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Extensive literature discusses traction control system designs for electric vehicles. In general, the proposed control structures do not include consideration of the actuation dynamics, which are especially important for vehicles with on-board drivetrains, usually characterized by significant torsional dynamics of the half-shafts. This paper compares the performance of a selection of traction controllers from the literature, with that of PID and \boldsymbolH_\infty control structures specifically designed for on-board electric drivetrains. The analysis in the frequency domain and the simulation results in the time domain show the significant performance improvement provided by the control system designs considering the actuation dynamics. [ABSTRACT FROM AUTHOR]

Current consumption measurements are useful in a wide variety of applications, including power monitoring and fault detection within a lithium-ion battery management system (BMS). This measurement is typically taken using either a shunt resistor or a Hall-effect current sensor. Although both methods have achieved accurate current measurements, shunt resistors have inherent power loss and require isolation circuitry, and Hall-effect sensors are generally expensive. This paper explores a novel alternative to sensing battery current by measuring terminal voltages and cell temperatures and using an unknown input observer to estimate the battery current. An accurate model of a LiFePO4 cell is created, validated, and then used to characterize a model of the proposed current estimation technique. Finally, the current estimation technique is implemented in hardware and tested in an online BMS environment. Results show that the current estimation technique is sufficiently accurate for a variety of applications, including fault detection and power profiling. [ABSTRACT FROM AUTHOR]

A flexible, automatic, reliable, and safe charging and discharging system for electric vehicles (EVs) and hybrid EVs is crucial in vehicle-to-grid (V2G) operation. The bidirectional inductive wireless power transfer system (BIWPTS) is an ideal solution in this situation. With authentic power-flow models for the BIWPTS in hand, designers and operators can predict, optimize, and analyze the interaction performance between the EV and the grid. Thus, this paper presents a harmonics-based theoretical power-flow model for BIWPTS for charging and discharging EVs in V2G applications. The proposed model provides accurate estimation for the active and reactive power flow during V2G and grid-to-vehicle modes. Moreover, the impact of the circuit losses and harmonics, due to the high-frequency power inverters, on the power-flow performance is investigated. This effect is stated mathematically by four novel power-flow criteria formulas. New conditions for achieving maximum active and reactive power, and unity and zero power-factor system operation, are also developed. The sensitivity of the circuit performance to the variation of its parameters has been studied. For validation purposes, a computer-based model and experimental platform of BIWPTS for EV applications are developed and tested. The comparison between the theoretical, simulation, and experimental results verified the proposed models and analysis. [ABSTRACT FROM PUBLISHER]

Vehicular ad hoc networks (VANETs) can significantly improve traffic safety and efficiency. The basic idea is to allow vehicles to send traffic information to roadside units (RSUs) or other vehicles. Vehicles have to be prevented from some attacks on their privacy and misuse of their private data. For this reason, security and privacy preservation issues are important prerequisites for VANETs. The identity-based batch verification (IBV) scheme has been recently proposed to make VANETs more secure and efficient for practical use. In this paper, we point out that the current IBV scheme has some security risks. We introduce an improved scheme that can satisfy the security and privacy desired by vehicles. The proposed IBV scheme provides the provable security in the random oracle model. In addition, the batch verification of the proposed scheme needs only a small constant number of pairing and point multiplication computations, independent of the number of messages. We show the efficiency advantages of the proposed scheme through performance evaluations in terms of computation delay and transmission overhead. Moreover, the extensive simulation is conducted to verify the efficiency and applicability of the proposed scheme in the real-world road environment and vehicular traffic. [ABSTRACT FROM PUBLISHER]

In typical wireless networks, multihop communication is a method used to establish connectivity between distant nodes. Adapting this technique to vehicular networks requires bypassing several challenging constraints imposed by the nature of vehicular environments (e.g., high mobility and speeds and repetitive link disruptions). This paper revolves around establishing a multihop connectivity path between an isolated source vehicle $S$ and a faraway gateway roadside unit (RSU) $D$ through cooperative vehicles serving as intermediate relays. A stochastic model is formulated for the purpose of deriving an expression for the probability of the existence of a connectivity path between $S$ and $D$. Then, the dynamic changes in the network topology are carefully examined to present a tight upper bound for the average end-to-end packet delivery delay. Finally, taking into account the inherent contention-based nature of the employed IEEE 802.11p medium access control (MAC) protocol, together with several other limiting factors such as relay unavailability and hidden terminals, the per-hop and the end-to-end throughput expressions are presented. Extensive simulations are conducted for the purpose of validating the proposed model and examining the system's performance. [ABSTRACT FROM PUBLISHER]

Accurate information on vehicle longitudinal and lateral velocities is vital for the efficient operation of many vehicle control systems. In this paper, an estimation structure to simultaneously estimate vehicle velocities in longitudinal and lateral directions is developed and experimentally validated. This structure includes two parallel estimators: The first estimator is a kinematic-based observer for longitudinal velocity estimation, and the second is a combination of a kinematic-based observer and an inverse tire model to estimate vehicle lateral velocity. The proposed structure can effectively handle the additive biases, which are common in a vehicle's stock accelerometers' signals, and provide an accurate estimate of vehicle velocities when one (or more than one) wheel experiences excessive slippage. Additionally, the proposed structure is not sensitive to changes in parameters of the tire model and vehicle mass. The performance of this estimation structure is validated by experimental studies. [ABSTRACT FROM PUBLISHER]

In this paper, a new mathematical approach for the analysis and optimization of cellular-enabled low-energy mobile devices with simultaneous wireless information and power transfer capabilities is introduced. The proposed methodology relies on modeling the locations of the base stations as points of a spatial Poisson point process, and it leverages stochastic geometry for system-level analysis. The tradeoffs emerging from simultaneous wireless information and power transfer transmission are characterized through the concept of “feasibility regions” and are quantified through the joint cumulative distribution function of harvested power and rate. To gain insight on the achievable performance, in addition, an upper bound is proposed, and its accuracy is discussed. The system model encompasses a realistic channel model that accounts for line-of-sight (LOS) and non-LOS (NLOS) links, different cell association criteria, practical receivers based on time switching and power splitting schemes, and directional beamforming. The analysis shows that optimal values for the time switching and power splitting ratios exist and that directional beamforming and network densification are capable of enhancing system performance. More specifically, high directional antennas lead cellular networks to operate in the noise-limited regime, which is proved to provide optimal performance, and because of the existence of LOS and NLOS links, an optimal deployment density for the base stations is proven to exist for typical system setups. [ABSTRACT FROM PUBLISHER]

Multimedia streaming over multihop ad hoc networks is an emerging application. Consequently, a novel routing algorithm designed for video streaming is necessary. Existing video routing solutions focus on technical parameters such as bandwidth, jitter, delay, etc. However, these parameters are not perfectly correlated to the quality of experience (QoE) perceived by users. In this paper, we study a QoE-based routing problem in multihop wireless ad hoc networks. We use the pseudosubjective quality assessment (PSQA) tool to derive the mean opinion score (MOS). As PSQA is an implicit nonlinear function, we use a linearization method to derive its approximate mathematical form. Then, we formulate the optimal QoE-based routing problem as a mixed-integer linear programming. We show major properties of feasible solutions of the problem and then exploit them to propose a heuristic algorithm. The results show that the proposed algorithm can enhance resource utilization and the users' experience with wireless networks. Moreover, the feasible solution can be reached in real time using the proposed heuristic approach. [ABSTRACT FROM PUBLISHER]

Millimeter-wave (mmWave) cellular systems are power limited and susceptible to blockages. One promising approach to increasing mmWave connectivity and range is to use relays. Device-to-device (D2D) communications open the door to the vast opportunities of D2D relaying for mmWave cellular systems. In this paper, we study how to select a good relay for a given source–destination pair in a two-hop mmWave cellular system, where the mmWave links are subject to random Bernoulli blockages. In such a system, probing more relays could potentially lead to the discovery of a better relay but at the cost of more overhead. We find that the throughput-optimal relay probing strategy is a pure threshold policy: The system can stop relay probing once the achievable spectral efficiency of the currently probed two-hop link exceeds an optimized threshold. We further characterize the optimal spectral efficiency threshold via a fixed-point equation. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new transmission scheme, called integer forcing-and-forward (IFF), for communications among multipair multiple-antenna users in which each pair exchanges their messages with the help of a single multiple-antenna relay in the multiple-access and broadcast phases. The proposed scheme utilizes integer forcing linear receiver (IFLR) at the relay, which uses equations, i.e., linear integer combinations of messages, to harness intrapair interference. Accordingly, we propose the design of a mean square error (MSE)-based transceiver, including precoder and projection matrices for the relay and users, assuming that perfect channel state information (CSI) is available. In this regard, in the multiple-access phase, we introduce two new MSE criteria for the related precoding and filter designs, i.e., the sum of the equations MSE (Sum-Equation MSE) and the maximum of the equations MSE (Max-Equation MSE), to exploit the equations in the relay. In addition, the convergence of the proposed criteria is proven as well. Moreover, in the broadcast phase, we use the two traditional MSE criteria, i.e., the sum of the users% mean square errors (Sum MSE) and the maximum of the users% mean square errors (Max MSE), to design the related precoding and filters for recovering the relay%s equations by the users. Then, we consider a more practical scenario with imperfect CSI. For this case, the IFLR receiver is modified, and another transceiver design is proposed, which takes into account the effect of channel estimation error. We evaluate the performance of our proposed strategy and compare the results with the conventional amplify-and-forward (AF) and denoise-and-forward (DF) strategies for the same scenario. The results indicate the substantial superiority of the proposed strategy in terms of outage probability and sum rate. [ABSTRACT FROM PUBLISHER]

Searching for the minimum exposure path (MEP) is one of the critical issues in wireless sensor networks (WSNs). The exposure corresponding to the MEP is one indicator to measure the coverage quality of a WSN, reflecting how well a mobile target is monitored through the sensing field. The classical methods, such as the grid-based method and the Voronoi-diagram-based method, are not accurate enough to obtain the MEP, are too complex, and are not applicable to networks with heterogeneous sensor nodes, a large number of sensor nodes, or all-sensor field intensity function. To overcome these challenges, a numerical functional extreme (NFE) model is proposed for the MEP problem in this paper. The NFE model is a high-dimensional and nonlinear optimization problem. To efficiently solve this problem, based on the characteristics of the sensor node coverage, a new crossover operator is designed, a new local search scheme is proposed, and an upside-down operator to escape from local optima is developed. Integrating all of these, a hybrid genetic algorithm (HGA) is proposed for the NFE model, and its global convergence with probability one is proven. An extensive collection of experiments were conducted, and the results indicate that the proposed NFE model and the developed HGA can improve the solution accuracy and can be applicable to not only the case with heterogeneous sensors but the case with a large number of sensor nodes and all-sensor field intensity function as well. [ABSTRACT FROM PUBLISHER]

This paper analyzes the information delivery delay for the purpose of roadside unit (RSU) deployment in a vehicular ad hoc network (VANET) with intermittent connectivity. A mathematical model is developed to describe the relationship between the average delay for delivering road condition information and the deployment distance between two neighbor RSUs. The derived mathematical model considers a sparse highway scenario where two neighbor RSUs are deployed at a distance without a direct connection, and vehicles are sparsely distributed on the road with road condition information randomly generated between the two neighbor RSUs. Moreover, the model takes into account the vehicle speed, the vehicle density, the likelihood of an incident, and the deployment distance between two neighbor RSUs. The correctness and accuracy of the derived mathematical model is verified, and the impacts of different parameters on the average information delivery delay are investigated through simulation results. Given an information delivery delay constraint for time-critical applications, this model can be used to estimate the maximum deployment distance allowed between two neighbor RSUs, which can provide a reference for the deployment of RSUs in such scenarios. [ABSTRACT FROM PUBLISHER]

This paper develops efficient compute-and-forward (CMF) schemes in multiuser multirelay networks. To solve the rank failure problem in CMF setups and to achieve full diversity of the network, we introduce two novel CMF methods, namely, extended CMF and successive CMF. The former, having low complexity, is based on recovering multiple equations at relays. The latter utilizes successive interference cancelation (SIC) to enhance system performance compared with the state-of-the-art schemes. Both methods can be utilized in a network with different number of users, relays, and relay antennas, with negligible feedback channels or signaling overhead. We derive new concise formulations and explicit framework for the successive CMF method, as well as an approach to reduce its computational complexity. Our theoretical analysis and computer simulations demonstrate the superior performance of our proposed CMF methods over the conventional schemes. Furthermore, based on our simulation results, the successive CMF method yields additional signal-to-noise ratio (SNR) gains and shows considerable robustness against channel estimation error (CEE), compared with the extended CMF method. [ABSTRACT FROM PUBLISHER]

In this paper, novel closed-form expressions for the level crossing rate and average fade duration of $\kappa-\mu$ shadowed fading channels are derived. The new equations provide the capability of modeling the correlation between the time derivative of the shadowed dominant and multipath components of the $\kappa-\mu$ shadowed fading envelope. Verification of the new equations is performed by reduction to a number of known special cases. It is shown that as the shadowing of the resultant dominant component decreases, the signal crosses lower threshold levels at a reduced rate. Furthermore, the impact of increasing correlation between the slope of the shadowed dominant and multipath components similarly acts to reduce crossings at lower signal levels. The new expressions for the second-order statistics are also compared with field measurements obtained for cellular device-to-device and body-centric communication channels, which are known to be susceptible to shadowed fading. [ABSTRACT FROM PUBLISHER]

This paper aims to analyze two major quantities related to the performances of IEEE 802.11p-based vehicle ad hoc networks (VANETs): the mean number of simultaneous transmitters and the distribution of the distance between them. The first quantity is directly related to the network capacity as it limits the number of frames transmitted in the network at the same time. The second quantity is a crucial element to describe interference generated by the simultaneous transmitters and to deduce important wireless properties such as the signal-to-noise-plus-interference ratio, bit error rate (BER), and the frame error rate (FER). We propose two different models to approach these quantities: an extension of the Rényi's packing model and a Markovian point process. Our theoretical propositions are compared with the performance of simulations with the network simulator ns-3 and show good agreement. In addition, we have also incorporated two new models in ns-3. The radio model has been set according to real experimentations. These experimentations involved two vehicles equipped with IEEE 802.11p wireless cards. Moreover, we used a realistic vehicle traffic simulator, emulating highway traffic, which we have combined with ns-3. [ABSTRACT FROM AUTHOR]

In this paper, an intrusion detection system (IDS) for vehicular ad hoc networks (VANETs) is proposed and evaluated. The IDS is evaluated by simulation in the presence of rogue nodes (RNs) that can launch different attacks. The proposed IDS is capable of detecting a false information attack using statistical techniques effectively and can also detect other types of attacks. First, the theory and implementation of the VANET model that is used to train the IDS is discussed. Then, an extensive simulation and analysis of our model under different traffic conditions is conducted to identify the effects of these parameters in VANETs. In addition, the extensive data gathered in the simulations are presented using graphical and statistical techniques. Moreover, RNs are introduced in the network, and an algorithm is presented to detect these RNs. Finally, we evaluate our system and observe that the proposed application-layer IDS based on a cooperative information exchange mechanism is better for dynamic and fast-moving networks such as VANETs, as compared with other techniques available. [ABSTRACT FROM AUTHOR]

As an important scatterer in vehicle-to-X (V2X) communication scenarios, traffic signs frequently appear along roads and can affect the propagation channel appreciably. Although traffic signs can be considered as an important part in such scattering scenarios, knowledge of their bistatic radar cross section (RCS) is still absent. This paper presents an integration of the analytical models, full-wave simulation, measurement, and validation of the bistatic RCS of three types of representative traffic signs for V2X communication. First, the total RCS of the traffic signs is calculated by summing up the field contributions of the individual parts with their own phase relationships. Furthermore, simulations using full-wave analysis are performed to validate the analytical models for RCSs in the far field under plane-wave illumination. Since the high-frequency calculation techniques used here are applicable only under plane-wave excitation, the traffic-sign parts are divided into small elements, where this precondition is locally valid. Measurements are conducted in an open-area test site at the National Metrology Institute of Germany to validate the model in the near field (spherical wave from a point source). Corresponding comparisons show that the method of decomposing the traffic sign into small parts supports an effective way to embed the analytical models into ray-tracing tools, thus improving propagation modeling in V2X communication. [ABSTRACT FROM AUTHOR]

At present, battery-charging operations constitute one of the most critical obstacles toward a large-scale uptake of electric mobility (EM), due to performance issues and implementation complexities. Although several solutions based on the utilization of information and communication technologies and on mobile applications have been investigated to assist electric vehicle (EV) drivers and to coordinate charging operations, there is still the problem of how to evaluate and validate such solutions on realistic scenarios, due to the lack of accurate simulators integrating vehicular mobility, wireless communication, and battery charging/discharging models. In this paper, we attempt to fill this gap by proposing a novel EV simulation platform that can assist in the predeployment of charging infrastructures and services on realistic large-scale EM scenarios. The simulation platform, which is realized within the ARTEMIS EU project “Internet of Energy for Electric Mobility,” supports two utilization modes, i.e., evaluation of EM scenarios and immersive emulation of EM-related mobile applications, due to a semantic architecture through which virtual and real components can be integrated in a seamless way. We provide three major contributions with respect to the state of the art. First, we extend the existing cosimulation platform composed of SUMO (a vehicular traffic simulator) and OMNET++ (a network simulator) with realistic models of EVs, electric vehicle supply equipment, and ontology-based communication protocols that enable the deployment of city-wide mobile services (e.g., charging reservation). Second, we validate the battery model against the consumptions data of target EVs, and we evaluate the operations of EVs on a large-scale scenario (the city of Bologna, Italy), by analyzing the effectiveness of the charging reservation process and the resulting impact to the smart grid. Finally, we introduce the Mobile Application Zoo, which is a sandbox through which EM-related mobile applications can be seamlessly integrated within the simulation platform to be validated on virtual environments before their deployment on real scenarios, and we describe the implementation of an Android application for battery monitoring and charging reservation. [ABSTRACT FROM AUTHOR]

This paper proposes a particle filter framework to perform an online estimation of the unsaturated buffers of the stations in the IEEE 802.11 distributed coordination function (DCF) network. Using this framework, an access point can adapt flow control to its serving stations and configure related parameters dynamically, thus improving the system throughput and reducing the packet latency. Current research analyzing the unsaturated condition in the IEEE 802.11 DCF network is based on the steady-state model, whereas this proposed method is devoted to the dynamic estimation for the probability distribution of the unsaturated buffer in the stations, in either homogeneous or heterogeneous networks. This study also employs theoretical support from Bayesian inference to the particle-filtering algorithm. The estimation accuracy and effectiveness were evaluated via root mean square error (RMSE) and time complexity. Furthermore, we considered different network loads and convergence speeds in our analysis. Our analysis demonstrated that the proposed dynamic estimation scheme has greater awareness of the traffic changes in the varying wireless network when compared with the traditional static traffic model. [ABSTRACT FROM AUTHOR]

The discontinuous reception (DRX) mechanism is adopted in Long-Term Evolution (LTE) systems as a core technology to prolong the battery lifetime of user equipment (UE). With the development of mobile Internet, there is an increasingly urgent need to optimize the DRX performance to accommodate the emerging applications. In this paper, to describe the self-similarity exhibited by the applications, a truncated-Pareto-distributed arrival traffic model is introduced into the LTE-DRX modeling framework. With this premise, a DRX analytical model based on a discrete-time semi-Markov process (DTSMP) is established. Using the proposed model, the performance of DRX operations under certain configurations can be evaluated precisely. To deploy it in practical use, we have designed an online power-saving strategy (OPSS) to improve the energy efficiency of the UE. The OPSS is conducted in two phases: estimation and optimization. In the first phase, several derived statistical estimators are deployed to capture the fluctuations of the traffic conditions and DRX operations. It is proved that these estimators could unbiasedly estimate the statistics within just 1 s. In the second phase, the DRX configuration is optimized by considering the trade off between the packet delay and the power-saving performance. Solid simulations are conducted to verify the accuracy of the DRX analytical model and to evaluate the efficiency of the OPSS. The well-matched results demonstrate that the analytical model is correctly derived. Moreover, we have proved that the proposed OPSS could outperform the conventional LTE DRX mechanism in terms of both energy conservation and packet delay. [ABSTRACT FROM AUTHOR]

Precision control of clutch pressure is critical in heavy-duty automatic transmission applications in which the fast response of the clutch actuator is required. A conventional clutch actuator system with a pressure-reducing valve (PRV) is not applicable in this kind of application due to the fact that a large transient flow and high output power for power-on shift are necessary. In this paper, a pilot-operated PRV is developed for heavy-duty automatic transmission systems. The developed PRV can make the clutch actuator system have a fast response and a high flow capacity simultaneously. The PRV utilizes a three-stage structure with a high-speed proportional solenoid valve (PSV) as the pilot stage to do the tradeoff between the valve response and the flow capacity. First, a linearized input–output dynamic analytical model for the clutch pressure control system is developed based on fluid dynamics. Then, the parameters are identified, and the model is validated by using experimental data. For the validated input–output model, both open- and closed-loop (feedback) pressure control strategies are designed and implemented in a test setup. It infers from the experimental results that the feedback control can lead to excellent control precision. The developed clutch actuator system is applicable for heavy-duty automatic transmissions. [ABSTRACT FROM AUTHOR]

This paper discusses the energy- and spectral-efficient transmissions for delay-constrained wireless communication systems utilizing hybrid automatic repeat request (HARQ). Three design metrics are considered: 1) the energy efficiency (EE) measured by the average energy required to successfully deliver one information bit from a source to its destination; 2) the spectral efficiency (SE) defined as the average data rate per unit bandwidth; and 3) the average energy per bit normalized by the SE. Optimum system designs with respect to different metrics are developed by analytically identifying the sequence of transmission energy that should be employed at different retransmissions. The optimum energy distributions are calculated with a new backward sequential calculation method, where the energy at each retransmission round is expressed as a closed-form expression of a wide range of practical system parameters, such as circuit power and channel coding in the physical layer and frame length and protocol overhead in the media access control layer. Reducing the SE-normalized energy per bit yields a balanced tradeoff between EE and SE. Numerical results demonstrate that reducing the SE-normalized energy per bit achieves a 26.7% SE gain over the system that minimizes the average energy per bit; yet, the EE of the two systems is almost identical. [ABSTRACT FROM AUTHOR]

This paper aims to present a simple but robust model characterizing the frequency-dependent transfer function of an in-vehicle ultrawideband (UWB) channel. A large number of transfer functions spanning the UWB (3–11 GHz) are recorded inside the passenger compartment of a four-seated sedan. It is found that the complex transfer function can be decomposed into two terms, the first term being a real-valued long-term trend that characterizes frequency dependence with a power law and the second term forming a complex correlative discrete series that may be represented via an autoregressive (AR) model. An exhaustive simulation framework is laid out based on empirical equations characterizing trend parameters and AR process coefficients. The simulation of the transfer function is straightforward as it involves only a handful of variables; however, it is in good agreement with the actual measured data. The proposed model is further validated by comparing different channel parameters, such as coherence bandwidth, power delay profile, and root-mean-square delay spread, obtained from raw and synthetic data sets. It is also shown how the model can be compared with existing time-domain Saleh–Valenzuela-influenced models and related IEEE standards. [ABSTRACT FROM PUBLISHER]

Supercapacitors have received increasing interest from the vehicular community due to their high power density and compact size, making them good candidates for high-performance applications, such as electric/hybrid vehicles and railway transportation. However, supercapacitors have a finite lifespan due to the occurrence of unwanted physical changes. These changes are usually irreversible, which yield capacitance loss and performance deterioration, regardless of the components' usage. Most of the available aging models are based on particular assumptions of capacitance loss. Unlike these techniques, this paper considers different capacitance aging models and compared them to verify their accuracy. In addition, a generalized prediction aging model for the calendar life is presented based on chemical reactions causing degradation. The performance of the proposed model is compared with the classical and modified Eyring's law. To better show the effectiveness of the proposed prediction aging strategy and capacitance loss models, 12 supercapacitors are tested under 12 different operating temperatures and bias voltage conditions. Experimental results highlight the high estimation accuracy of the proposed prediction aging model under various operating conditions. [ABSTRACT FROM PUBLISHER]

This paper describes an integrated chassis control (ICC) algorithm of differential braking, front/rear traction torque, and active roll moment control. The integrated control algorithm is designed to maximize driving velocity and enhance vehicle lateral stability in cornering. The target longitudinal acceleration is determined based on the driver's intention and vehicle current status to ensure vehicle lateral stability in high-speed maneuvering. An optimization-based control allocation strategy is used to distribute the actuator control inputs optimally under consideration of tire and vehicle limitations. Closed-loop simulations of a driver–vehicle–controller system were conducted to investigate the performance of the proposed control algorithm. The performance of the ICC has been compared with those of individual chassis control systems, such as electronic stability control, four-wheel drive (4WD), and active roll control system. The simulation results show that the proposed ICC algorithm improves the performance in high-speed cornering with respect to driving speed without losing stability, compared with individual chassis control systems. [ABSTRACT FROM PUBLISHER]

An electrical variable transmission (EVT) is an electromagnetic device with dual mechanical and electrical ports. In hybrid electric vehicles (HEVs), it is used to split the power to the wheels in a part coming from the combustion engine and a part exchanged with the battery. The most important feature is that the power splitting is done in an electromagnetic way. This has the advantage over mechanical power splitting devices of reduced maintenance, high efficiency, and inherent overload protection. This paper gives a conceptual framework on how the torque on both rotors of the EVT can be simultaneously controlled by using a field-oriented control (FOC) scheme. It describes an induction-machine-based EVT model in which no permanent magnets are required, based on classical machine theory. By use of a predictive current controller to track the calculated current reference values, a fast and accurate torque control can be achieved. By selecting an appropriate value for the flux coupled with the squirrel-cage interrotor, the torque can be controlled in various operating points of power split, generation, and pure electric mode. The conclusions are supported by simulations and transient finite-element calculations. [ABSTRACT FROM PUBLISHER]

Hybrid electric vehicles (HEVs) are capable of improving fuel economy with reduced emissions over traditional vehicles powered only by the internal combustion (IC) engine. HEV durability is often significantly limited by the useful life of the battery, and battery life can be significantly reduced if the battery is often operated outside its allowed discharging/charging limits. This could occur particularly at extremely low temperature, leading to permanent damage and reduced useful life of the battery. To reduce battery overdischarging duration for extended battery life, this paper proposes a stochastic predictive boundary management (SPBM) strategy to proactively make the engine power available to the powertrain based on the predicted torque requirement and its prediction error variance. The effectiveness of the proposed SPBM is validated in simulations, and its performance is compared with the baseline power follower control strategy (PFCS) and predictive boundary management (PBM) under five typical driving cycles with selected initial battery temperature. The simulation results show that the proposed control strategy dramatically reduces the battery overdischarging energy under all five typical driving cycles over both PFCS and PBM, where the average reduction is 82% under aggressive US06 and ARB02 driving cycles, 67% under NYCC and FTP driving cycles, and 37% under the IM240 highway driving cycle. Note that the adaptive prediction and its error variance can be calculated in real time with fairly low computational load. [ABSTRACT FROM PUBLISHER]

Accurate battery state-of-charge (SOC) estimation in real time is desired in many applications. Among other methods, the extended Kalman filter (EKF) allows for high-accuracy real-time tracking of the SOC. However, an accurate SOC model is needed to guarantee convergence. Additionally, knowledge of the statistics of the process noise and the measurement noise is needed for high-accuracy SOC estimation. In this paper, two methods, namely, the multiple-model EKF (MM-EKF) and the autocovariance least squares technique, are proposed for estimating the SOC of lithium-ion (Li-ion) battery cells. The first method has the advantage of minimizing the EKF algorithm's dependence on the correct assumptions of the measurement's noise statistics, thus, minimizing the impact of model mismatch. The MM-EKF assumes a number of hypotheses for the unknown measurement noise covariance. An EKF is assigned for each assumed measurement noise covariance. The SOC estimate is then obtained by probabilistically summing up the estimates of the hypothesized EKFs. On the other hand, the second method assumes that the measurement noise is unknown and determines its value from the statistics of the EKF. Given an initial and possibly wrong assumption of the measurement noise covariance, the method accounts for possible correlation in the measurement innovations. The estimated measurement noise covariance is subsequently used to obtain an optimal SOC estimate. The proposed methods are evaluated and compared with the conventional EKF method on experimental test data obtained from a 3.6-V Li-ion battery cell. [ABSTRACT FROM PUBLISHER]

This paper aims at developing integrated onboard diagnosis and fault-tolerant control methods with experimental validation for a urea selective catalyst reduction (SCR) aftertreatment system to reduce vehicle tailpipe emissions. Diagnostics are performed for an SCR urea injection system by estimating and monitoring the injected urea mass flow with no need for a costly physical flow sensor. The estimation is derived from a first-principle-based urea injection system model, and the model parameters are identified by using system identification. During vehicle transient maneuvers, a Kalman filter (KF) is formulated to further reduce the estimation noise and improve diagnostic robustness. Once an injection fault is detected, an adaptation control algorithm is applied to compensate the urea injection command, thus correcting certain types of urea under/overdosing faults and maintaining the SCR \mboxNOx conversion performance. These methods have been validated through vehicle tests by utilizing an onboard rapid prototyping control system. [ABSTRACT FROM PUBLISHER]

In this paper, a higher fidelity battery equivalent circuit model incorporating asymmetric parameter values is presented for use with battery state estimation (BSE) algorithm development; particular focus is given to state-of-power (SOP) or peak power availability reporting. A practical optimization-based method is presented for model parameterization fitting. Two novel model-based SOP algorithms are proposed to improve voltage-limit-based power output accuracy in larger time intervals. The first approach considers first-order extrapolation of resistor values and open-circuit voltage (OCV) based on the instantaneous equivalent circuit model parameters of the cell. The second proposed approach, which is referred to as multistep model predictive iterative (MMPI) method, incorporates the cell model in a model predictive fashion. Finally, a SOP verification methodology is presented that incorporates drive cycle data to realistically excite the battery model. Simulation results compare the proposed SOP algorithms to conventional approaches, where it is shown that higher accuracy can be achieved for larger time horizons. [ABSTRACT FROM PUBLISHER]

Low-data-rate wireless networks can be deployed for physical intrusion detection and localization purposes. The intrusion of a physical object (or human) will disrupt the radio-frequency magnetic field and can be detected by observing the change in radio attenuation. This gives the basis for the radio tomographic imaging technology, which has recently been developed for passively monitoring and tracking objects. Due to noise and the lack of knowledge about the number and the sizes of intruding objects, multiobject intrusion detection and localization is a challenging issue. This paper proposes an extended variational Bayesian Gaussian mixture model (VB-GMM) algorithm in treating this problem. The extended VB-GMM algorithm applies a Gaussian mixture model to model the changed radio attenuation in a monitored field due to the intrusion of an unknown number of objects and uses a modified version of the variational Bayesian approach for model estimation. Real-world data from both outdoor and indoor experiments (using the radio tomographic imaging technology) have been used to verify the high accuracy and the robustness of the proposed multiobject localization algorithm. [ABSTRACT FROM AUTHOR]

In the Long-Term Evolution (LTE) mobile communication standard, there is provision for a random-access channel (RACH), which provides users the opportunity to gain access to the network. Future fourth-generation and fifth-generation systems will undoubtedly also have some similar scheme. Therefore, better understanding of such systems is desirable. To this end, we present, in this paper, an abstract representation of the RACH queue that is in the form of a leaky Markov “ball and urn” model, where the number of balls in $M$ successive bins represents the number of users contending for access after each of M$ attempts. We analyze this model by evolving a multidimensional probability distribution for the number of balls in each bin, from which the individual probabilities are then determined as marginals. In particular, the probability of the M$th bin will determine the probability of access or failure. An efficient computational scheme is developed to evolve the bin probabilities, and the results are shown to be in excellent agreement with Monte Carlo simulation, which, for , requires 10 000 times longer run time. [ABSTRACT FROM AUTHOR]

The design of optimal input for fault identification in electric ground vehicles (EGVs) that use four independent in-wheel motors and four-wheel steering is presented in this paper. As the number of motors and steering actuators increases beyond the number of controlled variables, an EGV becomes an overactuated system, which provides actuator redundancy and the possibility of fault-tolerant control in the case of faults that occur in vehicle elements, such as its sensors and actuators. To ensure the reliability of the EGV during driving, online fault identification is needed, and its performance is directly dependent on the input signals. The input can be designed using the control allocation method, which is one approach to manage actuator redundancy. The proposed control allocation maximizes the sensitivity of the system output to parameters related to the fault position, while the system output is simultaneously controlled to maintain stability and follow the desired vehicle motions, even when faults occur. Simulations using the commercial software CarSim are performed to show the effectiveness of the proposed optimal input design method for fault identification; the performance of the system is compared with the conventional white noise perturbation input with equal power. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) can efficiently offer safety-related and commercial contents for in-vehicle consumption. In this paper, we analyze the vehicle-to-vehicle (V2V) data dissemination with network coding in two-way road networks, where the vehicles move in opposite directions. In particular, depending on whether the broadcasting coverage areas overlap or not, two-way data dissemination is usually carried out in two phases, namely, the encountering phase and the separated phase. We first derive the probability mass function (pmf) of the dissemination completion time for the encountering disseminators during the encountering phase. The data dissemination velocity in the separated phase is mathematically derived. We prove that, without the help of the data dissemination in their own direction, the vehicles cannot recover all original packets from the opposite direction under a scarce handover condition. Furthermore, the dissemination slope and cache capacity of the vehicles in the proposed model are also analytically presented. Simulation results are provided to confirm the accuracy of the developed analytical results. [ABSTRACT FROM AUTHOR]

Wave propagation experiences extra loss in curved tunnels, which is highly desired for network planning. Extensive narrow-band propagation measurements are made in two types of Madrid subway tunnels (different cross sections and curvatures) with various configurations (different frequencies and polarizations). A ray tracer validated by the straight and curved parts of the measuring tunnels is employed to simulate the reference received signal power by assuming the curved tunnel to be straight. By subtracting the measured received power in the curved tunnels from the simulated reference power, the extra loss resulting from the tunnel curve is extracted. Finally, this paper presents the figures and tables quantitatively reflecting the correlations between the extra loss and radius of curvature, frequency, polarization, and cross section, respectively. The results are valuable for statistical modeling and the involvement of the extra loss in the design and network planning of communication systems in subway tunnels. [ABSTRACT FROM AUTHOR]

The connectivity of moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) that critically influences the performance of data transmission. Due to lack of in-depth analysis of real-world vehicular mobility traces, we do not understand the connectivity in realistic large-scale urban scenarios. Specifically, the mechanism of how the mobility of networked vehicles impacts the network connectivity remains unknown. In this paper, we aim to unveil the underlying relationship between the mobility and connectivity of VANETs. To achieve this goal, we employ some key topology metrics, including component speed and component size, to characterize mobility and connectivity. In our investigation of a large-scale real-world urban mobility trace data set, we discover, to our surprise, that there exists a dichotomy in the relationship between component speed and size. This dichotomy indicates that mobility destroys the connectivity with a power-law decline when the component speed is larger than a threshold; otherwise, it has no apparent impact on connectivity. Based on this observation, we propose a mathematical model to characterize this relationship, which agrees well with empirical results. Our findings thus offer a comprehensive understanding of the relationship between mobility and connectivity in urban vehicular scenarios, and based on this, helpful guidelines can be provided in the design and analysis of VANETs. [ABSTRACT FROM AUTHOR]

Appropriate structure is the first key issue that a designer of an efficient hybrid vehicle should pay attention to. This paper presents three different structures for the concept of series–parallel structure in hybrid electric vehicles (HEVs) and compares them with each other for the application of Toyota Prius 2004 as a well-known case study. The first structure consisting of one electric motor, one electric generator, and one power split device is the conventional structure as one existing in Toyota Prius 2004 and called THS-II in the literature. The second and third structures are based on two special electric machines named transmotor-based structure (TBS) and dual-mechanical-port (DMP) machine-based structure (DMPBS), respectively. The three structures are compared by considering different aspects such as weight, occupied volume, and overall efficiency. A surface-mounted fractional-slot concentrated winding DMP machine is designed, and the design is verified by applying the finite-element analysis. Simulation results have been presented to support the accomplished comparison of the structures under study. Simulation results show that all structures have approximately equal weight, volume, and efficiency, but DMPBS is a compact, efficient, and simple way to convert the existing fossil-fuel cars into an HEV. One DMP machine prototype has been implemented, and experimental results have been included. [ABSTRACT FROM PUBLISHER]

Node clustering is a potential approach to improve the scalability of networking protocols in vehicular ad hoc networks (VANETs). High relative vehicle mobility and frequent network topology changes inflict new challenges on maintaining stable clusters. As a result, cluster stability is a crucial measure of the efficiency of clustering algorithms for VANETs. This paper presents a stochastic analysis of the vehicle mobility impact on single-hop cluster stability. A stochastic mobility model is adopted to capture the time variations of intervehicle distances (distance headways). First, we propose a discrete-time lumped Markov chain to model the time variations of a system of distance headways. Second, the first passage time analysis is used to derive probability distributions of the time periods of the invariant cluster-overlap state and cluster membership as measures of cluster stability. Third, queueing theory is utilized to model the limiting behaviors of the numbers of common and unclustered nodes between neighboring clusters. Numerical results are presented to evaluate the proposed models, which demonstrate a close agreement between analytical and simulation results. [ABSTRACT FROM PUBLISHER]

An intelligent location-aware data aggregation mechanism for real-time observation, estimation, and efficient dissemination of any kind of traffic information in vehicular ad hoc networks (VANETs) is presented in this paper. The mechanism introduces a location awareness algorithm, enabling spatiotemporal database indexing and providing the location context of the messages without the use of advanced positioning systems such as satellite navigation and digital maps. Intelligent passive clustering and adaptive broadcasting are used to minimize the number of messages exchanged, packet collisions, and network load. The incoming messages are fused by a Kalman filter, allowing the description of the traffic-related information as a system characterized by as many variables as needed, depending on the application design. The scheme allows the comparison of aggregates and single observations, which enables their merging and better overall accuracy. Old information in aggregates is removed by real-time database refreshing, thus leaving only newer relevant information for the driver to make real-time decisions in traffic. The mechanism is generic and can be used for any kind of VANET information. It is evaluated by extensive simulations to show the efficiency and accuracy. [ABSTRACT FROM PUBLISHER]

With the widespread penetration of plug-in hybrid electric vehicles (PHEVs), the overall demand on microgrids (MGs) may increase manifold in the near future. Unregulated power demands from PHEVs may increase the demand–supply gap at MGs. Thus, to keep MGs stabilize and cater the ever-growing energy demands, there is a requirement of an intelligent solution to regulate and manage PHEVs in vehicle-to-grid (V2G) environment. Keeping in view the given issues, this paper proposes a novel scheme that aims to regulate PHEVs' charging and discharging activities based on MGs' day-ahead load curves. These load curves are obtained by utilizing the existing load forecasting techniques such as fuzzy logic (FL) and artificial neural networks (ANNs). Efficient utilization of PHEVs according to these curves may play a vital role in flattening MG's load profile. Thus, the proposed scheme works by reserving resources such as time slots and charging points (CPs) for PHEVs during peak shaving and valley filling. Different algorithms pertaining to resource reservation for PHEVs have also been designed. These algorithms employ the concepts of game theory and the 0/1 knapsack problem for supporting peak shaving and valley filling, respectively. Moreover, PHEVs are also utilized when there are transitions from valley filling to peak shaving areas in the load curves and vice versa. PHEVs involved in this process have both charging and discharging capabilities and are referred to as dual-mode PHEVs. The proposed scheme has been tested with respect to various parameters, and its performance was found satisfactory. [ABSTRACT FROM PUBLISHER]

As a benefit of their inherent rateless nature, fountain codes constitute a favorable choice for protecting packet-based transmissions in the physical layer for wireless channels having varying quality. However, previous research has revealed that the performance of fountain codes substantially degrades as their block length is reduced. Three structural phenomena of the Tanner graph were identified by Mackay in the hard decoding of fountain codes on binary erasure channels (BECs), which may be referred to as having “no degree-one check nodes (CNs),” “no emerging degree-one CNs,” and “uncovered variable nodes (VNs).” In this paper, we explicitly analyzed how these structural phenomena influence their soft decoding algorithm. Furthermore, these phenomena are shown to be responsible for the high error floors when fountain codes are transmitted over noisy fading channels, particularly for the transmissions of short blocks. To eliminate the influence of these structural phenomena, we conceived a technique of generating a few specifically encoded bits with the aid of the associated Tanner graph. Simulation results have demonstrated that our improved Raptor (IRaptor) codes significantly reduce the packet loss ratio (PLR) of conventional fountain codes, despite imposing reduced low complexity. Finally, we conceive a novel adaptive hybrid automatic repeat request (HARQ) scheme based on a lookup table (LUT)-aided technique, which may adapt its coding rate for each transmission. Our simulation results demonstrated that the proposed IRaptor HARQ achieves a similar performance to the Long-Term Evolution (LTE) turbo-coded HARQ scheme or even outperforms the LTE arrangement for block length in excess of 1000 bits. [ABSTRACT FROM PUBLISHER]

This paper presents the use of nonlinear distributed estimation in a wireless system transmitting over channels with random gains. Specifically, we discuss the development of estimators and analytically determine their attainable variance for two conditions: 1) when full channel state information (CSI) is available at the transmitter and receiver; and 2) when only channel gain statistics and phase information are available. For the case where full CSI is available, we formulate an optimization problem to allocate power among each of the transmitting sensors while minimizing the estimate variance. We show that minimizing the estimate variance when the transmitter is operating in its most nonlinear region can be formulated in a manner very similar to optimizing sensor gains with full CSI and linear transmitters. Furthermore, we show that the solution to this optimization problem in most scenarios is approximately equivalent to one of two low-complexity power allocation systems. [ABSTRACT FROM AUTHOR]

This paper focuses on external volume and power consumption optimization of an electromagnetic actuator used to move pins in a two-stage variable valve lift component within an automotive engine. This system is designed to change the valve lift at each engine cycle. To achieve this optimization, three models were developed: a magnetic, an electric, and a dynamic model. They evaluate the performances of a given actuator, check if it fulfills its role, and define the springs needed for the displacement of the pins. The optimization is made using a particle swarm method and can reduce the volume up to 20% with the same electric consumption. [ABSTRACT FROM PUBLISHER]

In a vehicular ad hoc network (VANET), the performance of the communication protocol is heavily influenced by the vehicular density dynamics. However, most of the previous works on VANET performance modeling paid little attention to vehicle distribution or simply assumed homogeneous car distribution. It is obvious that vehicles are distributed nonhomogeneously along a road segment due to traffic signals and speed limits at different portions of the road, as well as vehicle interactions that are significant on busy streets. In light of the inadequacy, in this paper, we present an original methodology to study the broadcasting performance of 802.11p VANETs with practical vehicle distribution in urban environments. First, we adopt the empirically verified stochastic traffic models, which incorporate the effect of urban settings (such as traffic lights and vehicle interactions) on car distribution and generate practical vehicular density profiles. Corresponding 802.11p protocol and performance models are then developed. When coupled with the traffic models, they can predict broadcasting efficiency, delay, and throughput performances of 802.11p VANETs based on the knowledge of car density at each location on the road. Extensive simulation is conducted to verify the accuracy of the developed mathematical models with the consideration of vehicle interaction. In general, our results demonstrate the applicability of the proposed methodology on modeling protocol performance in practical signalized road networks and shed insights into the design and development of future communication protocols and networking functions for VANETs. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel tractable approach for accurately analyzing both the coverage probability and the achievable throughput of cellular networks. Specifically, we derive a new procedure referred to as the equivalent uniform-density plane-entity (EUDPE) method for evaluating the other-cell interference. Furthermore, we demonstrate that our EUDPE method provides a universal and effective means to carry out the lower bound analysis of both the coverage probability and the average throughput for various base-station distribution models that can be found in practice, including the stochastic Poisson point process (PPP) model, a uniformly and randomly distributed model, and a deterministic grid-based model. The lower bounds of coverage probability and average throughput calculated by our proposed method agree with the simulated coverage probability and average throughput results and those obtained by the existing PPP-based analysis, if not better. Moreover, based on our new definition of cell edge boundary, we show that the cellular topology with randomly distributed base stations (BSs) only tends toward the Voronoi tessellation when the path-loss exponent is sufficiently high, which reveals the limitation of this popular network topology. [ABSTRACT FROM PUBLISHER]

Hybrid powertrain provides driving torque based upon the vehicle acceleration pedal position. Due to the slow response of the internal combustion (IC) engine, the electric motor(s) (EM) often has to provide additional torque during transient vehicle operations. During cold-start operations, the available battery power is significantly reduced due to cold temperatures, and due to the starting delay of the IC engine, the battery has to provide power at the level beyond its normal operation limit for the electrical motor to meet the torque demand, which would dramatically shorten the battery life. However, if the desired powertrain torque can be predicted, the IC engine could be started early when the predicted future powertrain power is higher than the battery normal operational range, and hence, the duration of battery operated over its normal operational range could be significantly reduced, leading to extended battery life. This makes predicting multistep future torque demand, based upon current and past torque demand data, an important subject. Amid many existing studies, few research studies were focused on this subject. This paper proposes an adaptive recursive prediction algorithm to estimate the future desired powertrain torque and compares the performance of the proposed algorithm with two existing prediction algorithms, i.e., step-by-step and fixed-gain algorithms. These three prediction algorithms are studied under the federal test procedure (FTP) and another four typical driving cycles. The simulation results show that the adaptive recursive prediction algorithm reduces the prediction error significantly with a 4% maximum prediction error, and in addition, the reduced computational load makes it feasible for real-time implementation. The introduced weighting factors for the past and current data are the key to improve prediction accuracy and avoid prediction calculation over- and underflow. In addition, due to the online update of regression gain, the proposed algorithm is also robust to driving behavior variations. [ABSTRACT FROM PUBLISHER]

In this paper, we propose generalized moment-based estimators of the Gamma-Gamma fading distribution. Specifically, the moment-based estimators are defined by using the $k$ -th, the $(k+1)$-th, the $l$-th, and the $(l+1)$-th order moments. Hence, the new moment-based estimators, which can choose lower order moments, have more degrees of freedom than existing estimators. In order to compare their performance, the normalized mean square error is taken into account. We validate, by Monte Carlo simulations, that the new moment-based estimators with lower order moments are superior to the existing estimators over various channel conditions. [ABSTRACT FROM PUBLISHER]

Recent studies have shown that utilizing a mobile sink (MS) to harvest and carry data from a wireless sensor network (WSN) can enhance network operations and increase the network lifetime. Since a significant portion of sensor nodes' energy is consumed for data transmission to an MS, the specific trajectory has a profound influence on the lifetime of a WSN. In this paper, we study the problem of controlling sink mobility in event-driven applications to achieve maximum network lifetime. In these applications, an MS with limited velocity has to harvest the captured data of events from a group of sensor nodes in a specific time as a reporting time slot. We show that this problem is NP-hard, and then, we propose a convex optimization model inspired by the support vector regression technique to determine an optimal trajectory of an MS without considering predefined structures such as a virtual grid or rendezvous points. The effectiveness of our approach is validated via the extensive number of simulation runs and comparison with other algorithms. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel unified structure to estimate tire forces. The proposed structure uses estimation modules to calculate/estimate tire forces by means of nonlinear observers. The novelty in the proposed approach lies in the independence of the estimates from the vehicle tire model, thereby making the structure robust against variations in vehicle mass, tire parameters due to tire wear, and, most importantly, road surface conditions. In the proposed structure, we have a dedicated module to estimate the longitudinal tire forces and another to calculate the vertical tire forces. Subsequently, these forces are fed into a third module that utilizes a nonlinear observer to estimate lateral tire forces. The proposed structure is validated through experimental studies. [ABSTRACT FROM PUBLISHER]

This paper presents a supervisory control strategy for plug-in hybrid electric vehicles based on energy demand prediction and route preview. The aim is to minimize the fuel consumption in real-time operation. This strategy is realized through three successive steps. First, a neural network model is established to predict the energy demand of the vehicle. It reduces the complete traffic data to several statistical parameters, which contributes to ease the prediction process. Second, a mathematical model is proposed to translate the predicted energy demand into a state of charge (SOC) reference of the battery, which significantly simplifies the SOC-programming method. Finally, the adaptive equivalent consumption minimization strategy (ECMS) is used to track the SOC reference and determine the powertrain state. The proposed strategy can optimally distribute the energy between the engine and the motor on a global range and achieve an optimal torque split on a local range. Simulations are carried out on a power-split plug-in hybrid electric bus, and the proposed strategy shows substantial improvements in fuel economy and other indexes compared with the rule-based strategy and the ECMS. [ABSTRACT FROM PUBLISHER]