Search

Device-to-device (D2D) based offloading stands out as a promising approach to mitigate the pressure over cellular networks. From the perspectives of both operators and users, an ideal solution is expected to maximize the offloading efficiency (ratio) while guaranteeing the quality-of-service (QoS) of users. However, existing approaches seldom jointly take into consideration offloading efficiency optimization and QoS guarantee. In this paper, we study how to maximize the percentage of total offloaded traffic of mobile content downloading via D2D, while guaranteeing the user minimum throughput under limited buffer size. The major challenge is how to incorporate all potential D2D opportunities to maximize the offloading performance. We propose a spatial–temporal dynamic graph (STDG) to capture all possible D2D opportunities in D2D-based traffic offloading. Based on STDG, we mathematically formulate an optimization problem of maximizing the offloading efficiency, subject to throughput requirement and buffer size constraint. The formulated problem is a linear-fractional problem and can be transformed into an equivalent linear program, whose solution reveals the maximum offloading ability that D2D achieves under QoS satisfaction. Trace-based simulations validate the superiority of our approach over three benchmark approaches. [ABSTRACT FROM AUTHOR]

Altogether with extent of automotive role in today's life, governments are approving stringent laws to lower the permitted level of automotive emissions. An important specie of automotive engines emissions is NOx. Different methods are developed to decrease the level of NOx generation; one of which is exhaust gas recirculation (EGR). Since the performance of EGR is strictly depended on burned gas fraction (BGF) in inlet manifold, the precise control of BGF is of importance. Unfortunately, due to inlet manifold temperature, no economical sensor is available to measure the BGF; therefore, estimators are employed instead. In this paper, a stable observer is designed for estimation of air fraction in inlet manifold. The governing equations are in the form of linear parameter varying (LPV). Since the LPV parameters are not directly measured, a hierarchical estimator structure is developed. Lyapunov theory is employed to design the higher level estimator, while high gain estimators and open-loop estimators are developed for estimation of the lower level parameters. Experimental test results show that the higher level estimator is able to estimate the BGF with high accuracy in both transient and steady states. Furthermore, it is shown that BGF estimator is more sensitive to aspirated gas flow estimation rather than exhaust temperature and pressure. [ABSTRACT FROM AUTHOR]

In this paper, a new mathematical model of quadrotor is proposed by using the Hamiltonian approach, which has more advantages than Newtonian and Lagrangian approaches in some aspects such as compactness, easiness of controller design, and its symplectic structure. The Hamiltonian model of a quadrotor is a first-order differential equation whose states include generalized position and momentum that contain information about both the corresponding velocity and generalized mass. A nonlinear controller is proposed based on the proposed symplectic Hamiltonian model. The controller includes two sub-controllers—attitude sub-controller and position sub-controller. Furthermore, the stability of the designed controller is verified by Lyapunov stability theorem and the controller is provided with exponential stability. The effectiveness of the proposed model and the designed controller is demonstrated not only by software-in-the-loop simulations but also by hardware-in-the-loop simulations. Real-time experimental results show that the proposed controller is able to carry out the tasks of taking off, tracking fixed point, hovering, and landing. [ABSTRACT FROM AUTHOR]

The paper presents a novel technique for modeling ladder-secondary single-sided linear induction machines. Saturation effect is considered by core non-linearity characteristic for both primary and secondary. The proposed modeling method is based on magnetic equivalent circuit with adjustable accuracy. The machine parameters such as its dimensions, pole numbers, bar numbers, and also model accuracy can be chosen arbitrary in the proposed model. The end effect is considered by virtual teeth at the entry and exit ends of the machine primary. The results of the proposed method are compared with finite element method (FEM) in terms of accuracy and processing time. The results show the effectiveness of the proposed method. In addition, due to having long processing time of the FEM, the proposed method is a very suitable manner for modeling of this particular machine. Also, the accuracy of the proposed method has been confirmed by experimental results, in starting. [ABSTRACT FROM AUTHOR]

The differential drive assist steering (DDAS) system improves the handiness of steering by torque difference between two-side wheels. However, the DDAS and the vehicle stability control (VSC) will interfere with each other due to the same actuators. This paper proposes a hierarchical coordination control approach for the DDAS and VSC. The system is divided into four layers: the parameter estimation layer, the control region division layer, the coordination decision layer, and the control allocation layer. In the control region division layer, the stable area is firstly determined on the $\beta - \dot{\beta }$ phase portrait, and the coordinated control region is obtained by applying the simulated annealing algorithm inwardly and outwardly to the boundary of the stable area. In the coordination decision layer, the direct control strategy of steering hand wheel torque is used to establish the controller of the DDAS based on the fuzzy adaptive PID algorithm. At the same time, the controller of yaw moment is established based on the sliding mode control theory. In the stable region, the DDAS works separately. The VSC works separately in the unstable region. In the coordination region, the Sigmoid function is used to adjust the weight coefficient of the DDAS dynamically. Besides, the control allocation layer includes torque distribution controller and vehicle slip ratio controller. Finally, the vehicle model is established in CarSim, the control strategy, and the wheel motor model are established in Simulink. The joint simulation results of several typical conditions show that the designed control strategy can improve the handling stability of the vehicle effectively while DDAS working even in severe driving condition. [ABSTRACT FROM AUTHOR]

A 4-independent wheel driving (4-IWD) electric vehicle has distinctive advantages with both enhanced dynamic and energy efficiency performances since this configuration provides more flexibilities from both the design and control aspects. However, it is difficult to achieve the optimal performances of a 4-IWD electric vehicle with conventional design and control approaches. This paper is dedicated to investigating the vehicular optimal design and control approaches, with a 4-IWD electric race car aiming at minimizing the lap time on a given circuit as a case study. A 14-DOF vehicle model that can fully evaluate the influences of the unsprung mass is developed based on Lagrangian dynamics. The 14-DOF vehicle model implemented with the reprogrammed Magic Formula tire model and a time-efficient suspension model supports metric operations and parallel computing, which can dramatically improve the computational efficiency. The optimal design and control problems with design parameters of the motor, transmission, mass center, anti-roll bar and the suspension of the race car are successively formulated. The formulated problems are subsequently solved by directly transcribing the original problems into large-scale nonlinear optimization problems based on trapezoidal approach. The influences of the mounting positions of the propulsion system, the mass and inertia of the unsprung masses, the anti-roll bars, and suspensions on the lap time are analyzed and compared quantitatively for the first time. Some interesting findings that are different from the ‘already known facts’ are presented. [ABSTRACT FROM AUTHOR]

The Very High Throughput (VHT) supplied by the IEEE 802.11ac standard is the outcome of several amendments incorporated at its both Medium Access Control (MAC) and PHYsical (PHY) layers. In particular, the Aggregate-MAC Protocol Data Unit (A-MPDU) frame aggregation allows, at the MAC level, increasing the amount of data to be transmitted and the spatial multiplexing technique serves at the PHY level to accelerate the rate at which such data are forwarded. Therefore, the goals we are seeking to achieve in this paper are, on one hand, to demonstrate that A-MPDU aggregation and spatial multiplexing may, respectively, lead to a decrease in throughput and an increase in overhead, and on the other hand, to highlight that A-MPDU aggregation and spatial multiplexing have mutual needs that could mutually satisfy operational challenges. For this purpose, we model the 802.11ac station enabling A-MPDU aggregation and spatial multiplexing, and we compute the throughput and the overhead of the 802.11ac network. After that, we run Monte Carlo simulations for validating the accuracy of the mathematical model, and we calculate 95% confidence intervals to establish credibility to the simulation results. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have emerged as the state-of-the-art energy storage for portable electronics, electrified vehicles, and smart grids. An enabling Battery Management System holds the key for efficient and reliable system operation, in which State-of-Charge (SOC) estimation and State-of-Health (SOH) monitoring are of particular importance. In this paper, an SOC and SOH co-estimation scheme is proposed based on the fractional-order calculus. First, a fractional-order equivalent circuit model is established and parameterized using a Hybrid Genetic Algorithm/Particle Swarm Optimization method. This model is capable of predicting the voltage response with a root-mean-squared error less than 10 mV under various driving-cycle-based tests. Comparative studies show that it improves the modeling accuracy appreciably from its second- and third-order counterparts. Then, a dual fractional-order extended Kalman filter is put forward to realize simultaneous SOC and SOH estimation. Extensive experimental results show that the maximum steady-state errors of SOC and SOH estimation can be achieved within 1%, in the presence of initial deviation, noise, and disturbance. The resilience of the co-estimation scheme against battery aging is also verified through experimentation. [ABSTRACT FROM AUTHOR]

In this paper, a novel multirate lane-keeping system (LKS) is introduced by using a kinematic-based model considering a look-ahead distance. First, we developed a kinematic lateral motion model for LKS with respect to the road. To increase a system damping of vehicle, the unique look-ahead output measurement matrix was introduced. The decentralized multirate lane-keeping control scheme with a multirate Kalman filter was also applied to resolve the asynchronous and irregular sampling time of multi sensors in autonomous vehicles. We validated the performance of the proposed kinematic-based multirate LKS using the dynamic vehicle solver CarSim, MATLAB/Simulink, and electric power steering hardware-in-the-loop system. Experimental validation was also conducted using a real test vehicle. Both simulation and experimental results showed that the proposed kinematic-based LKS performed lane-keeping capability comparable to that of dynamic-based LKS. In particular, the experimental results showed that the proposed method, compared to a human driver, can also maintain the lane within reasonable specifications. The experimental validation was performed at vehicle speeds of up to 120 km/h. [ABSTRACT FROM AUTHOR]

In today's hybrid or fully electric vehicles, two types of propulsion machines can mainly be found: induction machines (IM) and interior permanent magnet synchronous machines. They are highly utilized and operated in magnetic saturation regions where inductance parameters change nonlinearly. The energy efficiency of both drives depends strongly on the choice of reference currents within field-oriented control. The paper shows two approaches for the determination of well-adjusted current components for both machines. First, model-based optimization is performed. It uses machine models considering saturation and loss models including the inverter. Second, a newly developed online-optimization scheme based on a discrete search method is presented. Simulation results show its advantages over recently published solutions for both machine types. Finally, experimental results at a drive test bench are presented. They prove the fast convergence and correct function of the proposed method. [ABSTRACT FROM AUTHOR]

With the increasing demand for high-data mobile traffic and applications, Device-to-Device (D2D) communication has emerged as a promising technique for next generation of cellular networks to increase the network capacity. In this paper, to offload traffic from network backhaul, we investigate the problem of relaying data through multihop nodes, using D2D communication, for a set of transmitter-receiver pairs, by sharing cellular resource blocks. Furthermore, the problem is extended to schedule the transmission links within a maximum time frame to obey a delay constraint. If the transmission path could not be scheduled within the maximum time frame for any D2D communication pair, the communication is redirected through the base station similar to cellular mode. Our problem aims at minimizing the overall D2D transmission power by giving priority to maximizing the number of paths that go through D2D relaying. We mathematically formulate the above problem and find optimal solutions for small networks. We show the NP-hardness of the problem, and owing to its complexity, we propose an algorithmic method for more extensive networks. Through numerical results, the performance of our mathematical optimization model and algorithm is compared and the efficiency of the proposed algorithmic method is shown. [ABSTRACT FROM AUTHOR]

This paper presents a coupling control strategy of force and displacement for an electric differential power steering (EDPS) system to improve steering maneuverability and handling stability of electric vehicles with motorized wheels. In order to investigate mutual influences between vehicles and drivers, models of the EDPS system, the vehicle, the tire as well as the actuate motor are introduced. Then, by analyzing key factors that affect the interaction between vehicles and drivers, the optimum hand wheel torque of the EDPS system is developed and actualized by torque difference between two front wheels based on the ${\text{H}}_{2}{\text{/ H}}_{{{\infty }}}$ control method. In addition, a yaw rate control method is schemed to diminish the yaw moment interference to vehicles caused by the torque difference between two front wheels, and μ-synthesis theory is applied here to improve the yaw rate tracking performance as well as attenuate influences of system uncertainties and disturbances. Finally, simulations by MATLAB/Simulink and hardware-in-the-loop experiments based on dSPACE are conducted and effectiveness of the proposed control strategy is demonstrated by simulation and experiment results and numerical analyses. [ABSTRACT FROM AUTHOR]

Model-based observers are widely applied in state-of-charge (SOC) estimation. The existing model-based observers can achieve high precision in theory, but the estimation precision is influenced by many factors in practical application. The impact of the possible error sources on the SOC estimation needs to be clarified in order to enhance the practical estimation accuracy. In this paper, the effect of five error sources in the model-based SOC observer is analyzed, including the initial error, the capacity error, the current measurement error, the voltage measurement error, and the model prediction error. Both theoretical derivation and simulation analysis are implemented. The results show that the initial SOC error converges to zero during the iteration, while the errors of the capacity, current, voltage, and model cause the SOC estimation error in different extents. Quantitative comparisons of these different error sources are carried out in order to determine the critical factors of the SOC estimation. The SOC estimation accuracy is mainly limited by the model precision under the current hardware and software conditions of the battery management system, indicating a possible improving direction of the SOC estimation. [ABSTRACT FROM AUTHOR]

The main objective of this paper is the design of an Electric Naval Propulsion System (ENPS) emulator based on a low power multiphase machine. The whole drive system (control, power electronics, and electric machine) is tested and the mechanical part is emulated based on a Hardware-in-the-Loop principle. First, the modeling of all system components including mechanical and electrical parts are reviewed, and the total hull resistance forces are also developed. Then, a pure software simulation of the ENPS is carried out. After that, the designed emulator architecture is presented, and for adaption purpose, a scaling factor is introduced. The developed emulation tool allows to validate optimal control strategies for the propulsion system under a realistic navigation cycle under healthy and faulty operating conditions. Finally, simulation and experimental results are compared to investigate the accuracy and the performance of the developed emulator, and the effectiveness of the proposed control strategies. [ABSTRACT FROM AUTHOR]

The generalized memory polynomial (GMP) model is one of the most commonly used models in digital predistortion to compensate for the nonlinearities and memory effects of power amplifiers (PAs). A very difficult, yet crucial, aspect of behavioral modeling is model dimensioning, i.e., determining nonlinear orders and memory depths. In this paper, we propose an algorithm based on hill-climbing (HC) heuristic to search for a GMP model structure, which provides the best tradeoff between modeling accuracy and complexity with different search criteria. Algorithm performance and convergence are evaluated for two different search criteria. Some optimization methods of the algorithms are then proposed to reduce the execution time by controlling its search path. The algorithm has been evaluated using real measurements from two different Doherty PAs. The solutions of the algorithm are proved robust in linearization of PA on the test bench. The results show that the proposed algorithm allows us to decrease the searching time by a factor typically greater than $10^4$ compared with exhaustive search. [ABSTRACT FROM AUTHOR]

The generalized normal (GN) distribution has recently attracted enough attention within the research community because of its ability to model additive noise and interference. In this paper, the Type II and VII Pearson distributions as well as the so-called normal-gamma distribution are proposed to approximate the distribution of the sum of GN random variables. The proposed probability distributions can exploit not only the mean and variance but also higher order statistical characteristics, i.e., the kurtosis, in order to accurately approximate the GN sum distribution. Using the moment matching method, simple moment-based estimators for the parameters of these distributions are also presented. It is shown that the proposed approximations yield accurate results over a wide range of distribution parameters and numbers of summands. The proposed approximation is further applied to analyze the error performance of diversity receivers with binary modulation and equal gain combining in the presence of generalized fading and noise. To this end, the characteristic function based approach is employed to transform the average error-rate integral into the frequency domain using the Gil-Pelaez inversion theorem. Based on this approach, the performance of diversity receivers with equal gain combining operating in the presence of double Rayleigh or double Nakagami- $m$ fading environments, which are regarded as appropriate choices to model vehicle-to-vehicle fading channels, is further assessed. Numerically evaluated results accompanied with Monte Carlo simulations are presented to validate the underlying mathematical analysis. [ABSTRACT FROM AUTHOR]

The trend in traction motor design and optimization is to achieve high torque and power densities, wider speed range, and high efficiency within the area defining the most frequent operating points. A fast tool to evaluate different variables within the torque-speed map is convenient for this purpose. In this context, starting from a preliminary motor design, and taking into account motor cross-coupling effects and power losses, this paper presents a new tool for evaluating the behavior of permanent magnet machines, such as synchronous reluctance machines, and permanent magnet assisted synchronous reluctance machines, in all operational points. Apart from the torque and efficiency, many other electrical variables can be obtained, such as the current space vector angle, power factor or electrical power among others. The proposed tool also allows optimizing the design of the machine under a pre-established control law, thus obtaining the current set point trajectory in the dq frame and allowing a fast and accurate evaluation of motor performance. The results obtained by means of the proposed simulation tool are compared against finite element analysis simulations and experimental data, thus validating the usefulness and accuracy of the proposed methodology. [ABSTRACT FROM AUTHOR]

Real-time classification of road surface conditions is very important for the control of a vehicle properly within its handling limits. There have been numerous attempts to estimate the road surface conditions, but there is a plenty of room for improvement, since most of the estimation methods have some robustness issues in real-world applications. The method proposed in this paper utilizes the relationship between the road surface condition and the tire cornering stiffness. Since the tire cornering stiffness varies significantly depending on the road surface condition, it can be estimated out of the tire cornering stiffness, which is measured accurately at the early state of wheel slip. In this study, normalized tire cornering stiffnesses are estimated in real time, exploiting the fact that production vehicles are generally built to show some understeering characteristics. The proposed method compares the front and rear wheel slips simultaneously, unlike other studies that focus on individual wheel slip. In this way, estimation of unmeasurable major vehicle states such as wheel sideslip angle and absolute vehicle speed can be eliminated from the entire algorithm. The experimental results show satisfactory estimates of less than 10% error and confirm the feasibility of the proposed algorithm in production vehicles without exotic extra sensors. [ABSTRACT FROM AUTHOR]

The performance of vehicular ad hoc networks (VANETs) is subject to frequent changes due to node mobility so that nonstationary/transient network behavior always exists. In addition, safety applications in VANETs are very sensitive to the real-time performance of delay and packet delivery ratio (PDR). Although there are extensive studies on the steady-state (SS) performance analysis of vehicular networks, little work exists on evaluating their time-varying behavior. In this paper, we develop a performance model to efficiently estimate the dynamic behavior of VANETs. In this paper, vehicle's transmission queue is modeled using fluid-flow (FF)-based differential equations, which are solved using numerical methods, whereas the network hearing topology is modeled by a time-varying adjacency matrix that can be determined from stochastic models, measurements, or discrete event simulations. Numerical results illustrate that our performance model is able to promptly respond to the ongoing dynamic conditions of VANETs and provide reasonably accurate performance results. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear model predictive controller (NMPC) with a multiagent heuristic optimizer, which is called dynamic particle swarm optimization (DPSO), is proposed to reduce the cold-start hydrocarbon (HC) emission of an automotive spark-ignited engine. In general, the cold-start HC emission reduction has been proven to be a very challenging control problem that has attracted increasing attention from the automotive research community. The main contribution of this paper lies in the implementation of a model-based predictive control scheme that uses a discrete nonlinear control-oriented model for calculating the control commands. In the first part of the experiments, the developed control-oriented model is validated with some data obtained through experiments. Then, by applying the controller to different cold-starting conditions, the values of exhaust gas temperature T\mathrm{exh} and engine-out HC emissions are controlled to reduce the cumulative tailpipe emissions \textHC\mathrm{cum}. To ascertain the veracity of the proposed control scheme, the same problem is solved using Pontryagin's minimum principle. The conducted simulations indicate the applicability of DPSO as an effective solver at the heart of the NMPC. Moreover, the results of model-in-the-loop simulation and hardware-in-the-loop testing demonstrate the acceptable performance of the proposed control scheme for real-time applications, which is based on the NMPC method for the considered problem. [ABSTRACT FROM PUBLISHER]

The aim of this paper consists of developing a hybrid active control system to reduce the impact of vertical forces on roads and, therefore, to avoid the rollover of heavy vehicles (HVs). The controller is based on a super twisting algorithm. Two states are then fixed, which correspond to the controller being on or off. They are based on the lower limit and the upper limit of lateral acceleration. These states have been computed from the fixed lower limit and upper limit of the load transfer ratio (LTR). The system switches between these two states to control the vehicle. An estimator based on the sliding-mode observer is also developed to estimate the vehicle dynamics, such as yaw rate, roll angle, and lateral acceleration. The lateral positions and speeds are then controlled to limit the lateral acceleration of the vehicle, reduce the effect of impact forces, and prevent the risk of vehicle rollover. To show the quality of this assistance system, simulation and experimental results are given in this paper. Ramp and chicane tests are then presented. [ABSTRACT FROM PUBLISHER]

In Part I of this paper (“Delay-Optimal Dynamic Mode Selection and Resource Allocation in Device-to-Device Communications—Part I: Optimal Policy”), we investigated dynamic mode selection and subchannel allocation for an orthogonal frequency-division multiple access (OFDMA) cellular network with device-to-device (D2D) communications to minimize the average end-to-end delay performance under the dropping probability constraint. We formulated the optimal resource control problem into an infinite-horizon average-reward constrained Markov decision process (CMDP), and the optimal control policy derived in Part I using the brute-force offline value iteration algorithm based on the reduced-state equivalent Bellman equation still faces the well-known curse-of-dimensionality problem, which limits its practical application in realistic scenarios with multiple D2D users and cellular users. In Part II of this paper, we use linear value approximation techniques to further reduce the state space. Moreover, an online stochastic learning algorithm with two timescales is applied to update the value functions and Lagrangian multipliers (LMs) based on the real-time observations of channel state information (CSI) and queue state information (QSI). The combined online stochastic learning solution converges almost surely to a global optimal solution under some realistic conditions. Simulation results show that the proposed approach achieves nearly the same performance as the offline value iteration algorithm and outperforms the conventional CSI-only scheme and throughput-optimal scheme in a stability sense. [ABSTRACT FROM AUTHOR]

Modeling the variations in the local mean received power, the shadow fading is a relatively understudied effect in the literature. The inaccuracy of the universally accepted lognormal model is shown in many works. However, proposing other statistical distributions, such as gamma, which are not stemmed from the natural underlying physical process, cannot provide sufficient insights. Conceding the physical process of multiple reflections generating the lognormal distribution, in this paper, we propose a generalized mixture model that can address the modeling inaccuracies observed with a single lognormal distribution that may not correctly represent empirical data sets. To show that lognormal mixture model can be used under any shadow fading condition, we prove that an arbitrary probability density function can accurately be represented by a mixture of lognormal random variables (RVs). One of the main issues associated with mixture models is the determination of the mixture components. Here, we propose two solutions. Our first solution is a Dirichlet-process-mixture-based estimation technique that can provide the optimum number of components. Our second solution is an expectation–maximization (EM) algorithm-based technique for a more practical implementation. The proposed model and solution approaches are applied to our empirical data set, where the accuracy of the mixture model is verified by using both confidence-based and error-vector-norm-based techniques. Concluding this paper, we provide outage and cellular coverage probability expressions, where we show that more accurate shadow fading models yield more realistic performance estimates. [ABSTRACT FROM PUBLISHER]

A ticket dispenser (TD) is used to assist customers with the waiting process in, e.g., a restaurant. This paper deploys a mobile TD system (MTDS) with waiting time prediction to enhance the user experience while waiting. For example, the MTDS for a restaurant allows a customer to remotely draw a ticket for a meal order anywhere through a smartphone before she/he arrives at the restaurant and therefore reduces her/his waiting time. The developed MTDS system can dynamically adjust the predicted waiting time. We propose two output indicators and develop a discrete-event simulation model to investigate the performance of the predicted time adjustment (PTA) mechanism for the MTDS. With a proper frequency of recalculation for the predicted waiting times, this paper indicates that the waiting times can be more accurately predicted without consuming more wireless network resources and power of mobile devices. [ABSTRACT FROM PUBLISHER]

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

In delay-tolerant networks (DTNs), stable end-to-end connections do not always exist. Messages are forwarded, assisted by the mobility of nodes, in a store–carry–forward paradigm. The mobility of nodes in most DTNs has a certain statistical regularity; thus, using historical information in DTNs to compute the delivery quality of nodes can help to select good forwarding nodes. This paper aims to establish a routing scheme based on multihop delivery quality, which is designed to reduce the energy consumption of message forwarding while maintaining a high delivery rate. We characterized the multihop delivery quality of each node with an expected delay and an expected probability, parameterized by the remaining hop count. Based on these two quality metrics, we developed two algorithms, namely, the delay-inferred forwarding (DIF) algorithm and the probability-inferred forwarding (PIF) algorithm. The basic idea of DIF and PIF is to find the optimal forwarding path by minimizing the expected delay and by maximizing the expected probability, respectively, in the hop graph that is defined in this paper. We performed extensive trace-driven simulations to compare our algorithm to other representative routing algorithms using several real traces. We observed the following: 1) Compared with the delegation algorithm, which uses one-hop delivery quality, both DIF and PIF significantly improve the message delivery rate, and they yield more improvements as the mobility of nodes becomes more regular; and 2) compared with the state-of-the-art optimal opportunistic forwarding (OOF) algorithm, which also uses a multihop delivery quality, DIF and PIF have significantly smaller forwarding overhead (with the maximum reduction in the number of forwarding being over 40%), whereas they are quite close to OOF in terms of both delivery rate and average delay. [ABSTRACT FROM PUBLISHER]

Batteries are the most commonly used electrical energy storage devices. The cost and performance of an electric vehicle (EV) are strongly affected by the proper selection of technology, number, and arrangement of the battery cells used. This paper formulates the problem of optimal sizing of the battery unit (BU) for a plug-in EV (PEV), given the specifications of the vehicle, battery cell data, and drive cycle. This paper employs a metaheuristic optimization algorithm to find the optimal size of the BU with the objective of minimizing the overall cost. Feasibility of the optimization results in terms of respecting the constraints is verified via simulation. The developed optimization platform is highly flexible and allows use of different vehicle designs, component data, and drive cycles. [ABSTRACT FROM PUBLISHER]

This paper presents an adaptive radial basis function (RBF) network based speed control approach for interior permanent magnet synchronous machine (IPMSM) based vehicles. The presented control strategy achieves speed tracking without current measurement or control by acting directly on the machine's voltages. As a result, the current control loop is avoided, greatly simplifying the overall control strategy when compared to most existing speed control schemes. Moreover, no parameter knowledge is required unlike other control strategies. Furthermore, the closed-loop control system's stability is guaranteed by a Lyapunov theorem. Simulation and experimental results for different scenarios along with comparison against the well-established field oriented vector control technique demonstrate the controller's satisfactory behavior in both transient and steady state. The proposed method yields 99% and 97% tracking accuracy in the presence of parametric uncertainties and friction nonlinearities, respectively. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction of lithium-ion batteries can assess the battery reliability to determine the advent of failure and mitigate battery risk. The existing RUL prediction techniques for lithium-ion batteries are inefficient for learning the long-term dependencies among the capacity degradations. This paper investigates deep-learning-enabled battery RUL prediction. The long short-term memory (LSTM) recurrent neural network (RNN) is employed to learn the long-term dependencies among the degraded capacities of lithium-ion batteries. The LSTM RNN is adaptively optimized using the resilient mean square back-propagation method, and a dropout technique is used to address the overfitting problem. The developed LSTM RNN is able to capture the underlying long-term dependencies among the degraded capacities and construct an explicitly capacity-oriented RUL predictor, whose long-term learning performance is contrasted to the support vector machine model, the particle filter model, and the simple RNN model. Monte Carlo simulation is combined to generate a probabilistic RUL prediction. Experimental data from multiple lithium-ion cells at two different temperatures is deployed for model construction, verification, and comparison. The developed method is able to predict the battery's RUL independent of offline training data, and when some offline data is available, the RUL can be predicted earlier than in the traditional methods. [ABSTRACT FROM AUTHOR]

The American automobile industry is facing substantial environmental and energy challenges driving the pursuit of alternative powertrain technologies—“The intervening mechanism by which power is transmitted from an engine to a propeller or axle that it drives,” which nominally includes engine, transmission, drive shafts, differentials, and the final drive. Emerging alternative fuel vehicles are showing their potential to address these challenges. However, diffusion of new technologies has many complications. This paper uses system dynamics modeling to investigate the impacts of individual and organizational parameters. This model embeds two traditional modeling methods for technology adoption, the Bass diffusion model and multinomial logit regression method. Five types of powertrain systems and three major stakeholders are considered. Mathematical relationships among different variables are elaborated. The qualitative impacts of government feebate, manufacturer willingness, and consumer purchasing preferences on economic and environmental issues are addressed using scenario analysis. [ABSTRACT FROM AUTHOR]

The heterogeneous networks (HetNets) performance is highly dependent on the interference coordination among cells since both macrocells and small cells share the same spectrum. This paper focuses on the configuration of eICIC parameters: almost blank subframe ratio and cell range expansion bias defined for LTE-A. We propose an online learning mechanism based on a big data-driven framework and multiarmed bandit algorithms that retrieves data from the network to learn efficient configurations of eICIC parameters without requiring any previous knowledge about the network (e.g., traffic load, topology, scheduling algorithm). Our numerical results show that our approach attains a significant improvement with respect to the state of the art of online learning algorithms in networks under stationary and variable conditions (e.g., number of small cells, traffic load). [ABSTRACT FROM AUTHOR]

Incipient fault detection and diagnosis (FDD) is a key technology for enhancing safety and reliability of high-speed trains. This paper develops a real-time incipient FDD method named deep principal component analysis (DPCA) for electrical drive in high-speed trains. This method can effectively detect incipient faults in electrical drive before they develop into faults or failures. This scheme adopting multivariate statistics is composed of multiple data processing layers to extract more accurate signal features of electrical drive, which exhibits several salient advantages: 1) It can establish precise data models containing both systematic and noise information of electrical drive, which are helpful for incipient fault detection; 2) the incipient faults are described by multicharacteristics which can improve the fault diagnosis ability; 3) it can be easily implemented even if the system models and parameters of electrical drive are unknown. The effectiveness and feasibility of the proposed FDD scheme are authenticated via a mathematical analysis and validated via two experiments. Results of two experiments show that the missing alarm rate and detection delay by using the proposed DPCA-based FDD method are less than 10% and 0.68 s, respectively. In comparison with the standard PCA-based FDD method, the proposed DPCA-based FDD method can show its superiorities by the detailed performance comparisons. [ABSTRACT FROM AUTHOR]

This paper examines a symbolic numerical approach to optimize a vehicles track for autonomous driving and collision avoidance. The new approach uses the classical cost function definition incorporating the essential aspects of the dynamic state of the vehicle as position, orientation, time sampling, and constraints on slip angles of tires. The optimization processes minimize the cost function and simultaneously determine the optimal track by varying steering and breaking amplitudes. The current velocity of the vehicle is limited to a maximal velocity, thus, allowing a stable search of the optimal track. The parametric definition of obstacles generates a flexible environment for low and high speed simulations. The minimal number of influential optimization variables guarantees a stable and direct generation of optimal results. By the current new approach to control a vehicle on an optimal track, we are able to autonomously move the vehicle on an arbitrary track approximated by low order polynomials. The optimization approach is also able to deal with a variety of different obstacles and the corresponding optimal smooth obstacle path. The computations demonstrate the effective control of a four wheel vehicle in normal operation and exceptional obstacle avoidance with continuously differentiable obstacle avoidance tracks. Simulation tests are done using vehicles velocities of 3, 6, 7.6, 10, 12, and 18 m/s. At higher vehicles velocities, a mathematical-only approach is not sufficient and a mechanical intervention for tires is needed as a complimentary part to control the slip angle. The results shows that the cost function reached a considerably high average convergence-to-zero rate success in most of the tested scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we propose a scheme referred to as integer-forcing message recovering (IFMR) to enable receivers to recover their desirable messages in interference channels. Compared to the state-of-the-art integer-forcing linear receiver (IFLR), our proposed IFMR approach needs to decode considerably less number of messages. In our method, each receiver recovers independent linear integer combinations of the desirable messages each from two independent equations. We propose an efficient polynomial-time algorithm to sequentially find the equations and integer combinations with maximum rates and analyze its complexity. We evaluate the performance of our scheme and compare the results with the minimum mean-square error linear receiver (MMSELR) and lattice-reduction-aided successive interference cancellation with signal-to-interference-plus-noise ratio maximizing preprocessing (LaR-aided SIC with SINR-Max), as well as the IFLR schemes. The results indicate that our IFMR scheme outperforms the MMSELR and LaR-aided SIC with SINR-Max schemes, in terms of achievable rate, considerably. Also, compared to the IFLR, the IFMR scheme achieves slightly less rates in moderate signal-to-noise ratios, with significantly less overall implementation complexity. [ABSTRACT FROM PUBLISHER]

This paper studies the network connectivity problem in a vehicular network and focuses on the two-way connectivity of vehicles moving on a one-way highway road with one entry and exit. The entry and exit separates the road into two segments, where vehicle arrivals follow a Poisson process. At the entry and exit, a vehicle may enter or exit from the road, or drive through the entry and exit. A connectivity analytical model is derived for analyzing the connectivity probability of vehicles moving on the road. The analytical model takes into account several parameters that may affect the connectivity probability, including a vehicle's arrival rate and moving speed as well as the probability that a vehicle drives through the entry and exit. Moreover, it considers two entry and exit scenarios: one with no roadside unit (RSU) deployed, and the other with one RSU deployed. The analytical model is justified through simulation results and the impacts of different parameters on the connectivity probability are investigated based on the derived analytical model. [ABSTRACT FROM PUBLISHER]

For the sake of reducing the computational load and enhancing the implementation possibility, a dual time-scale extended Kalman filter is developed in this paper. First, a conventional multistate mathematical selective catalytic reduction (SCR) model is simplified into a single-state SCR model. This simplification would reduce the computational cost a lot. Inspired by the fact that the variation of NH3 coverage ratio between two cells changes relatively slow, then, the dual time-scale extended Kalman filter is proposed. The dual time-scales include one fast time-scale for the average NH 3 coverage ratio and a slow time-scale for the difference of NH3 coverage ratios between the actual cell and the average value. In the dual time-scale scenario, it is unnecessary to estimate the NH 3 coverage ratio of each SCR cell every time. Finally, simulation results for a medium-duty diesel engine through the MATLAB/SIMULINK show the proposed method has good performance and lower computational load. [ABSTRACT FROM PUBLISHER]

This paper introduces a novel control structure for integrated vehicle stability control that can be reconfigured for a wide range of vehicles with different types and combinations of control actuation. The proposed structure can be easily adjusted for a specific vehicle as it enables any actuator to be readily added or removed without reformulating the control problem. Different types and combinations of control actuation can be selected for the control design including differential braking and torque vectoring on all or selected wheels as well as active front or rear steering. To develop this model-based reconfigurable control structure, vehicle modeling is provided in a matrix form and an actuator reconfiguration matrix is defined. The proposed structure realizes integrated lateral stability control and slip control in traction/braking. The Model Predictive Control (MPC) approach is used to develop the controller. By employing the reconfigurable structure, optimal control solutions can be achieved with respect to only available control actuations. Simulation results show that the proposed control structure can facilitate the design of stability control regardless of the actuators configuration. [ABSTRACT FROM PUBLISHER]

Due to demands for active control systems that can enhance driving performance, active-type all-wheel drive (AWD) systems have drawn interest recently. Equipped with an electronically actuated motor to control a wet clutch in a transfer case, the active-type AWD facilitates the variable distribution of torque from the main driveshaft to the subdriveshaft. The conventional approaches to the estimation of tire force, developed to implement chassis control, have only focused on the two-wheel drive vehicle dynamics model. In this paper, an individual tire force estimation algorithm that is particularly designed for AWD vehicles is proposed. Using the interacting multiple model filter method, the suggested algorithm can help avoid the chattering response caused by immediately switching between vehicle dynamic models. Data obtained from the controller area network of production vehicles were used for the real-time application of the proposed estimator to chassis control. Then, the proposed estimator was validated using an AWD vehicle in various driving scenarios. [ABSTRACT FROM PUBLISHER]

In this paper, vehicle cornering along roads with low friction coefficient is studied, and an autopilot design is proposed to satisfy desired handling performance. A novel hierarchical optimization approach is presented to generate offline solutions for vehicle cornering problem along roads with different friction coefficients and radii of curvature. A vehicle status definition is introduced as a function of vehicle states that contains data to indicate handling performance. At each control instant, vehicle statuses are calculated, and at the end of each scenario, they are clustered with k-means clustering technique. The vehicle status cluster centres are associated with the applied control commands. The pair formed by the vehicle status at cluster centre and the control command corresponds to a rule that produces the unique control command to be applied as a function of the vehicle status. The autopilot is constructed by a convex combination of these rules. This basic idea of autopilot design has been extended for motion along a specific rotation radius and friction coefficient; the control commands corresponding to arbitrary scenario parameters are obtained by a runtime scheduling of the weighted interpolation among the control commands corresponding to different scenario parameters. The proposed autopilot design is verified with vehicle motion under offline optimized control commands. The autopilot is also tested along roads with varying friction coefficient, and it is shown that the vehicle satisfies the desired handling performance. [ABSTRACT FROM AUTHOR]

This paper proposes a co-estimation scheme of state of charge (SOC), state of health (SOH), and state of function (SOF) for lithium-ion batteries in electric vehicles. The co-estimation denotes that the SOC, SOH, and SOF are estimated simultaneously in real-time application. The model-based SOC estimation is fulfilled by the extended Kalman filter. The battery parameters related with the battery SOH and SOF are online identified using the recursive least square algorithm with a forgetting factor. The capacity and the maximum available output power are then estimated based on the identified parameters. The online update of the capacity and correlated parameters help improve the accuracy of the state estimation but with limited increase in the computation load, by making good use of the correlations among the states. The co-estimation scheme is validated in a real battery management system with good real-time performance and convincible estimation accuracy. [ABSTRACT FROM PUBLISHER]

Inductive power transfer (IPT) technology has been approved to be convenient and reliable interface for charging and discharging the electric vehicles (EVs). Precise model for such system can help designers and researchers to anticipate, optimize, and evaluate its behavior amid the development. Thus, this paper presents a physics-based co-simulation platform for the bidirectional IPT system (BIPTS) in EVs’ applications. The platform is established through the coupling between finite element and circuit analysis. The power electronic converters and controllers are developed in Simulink; and the power pads are modeled in Magnet environment. The two parts are lined together through the compatible Simulink plug-in tool. In addition, a state-space dynamic mathematical model for the same BIPTS is derived and implemented in MATLAB environment. A 1.2 kW BIPTS is analyzed under different dynamics by both models and the results are compared. The effect of the nonlinearities and the magnetic material characteristics on the BIPTS's performance is assessed, in terms of errors and harmonics analysis. The analysis considered both the full and light loading operating conditions in the system. Finally, a small-scale prototype for a BIPTS is built, tested, and compared with the co-simulation results for verification purposes. The proposed co-simulation could provide accurate prediction for the system's dynamics, during both charging and discharging operation. The scheme is generic and can be easily expanded to different pad structures, compensation networks, and converter topologies. [ABSTRACT FROM PUBLISHER]

Some studies indicate the potential of multispeed transmissions (MSTs) in improving performance of electric vehicles (EVs), which has led to many developments of MSTs in this context. However, comprehensive dynamics analyses have not been reported yet. For this reason, a mathematical model for MSTs in EVs is being developed, as reported in this paper. This model will be beneficial to support the design and control of EV-oriented MSTs. The transient dynamic response of the transmission is of interest in this study. Therefore, backlash, flexibility, and dissipation of the gear mesh and connecting parts between two planetary gear sets are studied, while taking all these items into account. The system topology variations induced upon gear shifting are given due attention. Simulation results are validated with experiments. The results show that the model can provide a realistic dynamic response of the transmission. [ABSTRACT FROM PUBLISHER]

In this paper, a reduced-order electrochemical model of lithium-ion batteries is developed for control and estimation applications through analytical model order reduction based on a Galerkin projection method. The governing diffusion partial differential equations in the liquid and solid phases are approximated into low-order systems of ordinary differential equations while the physical meaning of all model parameters is preserved, allowing one to perform state and parameter estimation. The selection of basis functions for the Galerkin projection method and model order truncation is carefully determined based on analysis both in the frequency and time domains. With the reduced-order diffusion models in the liquid and solid phases, an extended single particle model incorporating the electrolyte dynamics is developed. The model is then validated against the experimental data gathered from two batteries with different chemistries (lithium nickel manganese cobalt oxide/graphite and lithium iron phosphate oxide/graphite) at different input conditions. Results show that the reduced-order model agrees very well with experimental data at various conditions. Meanwhile, it can be simulated thousands of times faster than the real time, making it suitable for long-term-life simulation, control, and estimation applications. [ABSTRACT FROM PUBLISHER]

This paper focuses on the state-of-charge (SOC) estimation of the Lithium-ion battery in electric vehicles based on an H\infty -based nonlinear observer. First, the second-order RC equivalent circuit model is introduced by utilizing the physical behavior of the battery. Then, the parameters in the second-order RC model are identified as polynomial functions with respect to SOC. Meanwhile, the battery model with constant parameters is also introduced for comparison. Due to that the battery model is undetectable, an one-sided Lipschitz condition is proposed to ensure that the nonlinear function in output equation can play a positive role in the observer design. After that, a nonlinear observer design criterion is presented based on the H\infty method, which is formulated as linear matrix inequalities. Compared with existing nonlinear observer-based SOC estimation methods, the proposed observer design criterion does not depend on any estimates of the unknown variables. Consequently, the convergence of the proposed nonlinear observer is guaranteed for various operating conditions. Finally, one static and three dynamic operation conditions are given to show the efficiency of the proposed nonlinear observer by comparing with the classic extended Kalman filter and the nonlinear observer for constant parameters. [ABSTRACT FROM AUTHOR]

This paper studies base station (BS) cooperation in nonuniform heterogeneous networks. Considering the limited capacity at BS and existence of untrusted small cell BSs (SBSs) in practical scenarios, a novel capacity- and trust-aware BS cooperation strategy is proposed. The BS cooperation is performed in a user centric manner, based on the average received signal strength at users, the capacity of BSs, and the trustworthiness of SBSs. Furthermore, with the proposed BS cooperation, the statistics of aggregate information-signal strength and interference strength are theoretically analyzed, based on stochastic geometry. Then, expressions for spectral efficiency (SE) and area SE (ASE) are analytically derived. In addition, to study the impact of the SBS density on the SE and ASE, the optimal densities of normal SBSs to maximize the SE and ASE are proved to exist and obtained. Finally, simulations and numerical evaluations validate the theoretical analysis and reveal that with the awareness of BS capacity and trustworthiness, optimal cooperative thresholds achieving the maximum SE and ASE exist, which decrease with high path-loss exponent; and in high path-loss fading environment with high existence probability of untrusted SBSs, more normal SBSs are required to be deployed to achieve the maximum SE and ASE performance. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) propagation is known to be severely affected by the blockage of the line-of-sight (LoS) path. In contrast to microwave systems, at shorter mmWave wavelengths such blockage can be caused by human bodies, where their mobility within environment makes wireless channel alternate between the blocked and non-blocked LoS states. Following the recent 3GPP requirements on modeling the dynamic blockage as well as the temporal consistency of the channel at mmWave frequencies, in this paper, a new model for predicting the state of a user in the presence of mobile blockers for representative 3GPP scenarios is developed: Urban micro cell street canyon and park/stadium/square. It is demonstrated that the blockage effects produce an alternating renewal process with exponentially distributed non-blocked intervals, and blocked durations that follow the general distribution. The following metrics are derived 1) the mean and the fraction of time spent in blocked/non-blocked state, 2) the residual blocked/non-blocked time, and 3) the time-dependent conditional probability of having blockage/no blockage at time t1 given that there was blockage/no blockage at time t0 . The latter is a function of the arrival rate (intensity), width, and height of moving blockers, distance to the mmWave access point (AP), as well as the heights of the AP and the user device. The proposed model can be used for system-level characterization of mmWave cellular communication systems. For example, the optimal height and the maximum coverage radius of the mmWave APs are derived, while satisfying the required mean data rate constraint. The system-level simulations corroborate that the use of the proposed method considerably reduces the modeling complexity. [ABSTRACT FROM PUBLISHER]

In wireless sensor networks, there are usually a small number of position-aware nodes, referred to as anchors. Every other node, called target node (or sensor), can estimate its distances to surrounding anchors according to the received signal strength. Then, trilateration can be employed for self localization. This system is low-cost, simple, and thus popular, but it suffers from two major drawbacks. The first problem is the requirement for realtime channel parameters, which should be estimated and transmitted to target sensors by a central node. Another problem arises from the fact that positioning accuracy is dependent on number of anchors, but anchor density is quite limited for economic consideration in the conventional localization systems. To solve these problem, we proposed in this paper, a new localization framework for wireless sensor networks, which employs a mobile node to serve as virtual anchors. This new framework has two advantages over the conventional one: 1) Offline measurements are totally unnecessary; 2) the number of virtual anchors can be vastly increased with negligible cost, leading to very high positioning accuracy. To justify the superiority of this new framework, extensive analysis and simulations were conducted. Besides, we implemented it on a drone and did field experiments, which showed very promising results. [ABSTRACT FROM PUBLISHER]

Accurate estimation of state-of-charge (SoC) is vital to safe operation and efficient management of lithium-ion batteries. Currently, the existing SoC estimation methods can accurately estimate the SoC in a certain operation condition, but in uncertain operating environments, such as unforeseen road conditions and aging related effects, they may be unreliable or even divergent. This is due to the fact that the characteristics of lithium-ion batteries vary under different operation conditions and the adoption of constant parameters in battery model, which are identified offline, will affect the SoC estimation accuracy. In this paper, the joint SoC estimation method is proposed, where battery model parameters are estimated online using the H-infinity filter, while the SoC are estimated using the unscented Kalman filter. Then, the proposed method is compared with the SoC estimation methods with constant battery model parameters under different dynamic load profiles and operation temperatures. It shows that the proposed joint SoC estimation method possesses high accuracy, fast convergence, excellent robustness and adaptability. [ABSTRACT FROM PUBLISHER]

Unicast Internet protocol television (IPTV) services that can support live TV, video-on-demand, video conferencing, and online gaming applications over broadband wireless cellular networks have been becoming popular for many years. However, video streaming services significantly impact the performance of wireless cellular networks because they are bandwidth hogs. To maintain the system performance, effective admission control and resource allocation mechanisms based on an accurate mathematical analysis are required. On the other hand, the quality of a wireless link usually changes with time because of user mobility or time-varying channel characteristics. To counteract such time-varying channels and improve the spectral efficiency, an adaptive modulation and coding (AMC) scheme is adopted in offering unicast IPTV services for mobile users. In this paper, closed-form solutions for the bandwidth usage, blocking rate, and dropping rate of unicast IPTV services over wireless cellular networks were derived based on the novel queueing model that considers AMC. Simulations were also conducted to validate the accuracy of analytical results. Numerical results demonstrate that the presented analytical results are accurate. Based on the accurate closed-form solutions, network providers can implement precise admission control and resource allocation for their networks to enhance the system performance. [ABSTRACT FROM PUBLISHER]

In cognitive radio (CR), spectrum sensing is a critical task to effectively detect the spectrum holes. Through dynamically accessing such idle spectrum, CR can improve spectrum utilization and provide broad wireless band for wireless services. There are two types of spectrum sensing algorithms for CR, which are cooperative and noncooperative spectrum sensing. Noncooperative spectrum sensing is easier to implement, has less power costs, and is more robust to node failure, compared with cooperative spectrum sensing. In this paper, a novel noncooperative spectrum sensing algorithm has been proposed to exploit historical sensing data and boost the spectrum sensing performance. The hierarchical Dirichlet process is used to cluster the historical sensing data into groups, where each group shares a consensus spectrum state. Through exploiting the statistical correlations among historical sensing data, the uncertainty in spectrum sensing can be significantly minimized. The simulation results show that the proposed algorithm is better than the other three typical algorithms and has more than 10% detection performance improvement at false alarm probability 0.2 when received signal-to-noise ratio (SNR) is 5 dB. [ABSTRACT FROM PUBLISHER]