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A communication-based train control (CBTC) system is an automated train control system using bidirectional train–ground communications to ensure the safe operation of rail vehicles. CBTC systems have stringent requirements for communication availability and latency. Most existing CBTC train–ground communication systems work in the infrastructure mode without train–train communications. Due to unreliable wireless communications and frequent handoffs, existing CBTC systems can severely affect train control performance, train operation efficiency, and the utility of the railway. In this paper, we use recent advances in cooperative relaying to enable train–train communications and, consequently, enhance the train control performance of CBTC systems. Trains act not only as end communication points but as relays for other trains as well. Unlike existing works on cooperative relaying, in this paper, linear quadratic cost for the train control performance in CBTC systems is considered as the performance measure. We jointly consider cooperative relaying and handoff decision issues in CBTC systems. Moreover, to mitigate the impacts of handoff latency on the train control performance, we propose an optimal guidance trajectory calculation scheme in the train control procedure that takes full consideration of the tracking error caused by handoff latency. Simulation results show that the train control performance can be substantially improved in our proposed CBTC system with cooperative relaying.

Effective capacity (EC) is a valuable metric in investigating the impact of the physical-layer infrastructure on the quality of service (QoS) of the data link layer. By means of the EC, this paper provides a link-layer look at the concept of multirelaying transmission. A closed-form expression for the EC of a multirelay network is derived to simplify the computation of this quantity. It is shown that increasing the number of relays leads to an unavoidable reduction in EC. Some techniques such as best relay selection (BRS) and distributed space–time coding (DSTC) are used to solve this problem. It is found that the EC performance of BRS, DSTC, and direct transmission schemes extremely depends on the amount of QoS exponent $\theta$ . Inspired by this dependence, a novel interesting cross-layer relaying transmission framework is introduced, in which the best scheme of transmission is selected for maximizing the EC, according to the amount of $\theta$ and the channel gain. Then, by the goal of EC maximization under a given $\theta$ , a cross-layer fixed power-allocation (PA) algorithm is proposed based on the particle swarm optimization (PSO) algorithm. The numerical results validate our analytical outcomes and evaluate our proposed algorithms. The complexity of the proposed algorithms is analyzed at the end of this paper.

This paper analyzes the feasibility of improving the steering behavior of a trailer self-steering system using variable steering geometry. The motivation is to overcome the performance limitations of passive trailer self-steering systems, without introducing any expensive high-power actuation hardware. The final objective is to realize a low-cost system that reaches the same performance levels as directly actuated steering systems, including the capability of reverse-travel steering and the controllability of the steering angle. With the novel approach described in this paper, only a low-power actuation is required. The objective of this paper is a description of the system structure and a comprehensive assessment of its properties and limitations using simulation experiments.

This paper proposes a methodology for overcoming Mamdani-type inference failures on a fuzzy-logic-based decision-making process applied to collision avoidance in ship navigation. The fuzzy inference failures are observed in three distinct situations: 1) intersected contradictory decision boundaries; 2) an improper transition region between the inference boundaries of nonintersected contradictory decisions; and 3) contradictory decision accumulation under multiple obstacle scenarios. The solutions for overcoming these fuzzy inference failures and their limitations are also discussed in this paper. The proposed solutions consist of insertion of smooth transition regions, determination of the proper size of the smooth transition regions, and use of multilevel decision/action formulations. Furthermore, this paper analyzes a decision-making process for ship navigation, derives input and output fuzzy membership functions (FMFs), formulates an if–then -rule-based fuzzy inference system (FIS), and presents simulation results that support recovery from rule inference failures in several contradictory decision boundary conditions.

In this paper, we develop an approach of embedded Markov chain to analyze the signaling cost of a movement-based location management (MBLM) scheme. This approach distinguishes itself from those developed in the literature in the following aspects. 1) It considers the location area (LA) architecture used by personal communication service (PCS) networks for location management. 2) It considers two different call handling models that determine after a call whether a location update should be performed. 3) It considers the effect of the call holding time on the call handling models. 4) It proposes to use a fluid flow model to describe the dependence between the cell and the LA residence time. We derive closed-form analytical formulas for the signaling cost, whose accuracy is manifested by a simulation. Based on the analytical formulas, we conduct a numerical study to evaluate the influence of various parameters on the signaling cost. The formulas can contribute to the implementation of the MBLM scheme in PCS networks including Fourth-Generation (4G) Long-Term Evolution. The modeling approach developed in this paper can be exploited to model other location management schemes.

This paper deals with the lateral motion control of four-wheel-independent-drive electric vehicles (4WID-EVs) subject to onboard network-induced time delays. It is well known that the in-vehicle network and x-by-wire technologies have considerable advantages over the traditional point-to-point communication. However, on the other hand, these technologies would also induce the probability of time-varying delays, which would degrade control performance or even deteriorate the system. To enjoy the advantages and deal with in-vehicle network delays, an H∞-based delay-tolerant linear quadratic regulator (LQR) control method is proposed in this paper. The problem is described in the form of an augmented discrete-time model with uncertain elements determined by the delays. Delay uncertainties are expressed in the form of a polytope using Taylor series expansion. To achieve a good steady-state response, a generalized proportional-integral control approach is adopted. The feedback gains can be obtained by solving a sequence of linear matrix inequalities (LMIs). Cosimulations with Simulink and CarSim demonstrate the effectiveness of the proposed controller. Comparison with a conventional LQR controller is also carried out to illustrate the strength of explicitly dealing with in-vehicle network delays.

This paper presents the modeling and control of an ultrahigh-speed centrifugal compressor for the air management of proton exchange membrane fuel-cell (PEMFC) systems. Centrifugal compressors have the advantages of compactness, high efficiency, and low noise, compared with other kinds of displacement compressors. Moreover, ultrahigh-speed technology can also reduce the size and weight of the compressor, which makes it more feasible for automotive applications. However, the adoption of a centrifugal compressor results in control being difficult because of coupling between mass flow and pressure. In this paper, a neural network model of the compressor is developed, and a decentralized sliding-mode controller based on twisting and super twisting algorithms is proposed and tested to control the compressor pressure and mass flow. The experimental results show good dynamic characteristics and faster response compared with conventional proportional-integral (PI) control.

This paper describes a new function for electrical-power-assisted steering (EPAS) systems applied for hybrid electric trucks (HETs). EPAS systems consume power to provide torque to steer the wheels. The required power must be equal to the power caused by the self-alignment torque that forces the wheels back into the aligned position. This paper describes, for the first time, how self-alignment torque is used to recover energy that is required to turn the wheels. This paper shows that, for a common steering maneuver, a maximum of 20% of the energy required to complete the steering maneuver can be recovered. This energy saving relates to a fuel gain of 2094.5 gal over the lifetime of a truck of 500 000 mi. This paper also shows that the new EPAS function can be embedded in existing systems without changes to the hardware or the motor configuration. An EPAS model is developed in Dymola to show how steering at various vehicle speeds and various steering wheel angles contribute to the energy savings. An EPAS drive has been designed, constructed, and tested for an HET to validate the simulation results.

In this paper, we consider a multiantenna system where the receiver should harvest energy from the transmitter by wireless energy transfer to support its wireless information transmission. To maximize the harvesting energy, we propose the performance of adaptive energy beamforming according to the instantaneous channel state information (CSI). To help the transmitter obtain the CSI for energy beamforming, we further propose a win-win CSI quantization feedback strategy to improve the efficiencies of both power and information transmission. The focus of this paper is on the tradeoff of wireless energy and information transfer by adjusting the transfer duration with a total duration constraint. By revealing the relationship between transmit power, transfer duration, and feedback amount, we derive two wireless energy and information transfer tradeoff schemes by maximizing an upper bound and an approximate lower bound of the average information transmission rate, respectively. Moreover, the impact of imperfect CSI at the receiver is investigated, and the corresponding wireless energy and information transfer tradeoff scheme is also given. Finally, numerical results validate the effectiveness of the proposed schemes.

This paper investigates and compares the performance of wireless sensor networks where sensors operate on the principles of cooperative communications. We consider a scenario where the source transmits signals to the destination with the help of L sensors. As the destination has the capacity of processing only U out of these L signals, the strongest U signals are selected, while the remaining (L - U) signals are suppressed. A preprocessing block similar to channel shortening (CS) is proposed in this paper. However, this preprocessing block employs a rank-reduction technique instead of CS. By employing this preprocessing, we are able to decrease the computational complexity of the system without affecting the bit-error-rate (BER) performance. From our simulations, it can be shown that these schemes outperform the CS schemes in terms of computational complexity. In addition, the proposed schemes have a superior BER performance as compared with CS schemes when sensors employ fixed-gain amplification. However, for sensors that employ variable-gain amplification, a tradeoff exists in terms of BER performance between the CS scheme and these schemes. These schemes outperform the CS scheme for a lower signal-to-noise ratio.

Wireless vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g., vehicle speed and location or traffic signal state and timing). Unless controlled, the aggregate of these broadcasts will congest the channel under dense traffic scenarios, reducing the effectiveness of collision avoidance applications that use transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology. This paper defines a new adaptive congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full-precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This “perfect” convergence avoids the limit cycle behavior that is inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust convergence under all conditions, convergence with asynchronous updates, and using channel load to determine the aggregate message rate that is observable at a receiver. This paper also extends the convergence analysis for two important cases, i.e., measurement noise in the input signal and delay in the update process. This paper illustrates key analytical results using MATLAB numerical results and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high-density scenarios.

This paper studies the problem of joint allocation of subchannel, transmission power, and phase duration in relay-enhanced bidirectional multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) networks. The goal of resource allocation is to minimize transmission energy consumption in networks with multiple decode-and-forward relay stations (RSs) under the data rate constraints of user equipment (UE). The challenges of this resource allocation problem arise from the complication of multiple-phase assignments within a subchannel since the RS can provide an additional transmission path from the base station to the UE. Existing research does not fully take into account all of the influential factors to achieve feasible resource allocation for relay-enhanced MIMO-OFDM networks. Green resource allocation (GRA) schemes with reduced computational complexity are proposed in this paper to develop joint allocation of subchannel, power, and phase duration for the UE with the consideration of direct and two-hop communications. Both the separate-downlink (DL)-and-uplink (UL) and mixed-DL-and-UL relaying assignments and the linear block diagonalization (LBD) technique are adopted to obtain the solutions for the proposed GRA schemes. Simulation results show that the proposed GRA schemes can provide comparably better energy conservation with the consideration of quality-of-service (QoS) support.

Because it achieves similar performance to that of orthogonal frequency-division multiple access (OFDMA) with much more favorable envelope characteristics and lower peak-to-average power ratio (PAPR), single-carrier frequency-division multiple access (SC-FDMA) was chosen as the uplink transmission technology for Long Term Evolution (LTE) and LTE-Advanced systems. This choice has provoked a growing interest in this transmission technology; therefore, there is extensive literature on its performance in terms of bit error rate (BER) and PAPR. However, research on its spectral efficiency has been scarce until now. This paper aims to help fill this gap with an analytical study of the spectral efficiency in SC-FDMA when adaptive modulation and coding (AMC) and linear frequency-domain equalization are applied. This paper therefore computes the spectral efficiency for zero-forcing (ZF) and minimum-mean-square-error frequency-domain equalization (MMSE-FDE) for different Nakagami-m fading channels.

This paper studies the precoder design of bidirectional networks in which each node is equipped with multiple antennas. In contrast to the conventional model of bidirectional relay networks, this paper considers a more general scenario, i.e., a multi-user multi-relay network. First, by assuming that perfect channel state information (CSI) is available, we investigate the precoding design for the relays and users to minimize the sum mean squared error (MSE) and the maximum of single user's MSE, respectively. Then, we consider a more practical scenario where CSI estimation error is taken into account. By means of alternating optimization approach, we decompose the main problem into several decoupled subproblems with tractable solutions. It is shown that, in both the perfect and imperfect CSI cases, the proposed precoding algorithms outperform the existing solutions in terms of MSE and bit-error-rate (BER) performance.

It has been shown that adaptive power allocation can provide a substantial performance gain in wireless communication systems when perfect channel state information (CSI) is available at the transmitter. In the case when only imperfect CSI is available, the performance may significantly degrade, and robust power-allocation schemes are developed to save this kind of degradation. In this paper, we investigate power-allocation strategies for multicarrier systems, in which each subcarrier employs single amplify-and-forward (AF) relaying scheme. Optimal power-allocation schemes are proposed by maximizing the approximated channel capacity under an aggregate power constraint (APC) and a separate power constraint (SPC). By comparison with the uniform power-allocation (UPA) scheme and the best channel power-allocation (BCPA) scheme, we confirm that both the APC and SPC schemes achieve a performance gain over benchmark schemes. In addition, the impact of channel uncertainty is also considered in this paper by modeling the uncertainty regions as bounded sets, and results show that the uncertainty can degrade the worst case performance significantly.

In this paper, we investigate novel soft mutual information forwarding (MIF) protocols in a two-way relay channel (TWRC), where two sources exchange information with the help of an intermediate relay. Based on the estimated signals from the two sources, the relay calculates the soft mutual information and then broadcasts it to the two sources. In particular, we propose two MIF protocols, namely, network-coded MIF (NC-MIF) and superposition-coded MIF (SC-MIF), which is suitable for different channel conditions. The expressions derived for the received signal-to-noise ratio (SNR) at the receivers reveal that, if both source-to-relay channels are in good condition, the NC-MIF outperforms the SC-MIF. Otherwise, the SC-MIF is superior to the NC-MIF. For the TWRC with varying channels, we further develop an adaptive scheme, which enables the dynamic switch between the two protocols, depending on the received SNR at the sources. Furthermore, the threshold that determines the switch of the protocols is developed as a close-form expression. Simulation results show that our adaptive scheme outperforms other protocols discussed in this paper over fading channels.

Next-generation cellular networks will provide users with better experience employing smaller cells, which results in high dynamics and strong interference. Conventional intercell interference coordination (ICIC) approaches are based on fixed or dynamic frequency reuse, which more or less underutilize frequency resources or reduce network-wide performance. As a result, the benefit brought by ICIC is questionable in practical networks. In this paper, we tackle this problem from the self-organizing network (SON) viewpoint by optimizing fractional frequency reuse (FFR) and adapting to dynamic traffic maps. The approach proposed by this paper formulates this problem as an optimization problem with multiple key performance indicators (Multi-KPIs), and a traffic-based dynamic spectrum management (DSM) algorithm is proposed to reduce the call drop and block ratio (CDBR) and to also improve the network throughput. To reduce further the cost of spectrum partition and assignment, we conduct data mining for the traffic maps from realistic networks and then obtain traffic stable states for a longer specific period of time. DSM based on the stable states (DSM-SS) approach brings further benefits for Long Term Evolution networks in terms of operational costs. Simulation results show that the proposed schemes significantly outperform the traditional schemes.

In this paper, the tradeoff relationship between the spectral efficiency (SE) and energy efficiency (EE) of homogenous cellular networks in which the BSs are arbitrarily distributed is investigated. The network performance metrics of SE and EE are assessed subject to a downlink transmission outage constraint in interference-limited operational environments. The EE is expressed in closed form as a function of SE, based on which the performance bounds of the network are derived. Unlike the traditional inverse relationship between SE and EE, it is found in this paper that there exists an operational regime for which both the SE and EE increase while satisfying the outage requirement, and the density of base stations (BSs) simultaneously sharing the spectrum is optimal. The difference in the performance achieved for the SE when operating in the EE maximizing mode as compared with the SE maximizing mode strongly depends on the received signal-to-interference ratio (SIR) threshold. In the SE–EE tradeoff regime, the analytical tools from microeconomics theory are applied to determine the optimal BS density with respect to the utility achieved by the network operator via balancing the SE and EE objectives. Numerical results show that, by tuning a preference factor toward either the SE or EE metrics, it is feasible to realize Pareto optimal performance.

In this paper, we investigate the effect of channel state information at the transmitter (CSIT) on the performance of a class of transmission schemes for multi-input–multi-output (MIMO) systems that can be collectively called space modulation (SMod) schemes. Space shift keying (SSK) is a simple example of SMod. In SMod, multiple antennas at the transmitter are employed to spatially modulate the information. The constellation vectors at the receiver are linear combinations of columns of the channel matrix, and therefore, the symbol error rate (SER) performance of the system highly depends on the Euclidean distance between each pair of these vectors. In this paper, we propose two novel methods to design the transmit vectors using CSIT such that the distance between each pair of constellation vectors at the receiver becomes larger, which, in turn, reduces the SER. Whereas in the first method we do not impose any constraint on the structure of the transmit vectors, in the second method, we confine the transmit vectors to have only one nonzero entry, and as such, it can be looked at as a modified SSK (MSSK) transmission scheme. This is to avoid interantenna synchronization (IAS) as only one transmit antenna is active at a time. Compared to SSK, the first method provides a significant performance improvement at the expense of increased complexity at the transmitter due to IAS. The second method provides an appealing SER improvement while keeping the complexity the same as that of SSK. Therefore, one can look at the second method as a modified version of SSK in which instead of transmitting a fixed symbol from one of the transmit antennas, in our proposed method, different transmit powers and phases are assigned to different transmit antennas. As acquiring full CSIT is difficult in some cases, we also consider the case of imperfect CSIT and derive alternative SMod transmission schemes in this case. By simulation, we show that the proposed SMod transmission schemes provide a significant performance improvement in terms of the SER in the presence of full or imperfect CSIT.

With macro and micro base stations (BSs) of different transmission and processing capabilities jointly deployed, heterogeneous networks effectively extend the coverage and capacity of wireless networks and have been adopted as one of the key technologies in Fourth-Generation-Long-Term Evolution (4G-LTE). To fully exploit the benefits of heterogeneous networks, proper mobile association, interference management, and radio resource management schemes need to be developed. In this paper, we investigate radio resource allocation for heterogeneous networks with cooperative relays, where the relay nodes (RNs) with in-band backhaul act as micro BSs and are able to serve user equipment (UE) either independently or cooperatively with the BSs. In such a network, radio resource-allocation schemes need to decide for each resource block: 1) whether a UE is served by a BS or an RN, and 2) whether a UE should be cooperatively served or not. In making these decisions, radio resource consumption on RN's in-band backhaul links and fairness among the UEs should be taken into consideration, which further complicates the resource-allocation problem. In this paper, we propose a radio resource-allocation framework and derive a resource-allocation strategy that is asymptotically optimal on the proportional fairness metric. The derived resource-allocation scheme gives insights on the optimal radio resource allocation for the heterogeneous networks with cooperative RNs using in-band backhauls.

This paper proposes a systematic method to develop electrohydraulic power steering (EHPS) motor speed maps. To overcome the weaknesses of the existing method, which depends on the test driver's steering feel at a proving ground, this paper utilized theoretical approaches and experimental developments to develop an optimal EHPS motor speed map. Steering torque of the target vehicle according to steering angle and vehicle speed was estimated through theoretical calculations. For the theoretical estimation of steering torque, tire properties of the target vehicle were measured, vehicle dynamics were employed to estimate slip angles, and the steering system was modeled. These estimation results were verified through field tests and then applied to the resistant motor of the EHPS Hardware-In-the-Loop Simulation (HILS) system, which represents the moments generated between the ground and tires. As the experimental development of EHPS motor speed map, an EHPS HILS system was set up, and the concept of desirable steering torque was established to quantify steering feel and catch-up effect. By means of the desirable steering torque, steering feel that was subject to the test driver's hands had criteria at various driving conditions. As the final procedure of this paper, the developed motor speed map was numerically compared with the existing map developed by the existing method, and then, it was verified through field tests with the target vehicle. The developed motor speed map provided sufficient steering assist to the driver and prevented the catch-up effect under all driving conditions. In addition, the steering torque from both maps had the same profiles at all driving conditions, even if the developed map demands lower motor speeds up to 1950 r/min than the existing map at almost all driving conditions.

Chained turbo equalization (CHATUE) has been recently recognized as a low-complexity frequency-domain turbo equalization technique that eliminates the necessity of transmitting the cyclic prefix (CP), and hence allows for spectrally efficient signaling in wireless communications. However, two issues arise from the original version of CHATUE (referred to as CHATUE1) as a consequence of eliminating the CP, which are the noise enhancement and the latency due to the time-concatenated structure. This paper proposes a new version of CHATUE (referred to as CHATUE2) to solve the noise enhancement problem. CHATUE2 retrieves the circulant structure of the channel matrix, originally inherent within the CP transmission, by utilizing composite replica signals that combine the received signals and the soft reference signals replicated from the log-likelihood ratio (LLR) fed back from the decoder. For this purpose, this paper determines the optimal combining ratio based on the minimum-mean-square-error (MMSE) criterion. CHATUE2 is hence able to achieve an improvement in bit-error-rate (BER) performance over CHATUE1. In addition, this paper provides a solution to solving the latency problem by making a practical assumption on the training sequence (TR) transmission, which is required to perform channel estimation generally, in practical systems. Furthermore, this paper proposes a new channel estimation technique, i.e., chained turbo estimation (CHATES), which improves the spectrum efficiency and asymptotically achieves the Cramer–Rao bound (CRB). CHATES assumes that the TR length is exactly equal to the channel impulse response (CIR) length, although the conventional technique requires twice as long or even longer TR lengths. Numerical results show that CHATUE2 with CHATES achieves a gain of 1 dB over conventional turbo equalization with a CP at a BER of $\hbox{10}^{-5}$ in realistic propagation scenarios represented by channel sounding measurement data and in model-based frequency-selective fading channels.

This paper presents a passive actuator fault-tolerant (FT) controller for a class of overactuated nonlinear systems and its experimental investigations on an electric vehicle. As the actuator fault information is unknown before the fault detection and diagnosis procedure finishes, the passive FT control is of great necessity in maintaining system stability and achieving acceptable performance. In this paper, three types of actuator faults are considered, and a passive FT controller that works for all of the studied fault types is designed. By grouping control efforts that have similar effects on the system into the same subsystem, the proposed control method can automatically distribute the higher level control signals to the actuators while minimizing a defined cost function. The FT control method was applied to control of a four-wheel-independently-actuated (FWIA) electric ground vehicle. Experimental results obtained on a FWIA electric vehicle show the effectiveness of the proposed method.

This paper examines the use of channel feedback to adaptively determine pilot placement among subcarriers in amplify-and-forward (AF) orthogonal frequency-division multiplexing (OFDM) relay systems. With feedback of the previous channel estimate, pilot subcarriers at the source and relay can be reassigned to reduce channel estimation error and ensure that good subcarriers are made available for data transmission. The minimum effective signal-to-noise ratio (SNR) among data subcarriers is utilized as the performance metric for the optimization of the pilot locations. The effective SNR is defined as the receive SNR at the destination, taking into consideration the source-to-relay (SR) and relay-to-destination (RD) channels, as well as their respective channel estimation errors. The feedback gain, which is defined as the ratio between the minimum effective SNR achieved with the optimal feedback-aided pilot placement and that achieved with the equal-spaced pilot placement, is used to measure the gains achieved through feedback. In this paper, we first derive an analytic lower bound on the optimal feedback gain and show its rate of increase with respect to the number of subcarriers. These studies are performed for systems both without and with subcarrier pairing (SP) at the relay. Specifically, in systems without SP, the relay forwards each datum on the same subcarrier on which it is received. In systems with SP, the relay is allowed to reassign the subcarriers used to forward the data received from the source. With the goal of maximizing the minimum effective SNR, the optimal SP scheme is shown to be an inverse mapping between the SR and RD subcarriers with respect to their channel gains. Due to the complexity of finding the optimal placement, a suboptimal but efficient algorithm called the iterative pilot relocation (IterPR) scheme is then proposed. The effectiveness of the proposed IterPR schemes is demonstrated through numerical simulations.

This paper considers the problem of network-based mobile-phone positioning when multiple providers are available. This paper proposes two cross-provider localization algorithms via a cooperative approach, which are called linear regression (LR) and locally cooperative embedding (LCE), to efficiently exploit the complementary information from each network provider. The former chooses the provider weights that minimize the mean-square positioning errors. The latter dynamically embeds the cooperation and iteratively refines the positioning process according to each provider's local accuracy surrounding the user. This paper applies the proposed cross-provider positioning algorithms to actual Global System for Mobile Communications (GSM) networks, using a dual-subscriber-identity-module (SIM) mobile phone connecting to multiple providers simultaneously. On-site experiments clearly demonstrated that the proposed cross-provider approaches provide better positioning accuracy than any individual provider, where LCE achieves the best performance.

This is the second paper in a series of two that describe our research in intelligent energy management in a hybrid electric vehicle (HEV). In the first paper, we presented the machine-learning framework ML_EMO_HEV, which was developed for learning the knowledge about energy optimization in an HEV. The framework consists of machine-learning algorithms for predicting driving environments and generating the optimal power split of the HEV system for a given driving environment. In this paper, we present the following three online intelligent energy controllers: 1) IEC_HEV_SISE; 2) IEC_HEV_MISE ; and 3) IEC_HEV_MIME. All three online intelligent energy controllers were trained within the machine-learning framework ML_EMO_HEV to generate the best combination of engine power and battery power in real time such that the total fuel consumption over the whole driving cycle is minimized while still meeting the driver's demand and the system constraints, including engine, motor, battery, and generator operation limits. The three online controllers were integrated into the Ford Escape hybrid vehicle model for online performance evaluation. Based on their performances on ten test drive cycles provided by the Powertrain Systems Analysis Toolkit library, we can conclude that the roadway type and traffic congestion level specific machine learning of optimal energy management is effective for in-vehicle energy control. The best controller, IEC_HEV_MISE, trained with the optimal power split generated by the DP optimization algorithm with multiple initial SOC points and single ending point, can provide fuel savings ranging from 5% to 19%. Together, these two papers cover the innovative technologies for modeling power flow, mathematical background of optimization in energy management, and machine-learning algorithms for generating intelligent energy controllers for quasioptimal energy flow in a power-split HEV.

This paper presents a new control method to improve the safety performance of the electric-vehicle (EV) steering system. It is found that the EV steering system exhibits unstable chaotic behaviors at certain speeds, which can deteriorate the steering performance and even make vehicles fall into spin. In this paper, a new dynamic model is proposed to describe the EV steering system, which takes into account the motor drive for EV propulsion. Moreover, both the driver's reaction time and the disturbance caused by irregularities of the road surface are also incorporated into the EV steering model. It can be identified that periodic, quasi-periodic, and chaotic motions occur at the EV steering system with respect to different forward speeds. Thus, a new control scheme, namely the adaptive time-delayed feedback control (ATDFC), is proposed and implemented to stabilize the EV steering system from chaos to stable operation. Finally, the validity of the proposed model and control are verified.

This paper provides a cross-layer analysis of uplink (UL) performance in femtocell (FC) networks. It characterizes the UL physical interference in FC networks and studies its impact on the delay and the data loss rate of constant-bit-rate (CBR) traffic and on the maximum achievable femto user (FU) throughput. This paper derives data-link layer quality-of-service (QoS) performances as a function of physical layer parameters, thereby establishing key cross-layer relationships that can be useful for designing efficient resource-allocation techniques for FC networks.

In this paper, we focus on adaptive time-variant channel estimation for vehicle-to-vehicle (V2V) communications in intelligent transportation systems (ITS) using the IEEE 802.11p physical layer. The IEEE 802.11p pilot pattern is identical to that in the well-known IEEE 802.11a/g (WiFi) standard, which was initially designed for indoor environments with little or no mobility. However, in a V2V drive-by situation, the channel impulse response rapidly changes due to the high relative velocity between transmitter and receiver, as well as the changes in the scattering environment. Hence, for such V2V channels, advanced decision directed channel estimation methods are needed to reach a frame error rate (FER) smaller than 10-1 . Even more importantly, the channels are nonstationary, which implies that the Doppler power spectral density (DSD) and the power delay profile (PDP) change on a timescale comparable with the frame length, which complicates the estimator design. In this paper, we develop an adaptation method for the channel estimation filter that is suitable for the following: 1) the short frame length in IEEE 802.11p; 2) the given pilot structure; and 3) the requirement of only a single received short frame for filter adaptation. We define a set of hypotheses on the support of the DSD and a second set of hypotheses on the support of the PDP. Each hypothesis is represented by a specific subspace spanned by orthogonal basis vectors. For basis vector calculation, we develop a numerically stable algorithm utilizing generalized discrete prolate spheroidal sequences. The adaptation algorithm chooses a hypothesis from both sets such that a probabilistic bound on the channel estimation error is minimized. We implement the hypothesis test by means of a novel subspace selection algorithm that allows utilizing correlated observations of a time- and frequency-selective (2-D) fading process. We validate the adaptive channel estimation scheme in an IEEE 802.11p compliant link level simulation for a relative velocity range from 0 to 111 m/s ≈ 400 km/h ≈ 249 mi/h. Adaptive filtering enables an up to fourfold reduction in the number of required iterations to reach an FER below 10-1 for an Eb/N0 = 12 dB.

This paper presents a Li-ion cell model parameterization technique for hybrid electric and electric vehicle control applications. The proposed method is based on an equivalent electrical circuit (EEC) model of the Li-ion cell and combines the advantages of the two main strategies employed for cell model parameterization, namely, the offline and online procedures. Offline methods are based on the identification of relevant EEC parameter values using a limited set of test data specific to the target cell chemistry. Conversely, online techniques employ adaptive algorithms that update the cell model as it is being used. The novel method presented in this paper employs recurrent offline updates of the EEC parameterization set, and thus, it integrates the advantages of the offline approach, such as flexibility, reduced complexity, and improved run-time performance, with the main benefit of the online counterpart, which is the capacity to adapt the model parameterization to uncharacterized operating conditions. Based on an extensive set of experimental and simulation results obtained from tests specified in the IEC 62660-1 standard, it is shown that the proposed approach offers a significant accuracy improvement over simple offline methods, as well as enhanced runtime speed in comparison with commonly employed online strategies.

In this series of two papers, we present our research on intelligent energy management for hybrid electric vehicles (HEVs). These two papers cover the modeling of power flow in HEVs, the mathematical background of optimization in energy management in HEVs, a machine learning framework that combines dynamic programming (DP) with machine learning to learn about roadway-type- and traffic-congestion-level-specific energy optimization, machine learning algorithms, and real-time quasi-optimal control of energy flow in an HEV. This first paper presents our research on machine learning for optimal energy management in HEVs. We will present a machine learning framework ML_EMO_HEV developed for the optimization of energy management in an HEV, machine learning algorithms for predicting driving environments, and the generation of an optimal power split for a given driving environment. Experiments are conducted based on a simulated Ford Escape Hybrid vehicle model provided by Argonne National Laboratory's Powertrain Systems Analysis Toolkit (PSAT). Based on the experimental results on the test data, we can conclude that the neural networks trained under the ML_EMO_HEV framework are effective in predicting roadway type and traffic congestion levels, predicting driving trends, and learning optimal engine speed and optimal battery power from DP.

The aim of this paper is to develop an active steering assistance system to avoid the rollover of heavy vehicles (HV). The proposed approach is applied on a single body model of HV presented in this paper. An estimator based on the high-order sliding mode observer is developed to estimate the vehicle dynamics, such as lateral acceleration limit and center height of gravity. Lateral position and lateral speed are controlled using a twisting algorithm to ensure the stability of the vehicle and avoid accidents. At the same time, the identification of unsprung masses and suspension stiffness parameters of the model have been computed to increase the robustness of the method. Some simulation and experimental results are given to show the quality of the proposed concept.

Electric vehicles (EVs) with a distributed drive train configuration offer great potential and flexibility for improving system efficiency, performance, reliability, and safety. This paper investigates a torque distribution scheme for a front- and rear-wheel-driven microsized EV to improve drive train efficiency over a wide torque and speed range. The loss model of the traction permanent-magnet (PM) motor is characterized in both the constant-torque and flux-weakening regions. The relationship between motor efficiency and torque at a given speed is then derived. It has been shown that maximum efficiency is achieved if the total torque required by the vehicle is equally shared between the two identical motors. In addition, the distribution of the energy consumption over a New European Driving Cycle (NEDC) is analyzed, and the regions of high speed and low torque are identified to have a high level of energy consumption; in these regions, motor efficiency improvement is the most important. Therefore, this paper further proposes to operate just one motor to provide the total required torque in the low-torque region. A clutch may be employed between one motor and gearbox (differential), thus “switching off” its idle loss (no-load loss and flux-weakening loss) and improving drive train efficiency. An online optimized torque distribution algorithm has been devised based on the motor efficiency map to determine whether the second motor should be disengaged by the clutch in the low-torque region. With the proposed optimization scheme, drive train efficiency can be improved by 4% over the NEDC. Experimental test results validate the proposed torque distribution strategy.

The impending global energy crisis has opened up new opportunities for the automotive industry to meet the ever-increasing demand for cleaner and fuel-efficient vehicles. This has necessitated the development of drivetrains that are either fully or partially electrified in the form of electric and plug-in hybrid electric vehicles (EVs and HEVs), respectively, which are collectively addressed as plug-in EVs (PEVs). PEVs in general are equipped with larger on-board storage and power electronics for charging or discharging the battery, in comparison with HEVs. The extent to which PEVs are adopted significantly depends on the nature of the charging solution utilized. In this paper, a comprehensive topological survey of the currently available PEV charging solutions is presented. PEV chargers based on the nature of charging (conductive or inductive), stages of conversion (integrated single stage or two stages), power level (level 1, 2, or 3), and type of semiconductor devices utilized (silicon, silicon carbide, or gallium nitride) are thoroughly reviewed in this paper.

Electric vehicle technology has been adopting fuel cells (FCs) for hybrid applications over the past few years. Therefore, the development of advanced power electronic systems for the integration of fuel cells with on-board energy management is fundamental for achieving high-performance systems. An FC for vehicular applications is usually a low-voltage current-source like device that produces electricity and heat directly from input hydrogen and oxygen. Most often, it is required that the FCs be stacked for high-voltage dc-link in order to supply the input power for the drivetrain and electric motor drive system. The FC has a nonlinear nature, and it must be controlled to operate in the high-efficiency operating range. Hybrid electric vehicles have physical constraints such as volume and weight under limited cost and expected lifetime. There is a need for high-voltage input/output ratio of dc-dc boost converters to be connected between the FC to the motor drive dc-link. In addition, it is necessary to have low input ripple at the dc-dc boost converter in order to maximize the FC lifetime, and the traditional dc-dc boost converter topologies have poor performance on these specifications. This paper proposes a new dc-dc converter family of topologies aimed at improving the application to electric vehicle power control. This family is defined as floating-interleaving boost converters (FIBCs). The paper will thoroughly show analysis and experimental verification of FIBC's, and they will be compared with conventional boost converter characteristics. The paper supports how performance figures related to the passive components, i.e., the inductor and capacitor, will have better volume and weight, extremely low input current ripple, and improved efficiency and transfer ratio. The analysis presented in this paper shows how to choose the most suitable topology in order to achieve the desired specifications. The selected topology is fully validated experimentally using advanced nonlinear sliding mode control, which has the additional feature of operating even in faulty conditions.

This paper presents a model predictive control (MPC) torque-split strategy that incorporates diesel engine transient characteristics for parallel hybrid electric vehicle (HEV) powertrains. To improve HEV fuel efficiency, torque split between the diesel engine and the electric motor and the decision as to whether the engine should be on or off are important. For HEV applications where the engines experience frequent transient operations, including start-stop, the effect of the engine transient characteristics on the overall HEV powertrain fuel economy becomes more pronounced. In this paper, by incorporating an experimentally validated real-time-capable transient diesel-engine model into the MPC torque-split method, the engine transient characteristics can be well reflected on the HEV powertrain supervisory control decisions. Simulation studies based on an HEV model with actual system parameters and an experimentally validated diesel-engine model indicate that the proposed MPC supervisory strategy considering diesel engine transient characteristics possesses superior equivalent fuel efficiency while maintaining HEV driving performance.

This paper adds some new contributions to the unified alternating current/direct current (ac/dc) power-flow method that is applied to railway power supply systems. These contributions are mainly focused on the way that the unified power-flow problem is implemented. The authors propose a new technique based on graph theory to model the motion of the trains without varying the system topology and dimensions, as well as the solution vector. Furthermore, a new matrix formulation is developed to provide an easy way of setting out the problem. The combination of these techniques makes the unified ac/dc power flow easier to implement, and the comparison among different instants can directly be done, representing the major contribution of this paper. Finally, as a minor contribution, a new technique based on previous matrix formulation is developed to easily obtain all active and reactive power magnitudes in compact form.

This paper provides a full study of a photovoltaic (PV)-aided plug-in hybrid electric vehicle (PHEV) charging facility by investigating the two most challenging technical issues: 1) sizing of the local energy storage (LES) unit and 2) control strategies of the facility. First, the paper proposes a method for determining the optimal size of LES for a charging facility. Second, the paper develops a control strategy for the integration of the PHEV charging stations with the proposed LES and PVs. The proposed LES-sizing method, which is based on optimal control theory, minimizes a cost function based on the average value of kilowatt-hour price, irradiance, and PHEVs' usage patterns. A power-loss/temperature-based battery model and a temperature-based charging strategy previously developed by the authors are utilized to determine the optimal LES size. Afterward, with the optimized facility parameters, a detailed circuit model of the charging facility, including PVs, PHEVs, and LES, is constructed with a real-time simulation system. While an experimental setup for this kind of complex and high-cost system was not readily feasible, real-time simulation was carried out to prove the effectiveness of the proposed control strategy. To validate the effectiveness and accuracy of the real-time simulation, control hardware-in-the-loop (HIL) and power-inverter-based experiments have been carried out at the subsystem level.

In this paper, we propose a novel method for gaze recognition of a driver coping with rotation of a driver's face. Frontal face images and left half profile images were separately trained using the Viola-Jones (V-J) algorithm to produce classifiers that can detect faces. The right half profile can be detected by mirroring the entire image when neither a frontal face nor a left half profile was detected. As an initial step, this method was used to simultaneously detect the driver's face. Then, we applied a regressional version of linear discriminant analysis (LDAr) to the detected facial region to extract important features for classification. Finally, these features were used to classify the driver's gaze in seven directions. In the feature extraction step, LDAr tries to find features that maximize the ratio of interdistances among samples with large differences in the target value to those with small differences in the target value. Therefore, the resultant features are more fitted to regression problems than conventional feature extraction methods. In addition to LDAr, in this paper, a 2-D extension of LDAr is also developed and used as a feature extraction method for gaze recognition. The experimental results show that the proposed method achieves a good gaze recognition rate under various rotation angles of a driver's head, resulting in a reliable headlamp control performance.

This paper studies the information-theoretic metric of outage probability for a half-duplex asymmetric two-way amplify-and-forward (AF) relaying. Unlike current research activities, our main purpose is to examine the impact of traffic asymmetry on system outage probability. First, an exact but two-integral-form outage probability is derived over Rayleigh fading channels. To provide more insight, a closed-form asymptotic outage probability is then obtained. It is shown that the outage probability in the system is determined only by the one-way channel or related to the two-way links, depending on the asymmetric level of the two senders' target rates and the terminals' average transmission powers. Following this analysis, optimum power allocation and opportunistic relay selection are studied to optimize the overall performance of the considered AF scenario. Next, with the aid of traffic knowledge, novel power allocation and relay selection criteria are proposed, followed by an analysis of the outage probability with the relay selection. Simulation experiments are performed to validate the proposed studies in this paper. The results show that the proposed power allocation and relay selection methods can achieve significant performance gains in terms of outage probability, regardless of symmetric or asymmetric traffic and channels.

Cell search and synchronization in the Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE) system is performed in each user equipment (UE) by using the primary synchronization signal (PSS) and secondary synchronization signal (SSS). The overall synchronization performance is heavily dominated by robust PSS detection, which can be achieved in the conventional noncoherent detector by exploiting the essential near-perfect autocorrelation and cross-correlation properties of Zadoff-Chu (ZC) sequences. However, a relatively high computational complexity is observed in conventional algorithms. As compared with them, two new detectors, i.e., almost-half-complexity (AHC) and central-self-correlation (CSC) detectors, are proposed in this paper to achieve reliable PSS detection with much lower complexity by exploiting the central-symmetric property of ZC sequences. The complexity of the proposed CSC detector is only 50% that of the AHC detector, which achieves exactly the same PSS detection accuracy as that of the conventional detector with one half of complexity being saved. An improvement of CSC, i.e., CSCIns, is also proposed in this paper to combat the large-frequency offset. The performance of the CSCIns detector is independent of the frequency offset, and numerical results show that the 90% PSS acquisition time of CSCIns can be well within an 80-ms duration, even in a heavy intercell-interference environment with a signal-to-noise ratio (SNR) of -10 dB.

Given the widespread deployment of orthogonal frequency-division multiplexing (OFDM)-based wireless systems, reliable spectrum sensing of OFDM waveforms is of great interest for future cognitive radio systems. Use of the embedded pilots of OFDM signals has been extensively studied in the literature. Such techniques rely on having correlated pilots to improve detection sensitivity. However, the impact of synchronization in accuracy and implementation impairments has not been thoroughly studied in the literature. This paper discusses the pilot-aided cyclostationary detection (PACSD) method as a practical structure to detect OFDM signals under asynchronous conditions. The presented analysis quantifies the performance of a detection method without exhaustive searching, compared with an ideal system. This paper goes on to investigate the impact of radio-frequency (RF) front-end nonidealities on the overall performance of the PACSD. We develop closed-form expressions for performance loss in the presence of in-phase/quadrature (I/Q) imbalance, carrier frequency offset, phase noise, and sampling clock frequency offset (SCFO). These results show that the most detrimental impairment is that of SCFO, where a 20-ppm offset can result in a 9-dB performance loss. We then present a modified detector structure to compensate for this huge loss. Analysis and simulation show that, using the modified technique, we can get within 2 dB of the ideal no-offset case.

This paper studies the power allocation problem for a cognitive radio (CR) multiple-input-multiple-output (MIMO) system under spectrum sharing with an existing primary radio network. In particular, the ergodic capacity maximization problem is studied in the Rayleigh fading cognitive MIMO channel under both the total transmit power constraint and a set of interference power constraints where each is applied at one of the primary receivers (PRs). This paper considers that the channel from primary transmitter (PT) to secondary receiver (SR), and the channel from secondary transmitter (ST) to SR are both in short-term fading due to the mobility of SR, whereas the channels from ST to PRs are in long-term fading. By using the Lagrangian dual method, the optimal transmit precoding matrix is derived, and some nearly optimal power allocation algorithms are proposed to maximize the bounds of secondary user ergodic capacity. Performance analysis and simulation results show that the proposed schemes can approach the comparative capacity of the optimal power allocation algorithm, which has perfect instant channel state information (CSI), particularly when the antenna number of the ST exceeds the total antenna number of PRs.

WiFi-enabled buses and stops may form the backbone of a metropolitan delay tolerant network, that exploits nearby communications, temporary storage at stops, and predictable bus mobility to deliver non-real time information. This paper studies the problem of how to route data from its source to its destination in order to maximize the delivery probability by a given deadline. We assume to know the bus schedule, but we take into account that randomness, due to road traffic conditions or passengers boarding and alighting, affects bus mobility. In this sense, this paper is one of the first to tackle quasi-deterministic mobility scenarios. We propose a simple stochastic model for bus arrivals at stops, supported by a study of real-life traces collected in a large urban network with 250 bus lines and about 7500 bus-stops. A succinct graph representation of this model allows us to devise an optimal (under our model) single-copy routing algorithm and then extend it to cases where several copies of the same data are permitted. Through an extensive simulation study, we compare the optimal routing algorithm with three other approaches: minimizing the expected traversal time over our graph, maximizing the delivery probability over an infinite time-horizon, and a recently-proposed heuristic based on bus frequencies. We show that, in general, our optimal algorithm outperforms the three, but it essentially reduces to either minimizing the expected traversal time when transmissions are always successful, or maximizing the delivery probability over an infinite time-horizon when transmissions fail frequently. For reliable transmissions and “reasonable” values of deadlines, the multi-copy extension requires only 10 copies to reach almost the performance of costly flooding approaches.; Bus et arrêts avec capacités de transmission WiFi peuvent constituer l'épine dorsale d'un réseau urbain tolérant aux délais, qui exploite les transmissions à courte portée, le stockage temporaire des données aux arrêts, et la mobilité prévisible de bus pour délivrer des informations non en temps réel. Cet article étudie le problème de savoir comment router les données à partir de la source jusqu'à la destination afin de maximiser la probabilité de livraison dans un délai donné. Nous supposons que nous connaissons les horaires de bus, mais nous prenons en compte le caractère aléatoire de la mobilité des bus, dû aux conditions de circulation ou au temps nécessaire pour l'embarquement et le débarquement des passagers. En ce sens, le présent document est l'un des premiers à aborder un scénario de mobilité quasi-déterministe. Nous proposons un modèle stochastique simple pour les arrivées de bus aux arrêts, motivé par une étude des traces réelles recueillies dans un vaste réseau urbain avec 250 lignes de bus et environ 7500 arrêts de bus. Une représentation graphique succincte de ce modèle nous permet de concevoir un algorithme de routage optimal (conformément à notre modèle) utilisant une seule copie des données, puis de l'étendre au cas où plusieurs copies des mêmes données sont permises. Grâce à une vaste étude de simulation, nous comparons l'algorithme de routage optimal avec trois autres approches : minimiser le temps attendu de traversement du graphe, maximiser la probabilité de livraison sur un horizon temporel infini, et un algorithme heuristique récemment proposé basé sur les fréquences de bus. Nous montrons que, en général, notre algorithme optimal surpasse les trois autres, mais il se réduit essentiellement à minimiser le temps de traversement quand les transmissions sont toujours couronnées de succès, ou à maximiser la probabilité de livraison sur un horizon temporel infini lorsque les transmissions échouent fréquemment. Pour les transmissions fiables et des valeurs des délais «raisonnables», l'extension multi-copie nécessite seulement 10 copies pour atteindre quasiment la même performance que celle du flooding, particulièrement coûteux en terme de nombre de transmissions et utilisation de buffers.

International audience; This paper presents a fast and precise electromagnetic-thermal model of a redundant dual star Flux-Switching Permanent Magnet (FSPM) motor for the embedded applications with driving cycles, e.g. Hybrid Electrical Vehicle (HEV), aerospace, etc. This model is based on a prior steady characterization by Finite Element Method (FEM) 2D of the FSPM motor via calculating the instantaneous torque, the normal and tangential components of magnetic flux density (Br and ) of each element of stator as well as rotor for different RMS current densities and different rotor positions. These results are then used in the analytical copper and iron losses models for calculating the instantaneous copper and rotor as well as stator iron losses during one driving cycle. The Lumped Parameter (LP) and Finite Element 2D transient thermal models are then carried out, in which the previously obtained instantaneous power losses are used as heat sources for calculating the temperatures of different motor parts during driving cycles. In the thermal studies, a transformation of irregular slot structure into a regular (rectangular) one is applied to simplify the calculation of winding thermal resistance. The Thermal-Electromagnetic Analysis method in this paper can also be extended for all the other applications with driving cycles. The experimental tests are carried out to validate the analytical and numerical results.

This paper proposes a novel demodulation-and-forward (DMF) scheme for the two-path succussive relay system. While the two-path relaying avoids the data rate loss that occurs in many one-relay cooperative systems, its performance is severely limited by interrelay interference. In this paper, we propose a hybrid DMF scheme for the two-path relay system so that the relays can switch between direct and differential demodulation modes according to channel conditions. The hybrid DMF scheme not only performs better than existing two-path approaches but is easy to achieve synchronization at the relays as well, which is particularly important as a relay receives signals from both the source and the other relay. The proposed hybrid DMF scheme provides an innovative way to implement the two-path relaying scheme.

The papers in this special section focus on the special features and applications supported by connected vehicles (CV) for intelligent transportation systems. CV systems use connectivity (via wireless communications), positioning (via Global Positioning System and digital maps), and data processing to enable vehicles, smart roadway infrastructure, and personal mobile devices to exchange information with each other and to provide road users with both safety and mobility advisories, warnings, and alerts. CV systems are creating new possibilities within the road transportation sector. They promise to reduce greenhouse gas emissions and fuel consumption; improve safety and security; enhance efficiency, mobility, and accessibility; and foster economic opportunities for advanced clean technology jobs and investments. The potential of CVs has been acknowledged with the establishment of ambitious research programs around the globe. In particular, radio spectrum has been allocated in North America, Europe, and Japan for dedicated short-range communications to facilitate safety technologies and applications for Intelligent Transportation Systems. The United States Department of Transportation has been taking steps to mandate car makers to enable vehicle-to-vehicle communication technologies for light vehicles for a safe driving experience. Despite the potential vision of CV systems, there are numerous design challenges remaining to be addressed before their widespread deployment. The aim of this series is to present a collection of high-quality research papers on the state of the art in emerging technologies for CVs, the latest development in standardizations and regulations, as well as the potential services and applications for CV systems.

This paper presents a novel approach to the solution of the energy management problem of a microturbine-powered plug-in hybrid electric vehicle (PHEV). A series hybrid midsize sedan, utilizing a microturbine and a chargeable Li-ion battery stack as its primary energy source and energy storage system, respectively, is modeled in this paper. The equivalent consumption minimization strategy (ECMS) is utilized to minimize the driving cost based on Pontryagin's minimum principle. To identify the equivalent factor (EF), a new concept called the energy ratio is defined, which is demonstrated to be closely related to the EF over all possible trips. By detecting the vehicle position with a telemetry system and measuring the battery state of charge (SOC), the EF is updated in real time and is used as an input for the computation of the ECMS. Simulation results demonstrate that the proposed ECMS exhibits driving cost and diesel consumption equivalent to that determined from numerical dynamic programming. Significantly, the proposed approach reduces the driving cost from 7.7% to 21.6%, compared with a baseline control over both urban and highway cycles. In addition, through numerical simulations, the computational cost of the proposed strategy is demonstrated to be acceptable for industrial applications. Furthermore, because this strategy uses the feedback of the battery SOC, the control performance is insensitive to the control parameter errors.