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This paper presents a decomposed co-design optimization framework for an electric vehicle powertrain equipped with a Continuously Variable Transmission (CVT). We jointly design the sizes of the powertrain components and the energy management strategy, considering the design and the dynamics of the transmission. Specifically, we divide the resulting nonlinear optimization problem into separate hierarchical optimization subproblems leveraging the concept of Analytical Target Cascading (ATC). Furthermore, we compare the proposed decomposition-based strategy with a centralized approach, in which we solve the same problem, and show that the method presented in this paper can be a potential alternative to a centralized optimization formulation for solving the large-scale, complex integrated plant and control system design. Finally, we compare the resulting performance of using a CVT on the vehicle total-cost-of-ownership with that equipped with a Fixed-Gear Transmission (FGT), which is what is commonly implemented in current electric vehicles. Our results show that by jointly optimizing the design of the transmission along with the control strategy, the CVT yields a lower energy consumption and total-cost-of-ownership compared to that of the FGT. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have attracted enormous attention in recent years due to their potentials in mitigating energy crisis and air pollutions. However, the long battery charging time and lack of sufficient charging infrastructure highly restrict the popularization of EVs. In this context, it is promising to establish battery charging and swapping systems (BCSSs) based on the concept of battery swapping services. To optimally achieve the combined operation of BCSSs, this paper proposes a hybrid swapped battery charging and logistics dispatch model in continuous time domain. Identifying the special structure of the mathematical models of the two problems, this paper innovatively formulated the swapped battery charging strategy as the rectangle packing problem and the battery logistics model as the vehicle routing problem. The two models are closely linked by the delivery time of transporting the well-charged batteries from battery charging stations to battery swapping stations. A hybrid optimal operation model of BCSSs is further formulated as a mixed-integer linear programming model by incorporating the interaction between the battery charging and battery logistics. Finally, case studies are conducted on several BCSSs and numerical results validate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) charging at low temperatures will easily accelerate the aging of LIBs and reduce the useful life. This paper applies advanced multi-factors coupling aging model and bi-objective particle swarm optimization (PSO) algorithm to derive suitable charging patterns for LIBs at low temperatures. Based on the results of orthogonal experiments, which consider the effects of charging rate, charging temperature and charging cut-off voltage on battery aging, the low-temperature capacity degradation model was established. Furthermore, two important yet competing charging objectives, including battery health and charging time, are taken into account simultaneously. These two conflicting charging objectives are traded off by solving a bi-objective optimization problem based on battery electrothermal and aging behavior. Combined with PSO algorithm, the optimal low-temperature charging strategy is obtained. As a result, the three-stage constant current and constant voltage (CC-CV) charging strategy is optimized to balance various combinations of charging objectives. Different tradeoffs are compared and analyzed based on the Pareto frontiers. The verification results show that the optimized three-stage CC-CV charging strategy can effectively offer feasible health-conscious charging with desirable tradeoffs among charging speed and battery health under low-temperature charging conditions. [ABSTRACT FROM AUTHOR]

While the vehicular network enables geographically distributed cooperative computation, its mature implementation has long been constrained due to the lack of an effective management platform. In this paper, employing the security and privacy attributes of blockchain, we propose a novel Blockchain-enabled Large-scale Parked Vehicular Computing (BLPVC) architecture to utilize the potential solar energy and vehicular computational resources in the outdoor parking lot. However, the uneven green power supply and random arrival time of electric vehicles compose the highly complex environment. Accordingly, in this paper, to handle the efficient utilization of the distributed resources by blockchain technology, we propose an integrated optimization framework which leverages the green energy utilization and service latency limit among the processes of block generation, task computing, and communication, whereas such a design leads to the mixed-timescale stochastic optimization problem. To this end, corresponding to the dynamic solar energy arrival, we propose a shaped deep deterministic policy gradient (DDPG) algorithm to accelerate the learning rate of computational frequency control in the short-term stage; while in the long-term stage, for the mixed-integer programming (MIP) of task offloading and blockchain parameters adjustment, a series of transformation is employed to preserve convexity. Finally, experiments are carried out on Python demonstrating that the proposed scheme achieves a balanced performance between service latency and distributed resources, while the battery depreciation cost is heavily reduced. [ABSTRACT FROM AUTHOR]

The aim of the paper is to define a limit performance of highly efficient battery electric quadricycles for urban mobility. The vehicle is employed for an energy efficiency competition in which urban concepts compete. A multi-physics (thermo-electro-mechanical) Tank To Wheels (TTW) model has been developed and validated. Given the information on the track route (track map and elevation) and on the throttle input command, the model computes the vehicle power demand and cruising speed. The model is validated by means of both indoor and outdoor experimental tests. The validated model is employed for the optimization of the transmission gear ratio and of the driving strategy to minimize the overall energy consumption on a given track. Design variables are related to the transmission gear ratio and to the throttle command profile. The algorithm aims to minimize the energy consumption, including constraints on the maximum current provided by the battery and on the maximum time available to complete the lap. Comparison with some common driving strategies confirmed the effectiveness of the proposed solution, with a foreseen 9% reduction of the vehicle overall energy demand. The single seater ultra-efficient electric vehicle, compared to other urban quadricycles, has a battery capacity ten times lower and a 50% longer driving range. [ABSTRACT FROM AUTHOR]

In multi-access edge computing systems with energy harvesting (MEC-EH), the mobile devices are empowered with unstable energy harvested from renewable energy sources. To prolong the life of mobile devices, as many computation-intensive tasks as possible should be offloaded to the MEC server. However, when the system states of mobile device and MEC server are unstable, e.g. poor communication channel conditions, a great number of tasks will be executed locally, leading to a long execution time. Even worse, some tasks may be dropped due to low energy levels. To address this problem, in this paper, we propose a multi-relay assisted computation offloading framework for MEC-EH systems. In this framework, a computation task can be executed by offloading to the MEC server with the help of multiple relay nodes, such as the neighboring nodes. We introduce execution cost as a performance metric to incorporate both the task execution time and task failure. We then develop a low-complexity online algorithm, namely MRACO algorithm, to minimize the average execution cost. MRACO algorithm can select the optimal execution strategy for each task from (1) executing the task locally, (2) offloading it to the MEC server directly, (3) offloading it to the MEC server with the help of the most suitable neighboring nodes, and (4) simply dropping it. Moreover, we also develop an algorithm for selecting the suitable neighboring devices to act as relays and determining the optimal task splitting ratio between them. Finally, performance evaluation shows that the proposed MRACO algorithm greatly outperforms the benchmarks in terms of both average execution time and task drop rate. [ABSTRACT FROM AUTHOR]

All-electric ships (AES) are regarded as a promising solution for decreasing greenhouse gas emissions since they are able to utilize clean technologies like fuel cells instead of fossil fuel. A zero-emission hybrid energy system comprising the Proton Exchange Membrane (PEM) fuel cell, battery, cold-ironing, and Recuperative organic Rankine cycle (RORC) is scrutinized in this paper. To use the waste heat produced by PEM fuel cells, a hybrid system which is composed of PEM fuel cells and RORC is suggested. In order to analyze this zero-emission all-electric ship, the real data of a ferry boat, containing the load profiles and routes is used to appraise the possibility of the suggested hybrid system. The structure of the boat and energy sources, along with mathematical models, are represented and examined. Eventually, the hourly energy management of the boat for one specific day is analyzed, and also, to optimize the power dispatch, the Deep Deterministic Policy Gradient (DDPG) is applied in actor-critic architecture. To corroborate the proposed models, the MATLAB software is employed. According to obtained results, the proposed models could ensure effective and efficient ways in marine vessels due to not only their high performances but also permissible energy cost to be a zero-emission ship. [ABSTRACT FROM AUTHOR]

In this paper, a model fusion method (MFM) is proposed for online state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs). Firstly, a particle swarm optimization-genetic algorithm (PSO-GA) method is cooperated with a 2-RCCPE fractional-order model (FOM) to construct battery open-circuit voltage (OCV)-SOC curve, which only relies on a part of dynamic load profile without the prior knowledge of an initial SOC. Secondly, a dual extended Kalman filter (DEKF) algorithm based on a 1-RC model is employed to identify the model parameters and estimate battery SOC with the extracted OCV-SOC curve. Furthermore, battery polarization dynamics in a SOP prediction window is analyzed from two aspects: (1) self-recovery; and (2) current excitation. They are separately simulated using 2-RCCPE FOM and 1-RC model, and then integrated through a model fusion for online SOP estimation, which enables an analytical expression of battery peak charge/discharge current in a prediction window without weakening the nonlinear characteristic of FOM. Experimental results demonstrate the improved performance of the proposed MFM for online discharge SOP estimation, where the mean absolute error and root mean square error are only 0.288 W and 0.35 W, respectively, under the urban dynamometer driving schedule profile. [ABSTRACT FROM AUTHOR]

Drones have been attracting the attention of diverse industries thanks to their superior maneuverability. Logistics companies especially keep trying to utilize drones for fast delivery following the growing market size of e-commerce. Accordingly, methods for safely operating multi-drone have been researched, and many researchers have proposed various optimal or near-optimal routing methods. However, such methods have some problems that cause routing failures or huge routing computation time in a drone-dense space due to many collisions. In this paper, we propose a centralized framework that deals with enormous collisions and obtains collision-free paths rapidly. We first build a drone energy consumption model with a data-driven method using flight experiment data of a commercial drone to estimate the drone battery state-of-charge (SoC). Then, we develop a novel routing method that generates collision-free paths by considering both the congestion of the space and the SoC of each drone. The proposed method is inspired by the VLSI circuit routing method that connects all signal nets among thousands of logic components. Through numerous delivery routing simulations, we confirm that the proposed method achieves a maximum of 6 times higher routing success rate with a 10x faster runtime compared with the state-of-the-art optimal method. In addition, we validate that the proposed method is applicable to delivery routing problems with various drone battery capacities. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the possibility and condition of exploiting wireless-powered signals as a cover for conducting covert communications in unmanned aerial vehicle (UAV) networks. Specifically, we aim to design the hovering altitude of a UAV and its prior transmission probability to strike a balance between energy replenishment and covert communications. We derive the necessary condition for guaranteeing communication covertness subject to an energy replenishment constraint, under which the UAV’s optimal hovering and the prior transmission probability are determined. Our simulation shows that varying the prior transmission probability can significantly improve the achievable communication covertness under the energy replenishment constraint, relative to a baseline scheme with a fixed prior transmission probability. [ABSTRACT FROM AUTHOR]

Wireless charging vehicle to grid (V2G) system is not-so-futuristic. It can maintain the power supply balance and stabilize the grid through a wireless link between vehicles using batteries based on energy supply-demand statistics. Most of the existing privacy preservation methods are based on data tampering approaches (e.g., encryption, adding noise, aggregation), which are not practicable over V2G due to complexity in billing and inaccurate state estimation. In this paper, we develop a novel adaptive demand-side energy management framework by employing federated learning-based privacy preservation for the wireless charging V2G systems. Our framework learns the temporal evolution of energy consumption of dynamic charging electric vehicles in a distributed fashion and exploits the reinforcement learning model for cost-saving and reward maximization. The convergence and the privacy preservation properties of the framework are demonstrated with extensive evaluations. [ABSTRACT FROM AUTHOR]

The use of multiple low-power fuel cells (FCs), instead of a high-power one, in the powertrain of a FC-hybrid electric vehicle (FC-HEV) has recently received considerable attention. This is mainly due to the fact that this configuration can lead to higher efficiency, durability, and reliability. However, the added degrees of freedom require an advanced multi-agent energy management strategy (EMS) for an effective power distribution among power sources. This paper puts forward an EMS based on game theory (GT) for a multi-stack FC-HEV with three FCs and a battery pack. GT is a well-approved method for characterizing the interactions in multi-agent systems. Unlike the other strategies, the proposed EMS is equipped with an online identification system to constantly update the time-varying characteristics of the power sources. The performance of the suggested strategy is investigated through two case studies. Firstly, a comparative study with two other EMSs, dynamic programming (offline), and a competent rule-based strategy (online), is conducted to realize the capability of GT. Secondly, to justify the necessity of online system identification, the degradation effect of each power source on the EMS performance is examined. The carried-out studies show that the total cost (hydrogen consumption and degradation) of the proposed strategy is almost 6% better than the rule-based EMS while keeping a reasonable difference with dynamic programming. Moreover, health unawareness of power sources can increase the hydrogen consumption up to 7% in the studied system. [ABSTRACT FROM AUTHOR]

The growing popularity and penetration of electric vehicles (EVs) bring in challenges and opportunities to the power grid. They represent a huge aggregate electricity consumption from the city power grid, while it is possible to make use of their batteries for supporting vehicle-to-grid (V2G) regulation service. Besides, there is a need to coordinate a collection of EVs in an intelligent manner through the smart transportation system in a smart city. In this paper, we propose a novel smart cross-system framework to support V2G regulation service in a smart city. Specifically, by considering electric buses (EBs) as a particular group of EVs with large battery packs installed and predictable operation schedules, we jointly solve the problems of EB allocation and scheduling with V2G regulation service. First of all, by using the deep learning approach, the short-term city traffic conditions can be predicted via the gated recurrent units (GRUs) neural network. With the predicted traffic conditions, we then formulate the EB allocation and V2G scheduling as a mixed-integer quadratic programming (MIQP) problem. In order to solve this problem effectively, we utilize the Lagrangian dual decomposition method to decouple the main problem into subproblems for EBs and devise a distributed algorithm to solve each subproblem. The simulation results show that our proposed approach is effective in EB allocation and V2G scheduling for the regulation service. Through efficient EB routing strategies under accurate predicted traffic conditions, the power fluctuations of the city power grid can be well flatten by providing the V2G regulation service. [ABSTRACT FROM AUTHOR]

The use of ultracapacitors in fuel cell electric vehicles holds the potential to promote their advancement by significantly extending the fuel cell lifetime. However, the design of suitable control strategies is critical to take full advantage of the additional degree of freedom and limit the principal causes of fuel cell degradation, such as transient operation and start-up/shut-down cycles. This paper studies the energy management of fuel cell-ultracapacitor-battery hybrid vehicles, investigating different criteria for fuel cell operation to find the best trade-off between hydrogen consumption and the number of start-up/shut-down cycles. In particular, the investigated control criteria are fixed-setpoint and charge-balancing operation. A third strategy is considered as a reference for potential performance improvements of predictive energy management. The study reveals that, in fuel cell-ultracapacitor-battery vehicles, the only significant source of fuel cell degradation are start-up/shut-down cycles. Here, harmful transients are limited because ultracapacitors enable stationary fuel cell operation without compromising battery life. Moreover, the investigation shows that the ultracapacitor and battery systems can sustain short driving cycles alone, avoiding fuel cell starts to preserve its life. The simulation results indicate that the fixed-setpoint strategy yields a better trade-off between hydrogen consumption and start-up/shut-down cycles than the charge-balancing one. Additionally, the study demonstrates that a predictive energy management strategy can significantly reduce degradation without hindering system efficiency. [ABSTRACT FROM AUTHOR]

Backscatter communication is an energy-efficient communication technique for the Internet of things (IoT) devices. It enables data transmission by reflecting the incident radio signals. In this paper, we propose a communication mode selection scheme for IoT devices that can communicate using either active transmission or backscattering. In the active transmission mode, the IoT devices can transmit data over narrowband subcarriers using power-domain non-orthogonal multiple access (NOMA). In the backscattering mode, which operates over shorter distance than active transmission, nearby user equipment (UE) devices are used as relays. The UEs receive the backscattered signals from the IoT devices and forward them to the base station. We formulate a connection density maximization problem to select the communication mode used by each IoT device. We determine the IoT device pairing for active transmission mode with NOMA and UE-IoT device association for backscattering mode. The formulated problem is a binary integer programming problem. Although it can be solved optimally, the optimal algorithm incurs exponential computational complexity. Hence, we propose a low-complexity suboptimal algorithm to solve this problem. Results show that our proposed algorithm can enhance the connection density of narrowband IoT systems by up to 64% when compared with using single communication mode. [ABSTRACT FROM AUTHOR]

In heavy-duty vehicle applications, the D+ alternator with the alternator-voltage-regulator (AVR) is widely used to supply electric loads due to lower cost and wide application possibilities. The phase voltage of D+ alternator is usually connected as auxiliary output, such as techometer; however, its wiring may short to ground through the vehicle frame due to the damage of the isolation. This would result in severe thermal events and safety issues. In this paper, a novel phase fault diagnose and protection method is proposed to prevent the thermal event and safety issue for phase auxiliary output fault in D+ alternator system of heavy duty vehicles. The method detects the voltage dip from D+ terminal without extra wiring or external components. Besides, prior art issues such as complex design, high noise sensitivity, and higher cost can be solved. The analysis and LTSpice simulation of D+ voltages in normal conditions and fault conditions set the foundation and parameter design for the proposed method. The experimental platform with a three-phase D+ alternator verified that the proposed method can diagnose and protect the ground fault while the alternator is generating power with rotating speed ranging from 2000 to 6000 rpm, and the AVR can stop the alternator output to prevent the thermal event. As soon as the fault is removed, the AVR can automatically reboot the system and return to a normal operation mode. [ABSTRACT FROM AUTHOR]

Mobile-edge computing (MEC) has been recognized as a promising solution to provide efficient communication and computation capabilities for mobile users (MUs). However, the problem relating to parallel offloading and load balancing with multiple cooperative MEC servers and massive delay-sensitive execution workloads remains to be investigated. In this paper, we study a joint parallel offloading and load balancing policy for such an MEC system. We formulate a long-term system average-cost (i.e., weighted sum of energy consumption and execution delays) stochastic programming problem under MU battery level stability and delay constraints. Our aims include optimizing the data sizes for uplink offloading transmissions for optimized communication and computation resource allocation among multi-users, and maximizing the computation capability utilization of cooperative MEC servers by load balancing. To solve this problem, we first design a Lyapunov-based centralized cost management algorithm (LYP-CCMA) to obtain the optimal system average-cost under the battery level stability constraints. Further, we propose two algorithms based on alternating direction method of multipliers (ADMM) to implement distributed resources allocation. Simulation results verify our analysis and demonstrate the superior performance of our proposed schemes over several baseline schemes. [ABSTRACT FROM AUTHOR]

In this paper, a detailed design methodology of a Resonant Dual Active Bridge (R-DAB) converter is proposed for battery charging applications. The converter design methodology is tailored to the charging profile of a Li-ion battery and is based on unique operating points (UOPs) that derive from a dual control method analysis. Specifically, both Pulse Frequency Modulation (PFM) and Phase-Shift Modulation (PSM) are adopted, to minimize the switching losses and the reactive power at the resonant tank. A detailed flowchart is presented, considering multiple design parameters and the UOPs, so that the resonant tank parameters are not overestimated. Simulation results of a conventional charger are presented, that validate the control method and design procedure. Finally, a 1kW|30A laboratory prototype charger is designed and evaluated, using a closed loop control system. The analysis of the experimental results proves the significance of the dual control method and verifies the proposed design methodology. [ABSTRACT FROM AUTHOR]

In this paper, a battery charging and swapping optimization model is established for electric vehicles (EVs) and battery charging/swapping stations (BCSS). The EVs are categorized into private electric vehicles (PrEVs) and electric taxis (ETs), where the charging behaviors of PrEVs are modeled based on the Monte Carlo (MC) method, and the battery swapping (BS) strategies of ETs are optimized by bi-level dynamic game. Moreover, the voltage deviation of the power grid is considered in the load regulation process of ETs. A “path-location” model is established combining with the Floyd algorithm in the simulation and the IEEE 14-Bus system is used to derive the node voltage. Numerical results show that the proposed strategy can simultaneously increase the revenue of BCSS and ETs and reduce the voltage deviation. [ABSTRACT FROM AUTHOR]

An efficient power battery thermal management strategy under low-temperature conditions can effectively alleviate mileage anxiety associated with electric vehicles and improve environmental adaptability. This paper aims to propose a novel coupled thermal management strategy for range extended electric vehicles (REEVs) power systems containing a range extender and a power battery. First, a novel coupled thermal management structure for REEVs power system is designed. Sequentially, a fuzzy controller is developed based on analysis of control rules for different working conditions on the basis of the structure. Furthermore, the stability of the proposed strategy is strictly proven by Lyapunov’s stability theory and phase plane analysis. Finally, a coupled thermal management model is built in AMEsim, and co-simulation between MATLAB/Simulink and AMEsim is developed. The results show that the proposed coupled thermal management strategy efficiently reduces the warm-up time and the steady-state temperature error of the engine and power battery and improves the energy utilization efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we develop centralized and distributed dynamic resource allocation schemes for an intelligent reflecting surface (IRS) aided energy harvesting (EH) system that optimize transmit power of a source node and phase-shift of passive reflecting elements of IRS. The source node randomly harvests renewable energy from the surrounding environment and performs data transmission with the harvested energy while satisfying statistical packet delay constraints in terms of maximum acceptable delay-outage probability. Our developed schemes do not require the statistical distributions of channel and energy profiles to be known and apply deep reinforcement learning (DRL) algorithm. Simulation results demonstrate the effectiveness of the proposed resource control scheme for IRS-aided EH system in different channel and energy conditions. [ABSTRACT FROM AUTHOR]

Accurate state of energy (SOE) estimation of the battery pack is very important for the electric vehicle's driving range estimation, which is still a very challenging problem under actual vehicle application conditions at present. This paper proposes a method for estimating the SOE of the battery pack based on prediction, taking into account the future voltage and temperature changes of the battery pack. Firstly, a novel definition of the battery pack SOE is proposed, which considers the inconsistency of the battery pack. Secondly, the thermo-electric lumped parameter equivalent circuit model of a single cell in the battery pack is applied. The electrical parameters are identified using the forgetting factor recursive least-squares algorithm, and the extended Kalman filter is used to estimate the SOC of each cell. Finally, the future current, voltage and temperature response of the cell in the battery pack are accurately predicted and iterative calculations are performed to get SOE under the condition that the future output power of the battery pack is predicted by the Markov chain model method. The effectiveness of the proposed method is verified under dynamic conditions at different temperatures. The results show that the above method has high precision and good robustness, and can meet the needs of actual vehicle applications. [ABSTRACT FROM AUTHOR]

Energy storage devices such as batteries and supercapacitors are arousing a growing interest in many areas of industrial applications since they allow improving the dynamic response and efficiency. In this sense, the availability of electric models of batteries and supercapacitors is necessary for the correct sizing, control, and energy management of power systems. In order to identify the parameters of the equivalent electric model, the identification current profile is often a pulse of charge or discharge. Subsequently, these models are validated with a current verification profile. However, the verification profile compared to the identification profile is more dynamic in amplitude and frequency, producing validation errors due to the identification signal's limited bandwidth. This paper proposes a novel Pulse – Pseudo Random Binary Sequence (Pulse – PRBS) identification current profile, with similar spectral characteristics to the verification profile, in order to improve the modeling accuracy. The parameters of a Lithium-ion battery and a supercapacitor are obtained with this new identification current profile and the Optimization Toolbox of Matlab, using the electrical models in Simulink or Simscape. Finally, the models are validated with the Urban Driving Cycle ECE-15 and the Hybrid Pulse Power Characterization (HPPC) for electric vehicle applications. [ABSTRACT FROM AUTHOR]

Recent improvements in vehicle-to-everything (V2X) communication and onboard computing power have enabled the development of control algorithms that jointly optimize the vehicle velocity and powertrain control in Connected and Automated Vehicles (CAVs), commonly referred to as the Eco-Driving problem. This paper presents a novel and computationally efficient algorithm to optimize the velocity planning and energy management in a CAV with a hybrid electric powertrain. The Eco-Driving problem is formulated as a dynamic, constrained optimization problem in the spatial domain, where information about the upcoming speed limits and road topography is assumed known. This problem is solved by embedding an Equivalent Consumption Minimization Strategy (ECMS) into a Dynamic Programming (DP) optimization to obtain a sub-optimal solution that provides results close to the global optimum at a fraction of the computational cost. Further, a multi-layer hierarchical control architecture is proposed as a path to a causal, real-time implementation. The DP-ECMS algorithm is converted into a Model Predictive Control (MPC) framework by using principles of Approximate Dynamic Programming (ADP). This causal implementation is finally benchmarked to a global optimal solution obtained with DP for different scenarios. [ABSTRACT FROM AUTHOR]

This paper focuses on the performance analysis of point-to-point communication, where the source node is powered with a fixed-power battery as well as a harvest-use battery. A time-switching scheme is implemented such that in each time frame the source either harvests energy or sends information to the destination node. Since switching takes place within a communication time frame, energy harvesting may not necessarily be beneficial in terms of improving the maximum achievable spectral efficiency or the energy efficiency of system. Here, we investigate and provide conditions under which harvesting can improve the system performance. We consider a scenario where the transmission power from the fixed battery is constant. Closed-form expressions for the conditions under which energy harvesting is proven beneficial are provided. The effects of important parameters, namely, time switching parameter, circuit powers, and harvested energy, on the system performance and on the condition for harvesting beneficialness, are investigated and discussed. [ABSTRACT FROM AUTHOR]

Lap time is the most relevant performance indicator in electric racing. In order to minimize it, it is important to carefully design the powertrain of the race car in terms of sizing and technology, and meticulously administer the energy available on-board. To facilitate efficient design, this paper presents a convex optimization framework to rapidly compute the minimum-lap-time control strategies for a battery electric race car. Specifically, we first identify a convex model of the electric powertrain, including the battery, the electric motor, and two transmission technologies: a fixed-gear transmission (FGT) and a continuously variable transmission (CVT). Second, assuming an expert driver, we formulate the minimum-lap-time control problem in a convex fashion for a given driving path, and compute its globally optimal solution with second-order conic programming algorithms. Third, we showcase our framework on the Le Mans track by comparing the performance achievable with an FGT and a CVT, and validate it with nonlinear simulations. Our results show that for the given setup a CVT can balance its lower efficiency and higher weight with a higher-efficiency and more aggressive motor operation, significantly outperforming the lap time achievable with an FGT. Finally, we leverage the computational efficiency of our framework to carry out parameter studies on the components, revealing that optimizing the size of the battery and the motor for the specific scenario can considerably improve the achievable lap time, and that the best transmission strongly depends on the sizing decisions. [ABSTRACT FROM AUTHOR]

Accelerated battery degradation, resulting in a reduced capacity, is the main concern when discussing vehicle-to-grid (V2G) services. This paper gives a unique empirical insight into the long-term effects on batteries performing an energy demanding V2G service. To this end, an extensive method for measuring the battery capacity of series produced electric vehicles (EVs) via the DC charge port was developed. The reproducibility of the method is demonstrated with repeated tests on new and aged vehicles and the results are compared with the EVs internal estimates, read via the on-board diagnostics port. The method is also validated with a state-of-the-art battery degradation model. The capacity test method is used to evaluate the battery degradation of a fleet of commercially owned EVs which, as a part of the world's first commercial operation, have been used for V2G services for five years. The EVs have, together with ± 10 kW V2G chargers, been delivering primary frequency regulation for 15 hours per day with a daily energy throughput of 50.6 kWh. The usable battery capacity, initially found to be 23 kWh, after two years is reduced on average to 20.7 kWh and after five years is reduced to 18.9 kWh. [ABSTRACT FROM AUTHOR]

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) network, where the UAV is responsible for sustaining the network with all devices on the ground. The sustainable charge process operates periodically, i.e., in each working period, the UAV flies from and back to a landing position. The initial remaining energy in the batteries and battery storage limits are considered in this study. We assume only the initial remaining network lifetime (RNL) known and no further lifetime updates available, and provide a sustainable design aiming at minimizing the average UAV consumed power while sustaining the network including all devices. We formulate the problem via determining the UAV trajectory and lengths of working period and charging phase, and introduce an iterative algorithm for an efficient solution. The repeats of designed working period are proved to guarantee the sustentation of the whole network. Moreover, when UAV behaves the same in all working periods, the obtained solution is strongly influenced by the initial RNL. To get rid of this influence, we further propose a second approach, namely a transition-based sustainable design, in which a transition state is introduced for improving the RNL, and the UAV updates its behaviour in each working period. The convergence and limit of this transition approach are addressed. After transitions, the network shifts to a steady state, and operates periodically as in the first design. Finally, through simulations, we validate the sustaining performance of both designs and the performance improvement via the transition approach. [ABSTRACT FROM AUTHOR]

The benefits of intermediate couplers (IC) in inductive power transfer (IPT) systems are well documented in the literature. However, the lack of guideline rules makes the selection process of the passive components and operation mode difficult. This paper presents an iterative design methodology based on the equivalent mathematical model of the system to determine the optimal magnetic coupler (MC) dimensions and resonant tanks parameters that operates the system in a quasi constant current mode. It was found that the transconductance function of a series-series (SS) with an intermediate coupler exhibits a quasi-load independent characteristic when the reflected reactance from the receiver side is null. The design methodology then combines the self and mutual coupling profiles of the MC, obtained from a finite element analysis (FEA) tool, with the system specifications and constraints to find all possible solutions. A set of selection metrics are proposed to identify the optimal solution. This work also presents an identification method of the load and mutual inductances based only in the information available from the transmitter side. The proposed method uses the steady-state equations and does not require any form of communication to the receiver side. An experimental prototype was built and assembled in a converted combustion vehicle with efficiency of 89 $\%$. [ABSTRACT FROM AUTHOR]

Energy management plays a decisive role in the design of efficient 48V mild hybrid drives owing to the limited amounts of energy and power. In particular, strong nonlinearities of an electrified engine air path and the high interaction between the 48V mild hybrid powertrain and the electrical system pose a major challenge. Current research results show the fundamental potential of optimization-based energy management strategies. However there is a lack of optimization-based solutions that allow online direct control of the electrified air path and the electric motor. In this context, this study presents a nonlinear model predictive control (NMPC) approach for a 48V mild hybrid powertrain with an electric supercharger, which is able to simultaneously improve the response behavior and energy consumption. The control concept is developed on the basis of a detailed system description. The special features of optimization-based control for the degrees of freedom of the powertrain, i.e., the belt starter generator (BSG) and the electrified air path through a throttle, waste gate, and electric supercharger (eC), are addressed. After an analysis and verification of the control properties, the NMPC is evaluated in dynamic driving cycle simulations through a comparison with a state-of-the-art rule-based control strategy. The investigations show that the NMPC is able to control the system well even under transient situations with a high influence of disturbance variables. In addition, the NMPC allows a specific and fuel saving use of the 48V system while improving the driving dynamics at the same time. [ABSTRACT FROM AUTHOR]

In this article, we propose an ecological predictive cruise control method for connected and automated vehicles (CAVs) using data-driven predicted leading vehicle speed in a car-following scenario. Many existing studies assume that the leading vehicle's behavior is known when planning the trajectory of the ego vehicle. Unfortunately, predicting the future behavior of adjacent vehicles is extremely uncertain and inaccurate for use in control. To overcome this, we adopt the vector autoregressive model (VAR) that is well suited for generating simultaneous forecasts of the response variables when predicting the short-term behavior of the vehicle ahead. As many human drivers behave similarly in a car-following situation, vehicle connectivity is specifically used to drop hints of the behaviors of the cars following the connected car. Once the leading vehicle's future trajectory is predicted, the ego vehicle is controlled in a way that minimizes energy consumption by optimizing its speed trajectory while remaining safe. Through simulation case studies, we demonstrate that our approach can achieve improved energy efficiency considerably than the conventional strategy that cannot predict the speed of vehicles ahead. Given the recent market penetration of vehicle connectivity technologies and advanced driving assistance systems (ADAS), the proposed method is expected to have a high commercialization potential. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are being actively adopted as a solution to sustainable transportation. However, a bottleneck remains with charging, where two of the main problems are the long charging time and the range anxiety of EV drivers. In this research, we investigate the deployment of dynamic charging systems, i.e., electrified roads that wirelessly charge EVs on the go, with a view to accelerating EV adoption rate. We propose a traffic-based deployment strategy, statistically quantify its impact, and apply the strategy to two case studies of real traffic in New York City (USA) and Xi’an (China). We find that our analytical estimates not only closely match the real data, but they also suggest that dynamic charging considerably extends the driving range of popular EV models in urban mobility. For example, when only 5% of the existing roads in New York City are equipped with this technology, an EV model such as the Nissan Leaf will approximately maintain its battery level without stopping to recharge. If the percentage of charging roads is increased to 10%, then the Leaf will gain nearly 10% of its battery after every 40 kilometers of driving. Our framework provides a solution to public and private organizations that support and facilitate vehicle electrification through charging infrastructure. [ABSTRACT FROM AUTHOR]

We propose an energy-efficient supervisory control method for the power management of parallel hybrid electric vehicles (HEVs) to improve the fuel economy and reduce exhaust gas emissions. Plug-in HEVs ((P)HEVs) have multiple power sources (e.g., an engine and motor) that should be cooperatively operated to meet the required instantaneous traction power for the desired vehicle speed while satisfying their physical limits. Because the efficiencies of the engine and motor vary with different operating speeds and torques, the main issue of energy-efficient power management is to allocate the power demand among the power sources by achieving maximum power conversion efficiencies and satisfy the operating limits. For an efficient power allocation, an optimal control problem is formulated, and a global solution is found through deterministic dynamic programming (DP). Owing to the curse of dimensionality and uncertainties in real driving, DP solutions are not directly applicable in real time. To resolve the limitations of DP, we employ a non-parametric Bayesian function approximation technique using a Gaussian process (GP). The offline DP solutions obtained from a set of real vehicle driving test data were used to learn a state-dependent probabilistic value function through Gaussian process regression. For online implementations, a receding horizon control scheme was applied for the feedback control of the power management. In comparison with the existing charge sustaining strategy and charge depleting and charge sustaining mixed controllers, we recorded fuel efficiency improvements of over 4.8% and 7.3%, respectively, in a mixed urban-suburban route. [ABSTRACT FROM AUTHOR]

Wireless rechargeable sensor networks (WRSNs) employ wireless charging transfer technology to provide dynamic energy replenishment for sensors. Previous related research considers enhancing the flexibility of WRSNs with the use of wireless charging vehicles (WCVs). Although this research prolongs network lifetime to some extent, there still exist two essential drawbacks: off-road limitation and traveling speed limitation. Benefiting from the intrinsic flexibility, high flight speed, low cost and small size, wireless charging drones (drones) have great potentials to be used to charge sensors in WRSNs. However, suffering from limited battery capacities, it is impossible to use drones for charging sensors in large WRSNs independently. To conquer the drawbacks of WCVs and drones, we propose a novel WRSN model which integrates WCVs, WCV-carried drones and independent drones to construct an efficient novel charging system. The proposed system, which aims to extend network lifetime and to minimize the time of charging scheduling on premise of satisfying charging requests with the cooperation of WCVs and drones. Thus, we develop a novel charging scheduling scheme, which divides the sensing region into three circular sub-regions for scheduling the WCVs or drones collaboratively. Sensor nodes located in the inner ring are charged by independent drones directly from the base station. Nodes located in the middle ring are charged by WCVs, while the outer ring charged by WCV-carried drones. Simulation results show that our scheduling scheme outperforms multiple WCVs which either employ temporal or distantial priority scheme and earliest-deadline first scheme with respect to the lifetime and network charging efficiency. [ABSTRACT FROM AUTHOR]

A simultaneous wireless information and power transfer (SWIPT)-assisted cognitive overlay-based multiple input multiple output (MIMO) downlink (DL) cooperative relaying system has been considered wherein a secondary network comprising of an energy harvesting (EH) base station (BS) and possibly multiple fully wireless powered secondary users (SUs) share the spectrum of a primary network for information transmission and energy harvesting. In exchange, the secondary BS acts as a relay for the primary network and aids in transmitting information from the primary BS to the primary users (PUs) along with its own information using non-orthogonal transmission and the harvested energy. Closed-form expressions for the end-to-end outage probability (OP) at all the PUs and SUs have been obtained considering Rayleigh fading channel links and also sensitivity and saturation impairments at the energy harvesting nodes. Moreover, all the energy harvesting nodes have been assumed to be equipped with a finite battery for storing the harvested energy and rely solely on this stored energy for running the transceiver circuitry. Closed-form expressions have also been obtained to characterize the degradation due to the energy harvesting impairments and finite battery on the end-to-end OP of all the users. Furthermore, closed-form expressions for the asymptotic outage floor and diversity order have been obtained to analyze the performance of the system at high transmit power. Finally, simulation results have been presented to validate the analytical results derived and provide further insights into the performance of the system. [ABSTRACT FROM AUTHOR]

As a typical energy storage in hydraulic hybrid powertrain, the hydraulic accumulator has high power density but low energy density. There are some efforts in improving the energy density of hydraulic energy storage to achieve balanced performance. Therefore in this study an electric-hydrostatic energy storage system is proposed to replace hydraulic accumulator in a hydraulic hybrid wheel loader. Through active control of proposed energy storage, constant system pressure is possible to provide good vehicle drivability. A rule-based energy management strategy without estimating final drive power is proposed. The vehicle performance and fuel consumption with different constant system pressures are studied. Results show the vehicle fuel consumption is reduced by 5.9% with constant system pressure reduced from 33 to 27 MPa. The energy density and power density of proposed energy storage are calculated, showing a much higher energy density and slightly lower power density than gas-charged accumulator. [ABSTRACT FROM AUTHOR]

Current research on Deep ReinforcementLearning (DRL) for automated on-ramp merging neglects vehicle powertrain and dynamics. This work considers automated on-ramp merging for a power-split Plug-In Hybrid Electric Vehicle (PHEV), the 2015 Toyota Prius Plug-In, using DRL. The on-ramp merging control and the PHEV energy management are co-optimized such that the DRL policy directly outputs the power split between the engine and the electric motor. The testing results show that DRL can be successfully used for co-optimization, leading to collision-free on-ramp merging. When compared with sequential approaches wherein the upper-level on-ramp merging control and the lower-level PHEV energy management are performed independently and in sequence, we found that co-optimization results in economic but jerky on-ramp merging while sequential approaches may result in collisions due to neglecting powertrain power limit constraints in designing the upper-level on-ramp merging controller. [ABSTRACT FROM AUTHOR]

This article proposes a predictive energy management strategy of plug-in hybrid electric vehicles by real-time optimization and data-driven calibration. The powertrain modelling and physical constraints, including engine, battery, and generator, are simplified by polynomial fitting approximations, which reserve the system nonlinearities with acceptable accuracy. To mitigate the control complexity, the physical constraints of engine, generator, and battery, are merged into a unified one by methodical derivatives. The nonlinear model predictive control problem is established, and the continuation/ general minimal residual (C/GMRES) algorithm is proposed for real-time optimization. Since the original C/GMRES algorithm can only deal with the equality constraints, the external penalty method is adopted for inequality constraints handling. To tackle the parameters’ tuning difficulties, the Bayesian optimization (BO) algorithm is proposed. Based on the prior knowledges of closed-loop experiments, the map between parameters and objective can be described by Gaussian process, and the control parameters can be optimized with few evaluations in BO. Moreover, owing to the real-time applicability of C/GMRES algorithm, the time of closed-loop experiments is reduced so that the calculation time of BO calibration can be saved, exhibiting the superior design for predictive energy management. Simulation and hardware-in-the-loop validations are carried out and verify the energy-saving effectiveness and real-time applicability for proposed approach. [ABSTRACT FROM AUTHOR]

A new equivalent consumption minimization strategy (ECMS) is proposed for a hybrid electric vehicle in which the equivalent factor varies dynamically with the current speed and the minimum engine operation time that leads to improvement in vehicle fuel economy and battery performance. To construct the strategy, a self-organizing map neural-network was utilized to recognize and group the speed patterns. The driving cycles corresponding to these speed patterns were reconstructed for solving the corresponding optimal equivalent factors. When applying the strategy on the road, the current vehicle speed pattern is recognized by using the historical vehicle speed data, and the corresponding optimal equivalent factor is adopted based on that pattern. Furthermore, to constrain the battery state of charge (SOC) to avoid exceeding the allowable values and to consider the hysteresis of engine starting, a fuzzy logic controller was used to dynamically adjust the minimum engine operation time. The simulated results indicated that the proposed ECMS improved the vehicle fuel economy by 7.87% and 6.23% over the conventional ECMS and the rule-based single-point strategy, respectively. Furthermore, the peak battery charging-current was reduced by 42.94% and 27.73%, and the peak discharging-current by 62.19% and 56.97%, respectively. In addition, the proposed strategy could lead to better SOC evolution of the battery. [ABSTRACT FROM AUTHOR]

A key task in Internet of Things (IoTs) networks is determining the active time of a set of devices or set covers to monitor static targets. This task, however, requires the battery level of devices. However, in practice, it is impractical to obtain an accurate battery level or channel state information from all devices, especially in large-scale networks. To this end, we present a number of approaches to construct set covers. We first propose a Two-Phase Algorithm (TPA) that requires devices to first determine their probability of being active in each time slot. This probability is then used by a Hybrid Access Point (HAP) to construct set covers. We then introduce learning approaches based on Gibbs and Thompson sampling. The Gibbs sampling based algorithm, aka GB, allows a sink/gateway to learn the best set cover to use over time. Similarly, our Thompson sampling solutions, namely TS-Random and TS-CB, construct set covers iteratively based on the probability that a device successfully monitors static targets. The numerical results show that GB performs better than TS-CB initially but has similar performance to TS-CB in the long term. [ABSTRACT FROM AUTHOR]

The boom of electric vehicles (EVs) has urged new designs and applications of power conversion technologies. Power converters, as one of the most critical components in EV battery systems, have seen significant developments in recent years. However, thus far forward design approaches for power converter topologies have been widely adopted, which are based on engineers’ empirical knowledge and instinct. This has been deemed having low design efficiency with high randomness and limited applicability, due to the absence of a systematic guideline. This approach, though widely adopted, can no longer meet the increasingly stringent and diverse needs of today’s research and industry, with the emerging trend of renewable energy sources. Aware of these drawbacks, in this study, we propose a novel reverse design approach through establishing general polynomial relations between the topology and voltage gain of high-step-up converters, which are found to be functions of basic boost converters’ voltage gains. With this newly formed polynomial relation, a generalized approach to devising power converters for various industrial needs can be derived, which is baptized the General Polynomial Reverse Design (GPRD) approach. Three polynomial converters are derived as examples to validate the proposed method, whereby their functionality and performances are verified and tested by prototype experiments, which substantiate the proposed design theory. [ABSTRACT FROM AUTHOR]

The equivalent consumption minimization strategy (ECMS) has been considered as a practical energy management strategy for the hybrid electric vehicles (HEVs) because it can be implemented in real time while providing satisfactory performance. However, it is still challenging to adjust an equivalent factor (EF) to its own optimal value in real time because the EF is fundamentally affected by the current driving condition. Although many adaptive ECMSs (A-ECMSs) have been developed to adjust the EF based on a charge-sustaining condition, they do not adequately respond to a change in the driving conditions. In this study, a novel ECMS for the HEVs is proposed to provide the near-optimal performance by considering actual driving conditions. First, the near-optimal condition for the EF is defined to consider a driving condition. Based on it, an iterative scheme is presented to numerically obtain the near-optimal EF. Then, the convergence analysis of the iterative scheme is conducted with practical considerations to implementing the proposed method into real-world applications. Simulation results show that the proposed strategy has better adaptability to changes in the driving conditions with a smaller loss of optimality than conventional A-ECMS which relies only on the charge-sustaining condition. The proposed strategy is also experimentally validated under real-world driving conditions. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) have emerged as one of the most promising solutions for reducing carbon emissions in smart cities. However, due to the limited battery life of EVs and the scarcity of charging stations, EV drivers are not willing to travel long distances. Thus, blockchain-enabled energy trading (BET) has lately been used to securely share energy among EVs via wireless power transfer (WPT) technology. Blockchain is used to ensure the security and privacy of transactions between untrustworthy EVs in the WPT process. Nevertheless, previous works on BET have relied on existing consensus mechanisms built on the requirements of the cryptocurrency systems. These consensus mechanisms have faced significant challenges in maintaining high reliability, throughput, low latency, and network scalability in V2V energy trading that requires real-time services. To address these issues, we propose a new consensus mechanism that leverages the benefits of Practical Byzantine Fault Tolerance (PBFT) and Proof of Reputation (PoR) called PBFT-based PoR (PPoR). The energy trading process runs in a clustered vehicular network, where validator selection, block generation, and consensus processes are performed in each cluster. We adopt an incentive mechanism based on a Stackelberg game model to optimize the utility of sellers, buyers, and validator nodes, which motivates honest and cooperative nodes. The simulation results show that the proposed scheme reduces buyers’ costs by 21.1% while increasing the utility of sellers by 18%. Moreover, compared to benchmarks, the proposed scheme reduces the transaction processing delay and increases the throughput by more than 47.1% and 15.7%, respectively. [ABSTRACT FROM AUTHOR]

Ultra-reliable and low-latency (URLLC) traffic in the upcoming sixth-generation (6G) systems utilize short packets, signalling a distancing from traditional communication systems devised only to support long data packets based on Shannon's capacity formula. This poses a formidable challenge for system designers and network operators. Many URLLC scenarios involve infrastructure-less unmanned aerial vehicle (UAV)-assisted communications. One of the biggest challenges with UAVs is their limited battery capacity, which can cause abrupt disruption of UAV-assisted communications. To overcome these limitations, we consider URLLC-enabled over-the-air charging of UAV relay system using a laser transmitter. Furthermore, we formulate a non-convex optimization problem to minimize the total decoding error rate subject to optimal resource allocation, including blocklength allocation, power control, trajectory planning, and energy harvesting to facilitate URLLC in such systems. In this regard, given its lower complexity, a novel perturbation-based iterative method is proposed to solve the optimization problem. The proposed method yields optimal blocklength allocation and power control for the two transmission phases, i.e., from the source node to the UAV and from the UAV to the robot acting as a ground station. It also maps the UAV trajectory from the initial position to the final position, and the UAV completes the flight using the laser's harvested energy. It is shown that the proposed algorithm and fixed baseline scheme, named fixed blocklength (FB), yield a similar performance as the exhaustive search in terms of UAV energy consumption. In contrast, fixed trajectory (FT) delivers the worst performance. Simultaneously, the proposed method yields the best performance in terms of the lowest average overall decoding error compared to fixed baseline schemes, including FB and FT, showing the efficacy of the proposed technique. [ABSTRACT FROM AUTHOR]

Energy storage systems in recent days are witnessing an increased trajectory of hybridization to decrease the burden on the single energy storage systems in renewable energy sources. The hybridization of energy storage imposes the need for an efficient power-sharing strategy. This article proposes the interval type2 fuzzy logic controller-based power-sharing strategy to utilize hybrid energy storages in the solar-powered charging station effectively. Under this study, the hybrid energy storage system consists of a battery and supercapacitor with solar power generation as a primary source. The battery supports the slow varying power and the supercapacitor supports the fast-varying power. Electric vehicles are connected as load and a multi-step constant current charging technique is proposed to charge its battery. In order to inject/absorb the power, the hybrid energy storage utilizes bidirectional DC-DC converters. The perturb and observe algorithm is used to track the maximum power from the solar panel. The proposed system is designed in MATLAB/Simulink platform and the real-time validation has been carried out through the dSPACE (DS1202) real-time processor. An obtained result demonstrates the performance of the interval type2 fuzzy logic controller-based power-sharing strategy under variable generation and load scenarios. [ABSTRACT FROM AUTHOR]

One of the main challenges for the energy management of heavy-duty fuel cell/battery hybrid vehicles is to control the battery charge within safe operating levels without hindering fuel efficiency. This article proposes a novel method that uses basic route information from navigation systems to implement an optimal and predictive energy management scheme to improve battery charge control, fuel economy, and fuel cell system lifetime. Indeed, it is possible to obtain a rough estimate of the electric load profile using elevation and speed forecasts and optimize the power split before starting the trip. The result of this offline optimization is saved as a location-based battery state of charge (SoC) reference for the online power-split strategy. The novelty of this work is related to the formulation of a quadratic program to generate the predictive SoC reference by optimizing the hydrogen economy while respecting the battery charge constraints. Afterward, a simple but effective online energy management strategy is used to track the predictive references during the simulations. The use of real-world driving cycles is essential to demonstrate the benefits of the proposed method. In particular, it is critical to consider the road elevation because it highly influences the power demand of heavy-duty vehicles. The simulation results prove that using the predictive SoC reference significantly improves the hydrogen economy and battery charge control compared to a non-predictive energy management scheme. In addition, the high-power operation time of the fuel cell system is reduced, resulting in slower system degradation compared to the non-predictive strategy. The effectiveness and robustness of the proposed method are tested against 1750 hours of real-world driving scenarios, considering uncertain speed and vehicle mass information for the reference generation. Statistical and global analyses of the results prove that the implementation of the predictive SoC reference generation method is highly beneficial for the performance of heavy-duty fuel cell vehicles. [ABSTRACT FROM AUTHOR]

Thanks to the feature of on-demand and flexible deployment, unmanned aerial vehicle base stations (UAV-BS) can enhance coverage and capacity beyond current 5 G cellular networks. The main challenge in deploying UAV-BS lies in the fact that the limited battery in UAB-BS cannot support long-time reliable services. Therefore, a serving UAV-BS being running out its power needs to be replaced with a new fully-charged UAV-BS. This is called UAV-BS replacement process. However, most existing UAV-BS replacement methods ignore the link outage problem during the replacement. Thus the outage-free replacement problem (OFRP) for UAV-BSs arises. We provide a method to calculate the outage probability of the UAV replacement approach. We further formulate the Outage Probability Minimization Problem (OPMP) for UAV-BS replacement. To solve this problem, a 3-Dimensional Outage-Free Replacement Approach (3D-OFRA) is presented, which exploits different heights of swapping UAV-BSs during the replacement process. More importantly, we provide the guidelines of determining the distance between two UAV-BSs to minimize the link outage possibility during the UAV-BS replacement process. Our numerical results show that with the recommended distance between two UAV-BSs based on 3D-OFRA, the outage probability of the served UEs approaches to zero, compared with 40% of the traditional direct replacement approach. [ABSTRACT FROM AUTHOR]

This work proposes a methodology for the energy-and cost-efficient 3-D deployment of an unmanned aerial vehicle (UAV)-based aerial access point (AAP), that exchanges a given amount of independent data with a set of ground user equipment (UE). Considering a fly-hover-communicate transmission scheme, the most energy-efficient 3-D hovering points (HPs) of the AAP are determined by decoupling the problem in the horizontal and vertical dimensions. First, we derive analytically the optimal hovering altitude that jointly maximizes the downlink and uplink global energy efficiency (GEE) of the system. Next, we propose the multilevel circle packing (MCP) algorithm to determine the minimal number of HPs and their associated horizontal coordinates, such that the AAP covers all the UEs in the given geographical area. A cost analysis is carried out to observe the variation of both fixed and variable costs; these are then minimized by suitably selecting the AAP's battery parameters, like the depth of discharge (DOD), defined as the portion of battery capacity that is consumed during a discharge cycle, and the velocity of the UAV. Simulation results show that: the UAV energy consumption has a significant impact on the 3-D HPs of the AAP; the time spent during the substitution swap of an out of power AAP has a major influence on the operational cost; the cost of the system can be optimized by suitably selecting the onboard battery and the UAV flight parameters. [ABSTRACT FROM AUTHOR]