and V2I communications. IEEE 802.11p is a protocol that has been standardized as the medium access control (MAC) layer of the DSRC standard. Due to the highly dynamic topology and low delay constraints in vehicular ad hoc networks (VANETs), direct (or one-hop) broadcast on the control channel (CCH) is an effective approach to inform the neighborhood of safety-related messages. The 802.11p enhanced distributed channel access (EDCA) mechanism allows four access categories (ACs) in a station for applications with different priorities according to their criticalities for the vehicle's safety. This paper focuses on the analysis of the 802.11p safety-critical broadcast on the CCH in a VANET environment and improves the existing work by taking several aspects into design consideration. Extensive performance evaluations based on the NS-2 simulator help to validate the accuracy of the proposed model and analyze the capabilities and limitations of the standard 802.11p broadcast on the CCH.
TL;DR: This paper focuses on developing a novel and nonintrusive driver behavior detection system using a context-aware system in VANETs to detect abnormal behaviors exhibited by drivers and to warn other vehicles on the road to prevent accidents from happening.
Abstract: Vehicular ad hoc networks (VANETs) have emerged as an application of mobile ad hoc networks (MANETs), which use dedicated short-range communication (DSRC) to allow vehicles in close proximity to communicate with each other or to communicate with roadside equipment. Applying wireless access technology in vehicular environments has led to the improvement of road safety and a reduction in the number of fatalities caused by road accidents through development of road safety applications and facilitation of information sharing between moving vehicles regarding the road. This paper focuses on developing a novel and nonintrusive driver behavior detection system using a context-aware system in VANETs to detect abnormal behaviors exhibited by drivers and to warn other vehicles on the road to prevent accidents from happening. A five-layer context-aware architecture is proposed, which is able to collect contextual information about the driving environment, to perform reasoning about certain and uncertain contextual information, and to react upon that information. A probabilistic model based on dynamic Bayesian networks (DBNs) in real time, inferring four types of driving behavior (normal, drunk, reckless, and fatigue) by combining contextual information about the driver, the vehicle, and the environment, is presented. The dynamic behavior model can capture the static and the temporal aspects related to the behavior of the driver, thus leading to robust and accurate behavior detection. The evaluation of behavior detection using synthetic data proves the validity of our model and the importance of including contextual information about the driver, the vehicle, and the environment.
TL;DR: In this article, the performance of spatial modulation (SM) and spatial multiplexing (SMX) with an experimental testbed is characterized. And the average bit-error ratio (ABER) of the system is validated through both theoretical analysis and simulation results for SM and SMX under the line-of-sight (LoS) channel conditions.
Abstract: In this paper, we seek to characterize the performance of spatial modulation (SM) and spatial multiplexing (SMX) with an experimental testbed. Two National Instruments (NI) PXIe devices are used for the system testing: one for the transmitter and one for the receiver. The digital signal processing (DSP) that formats the information data in preparation for transmission is described, along with the DSP that recovers the information data. In addition, the hardware limitations of the system are also analyzed. The average bit-error ratio (ABER) of the system is validated through both theoretical analysis and simulation results for SM and SMX under the line-of-sight (LoS) channel conditions.
TL;DR: Simulation results show how QUVoD is a highly efficient user-centric mobile VoD solution in urban vehicular networks in comparison with existing state-of-the-art solutions.
Abstract: Recently, many cities around the world have witnessed large-scale deployment of terrestrial broadcasting mobile television (TV) to vehicles. This service is similar to the cable or satellite TV already in the home, and user-centric interactive mobile Video-on-Demand (VoD) over urban vehicular networks is in fact expected. However, providing this new service with focus on user Quality of Experience (QoE) constitutes a significant challenge. This paper introduces a QoE-driven User-centric solution for VoD services in urban vehicular network environments (QUVoD). QUVoD relies on a multihomed hierarchical peer-to-peer (P2P) and vehicular ad-hoc network (VANET) architecture. Vehicles construct a low-layer VANET via Wireless Access in the Vehicular Environment interfaces; they also form an upper layer P2P Chord overlay on top of a cellular network via Fourth-Generation (4G) interfaces. A novel grouping-based storage strategy that uniformly distributes the video segments along the Chord overlay is proposed, reducing segment seeking traffic while also enabling load balancing. A novel segment seeking and multipath delivery scheme that achieves high lookup success rate and very good video data delivery efficiency is also introduced, which achieves high lookup success rate and very good video data delivery efficiency. Furthermore, a new speculation-based prefetching strategy is proposed, which analyses users' interactive viewing behavior and, by estimating video segment playback order, employs prefetching of the expected segments, smoothening the video playback. Simulation results show how QUVoD is a highly efficient user-centric mobile VoD solution in urban vehicular networks in comparison with existing state-of-the-art solutions.
TL;DR: The roadway type and traffic congestion level specific machine learning of optimal energy management is effective for in-vehicle energy control and the best controller, IEC_HEV_MISE, trained with the optimal power split generated by the DP optimization algorithm can provide fuel savings ranging from 5% to 19%.
Abstract: This is the second paper in a series of two that describe our research in intelligent energy management in a hybrid electric vehicle (HEV). In the first paper, we presented the machine-learning framework ML_EMO_HEV, which was developed for learning the knowledge about energy optimization in an HEV. The framework consists of machine-learning algorithms for predicting driving environments and generating the optimal power split of the HEV system for a given driving environment. In this paper, we present the following three online intelligent energy controllers: 1) IEC_HEV_SISE; 2) IEC_HEV_MISE ; and 3) IEC_HEV_MIME. All three online intelligent energy controllers were trained within the machine-learning framework ML_EMO_HEV to generate the best combination of engine power and battery power in real time such that the total fuel consumption over the whole driving cycle is minimized while still meeting the driver's demand and the system constraints, including engine, motor, battery, and generator operation limits. The three online controllers were integrated into the Ford Escape hybrid vehicle model for online performance evaluation. Based on their performances on ten test drive cycles provided by the Powertrain Systems Analysis Toolkit library, we can conclude that the roadway type and traffic congestion level specific machine learning of optimal energy management is effective for in-vehicle energy control. The best controller, IEC_HEV_MISE, trained with the optimal power split generated by the DP optimization algorithm with multiple initial SOC points and single ending point, can provide fuel savings ranging from 5% to 19%. Together, these two papers cover the innovative technologies for modeling power flow, mathematical background of optimization in energy management, and machine-learning algorithms for generating intelligent energy controllers for quasioptimal energy flow in a power-split HEV.
TL;DR: This paper reformulates the localization problem as a weighted least squares (WLS) problem and performs semidefinite relaxation (SDR) to obtain a convex semideFinite programming (SDP) problem, which is a relaxation of the original WLS problem and facilitates accurate estimate without postprocessing.
Abstract: Localization by a sensor network has been extensively studied. In this paper, we address the source localization problem by using time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements. Owing to the nonconvex nature of the maximum-likelihood (ML) estimation problem, it is difficult to obtain its globally optimal solution without a good initial estimate. Thus, we reformulate the localization problem as a weighted least squares (WLS) problem and perform semidefinite relaxation (SDR) to obtain a convex semidefinite programming (SDP) problem. Although SDP is a relaxation of the original WLS problem, it facilitates accurate estimate without postprocessing. Moreover, this method is extended to solve the localization problem when there are errors in sensor positions and velocities. Simulation results show that the proposed method achieves a significant performance improvement over existing methods.
TL;DR: This paper describes design techniques for electric vehicle (EV) traction machines to achieve high efficiency against a defined driving cycle such as the New European Drive Cycle (NEDC) while satisfying the required torque-speed operating range.
Abstract: This paper describes design techniques for electric vehicle (EV) traction machines to achieve high efficiency against a defined driving cycle such as the New European Drive Cycle (NEDC) while satisfying the required torque-speed operating range. A fractional-slot concentrated-winding (FSCW) surface-mounted permanent-magnet (SPM) machine has been identified as a suitable candidate for EV applications due to its high power/torque density, high efficiency, and good flux-weakening capability compared with other competing machine topologies. Based on the vehicle characteristics and the reference driving cycle, the motor specifications are established, and the design constraints for the SPM machine to satisfy the peak torque and flux-weakening capabilities are derived. Furthermore, the influence of the key parameters, such as slot-pole number combination, machine inductance, axial length, and number of turns, on the machine copper and iron losses over the NEDC is evaluated. Optimizations were carried for these parameters to minimize the total energy losses over the driving cycle. It has been shown that conventional design methodologies that aim to maximize efficiency in the region close to the rated operating condition may lead to less optimal designs and higher energy losses over the NEDC. A prototype motor for a front- and rear-wheel-driven EV has been designed, manufactured, and tested. The experimental results validate the proposed design methodology.
TL;DR: In this paper, the uplink performance of a multicell multiuser single-input multiple-output (SIMO) system with both small-and large-scale fading was investigated.
Abstract: We consider the uplink of a multicell multiuser single-input multiple-output system (MU-SIMO), where the channel experiences both small- and large-scale fading. The data detection is done by using the linear zero-forcing technique, assuming the base station (BS) has perfect channel state information of all users in its cell. We derive new exact analytical expressions for the uplink rate, the symbol error rate (SER), and the outage probability per user, as well as a lower bound on the achievable rate. This bound is very tight and becomes exact in the large-number-of-antenna limit. We further study the asymptotic system performance in the regimes of high signal-to-noise ratio (SNR), large number of antennas, and large number of users per cell. We show that, at high SNRs, the system is interference limited, and hence, we cannot improve the system performance by increasing the transmit power of each user. Instead, by increasing the number of BS antennas, the effects of interference and noise can be reduced, thereby improving system performance. We demonstrate that, with very large antenna arrays at the BS, the transmit power of each user can be made inversely proportional to the number of BS antennas while maintaining a desired quality of service. Numerical results are presented to verify our analysis.
TL;DR: This paper proposes an efficient cooperative authentication scheme for VANETs that maximally eliminates redundant authentication efforts on the same message by different vehicles, and uses an evidence-token approach to controlling the authentication workload, without the direct involvement of a trusted authority.
Abstract: Recently, vehicular ad hoc networks (VANETs) have emerged as a promising approach to increasing road safety and efficiency, as well as improving the driving experience. This can be accomplished in a variety of applications that involve communication between vehicles, such as warning other vehicles about emergency braking; however, if we do not take security and privacy issues into consideration, the attractive features of VANETs will inevitably result in higher risks for abuse, even before the wide deployment of such networks. While message authentication is a common tool for ensuring information reliability, namely, data integrity and authenticity, it faces a challenge in VANETs. When the number of messages that are received by a vehicle becomes large, traditional exhaustive (or per-message) authentication may generate unaffordable computational overhead on the vehicle and therefore bring unacceptable delay to time-critical applications, such as accident warning. In this paper, we propose an efficient cooperative authentication scheme for VANETs. To reduce the authentication overhead on individual vehicles and shorten the authentication delay, this scheme maximally eliminates redundant authentication efforts on the same message by different vehicles. To further resist various attacks, including free-riding attacks that are launched by selfish vehicles, and encourage cooperation, the scheme uses an evidence-token approach to controlling the authentication workload, without the direct involvement of a trusted authority (TA). When a vehicle passes a roadside unit (RSU), the vehicle obtains an evidence token from the TA via the RSU. This token reflects the contribution that the vehicle has made to cooperative authentication in the past, which enables the vehicle to proportionally benefit from other vehicles' authentication efforts in the future and thus reduce its own workload. Through extensive simulation, we evaluate the proposed cooperative authentication scheme in terms of workload savings and the ability to resist free-riding attacks.
TL;DR: In this article, the performance of an energy detector over generalized κ-μ and κ − μ extreme fading channels was investigated, and the authors derived analytic expressions for the corresponding average probability of detection for the case of single-user detection.
Abstract: Energy detection (ED) is a simple and popular method of spectrum sensing in cognitive radio systems. It is also widely known that the performance of sensing techniques is largely affected when users experience fading effects. This paper investigates the performance of an energy detector over generalized κ-μ and κ- μ extreme fading channels, which have been shown to provide remarkably accurate fading characterization. Novel analytic expressions are firstly derived for the corresponding average probability of detection for the case of single-user detection. These results are subsequently extended to the case of square-law selection (SLS) diversity and for collaborative detection scenarios. As expected, the performance of the detector is highly dependent upon the severity of fading since even small variations of the fading conditions affect significantly the value of the average probability of detection. Furthermore, the performance of the detector improves substantially as the number of branches or collaborating users increase in both severe and moderate fading conditions, whereas it is shown that the κ- μ extreme model is capable of accounting for fading variations even at low signal-to-noise values. The offered results are particularly useful in assessing the effect of fading in ED-based cognitive radio communication systems; therefore, they can be used in quantifying the associated tradeoffs between sensing performance and energy efficiency in cognitive radio networks.
TL;DR: This paper is the first to propose an evolving graph-based reliable routing scheme for VANETs to facilitate quality-of-service (QoS) support in the routing process and demonstrates, through the simulation results, that the proposed scheme significantly outperforms the related protocols in the literature.
Abstract: Vehicular ad hoc networks (VANETs) are a special form of wireless networks made by vehicles communicating among themselves on roads. The conventional routing protocols proposed for mobile ad hoc networks (MANETs) work poorly in VANETs. As communication links break more frequently in VANETs than in MANETs, the routing reliability of such highly dynamic networks needs to be paid special attention. To date, very little research has focused on the routing reliability of VANETs on highways. In this paper, we use the evolving graph theory to model the VANET communication graph on a highway. The extended evolving graph helps capture the evolving characteristics of the vehicular network topology and determines the reliable routes preemptively. This paper is the first to propose an evolving graph-based reliable routing scheme for VANETs to facilitate quality-of-service (QoS) support in the routing process. A new algorithm is developed to find the most reliable route in the VANET evolving graph from the source to the destination. We demonstrate, through the simulation results, that our proposed scheme significantly outperforms the related protocols in the literature.
TL;DR: An underlay cognitive network where the quality of service (QoS) of the secondary link is maintained by triggering an opportunistic regenerative relaying once it falls under an unacceptable level is investigated.
Abstract: Cooperative relay technology has recently been introduced into cognitive radio (CR) networks to enhance the network capacity, scalability, and reliability of end-to-end communication. In this paper, we investigate an underlay cognitive network where the quality of service (QoS) of the secondary link is maintained by triggering an opportunistic regenerative relaying once it falls under an unacceptable level. Analysis is conducted for two schemes, referred to as the channel-state information (CSI)-based and fault-tolerant schemes, respectively, where different amounts of CSI were considered. We first provide the exact cumulative distribution function (cdf) of the received signal-to-noise ratio (SNR) over each hop with colocated relays. Then, the cdf's are used to determine a very accurate closed-form expression for the outage probability for a transmission rate R. In a high-SNR region, a floor of the secondary outage probability occurs, and we derive its corresponding expression. We validate our analysis by showing that the simulation results coincide with our analytical results in Rayleigh fading channels.
TL;DR: It is shown that, for many system setups, a properly designed SM-MIMO outperforms, with lower decoding complexity, state-of-the-art MIMO.
Abstract: In this paper, we contribute to the theoretical understanding, analysis, and design of spatial modulation multiple-input-multiple-output (SM-MIMO) systems for transmit diversity without channel state information at the transmitter. The contribution is threefold: 1) The achievable transmit diversity of SM-MIMO is analytically studied by analyzing the impact of various design parameters, notably spatial constellation diagram and shaping filters at the transmitter; 2) the design of SM-MIMO providing transmit diversity and maximum-likelihood (ML) optimum single-stream decoding is investigated; and 3) via Monte Carlo simulations, a comprehensive performance assessment of SM-MIMO against state-of-the-art MIMO (e.g., spatial multiplexing, orthogonal space-time block codes, Golden code, and double space-time transmit diversity) is conducted. It is shown that, for many system setups, a properly designed SM-MIMO outperforms, with lower decoding complexity, state-of-the-art MIMO. In particular, SM-MIMO is particularly useful in the downlink, where many antenna elements (with only few of them active) are available at the transmitter, and few antenna elements are available at the receiver.
TL;DR: A novel method for the simultaneous optimization of energy management and powertrain component sizing of a parallel plug-in hybrid electric vehicle (PHEV) allows the study of the effect of some performance requirements, i.e., acceleration, top speed, and all-electric range, on the component sizes and total cost.
Abstract: This paper introduces a novel method for the simultaneous optimization of energy management and powertrain component sizing of a parallel plug-in hybrid electric vehicle (PHEV). The problem is formulated as a convex optimization problem to minimize an objective function, which is a weighted sum of operational and component costs. The operational cost includes the consumed fossil fuel and electrical energy, whereas the component cost includes the cost of the battery, electric motor (EM), and internal combustion engine (ICE). The powertrain model includes quadratic losses for the powertrain components. Moreover, the combustion engine and the electric motor losses are assumed to linearly scale with respect to the size and the losses of baseline components. The result of the optimization is the variables of the global optimal energy management for every time instant and optimal component sizes. Due to the dependency of the result on the driving cycle, a long real-life cycle with its charging times is chosen to represent a general driving pattern. The method allows the study of the effect of some performance requirements, i.e., acceleration, top speed, and all-electric range, on the component sizes and total cost.
TL;DR: PFQ-AODV is a flexible, portable, and practicable solution for routing in VANETs that learns the optimal route by employing a fuzzy constraint Q-learning algorithm based on ad hoc on-demand distance vector (A ODV) routing.
Abstract: Vehicular ad hoc networks (VANETs) have been attracting interest for their potential uses in driving assistance, traffic monitoring, and entertainment systems. However, due to vehicle movement, limited wireless resources, and the lossy characteristics of a wireless channel, providing a reliable multihop communication in VANETs is particularly challenging. In this paper, we propose PFQ-AODV, which is a portable VANET routing protocol that learns the optimal route by employing a fuzzy constraint Q-learning algorithm based on ad hoc on-demand distance vector (AODV) routing. The protocol uses fuzzy logic to evaluate whether a wireless link is good or not by considering multiple metrics, which are, specifically, the available bandwidth, link quality, and relative vehicle movement. Based on an evaluation of each wireless link, the proposed protocol learns the best route using the route request (RREQ) messages and hello messages. The protocol can infer vehicle movement based on neighbor information when position information is unavailable. PFQ-AODV is also independent of lower layers. Therefore, PFQ-AODV provides a flexible, portable, and practicable solution for routing in VANETs. We show the effectiveness of the proposed protocol by using both computer simulations and real-world experiments.
TL;DR: Five traffic rerouting strategies designed to be incorporated in a cost-effective and easily deployable vehicular traffic guidance system that reduces travel time are presented and can significantly improve the traffic even if many drivers ignore the guidance or if the system adoption rate is relatively low.
Abstract: Traffic congestion causes driver frustration and costs billions of dollars annually in lost time and fuel consumption. This paper presents five traffic rerouting strategies designed to be incorporated in a cost-effective and easily deployable vehicular traffic guidance system that reduces travel time. The proposed strategies proactively compute individually tailored rerouting guidance to be pushed to vehicles when signs of congestion are observed on their route. The five proposed strategies are the dynamic shortest path (DSP), the A* shortest path with repulsion (AR*), the random k shortest path (RkSP), the entropy-balanced kSP (EBkSP), and the flow-balanced kSP (FBkSP). Extensive simulation results show that the proposed strategies are capable of reducing the travel time as much as a state-of-the-art dynamic traffic assignment (DTA) algorithm while avoiding the issues that make DTA impractical, such as the lack of scalability and robustness, and high computation time. Furthermore, the variety of proposed strategies allows tuning the system to different levels of tradeoffs between rerouting effectiveness and computational efficiency. In addition, the proposed traffic guidance system can significantly improve the traffic even if many drivers ignore the guidance or if the system adoption rate is relatively low.