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This paper presents the concept of hybrid electric vehicles (HEVs) applied to sport utility vehicles (SUVs) with diesel engines. Two SUVs currently available in the U.S. market have been selected as the representative vehicles used in this study, which consists of the modeling and simulation of the vehicles as close as possible to the manufacturer's specifications and then running the models through the Advance Vehicle Simulator (ADVISOR). Effects of hybridization on the fuel economy and dynamic performances of the two representative SUVs are investigated. Different hybridization levels from mild to full hybrid electric drive trains are studied through comprehensive simulations.

The papers in this special section focus on connected autonomous vehicles and the technologies, methodologies, and applications that support this technology. During the last decade, there have been considerable developments in automated driving technology. This phenomenon is exemplified by the watershed event of the DARPA Challenges in the mid-2000s, followed by increased efforts from a number of industrial players and the testing of Google self-driving cars on public roads, which induced even greater investments by a number of technology providers as well as automotive manufacturers. Not only technological advancements have been demonstrated in many venues, several states and federal government in US have also moved forward to set up regulations and guidelines to prepare for the introduction of self-driving cars. Activities have intensified even more around the globe in recent years. In the next few years, many manufacturers may begin to offer automated driving system (ADS) that are equipped with various automation features. With their anticipated impacts, ADS definitely have the potential to lead to real and far-reaching ramifications in our society.

Non-orthogonal multiple access (NOMA)-based cooperative relaying systems (CRS) are very promising to overcome spectral inefficiency of conventional cooperative communications. Although NOMA-CRS have great recent attention, almost all studies investigate NOMA-CRS only in terms of capacity and outage probability. Error performances of NOMA-CRS have not been well-studied. In this paper, we analyze error performance of NOMA-based diamond relaying network (NOMA-DRN) with imperfect successive interference canceler (SIC) as a NOMA-CRS scheme. We derive exact bit error probability (BEP) for NOMA-DRN and provide a tight approximated BEP in the closed-form. In addition, high-SNR analysis is conducted to present that NOMA-DRN has an error floor. Moreover, it is proved that NOMA-DRN turns out to be a non-equiprobable communication system and we derive priori probabilities of symbols. All derived expressions are validated via computer simulations.

The papers in this special section focus on advanced vehicle power propulsion systems. Electrification f vehicles is one of the major trends in current research and development. In coming years, answers regarding more electric-based propulsion systems for a smooth and decided transition into more efficient and environmentally sensitive propulsion systems are required. Researches and developments should respond to these challenges by investigating more advanced energy storage, power propulsion and energy conversion systems in order to ensure better energy performance, reduced operating cost, and higher lifetime of the next generation of electric vehicles.

Prepaid personal communication service users have outnumbered postpaid users. This paper studies the charging issues of an integrated Global System for Mobile Communications (GSM) and General Packet Radio Service (GPRS) prepaid service, where a single prepaid account provides a user both voice and data services. The call setup and charging procedures for GSM and GPRS are presented using the Customized Applications for Mobile network Enhanced Logic network architecture. To reduce the probability of terminating both ongoing voice and data calls, we suggest that no new call be admitted when the user credit is below a threshold. An analytic model has been developed to evaluate the performance of the approach. Computer simulations have also been used to verify the results. The numeric results indicate that the force-termination probability can be significantly reduced by choosing an appropriate threshold of the user credit

In this paper, we investigate the uplink transmission performance of low-power wide-area (LPWA) networks with regards to coexisting radio modules. We adopt long range (LoRa) radio technique as an example of the network of focus even though our analysis can be easily extended to other situations. We exploit a new topology to model the network, where the node locations of LoRa follow a Poisson cluster process (PCP) while other coexisting radio modules follow a Poisson point process (PPP). Unlike most of the performance analysis based on stochastic geometry, we take noise into consideration. More specifically, two models, with a fixed and a random number of active LoRa nodes in each cluster, respectively, are considered. To obtain insights, both the exact and simple approximated expressions for coverage probability are derived. Based on them, area spectral efficiency and energy efficiency are obtained. From our analysis, we show how the performance of LPWA networks can be enhanced through adjusting the density of LoRa nodes around each LoRa receiver. Moreover, the simulation results unveil that the optimal number of active LoRa nodes in each cluster exists to maximize the area spectral efficiency., Comment: This paper has been accepted by IEEE Trans. Vehicular Technology (TVT)

In this paper we investigate energy-optimized wireless video transmission employing a hybrid automatic repeat request. We formulate the problem as maximizing the video quality, subject to a constraint on the wireless transmission energy consumption. We consider multiple parameters in multiple layers in a wireless video transmission system: Transmit power, alphabet size, FEC code rate, maximum number of transmissions, and unequal video data importance. An analytical framework is proposed to include these parameters, which allows us to divide this problem into two sub-problems: data transmission and unequal error protection. The problem is tackled by solving the two sub-problems, which are done by exhaustive search and convex optimization, respectively. Simulations of different videos show that the proposed scheme outperforms methods using conventional data transmission and/or unequal error protection.

Heterogeneous cellular network is considered as an important network architecture to improve spectral efficiency and energy efficiency. Extensive research has revealed that spectral efficiency and energy efficiency could be two conflicting performance metrics under most scenarios. Toward the next generation 5G wireless systems, both spectral efficiency and energy efficiency become increasingly important. How to design a wireless system that jointly considers/optimizes these two performance metrics is critical yet challenging. This paper jointly analyzes the area spectral efficiency and area energy efficiency in a wireless heterogeneous network with inter-tier fractional frequency reuse. Proportional fairness resource allocation is used to balance the spectral efficiency and user fairness within each cell. A theoretical framework is formulated to analyze the area spectral efficiency and area energy efficiency in such a system. Fractional frequency reuse and proportional fairness resource allocation define the optimal power reduction factor and fractional bandwidth partition to maximize the joint area spectral efficiency and area energy efficiency. The system performance is evaluated by using the proposed scheme with different system parameter settings including base station densities, cell fractional bandwidth partition, power threshold, and base station power consumptions. The study shows that fractional frequency reuse with proper settings can significantly increase the area spectral efficiency and area energy efficiency in wireless heterogeneous networks.

The optimization of handover (HO) parameters for in-building systems is investigated in this paper. We proposed a novel methodology that provides in-building base stations with the flexibility to customize HO parameters to specific radio frequency conditions at the cell-edge for different loading scenarios. We propose the use of machine learning and data mining techniques to allow the base stations to autonomously learn and identify characteristic patterns in the received signal strength values (reported by users during the HO process), and apply optimal HO parameters for each case. Our optimization strategy jointly considers the radio frequency conditions at the cell-edge and the load levels of the base stations, to determine optimal HO parameters that maximize the quality of service and guarantee the continuity of service at the cell-edge. We evaluated our methodology with experimental data collected from two fully operational LTE in-building systems deployed in a university campus. Our results show that with our methodology the spectral efficiency at the cell-edge can be greatly improved. Downlink data rate gains at the cell-edge reached a value close to 150% for a certain loading scenario compared to the traditional approach of selecting a unique set of HO parameters for the entire in-building system.

© 1967-2012 IEEE. Multiantenna beamforming has potential to improve the efficiency of simultaneous wireless information and power transfer (SWIPT). Existing designs are focused on the downlink of multiple-input-single-output under the assumption of single-antenna users and negligible energy consumption in users' circuitry, despite the fact that using multiple antennas on the user side can further improve system efficiency. In this paper, novel multiuser collaborative multiple-input multiple-output SWIPT systems are studied under the assumption of nonnegligible circuit energy consumption. Particularly, we convexify and maximize the uplink sum rate of active users, while maintaining the quality of service of their downlink data. The beamformers and durations of both links, and the power splitting factors of individual users are jointly optimized, using semidefinite programming and golden search. Further, the selection of active users is optimized, where all users are assumed to be active in the beginning and those detrimental to the sum-rate maximization are continually deactivated. Evident from simulations, the proposed approaches can eliminate the need for computationally prohibitive combinatorial integer programming at a marginal cost of the sum rate.

High mobility may destroy the orthogonality of subcarriers in OFDM systems, resulting in inter- carrier interference (ICI), which may greatly reduce the service quantity of high-speed railway (HSR) wireless communications. This paper focuses on ICI mitigation in the HSR downlinks with distributed transmit antennas.For such a system, its key feature is that the ICIs are caused by multiple carrier frequency offsets corresponding to multiple transmit antennas. Meanwhile, the channel of HSR is fast time varying, which is another big challenge in the system design. In order to get a good performance, low complexity real-time ICI reduction is necessary. To this end, we first analyzed the property of the ICI matrix in AWGN and Rician scenarios, respectively. Then, we propose corresponding low complexity ICI reduction methods based on location information. For evaluating the effectiveness of the proposed method, the expectation and variance of remaining interference after ICI reduction is analyzed with respect to Rician K-factor. In addition, the service quantity and the bandwidth and computation cost are also discussed. Numerical results are presented to verify our theoretical analysis and the effectiveness of proposed ICI reduction methods. One important observation is that our proposed ICI mitigation method can achieve almost the same service quantity with that obtained on the case without ICI at 300km/h,that is, ICI has been completely eliminated. Numerical results also show that the scenarios with Rician K-factors over 30dB can be considered as AWGN scenarios, which may provide valuable insights on future system designs., Comment: 30 pages, 6 figures, 3 tables

In electric and hybrid electric vehicles, series-connected battery packs are commonly used. Should the state-of-health (SOH) of one or several individual cells deteriorate, the entire battery pack is affected, reducing battery pack capacity, which in turn reduces the maximum distance able to be driven. In order to predict the SOH of the individual battery cells, this paper introduces the concept of effective battery capacitance. Effective capacitance is defined as the local slope of the voltage versus charge curve derived from a third-order polynomial relationship between these parameters. The location of maximum effective capacitance can be used as a means of identifying end-of-life and/or catastrophic failure of battery modules. Four different Toyota Prius battery packs were used in establishing this method in the proof of concept work. This paper presents a linear relationship between maximum effective capacitance and SOH; this relationship is confirmed with a larger dataset.

This paper investigates distributed MIMO precoding design to maximize the weighted sum rate in a heterogeneous network where the multiple input multiple output full-duplex (MIMO-FD) small cells reuse the downlink spectrum of the macro base-station (BS) to exchange backhaul information. The multi-antenna BS transmits signals to the cellular users using the dirty paper coding technique and the MIMO-FD small cells apply FD precoding structures to effectively balance the received signal, the self-interference (SI), and the co-channel interference (CCI). Since the optimization problem is shown to be nonconvex, obtaining the global optimum is challenging. A low-complexity solution with distributed implementation is introduced with proved convergence. By applying the successive convex approximation technique and the duality between the broadcast channel and the multiple access channel, the original nonconvex problem is decomposed into a sequence of convex subproblems, which can be solved analytically and separately at each small cell and macro BS with limited channel state information exchange. Simulation results confirm the convergence and demonstrate the benefits of the introduced algorithm. It is shown that the SI and CCI can be suppressed effectively with sufficient cancellation power and number of transmit antennas at the FD small cells.

In this paper, we consider an energy-harvesting underlay cognitive radio (CR) system, which is comprised of multiple cognitive users (CUs), a common cognitive base station (CBS), and multiple passive eavesdroppers (Es). When CUs transmit to CBS, Es may overhear the confidential transmissions from CUs. In the aforementioned system, all CUs are considered to be equipped with energy harvesters to collect energy from their surrounding environments. Meanwhile, the transmit power of CUs shall be limited by a tolerable interference temperature to guarantee the quality-of-service of a primary user. In order to improve the physical-layer security of energy-harvesting CR systems, we propose an Energy-AwaRe mulTiuser scHeduling (EARTH) scheme, which takes into account both the interference temperature constraint and residual energy level harvested from the ambient surroundings. We consider the use of security-reliability tradeoff (SRT) as a metric to evaluate the performance of proposed EARTH scheme. To be specific, we derive closed-form expressions of outage probability (OP) and intercept probability (IP) for the proposed scheme over Rayleigh fading channels. For the purpose of comparison, the SRT analysis of round-robin scheduling (RRS) and conventional multiuser scheduling (CMUS) methods is also carried out. Numerical results show that the SRT of proposed EARTH scheme is better than that of RRS and CMUS methods, demonstrating the advantage of our proposed scheme against eavesdropping attacks. Additionally, it is illustrated that the sum of IP and OP can be minimized for RRS and CMUS as well as EARTH schemes through a time allocation between the energy harvesting and information transmission phases.

This paper investigates cooperative security in wireless networks, where a source (Alice) intends to transmit a confidential message to a legitimate destination (Bob), with the help of a cooperative jammer (Charlie), coexisting with multiple passive eavesdroppers (Eves). In particular, by assuming knowledge of Bob's perfect channel state information (CSI) but only Eves’ statistical CSIs, secrecy beamforming with artificial noise (AN) is utilized for secure transmission, and cooperative jamming (CJ) is explored to further enhance secrecy. We first derive an accurate closed-form expression for the secrecy outage probability (SOP), and establish the condition under which positive secrecy rate is achievable. Then, we provide a secure transmit design for maximizing the SOP constrained secrecy rate. Moreover, based on a strict mathematical analysis, we characterize the impact of the main channel quality and the number of Eves on transmit design and secrecy performance. Specifically, optimal power allocation ratio between the information-bearing signal and the AN signal increases as the main channel quality improves, and decreases with the number of Eves. Numerical results confirm that our design achieves performance improvement in terms of both secrecy rate and secure energy efficiency, as compared to the approach without CJ.

Most applications of underwater acoustic sensor networks (UASNs) rely upon reliable location information of sensor nodes. However, the harsh underwater environment makes localization more challenging as compared to terrestrial sensor networks. This paper is concerned with a localization issue for UASNs, subject to clock asynchronization and node mobility. A hybrid architecture including autonomous underwater vehicles (AUVs), active and passive sensor nodes is designed, where AUVs act as anchor nodes to provide localization information for sensor nodes. In order to eliminate the effect of asynchronous clocks and compensate the mobility of sensor nodes, an asynchronous localization algorithm with mobility prediction is provided for active and passive sensor nodes. Then, two localization optimization problems are formulated as minimizing the sum of all measurement errors. To solve the localization optimization problems, iterative least squares estimators are designed. In addition, the convergence analysis and Crame r–Rao lower bound for the localization errors are also provided. Finally, simulation results show that the proposed localization approach can reduce the localization time by comparing with the exhaustive search-based localization method. Meanwhile, the asynchronous algorithm in this paper can effectively eliminate the impact of the clock asynchronization and node mobility.

This paper proposes a split power-driving scheme for electromagnetic interference robust design and its automatic stepwise digital slope control for suppression of spectral emission. This analog–digital hybrid method gives power saving effect up to 24.4%, and also production reliability can be improved by utilizing a failure detection and recovery scheme. For feasibility of the proposed electromagnetic compatibility (EMC) scheme, a Local Interconnect Network transceiver prototype is fabricated in a 0.18-μm BCD process, and experimentally verified to provide EMC-enhanced operation with low-cost and low-power overheads.

In this paper, an innovative method to minimize the intermediate dc-link capacitance in a cascaded two-stage combination of a three-phase six-switch power factor correction (PFC) and phase-shifted full-bridge (PSFB) dc/dc stage is introduced and analyzed. The proposed strategy has a significant impact on the minimization of the overall system weight and volume. Fundamentally, the method is based on minimizing the switching fundamental component of the dc-link current and is established by imposing a separate constraint on the phase duty ratios without compromising the unity PFC action. Furthermore, the control loop takes care of regulating the intermediate dc-link voltage along with the final output voltage. A 6 kW laboratory prototype of the integrated three-phase PFC and PSFB dc/dc as a part of auxiliary power unit in more-electric airplanes is developed and designed to validate the proposed algorithm. The experimental results show a conversion efficiency of 95.4% at full load, input total harmonic distortion of 4.1%, power factor of 0.998, output voltage ripple of ±1%, and a reduction of dc-link capacitor by 60%.

This paper proposes a unique integrated and isolated dual-output dc–dc resonant converter, which can interface both HV traction batteries and LV loads. In addition, the proposed topology is bidirectional, capable of delivering power from HV traction batteries to the grid for vehicle-to-grid (V2G) applications. To increase the power density of the converter, the dual-output dc–dc resonant converter combines magnetic components of resonant networks into a single three-winding electromagnetically integrated transformer. The resonant converter uses a half-bridge topology with split capacitors as the resonant network components to further reduce the size of a converter. The variable dc-link voltage strategy is utilized to enhance the efficiency over the entire output voltage range. A 3.3-kW converter is designed and developed for validation of various operation modes, including grid-to-vehicle, V2G, and HV-to-LV charging.

This paper presents a predictive braking control algorithm for electric vehicles with redundant braking actuators, composed of friction brake actuator(s) and electric motor(s). The proposed algorithm is based on a model predictive control framework and is able to optimally tackle several control goals, such as maximization of energy recuperation and wheel slip regulation, while taking into account actuation dynamics and constraints. The braking control algorithm was simulated and experimentally validated on the ROMO research vehicle. The obtained results demonstrate that in comparison with state-of-the-art control techniques, the proposed model predictive control approach is able to reduce the torque tracking error up to 60%, and improve the deceleration during emergency braking up to 10%.

This paper proposes the use of an adaptive control of a hybrid electric vehicle with dual clutch transmission (HDCT). First, this paper shows mathematical equations for the nonlinear system. Then it presents the linearized model for the proposed system. The control objective of the model reference adaptive controller (MRAC) considered in this paper is to minimize fuel consumption and reduce torque interruption in a hybrid electric vehicle. The MRAC can be used to control the electric motor during changes in speed and gear, and the system can adapt to a model that simulates different driver patterns. The effects of using different model responses as input combinations are analyzed in an effort to exploit the over-actuation feature of the system, as is the sensitivity of the performance to various design factors. The simulation results for an HDCT demonstrate that the MRAC achieves reduced torque interruption and less vehicle jerk compared to the conventional method of operation.

Interference alignment (IA) is a remarkable technique to manage interference, and artificial noise (AN) can be utilized to combat one main threat of security, passive eavesdropping. Nevertheless, in the existing schemes AN is only eliminated at each legitimate receiver, which is a waste of energy. In this paper, we propose an AN assisted IA scheme with wireless power transfer (WPT). In the proposed scheme, AN is generated by each transmitter along with data streams, which can disrupt the eavesdropping without introducing any additional interference. Due to the fact that the transmit power of AN should be high enough to ensure the security, energy harvesting (EH) is also performed in the scheme. A power splitter is equipped at each receiver, which can divide the received signal, including desired signal, interference and AN, into two parts: one for information decoding (ID) and the other for EH. To optimize the antieavesdropping performance, the total transmit power of AN is maximized by jointly optimizing the information transmit power and the coefficient of power splitting, with the requirements of signal-to-interference-plus-noise ratio (SINR) and harvested power satisfied. Due to the non-convex nature of the problem, a suboptimal solution is also derived to calculate the closed-form solutions with extremely low computational complexity. Extensive simulation results are presented to show the effectiveness of the proposed scheme.\ud

In this paper, we investigate the power allocation problem in massive multiple-input-multiple-output cognitive radio networks. We propose an orthogonal pilot sharing scheme at pilot transmission phase, where secondary users are allowed to use pilots for channel estimation only when there are temporarily unused orthogonal pilots. Following this, we formulate the power allocation optimization problem of the secondary network (SN) to maximize the downlink sum rate of the SN subject to the total transmit power and primary users' signal-interference-plus-noise-ratio constraints. Next, we transform the original (nonconvex) problem into a convex one by using convex approximation techniques and propose an iterative algorithm to obtain the solution. Furthermore, we prove that the proposed algorithm converges to Karush-Kuhn-Tucker points of the original problem. Meanwhile, the impact of the number of the secondary base station (SBS) antennas or the primary BS (PBS) antennas on the downlink rate of the SN and primary network is theoretically studied. Finally, the numerical results present the downlink sum rate of the SN under different parameters through our proposed algorithm.

Increasing safety and automation in transportation systems has led to the proliferation of radar and IEEE 802.11pbased dedicated short-range communication (DSRC) in vehicles. However, current implementations of vehicular radar devices are expensive, use a substantial amount of bandwidth, and are susceptible to multiple security risks. In this paper, we use the IEEE 802.11 orthogonal frequency-division multiplexing communications waveform, as found in IEEE 802.11a/g/p, to perform radar functions. In this paper, we present an approach that determines the mean-normalized channel energy from frequencydomain channel estimates and models it as a direct sinusoidal function of target range, enabling closest target range estimation. In addition, we propose an alternative to vehicular forward collision detection by extending IEEE 802.11 DSRC and WiFi technology to radar, extending the foundation ofjoint communications and radar frameworks. Furthermore, we perform an experimental demonstration near DSRC spectrum using IEEE 802.11 standard compliant software defined radios with potentially minimal modification through algorithm processing on frequency-domain channel estimates. The results of this paper show that our solution delivers sufficient accuracy and reliability for vehicular RADAR if we use the largest bandwidth available to IEEE 802.11p (20 MHz). This indicates significant potential for industrial devices with joint vehicular communications and radar capabilities.

Vehicular crowdsensing takes advantage of the mobility of vehicles to provide location-based services in large-scale areas. In this paper, mobile crowdsensing (MCS) in vehicular networks is analyzed and the interactions between a crowdsensing server and vehicles equipped with sensors in an area of interest is formulated as a vehicular crowdsensing game. Each participating vehicle chooses its sensing strategy based on the sensing cost, radio channel state, and the expected payment. The MCS server evaluates the accuracy of each sensing report and pays the vehicle accordingly. The Nash equilibrium of the static vehicular crowdsensing game is derived for both accumulative sensing tasks and best-quality sensing tasks, showing the tradeoff between the sensing accuracy and the overall payment by the MCS server. A Q-learning-based MCS payment strategy and sensing strategy is proposed for the dynamic vehicular crowdsensing game, and a postdecision state learning technique is applied to exploit the known radio channel model to accelerate the learning speed of each vehicle. Simulations based on a Markov-chain channel model are performed to verify the efficiency of the proposed MCS system, showing that it outperforms the benchmark MCS system in terms of the average utility, the sensing accuracy, and the energy consumption of the vehicles.

In the vehicular network, the smart car provides variously network services via user equipment (UE). However, the services might be interrupted since the handover procedure of the UE is performed frequently. The existing handover decision schemes in cellular networks are mostly based on received signal strength (RSS) to make the handover decision. However, these schemes suffer from a ping-pong effect and, as a result, perform unnecessary handovers incurring extra signaling overhead and packet loss, which is even more exaggerated in the ultra-dense environment. The failure probability of the handover procedure will increase when the velocity of the car is fast. Therefore, in this paper we propose a navigation-assisted seamless handover (NASH) scheme for reducing unnecessary handovers and the ping-pong effect. NASH uses the massive Multiple Input Multiple Output (MIMO) technique to perform the bicasting scheme and then it utilizes coordination multi-point (CoMP) and carrier aggregation (CA) mechanisms in the handover procedure to avoid the packet loss problem and enhance throughput. Moreover, a dynamic time to trigger (TTT) mechanism is proposed according to the velocity of the car. The simulation results show that the proposed scheme has a better performance in terms of handover latency, packet loss, network throughput, handover failure probability, and the number of unnecessary handovers.

The platooning of connected and automated vehicles has the potential to significantly benefit the road traffic, including enhancing highway safety, improving traffic capacity, and reducing fuel consumption. This paper presents a four-component analysis framework for platoon systems from a networked control perspective, including a literature review by network awareness, unified models of key components, and two application cases for controller synthesis. The networked control perspective naturally decomposes a platoon into four interrelated components, namely, 1) node dynamics (ND), 2) information flow topology (IFT), 3) formation geometry (FG), and 4) distributed controller (DC). The existing literature is categorized under this framework and analyzed according to the component features. The unified mathematical models are derived for platoons with linear dynamics and distributed controllers. As a case study, a distributed controller synthesis method is introduced for homogeneous platoons, which guarantees the internal stability in the presence of a broad class of topologies with/without uniform time-delays. The effectiveness is demonstrated by numerical simulations.

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

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.

A novel hybrid excitation flux reversal machine (HEFRM) is developed for application in electric vehicle propulsion. The proposed machine has a simple reluctance rotor and a stator with conventional ac armature windings and concentrated dc field windings. Besides, there are also permanent magnets (PMs) on the stator teeth. The PMs on each tooth have the same polarity, and the PMs on the adjacent two teeth have different polarities. In this paper, the feasible slot–pole combinations and equivalent circuits of the HEFRM are introduced, and the influences of several key design parameters such as rotor slot number, ratio of field winding to total winding, ferromagnetic pole arc, stator slot opening ratio, and rotor slot opening ratio on torque density and power factor are investigated by finite-element algorithm. Moreover, to verity the advantages of the hybrid excitation, the torque–speed envelop and power factor–speed envelop of the proposed HEFRM are compared to that of its solely PM excited counterpart. Finally, an HEFRM prototype is built and tested to validate the theoretical analysis.

This paper proposes two novel partitioned stator (PS) memory machines based on flux-reversal and switched-flux topologies, respectively. The proposed machines geometrically feature two separate stators with armature winding and hybrid PM excitations, i.e., NdFeB and low coercive force PMs. The proposed PS machines can offer increased available inner stator space for hybrid PM, which is beneficial to the torque improvement. The machine topologies are introduced first in terms of possible PM types with parallel magnetic circuits. The design considerations for hybrid PM are analytically analyzed. Then, the electromagnetic characteristics of the two proposed machines are comprehensively compared by using finite element (FE) method. The commercially available Toyota Prius 2010 machine is referenced as a benchmark for comparison with the optimal PS machine having identical overall dimension in potential context of traction applications. Finally, The FE analyses are validated by experiments on a small-scaled demonstration machine.

Electric vehicles with a distributed drive train configuration provide great possibilities for the improvement of the vehicle dynamics, handling, safety as well as efficiency. In this paper, an energy-efficient torque allocation scheme is proposed for the improvement of traction efficiency and braking energy recovery. In traction conditions, the traction distribution is developed using an objective function of minimizing power loss of four electric motors. In braking conditions, aiming at guaranteeing the braking stability and recapturing the braking energy as much as possible, the changeable distribution of braking torque is obtained based on the ideal front-rear braking force distribution curve, while complying with braking regulations of Economic Commission for Europe. The proposed allocation scheme does not rely on the complex online computation. It is obtained via an offline optimization procedure and utilized for online allocation by simple interpolation. The low calculation effort makes it easy to implement the algorithm on real vehicles. Additionally, a conventional torque allocation is introduced as a contrasting approach. Finally, the simulations are conducted in CarSim and MATLAB/Simulink environment. The results demonstrate that the energy-efficient torque allocation scheme considerably improves the vehicle efficiency and increases the braking energy recovery compared with the conventional approach.

A key issue of real-time applications is ensuring the operation by taking into account the stability constraints. For multisource vehicles, stability is impacted by the multisource interactions. Backstepping control ensures stable control for most classes of nonlinear systems. Nevertheless, no backstepping control in real time has been yet proposed for multisource vehicles. The objective of this paper is to apply the backstepping control to a multisource vehicle with fuel cell and supercapacitors for real-time implementation. A distribution criterion is used to allocate energy between sources. Experimental results demonstrate that the developed backstepping control can be implemented in real-time conditions. The supercapacitors can thus help the fuel cell to meet the requirements of the load with a guarantee of system stability.

The nested array, implemented by nonuniform antenna placement, is an effective approach to achieve $O(N^2)$ degrees of freedom (DOF) with an antenna array of $N$ antennas. Such DOF refers to the number of directions of incoming signals that can be resolved. With the increased number of DOF, an important application of nested array is to nullify interference signals from multiple directions. In this paper, we apply nested array in a massive multiple input multiple output (MIMO) heterogeneous network (HetNet) for interference management. With a nested array, a base station (BS) can nullify a certain number of interference signals based on their directions. Then, a key design issue is how to select the set of interference sources to be nullified at each BS. As the DOF of each BS is used to resolve both desired signals and interference, the number of interference signals that can be nullified depends on the number of users served by the BS. Thus, user association is another factor that impacts the system performance and should be jointly considered with interference nulling. We formulate the joint interference nulling scheduling and user association problem as an integer programming problem, aiming to maximize the sum rate of all users subject to BS DOF constraints. We first investigate the case of interference nulling with a given user association, and propose a scheme to solve a relaxed problem as well as derive a performance upper bound. Then, we propose a distributed joint interference nulling and user association scheme based on a poly matching between users and BSs. Simulation results show that the proposed schemes effectively improve the sum rate and achieves a near optimal performance.

In this paper, we investigate the resource allocation problem in two-tier heterogenous networks (HetNets) with massive MIMO, where two frequency bands, cellular frequency band and millimeter wave frequency band, are used for wireless backhaul links. To coordinate the interference among small cells (SCs), SCs are grouped into multiple SC clusters (SCCs), where SC users are jointly served by SC base stations (BSs) (SBSs) belonging to the same SCC. To eliminate the multi-user and inter-tier interference, we propose a block diagonalization based precoding scheme at macro-BS and each SCC, where the transmit precoding matrix for users is projected onto the null space of interference channels. Following this, we formulate a power allocation optimization problem to maximize the downlink sum rate of the system subject to per-SCC power and users’ quality of service requirements. Since the formulated problem is nonconvex, we transform it into a convex one by appropriate approximation, and a constrained concave convex procedure based iterative algorithm is proposed to obtain the solution. Furthermore, we prove that the obtained solution converges to the local optimum. Meanwhile, according to the structure of the original formulated problem, we divide it into multiple subproblems and propose an optimal iterative water-filling algorithm. Next, we extend the original problem with per-SBS power constraint, which results in that the precoding of each SCC has to be redesigned. On this basis, we propose an iterative water-filling algorithm to obtain the optimal power allocation and precoding. Numerical results are presented to verify the performance of our proposed algorithms.

This paper focuses on the secure transmission of wireless-powered full-duplex (FD) relay systems, where a multiantenna source communicates with a single-antenna destination with the help of a FD relay in the presence of a single-antenna eavesdropper. It is assumed that the FD relay is wireless energy harvesting-enabled, adopting both transmit and receive antennas to harvest energy in a time switching (TS) mode. As the objective of this paper is to maximize the system secrecy rate through jointly designing the energy beamforming vector, the information beamforming vector and the TS coefficient, an optimization problem is formulated. The formulated problem is proved to be nonconvex and the challenge to solve which is to concurrently solve out the three variables. To cope with this difficulty, an iterative algorithm is proposed to convert the formulated optimization problem into three convex subproblems, based on which the closed form solutions for the beamforming vectors are derived and the TS coefficient is obtained. Convergence property of the iterative method is analyzed. Simulations are also done to verify the theoretical derivations in terms of the convergence speed and the secrecy rate. Results reveal that the secrecy rate performance of exploiting transmit antenna together with receive antenna for energy harvesting at the FD relay outperforms only receive antenna case. Moreover, although there exists loopback interference between antennas, the FD relaying can always substantially boost the secrecy rate compared with half-duplex relaying architecture.

All over the world, there is a gigantic stir in the number of subscribers which gave rise to numerous challenges, like interference management and capacity enhancement. The enabling candidates to deal with this plight are the enabling technologies of the 5G wireless communication networks. Though the 5G technologies meet the mounting demands, yet, security remains a vital concern. In this paper, the security issues of 5G wireless communication networks have been emphasized upon, with the game theoretic analysis of bandwidth spoofing attack on the multistage 5G wireless communication network. The intrusion on the relay, small cell access point and base station, which are forming a multistage 5G wireless communication network, is detected using a proposed Adaptive Intrusion Detection System.

With the emergence of in-vehicle applications, providing the required computational capabilities is becoming a crucial problem. This paper proposes a framework named autonomous vehicular edge (AVE) for edge computing on the road, with the aim of increasing the computational capabilities of vehicles in a decentralized manner. By managing the idle computational resources on vehicles and using them efficiently, the proposed AVE framework can provide computation services in dynamic vehicular environments without requiring particular infrastructures to be deployed. Specifically, this paper introduces a workflow to support the autonomous organization of vehicular edges. Efficient job caching is proposed to better schedule jobs based on the information collected on neighboring vehicles, including GPS information. A scheduling algorithm based on ant colony optimization is designed to solve this job assignment problem. Extensive simulations are conducted, and the simulation results demonstrate the superiority of this approach over competing schemes in typical urban and highway scenarios.

Communication-based train control (CBTC) systems are automated train control systems based on continuous and bidirectional train-ground communications. CBTC is the direction of future train control systems. Complex channel condition and frequent handoff can severely affect urban rail CBTC train-ground communication performance, and consequently affect CBTC operation efficiency. Current research regarding CBTC does not consider the impact of train-ground communications on train control performance. This paper studies the joint optimization of communication and control in CBTC systems. With the objective to minimize the optimal operation profile tracking error and energy consumption, linear quadratic cost is defined as the control performance measure. In order to ensure train operation safety, the optimization model puts constraints on train control actions related to safety. Moreover, based on the stochastic channel characteristics and the real-time train position information, handoff decision and train control policy are jointly optimized using deep reinforcement learning. Extensive simulation results based on real-field channel measurements illustrate that the proposed optimization method can significantly improve the train control performance in CBTC systems, and CBTC systems need to sacrifice part of performance to ensure system safety.

This paper presents an integrated control strategy to achieve fast start-up and enable wide input frequency operation in a three-phase six-switch power factor correction (PFC) converter. Limiting the inrush current with simultaneously minimizing the start-up time is achieved by implementing a nonlinear compensator in the voltage control loop. In addition, the concerning issues regarding unity PFC operation with an accurate reference tracking in a system with wide input frequency variation (typical for more-electric-aircraft) are resolved by a second-order compensator integrated with a state-feedback controller in the current loop. For a proof-of-concept verification, a hardware prototype of 6 kW, three-phase six-switch PFC is developed and tested with the proposed integrated control strategy in a wide source frequency range (360–800 Hz) ac input that shows perfect unity power factor operation throughout. Both the start-up time and inrush current are reduced to $ of their values in conventional PI compensation.

The conventional state-of-charge (SoC) estimation methods based on the equivalent circuit model (ECM) integrate all state variables into one augmented state vector. However, the correlations between RC voltages and SoC degrade the stability and accuracy of the estimates. To address this problem, this paper presents an adaptive SoC estimation method based on the split battery model, which divides the conventional augmented battery model into two submodels: the RC voltage submodel and the SoC submodel. Hence, the cross interference between RC voltages and SoC is reduced, which effectively reduces the oscillation in the estimation and improves the estimation accuracy. In addition, the adaptive algorithm is applied on the SoC submodel to improve the system robustness to noise disturbances. A case of a second-order ECM is analyzed and two types of Lithium-ion batteries are employed to verify the universality of the proposed method. Experimental results show that the undesired oscillation is eliminated during the convergence stage and the maximum SoC error is within 1% over a wide SoC range.

Plug-in electric vehicles (PEVs) are equipped with onboard level-1 or level-2 chargers for home overnight or office daytime charging. In addition, off-board chargers can provide fast charging for traveling long distances. However, off-board high-power chargers are bulky, expensive, and require comprehensive evolution of charging infrastructures. An integrated onboard charger capable of fast charging of PEVs will combine the benefits of both the conventional onboard and off-board chargers, without additional weight, volume, and cost. In this paper, an innovative single-phase integrated charger, using the PEV propulsion machine and its traction converter, is introduced. The charger topology is capable of power factor correction and battery voltage/current regulation without any bulky add-on components. Ac machine windings are utilized as mutually coupled inductors, to construct a two-channel interleaved boost converter. The circuit analyses of the proposed technology, based on a permanent magnet synchronous machine (PMSM), are discussed in details. Experimental results of a 3-kW proof-of-concept prototype are carried out using a ${\textrm{220-V}}_{{\rm{rms}}}$ , 3-phase, 8-pole PMSM. A nearly unity power factor and 3.96% total harmonic distortion of input ac current are acquired with a maximum efficiency of 93.1%.

Since 2014, the Formula 1 power unit has consisted of an internal combustion engine equipped with an electrified turbocharger and a powerful electric motor with regenerative braking and boosting capabilities. This hybrid electric powertrain is controlled by a dedicated energy management system, whose decisions significantly influence the achievable lap time, as well as the fuel and electric energy consumption. Therefore, feedback control algorithms must be designed in order to follow lap time optimal strategies and properly react to disturbances. In this paper, we propose a real-time control algorithm based on a two-level model predictive control (MPC) scheme. A high-level controller, based on a convex model of the system, is used to periodically update the optimal strategies, whereas a zone MPC scheme is designed as a linear program to follow these trajectories in an optimal way. Each iteration of the low-level MPC can be calculated within few milliseconds, thus allowing for a suitable update frequency. The optimality of the presented controller is verified using a benchmark simulator, and its performance is finally tested on a third-party high-fidelity nonlinear simulator of the race car.

In this paper, we present a method of utilizing channel diversity to improve the secrecy outage probability and the intercept probability in a cognitive radio (CR) network. With the presence of channel diversity, an intended receiver can achieve a relatively better secrecy outage probability and intercept probability. We consider a CR network in which a single antenna, which maximizes the secrecy capacity, is selected at the CR transmitter. To demonstrate the advantages of our proposed framework, we derive new exact closed-form expressions for the secrecy outage probability and the intercept probability of the secondary network in Rayleigh fading. The positive impact of the number of receive antennas and the negative impact of the number of primary users on the secondary network are shown. The derived secrecy outage probability is found to be in precise agreement with simulation results. The numerical results strongly support our conclusion that maximal-ratio combining of channel diversity can enhance the physical layer security of a multiantenna based CR network. Furthermore, the numerical results reveal the impact of the primary network on the secondary network in the presence of a multiantenna wiretap channel.

Statistical signal transmission (SST) is an emerging orthogonal frequency-division multiplexing (OFDM) technology that can carry additional information over conventional OFDM signal streams without extra bandwidth cost. This additional information can be exploited to carry control information, location/speed data, or cooperative schedule signals, etc., to improve the performance of wireless communications. However, existing detection methods used in this technique work-based mainly on time-invariant channels (i.e., channel state is quasi-static within a detection period), and cannot work well in diverse channel variations caused by different user velocities. In this paper, we propose a mobility adaptive detection method for SST. First, we analyze the influence of nonstatic channels on SST detection and identify a negative factor, called cyclic autocorrelation function whitening effect. Correspondingly, we probe the feasibility of antiwhitening concept and carry out relevant experiments for verification purpose. Based on this, a complete version of mobility adaptive detection method is proposed. Numerical results manifest that the proposed method outperforms the existing ones in high velocity scenarios. In addition, the properties of the proposed method are investigated, based on which we show a specific application for SST to reveal the potential for SST to be used in 5G wireless communications.

The multitier heterogeneous structure of 5G with dense small cells deployment, relays, and device-to-device (D2D) communications operating in an underlay fashion is envisioned as a potential solution to satisfy the future demand for cellular services. However, efficient power allocation among dense secondary transmitters that maintains quality of service (QoS) for macro (primary) cell users and secondary cell users is a critical challenge for operating such radio. In this paper, we focus on the power allocation problem in the multitier 5G network structure using a noncooperative methodology with energy efficiency consideration. Therefore, we propose a distributive intuition-based online learning scheme for power allocation in the downlink of the 5G systems, where each transmitter surmises other transmitters power allocation strategies without information exchange. The proposed learning model exploits a brief state representation to account for the problem of dimensionality in online learning and expedite the convergence. The convergence of the proposed scheme is proved and numerical results demonstrate its capability to achieve fast convergence with QoS guarantee and significant improvement in system energy efficiency.