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We present the development of a novel smart ink pen instrumented with force and motion sensors designed for the quantitative and ecological assessment of daily-life handwriting. This work aims at testing the pen’s sensors and algorithms and the use of the smartpen to detect age-related changes in writing and tremor parameters during daily-life handwriting. A comparison against reference instruments was carried out to validate the pen tip force during static and dynamic conditions, the pen’s tilt angle, and the algorithm for the segmentation of the force signal into strokes. The smartpen was tested on 43 healthy adults divided into three age groups (young, middle-old, and old) during unconstrained handwriting on paper. The validation of the pen’s sensors and algorithms reported excellent results. A solid test–retest reliability was found in the writing and tremor indicators extracted from both young and old adults. As for the age-related analysis, the older age groups were characterized by an increase of temporal writing measures, a more uniform writing pressure, and more repetitive and predictable tremor oscillation components. The greatest accomplishment of our smart ink pen is the ability to combine the advantages of the digitizing tablet technology with the natural “feel,” the ease of use, and the ecological validity of the traditional pen-and-paper approach. The proposed solution represents an important step toward a simple, ecologically valid, reliable quantitative assessment of daily-life handwriting. Our smartpen is particularly suitable for daily-life telemonitoring applications in a number of important health-related fields. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries using flexible paper-based current collectors have been developed. These current collectors were fabricated from wood microfibers that were coated with carbon nanotubes (CNT) through an electrostatic layer-by-layer nanoassembly process. The carbon nanotube mass loading of the presented (CNT-microfiber paper) current collectors is 10.1 μg/cm^2. The capacities of the batteries made with the current collectors are 150 mAh/g for lithium cobalt oxide (LCO) half-cell, 158 mAh/g for lithium titanium oxide (LTO) half-cell, and 126 mAh/g for LTO/LCO full-cell. The fabrication approach of the CNT-microfiber paper current collectors, the assembly of the batteries, and the experimental results are presented and discussed. [ABSTRACT FROM PUBLISHER]

With the introduction of automated driving, new requirements for the design and control of a vehicle powernet and powertrain arise, since the increasing level of driving automation shifts the responsibility for passengers’ safety from a driver towards automation system. Therefore, the functionality of the subsystems required for the automated transition to a standstill in case of a failure must be guaranteed. The work presented in this paper proposes a novel and generic system architecture for the energy management of a fail-operational powernet and powertrain in fault-free and failure case operation. The underlying control strategy is based on a concept enabling safety based range extension with the main goal to complete a driving mission at the safest possible location for the passengers. The proposed Energy Management System (EMS) approximates a driving trajectory to the destination based on route preview, which is then used for the dynamic optimization of the torque split in a powertrain with multiple motors. In this way, the total energy required for propulsion, which is also used as an input for the energy distribution, can be accurately predicted. The optimal distribution of the energy for the supply of safety-critical subsystems and powernet auxiliaries is then estimated by solving a mixed-integer optimization problem. If the desired driving mission cannot be completed, a three-level-degradation concept adapting the driving mission is applied. The individual modules of the EMS are presented in this paper and a system architecture enabling model predictive and adaptive energy distribution in a vehicle powertrain and powernet with automated fault reactions is proposed and exemplified with simulation results. [ABSTRACT FROM AUTHOR]

Public Bike Systems (PBSs) offer the popular service for the short distance in daily life. The battery powered bike is an interesting and feasible method to extend the bike trip length, which can promote the PBS service but faces the challenges caused by the limited budget for the battery cabinet deployment and user demand. Thus, the realistic problem is how to deploy the cabinets near a part of public bike stations by considering the challenges. This paper is the first to study the novel problem, Cabinet Deployment Problem (CDP) in PBS, based on the features extracted from the real dataset of PBS in Hangzhou China, and proposes our strategies in the case of the Euclidean space and Manhattan model. In the Euclidean space, CDP can be specified as the ${e}$ lectric-bike Set Cover problem (e-SC), and this paper proposes a Greedy Station Coverage algorithm (GSC). Its distributed version, called the Localized Greedy Selection algorithm (LGS), is also presented because of the large amount of bike stations. In many cities, the roads have Manhattan-type directions, i.e., either east-west or south-north. In order to close to the realistic scenario, this paper develops a Genetic Algorithm based Cabinet Search algorithm (GAS) to determine the locations for the cabinet deployment in the Manhattan model. The extensive numerical experiment is conducted for our strategies, which are compared to a straightforward method, the Random Placement Strategy (RPS) under the diverse parameter settings. [ABSTRACT FROM AUTHOR]

This paper is concerned with online coverage of unknown planar environments by a mobile robot of size D operating with a limited energy capacity battery. The battery capacity is represented by the path length L that the robot can travel under a full battery charge. Starting at S, the robot has to cover a planar environment containing unknown obstacles, and return to S upon task completion. During task execution, the robot may return to S at any time to recharge its battery. This paper first describes a battery powered offline coverage methodology, then introduces the battery powered coverage (BPC) algorithm that performs online battery powered coverage using position and local obstacle detection sensors. The performance of the BPC algorithm is measured by its competitiveness, determined by measuring the mobile robot’s total online path length, l, relative to the optimal offline solution lopt. This paper establishes that the BPC algorithm has a competitive performance of l \leq ( L/ D) lopt. This paper additionally establishes a universal lower bound of l \geq \log ( L/ 4 D) lopt over all online battery powered coverage algorithms. Execution example illustrates the usefulness of the BPC algorithm. [ABSTRACT FROM AUTHOR]

A printed lateral resistive fuse-type write-once-read-many (WORM) memory on paper substrate is demonstrated. The memory writing process is based on breaking of a silver nanoparticle conductor. Low-voltage and low-current writability demonstrated with printed batteries are enabled by a \mum^2-range cross-sectional bit area that are achieved by super-fine inkjet technology. Supported by the statistical distribution of the writing times, the bit writing process is attributed to electromigration of silver and the required current density for fusing is found to be 34 mA/\mum^2. The results show an improvement in memory retention time when compared with structurally similar printed antifuse-type WORM memories. [ABSTRACT FROM AUTHOR]

The maximum efficiency point of an inductive power link depends on its load power consumption and the distance between the transmitter and the receiver. Matching networks (MNs) and adjustable ac–dc converters have been proposed in the literature as a means to achieve and track this maximum efficiency point. Most of the published papers in inductive power transmission only use one of these two methods without considering a joint design. In this paper, we analyze and compare both methods proposing a general joint design which exploits the advantages of each method. A proof-of-concept system was developed following the proposed flow, built and tested. This system, with a regulated output voltage, presents improvements in measured efficiencies of up to 80% (from 9.2% to 16%) at the largest distances, thus maximizing the distance range. The same approach can be used to maximize output dc voltage, instead of the link efficiency. The proposed system using our designed MN achieved a maximum output voltage that was 90% higher (from 1.36 to 2.59 V) than the case where only a step-up converter was used. [ABSTRACT FROM AUTHOR]

Battery-free wireless sensor network (BF-WSN) is a newly proposed network architecture to address the limitation of traditional wireless sensor networks (WSNs). The special features of BF-WSNs make the coverage problem quite different and even more challenging from and than that in traditional WSNs. This paper defines a new coverage problem in BF-WSNs which aims at maximizing coverage quality rather than prolonging network lifetime. The newly defined coverage problem is proved to be at least NP-Hard. Two sufficient conditions, under which the optimal solution of the problem can be derived in polynomial time, are given in this paper. Furthermore, three approximate algorithms are proposed to derive nearly optimal coverage when the sufficient conditions are unsatisfied. The time complexity and approximate ratio of the three algorithms are analyzed. Extensive simulations are carried out to examine the performance of the proposed algorithms. The simulation results show that these algorithms are efficient and effective. [ABSTRACT FROM AUTHOR]

This paper presents a novel power flow control system for a remote military microgrid with hybrid energy storage. A combination of batteries and supercapacitors (SCs) is managed by the novel control system to increase the battery life by redirecting the higher frequency current that would have to flow in the battery if SCs were not present. This paper offers a practical solution to manage the SC current and ensure that the SCs are never overcharged or commanded to support the system when they are discharged to the lower operating limit chosen. The new controller allows the independent selection of the low-pass filter parameter and the number of SCs. By making the most out of these two degrees of freedom, we investigate different configurations, identifying the one achieving the highest cash flow for the overall system. Modeling, simulations, and experimental verification are presented and linked to the sensitivity analysis of the economics of the military microgrid. [ABSTRACT FROM AUTHOR]

This paper aims to resolve a clear and unrealistic limitation found in recent works that have proposed solutions to the massive demand for the autonomous electric mobility-on-demand (AEMoD) services. Namely, this paper will focus on the matched charge-to-trip only service by enabling sub-class service; i.e., allowing vehicles to serve customer classes with trips needing less charge. The paper proposes a multi-class management system with a new queuing model for one city zone, and then derives the stability conditions of the system. Proportions of vehicles from each class that will immediately serve customers with sub-class service or full/partial charge are modeled, and the maximum expected response time of the system is then minimized by optimizing these vehicle service decisions. The advantages of the new model are then compared with previously proposed schemes and non-optimized models through simulations. [ABSTRACT FROM AUTHOR]

In this paper, we propose and study an energy-efficient trajectory optimization scheme for unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks. In such networks, a single UAV is powered by both solar energy and charging stations (CSs), resulting in sustainable communication services, while avoiding energy outage. In particular, we optimize the trajectory design of UAV by jointly considering the average data rate, the total energy consumption, and the fairness of coverage for the IoT terminals. A dynamic spatial-temporal configuration scheme is operated for terminals working in the discontinuous reception (DRX) mode. The module-free, action-confined on-policy and off-policy reinforcement learning (RL) approaches are proposed and jointly applied to solve the formulated optimization problem in this paper. We evaluate the effectiveness of the proposed strategy by comparing it with other dynamic benchmark algorithms. The extensive simulation results provided in this paper reveal that the proposed scheme outperforms the benchmarks in terms of data transmission, energy efficiency and adaptivity of avoiding battery depletion. By deploying the proposed trajectory scheme, the UAV is able to adapt itself according to the temporal and dynamic conditions of communication networks. [ABSTRACT FROM AUTHOR]

An uninterruptible power supply (UPS) can reduce power failure loss by responding to power demand during distribution network failures. For reliability-sensitive power consumers, deploying UPSs is a solution for improving power supply reliability. Nevertheless, because of UPS devices’ energy limits and potential failure probability, power consumers with UPSs cannot guarantee 100% adequacy. In future power systems with frequent reconfiguration and massive storage facilities, the power supply adequacy of a consumer with respect to the network configuration and the storage capacity will need to be frequently evaluated. Furthermore, the analytical formulation of a consumer with a UPS should be investigated since the simulation-based method might witness low efficiency and poor convergency. Therefore, this paper proposes a novel analytical availability index calculation framework for adequacy evaluation. The framework models the uncertainties of distribution networks and UPS devices as stochastic processes. Then, the availability index calculation formula is acquired based on the stochastic modeling. Compared with existing methods, this paper incorporates UPS devices’ capacity and characteristics. Moreover, the distribution network configuration and protection device placement are considered. Case studies on an illustrative distribution network, the modified RBTS system, and a real distribution system in China validate the proposed method. [ABSTRACT FROM AUTHOR]

Peer-to-peer interconnection of households having on-site batteries, multi-port converters and solar panels to form a multi-port converter-enabled solar DC nano-grid is an emerging approach for providing affordable energy access in rural areas. Battery charge and discharge losses, distribution losses and converter losses are significant problem when operating such nano-grids. This paper presents a centralized control algorithm that can help address the power loss problem. The proposed algorithm uses a new problem formulation where the power loss problem is formulated as a two-stage convex optimization problem. The first stage of the optimization problem is an optimal battery dispatch problem for determining optimal battery charge and discharge currents. The second stage is an optimal current flow problem for determining optimal distribution voltages which corresponds to the optimal battery currents. Simulation results of the nano-grid show that the proposed algorithm can minimize the nano-grid power losses while facilitating the power exchange between the households. The proposed algorithm is suitable for small nano-grids where privacy of households is not a concern. In Part II of this paper we propose a distributed control algorithm that preserves the privacy of the households especially where the size of the nano-grid is large. [ABSTRACT FROM AUTHOR]

Flexible loads, especially heating, ventilation, and air-conditioning (HVAC) systems can be used to provide a battery-like service to the power grid by varying their demand up and down over a baseline. Recent work has reported that providing virtual energy storage with HVAC systems leads to a net loss of energy, akin to a low round-trip efficiency (RTE) of a battery. In this paper, we rigorously analyze the RTE of a virtual battery through a simplified physics-based model. We show that the low RTEs reported in recent experimental and simulation work are an artifact of the experimental/simulation setup. When the HVAC system is repeatedly used as a virtual battery, the asymptotic RTE is 1. Robustness of the result to assumptions made in the analysis is illustrated through a simulation case study. [ABSTRACT FROM AUTHOR]

This paper introduces a supervised machine learning (ML) approach to predict and schedule the real-time operation mode of the next operation interval for residential PV/battery systems controlled by mode-based controllers. The performance of the mode-based economic model-predictive control approach is used as the benchmark. The residential load and PV data used in this paper are 1-min data downloaded from the Pecan Street Project website. The optimal operation mode for each control interval is first derived from the historical data used as the training set. Then, four ML algorithms (i.e., neural network, support vector machine, logistic regression, and random forest algorithms) are applied. We compared the performance of the four algorithms when using different number of features and length of the training sets extracted from different months of the year. Simulation results show that using the ML approach can effectively improve the performance of the mode-based control system and reduce the computation effort of local controllers because the training can be completed on a cloud-based ML engine. The work presented in this paper paves the way for using a shared-learning platform to design controllers of residential PV/storage systems. This may significantly reduce the cost for implementing such systems. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear dynamic simulation model of an ultracapacitor (UC) bank and the associated control system. The control system at hand consists of two levels: the lower level controls the inverter of the UC bank, while the upper control level is responsible for providing charging/discharging active power set points to be followed by the lower control level. This paper focuses on the development of the upper control level for frequency control. Specifically, two simulation case studies are developed so as to assess the performance of the proposed control framework. In the first case study the upper control level is developed using a classical Proportional-Integral-Derivative (PID) controller. In the second case study the upper control level is devised using a Model Predictive Control (MPC) algorithm based on internal linear prediction model of a nonlinear UC bank. In both cases, a nonlinear UC bank simulation model is used. The simulation case studies are modelled and tested in Matlab/Simulink. The response of the MPC-controlled UC bank is compared to the 3 existing PID-control algorithms for frequency control. The simulation results show that the MPC algorithm outperforms the conventional PID controllers. [ABSTRACT FROM AUTHOR]

In recent years, environmental issues have motivated the wide usage of electric vehicles (EVs) due to their zero tailpipe emission. However, this trend can pose severe challenges to power systems, such as decreasing equipment lifetime. Moreover, the CO2 emission of EVs is closer to that of internal combustion engine vehicles in some cases due to the carbon footprint of EV charging. Modeling the uncertain nature of EV users’ behavior is another obstacle due to the complex dynamics of these uncertainties. To overcome these problems, an overarching day-ahead smart charging method is proposed in this paper from the perspective of distribution system operators (DSOs), EV users, and governments simultaneously. The aim of the proposed method is to minimize the operating cost of microgrids, the degradation cost of EV batteries, and emission cost by scheduling the active and reactive power of EV parking lots integrated with photovoltaic (PV) systems as well as finding the optimum network configuration. Previous model-based methods cannot appropriately model uncertainties in EV users’ behavior because of some statistical assumptions. Nevertheless, this paper employs data-driven methods based on generative adversarial networks (GAN) to represent these uncertainties. The performance of the proposed method is evaluated by implementing it on a real reconfigurable microgrid. The results show that using the proposed method, the DSO and emission costs can reduce by 11.96% and 3.37% compared to the uncoordinated charging of EVs, respectively. Furthermore, the share of sustainable energy in EV charging increases by 9% using the proposed method. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is the key to the performance of fuel cell / battery hybrid system. At present, reinforcement learning (RL) has been introduced into this field and has gradually become the focus of research. However, traditional EMSs only take the energy consumption into consideration when optimizing the operation economy, and ignore the cost caused by power source degradations. It would cause the problem of poor operation economy regarding Total Cost of Ownership (TCO). On the other hand, most studied RL algorithms have the disadvantages of overestimation and improper way of restricting battery SOC, which would lead to relatively poor control performance as well. To solve these problems, this paper establishes a TCO model including energy consumption, equivalent energy consumption and degradation of power sources at first, then adopt the Double Q-learning RL algorithm with state constraint and variable action space to determine the optimal EMS. Finally, using hardware-in-the-loop platform, the feasibility, superiority and generalization of proposed EMS is proved by comparing with the optimal dynamic programming and traditional RL EMS and equivalent consumption minimum strategy (ECMS) under both training and unknown operating conditions. Results prove that the proposed strategy has high global optimality and excellent SOC control ability regardless of training or unknown conditions. [ABSTRACT FROM AUTHOR]

Soft open points (SOPs) are power electronic devices which can replace conventional normally open points in distribution networks. SOPs enable full control of active power flow between the interconnected feeders and can inject reactive power at each node to which they are connected. SOPs integrated with energy storage (ES) have been recently proposed to realize both spatial and temporal flexibility in active distribution networks. The flexibility provided by integrated ES-SOP devices will allow network operators to run their networks closer to their limits, but only if there is appropriate management of the uncertainty arising from demand and renewable generation. The only existing model of an ES-SOP uses nonconvex nonlinear equations, neglects uncertainty, and represents converter losses in an oversimplistic manner. This paper presents a robust mixed-integer convex model for the optimal scheduling of integrated ES-SOPs to ensure a zero probability of constraint violation. Losses of the subsystems comprising the ES-SOP are modeled using a proposed binary-polynomial model, enabling efficient scheduling of the energization state of subsystems to reduce no-load losses. The ES-SOP is considered in this paper to be owned by the network operator to: 1) manage power flow constraints, 2) minimize cost of losses, and 3) maximize arbitrage profit. [ABSTRACT FROM AUTHOR]

This paper presents a reconfigurable dc–dc converter for maximum thermoelectric generator (TEG) energy harvesting in a battery-powered duty-cycling wireless sensor node. The proposed dc–dc converter adopts discontinuous energy harvesting, which operates in single-input dual-output (SIDO) boost, battery-TEG pile-up buck (BTPB), dual-phase buck–boost (DPBB), and battery supplied buck modes with a single shared inductor. Fabricated in a 65-nm CMOS process, the converter adopts an adaptive dead-time controller for zero-voltage switching (ZVS) and an adaptive switch size (ASS) with maximum power point tracking (MPPT) to maximize efficiency in a wide TEG voltage range. The SIDO boost mode achieves 88.5% and 81.1% peak end-to-end efficiency with maximum and no-load conditions (only charging the battery), respectively. The combination of BTPB and DPBB modes to maximize the TEG power extraction during the battery-powered operation results in up to 44% saving in battery power. The battery-supplied buck mode achieves 93.3% peak efficiency. [ABSTRACT FROM AUTHOR]

State-of-charge (SoC) imbalance and bus voltage deviation are two of the main problems in autonomous dc microgrids. Based on this concern, this paper presents an improved dual-quadrant SoC weighted control strategy and a distributed optimization control method to achieve SoC balance, ensuring accurate power-sharing and bus voltage recovery. Firstly, this paper couples the injected/released power with the current SoC and observed average SoC value to weight the droop coefficient, which is based on the charge/discharge mode for the energy storage system. Then a secondary controller is designed based on distributed optimal control to eliminate the bus voltage deviation caused by the line impedance difference. The proposed optimal control method optimizes the average bus voltage to the nominal value and achieve accurate power-sharing by constructing the correlated variables and voltage independent intermediate variables exchanged among bulk energy storage units (ESUs). Since the voltage observer cannot accurately observe the true average bus voltage under the communication delay, the proposed distributed optimal control method without the voltage observer can ensure that the average bus voltage is optimized to the nominal value, thus improving the robustness of the control system. Finally, the correctness and effectiveness of the proposed method are verified in Simulink/MATLAB. [ABSTRACT FROM AUTHOR]

To enable the drone delivery service in a remote area, this paper considers the approach of deploying charging stations and collaborating with public transportation vehicles. From the warehouse which is far from a customer, a drone takes some public transportation vehicles to reach some position close to the remote area. When the customer is unreachable from the position where the drone leaves the public transportation vehicle, the drone swaps the battery at a charging station. The focus of this paper is the deployment of charging stations. We propose a new model to characterize the delivery time for customers. We formulate the optimal deployment problem to minimize the average delivery time for the customers, which is a reflection of customer satisfaction. We then propose a sub-optimal algorithm that relocates the charging stations in sequence, which ensures that any movement of a charging station leads to a decrease in the average flight distance. The comparison with a baseline method confirms that the proposed model can more accurately estimate the flight distance of a customer than the commonly used model, and the proposed algorithm can relocate the charging stations achieving lower flight distance. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic dynamic programming framework for the optimal energy management of a smart home with plug-in electric vehicle (PEV) energy storage. This paper is motivated by the challenges associated with intermittent renewable energy supplies and the local energy storage opportunity presented by vehicle electrification. This paper seeks to minimize electricity ratepayer cost, while satisfying home power demand and PEV charging requirements. First, various operating modes are defined, including vehicle-to-grid, vehicle-to-home, and grid-to-vehicle. Second, we use equivalent circuit PEV battery models and probabilistic models of trip time and trip length to formulate the PEV to smart home energy management stochastic optimization problem. Finally, based on time-varying electricity price and time-varying home power demand, we examine the performance of the three operating modes for typical weekdays. [ABSTRACT FROM PUBLISHER]

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]

This paper proposes a cooperative optimal power split (COPS) method for a group of intelligent electric vehicles with battery/supercapacitor hybrid energy storage systems (HESSs). To achieve good performance, the proposed COPS method is made up of the upper and lower layers: the upper layer aims at obtaining the optimal power demand sequence and sending it to the lower layer; the lower layer attempts to optimize the power split for HESS. Firstly, to ensure the performance of the proposed method in practice, the resistance of the battery and supercapacitor packs as well as the DC/DC converter efficiency are not assumed to be constants, but modeled well in the paper. Secondly, to achieve the optimal power demand sequence, an upper power demand sequence optimization problem is formulated based on distributed model predictive control (DMPC), in which energy demand is incorporated into the cost function. Thirdly, after receiving the optimal power demand sequence, a hybrid power split optimization strategy is presented to obtain the optimal power split, in which the lower HESS power split optimization problem is formulated based on DMPC to decrease battery aging process and energy consumption. Finally, an improved particle swarm optimization algorithm with multiple dynamic populations is used to solve the formulated upper and lower optimization problems. Simulation results demonstrate that, compared with the benchmark, the proposed COPS method can significantly extend battery lifespan and slightly decrease energy consumption. [ABSTRACT FROM AUTHOR]

In light of a reliable and resilient power system under extreme weather and natural disasters, networked microgrids integrating local renewable resources have been adopted extensively to supply demands when the main utility experiences blackouts. However, the stochastic nature of renewables and unpredictable contingencies are difficult to address with the deterministic energy management framework. The paper proposes a comprehensive distributionally robust joint chance-constrained (DR-JCC) framework that incorporates microgrid island, power flow, distributed batteries and voltage control constraints. All chance constraints are solved jointly and each one is assigned to an optimized violation rate. To highlight, the JCC problem with the optimized violation rates has been recognized as NP-hard and challenging to solve. This paper proposes a novel evolutionary algorithm that successfully solves this problem and reduces the solution conservativeness (i.e., operation cost) by around 50% compared with the baseline Bonferroni Approximation. We construct three data-driven ambiguity sets to model uncertain solar forecast error distributions. The solution is thus robust for any distribution in sets with the shared moments and shape assumptions. The proposed method is validated by robustness tests based on these sets and firmly secures the solution robustness. [ABSTRACT FROM AUTHOR]

This paper proposes a new battery management system (BMS) for a series string of battery cells. The proposed control structure is based on the duality of the adaptive droop control theory used in dc micro-grid connected sources. An adaptive virtual admittance is used in this paper similar to the virtual resistance control structure used with dc micro-grid connected sources. The virtual admittance value is adjusted according to the associated state of charge for every connected battery cell. The presented BMS control structure yields higher reliability compared to the conventional ones due to its decentralization features and its communication-less ability. Thus, balanced power-sharing is realized among the connected battery cells during charging/discharging conditions. The stability of the proposed control is analyzed using the small signal model. The proposed BMS control structure is validated using simulation results and an experimental prototype for three connected battery cells. [ABSTRACT FROM AUTHOR]

In this paper, the current distribution of an experimental lead cell is estimated using two methods and the results are compared. Noninvasive measurements are obtained using our magnetic tomography system, consisting of an array of magnetic sensors and a solver algorithm that is specially adapted for application to lead cells. Verification of this magnetic tomography system is achieved by comparing the results with data from a new invasive measurement instrument also reported in this paper. This new instrument is an array of ferrous cored current transducers submerged in the cell electrolyte, providing a direct spatially and temporally resolved measurement of the ionic current in a lead cell. Comparisons between the techniques and data are drawn. The new flowthrough internal sensor array has been designed specifically for this application and represents an improvement over a similar system reported in the literature. In addition, some auxiliary features of the experimental setup are designed to enable useful magnetic measurements to be taken by minimizing the complexity of the setup, and therefore the complexity of the models needed to interpret the data. Example results are presented for a typical automotive lead cell operating at a low state of charge and in a fatigued condition, to demonstrate the ability of both measurement systems to produce spatially and temporally resolved images of current distribution. The methods are evaluated, and the future work is recommended. [ABSTRACT FROM AUTHOR]

A Battery Energy Storage System (BESS) is a reliable resource to provide energy for various power system applications. The BESS can increase the flexibility and reliability of the renewable energy dispatch. Wind energy has the largest contribution among renewable energy resources and its control has become a research focus in power systems area. This paper introduces a novel BESS control to manage the net energy exchange between a wind farm and the grid in an electricity market. A Receding Horizon Control (RHC) scheme is proposed for optimal operation of the BESS in the presence of operational constraints. The proposed method seeks a decision policy to manage operation of the BESS to increase daily profits. Utilizing short-term wind and price forecasts provide valuable information for the BESS controller to obtain the best times to charge batteries, discharge the stored energy, or purchase energy from the DA market. An optimization problem is formulated considering BESS costs and operational constraints. This optimization problem, at each time step, is solved using the RHC scheme. All wind and electricity price data and case studies in this paper are based on MISO energy market data. [ABSTRACT FROM AUTHOR]

This paper studies the optimization of both the placement and controller parameters for Battery Energy Storage Systems (BESSs) to improve power system oscillation damping. For each BESS, dynamic power output characteristics of the power converter interface are modeled considering the power limit, State of Charge limit, and time constant. Then, a black-box mixed-integer optimization problem is formulated and tackled by interfacing time-domain simulation with a mixed-integer Particle Swarm Optimization algorithm. The proposed optimization approach is demonstrated on the New England 39-bus system and a Nordic test system. The optimal results are also verified by time-domain simulation. To improve the applicability and efficiency of the proposed method, seasonal load changes and the minimum number of BESS units to be placed are considered. The proposed controller is also compared to other controllers to validate its performance. [ABSTRACT FROM AUTHOR]

This paper investigates the impacts of incorporating energy storage devices (ESD) into a virtual power plant (VPP) model and subsequently using it for performing generating system adequacy studies. A two-step linearized optimization formulation is proposed for developing the overall VPP model featuring the aggregated characteristics and capacities of several distributed generators (DG), ESDs and loads together with distribution network information like topology and constraints. While the first step determines the (charging or discharging) operating mode of the batteries, the second step outputs the optimal dispatch schedules for the DGs and ESDs in accordance with a fair energy allocation based operating strategy adopted in this paper. The VPP is modeled using an augmented IEEE 69-bus distribution network, and the overall generation adequacy studies incorporating the VPP are carried out on the IEEE Reliability Test System using a sequential simulation algorithm. Several indices are presented for quantifying the impacts of the ESDs’ incorporation on the VPP model’s performance. Finally, sensitivity studies conducted for investigating the effects of variations in the ESD rated capacities and charging/discharging periods on the VPP performance and system adequacy indices are also described. [ABSTRACT FROM AUTHOR]

Mobile, wearable, and Internet-of-Things (IOT) electronic systems are typically powered by a single-cell lithium-ion (Li-ion) battery. Increasing demand for battery life and the constraint of the overall platform size impose an emerging desire for an efficient and compact power conversion stage that can directly deliver power from the battery, which ranges from 3.2 to 4.2 V, to the supplies of application/communication processors in the range of 1 V. The switched capacitor (SC) converter, though promising in some ways for this application, is inefficient at voltage regulation and is limited in capacitor utilization. This paper presents a hybrid dc–dc converter topology that eliminates charge sharing losses with a small inductor. The necessary inductance can be realized by PCB traces, which shows the potential for a compact system-in-package (SiP) solution. The designed converter is implemented in Texas Instruments’ 0.15- $\mu \text{m}$ CMOS process with a nominal 3:1 conversion ratio. It supports a 3.2–4.2-V input voltage with a continuous output regulation of 0.8–1.5 V. The peak efficiency is 87.2% with 83.4% efficiency at a power density of 0.46 W/mm2. This paper also presents an effective methodology of developing the large-signal model and small-signal dynamics for the generalized N:1 converter topology. A tight load line-based controller is integrated into the designed chip and experimentally verified with a transient performance of 50-mV voltage overshoot/undershoot under 400-mA on-die load step. [ABSTRACT FROM AUTHOR]

Installation of a large-capacity battery pack is a straightforward method to extend the range of battery electric vehicles (BEV or all-electric vehicles). However, at the same time, a large-capacity battery pack not only occupies a big space but also significantly increases the vehicle weight, which directly impacts the fuel economy and vehicle performance. This implies that increasing the battery capacity has an obvious limitation in extending the EV range. In this paper, we introduce a system-level framework to extend the range of BEV with the consideration of the vehicle dynamics, electric powertrain characteristics, road slopes, payload, and regenerative braking. This paper particularly takes into account driving time so that the resultant BEV power management does not impractically slow down the vehicle velocity. The BEV power management framework derives energy-aware velocity planning, i.e., a desirable instantaneous velocity at each distance step (or at each time instant). The major technical contributions of this paper compared with the previous work include: 1) practically applicable velocity planning for production BEVs from superb BEV power model fidelity; 2) new performance metrics to consider both driving energy and driving time: energy-delay product (EDP), energy-square-delay product, and energy-cubic-delay product; 3) heuristics to derive EDP-aware velocity planning; 4) comparative analysis of the velocity planning between BEV and internal combustion engine vehicles; and 5) analysis of the model fidelity impact on the energy-aware velocity planning. The proposed method results in up to a 46.2% improvement of the EDP compared with the least-energy constant velocity driving. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for quasi real-time and adaptive energy management for islanded ac/dc microgrids using advanced metering infrastructure (AMI) data. This paper presents a sophisticated multi-objective algorithm by employing AMI-based energy conservation and optimization techniques, tailored for different operational scenarios. This paper argues that optimal operation can be achieved by defining new concepts, such as operation value factor that can inherently reduce system operational costs. Moreover, the results of this paper demonstrate how utilizing accurate load models can improve ac/dc microgrid efficiency. To test the performance and the applicability of the proposed solution, a modified 33-node islanded ac/dc microgrid is used as a case study in the presence of various types of ac/dc generating units and loads. [ABSTRACT FROM AUTHOR]

This paper treats the problem of optimal resource allocation over time in a finite-horizon setting, in which the resource become available only sequentially and in incremental values, and the utility function is concave and can freely vary over time. Such resource allocation problems have direct applications in data communication networks (e.g., energy harvesting systems). This problem is studied extensively for special choices of the concave utility function (time invariant and logarithmic) in which case the optimal resource allocation policies are well-understood. This paper treats this problem in its general form and analytically characterizes the structure of the optimal resource allocation policy and devises an algorithm for computing the exact solutions analytically. An observation instrumental to devising the provided algorithm is that there exist time instances at which the available resources are exhausted, with no carryover to future. This algorithm identifies all such instances, which in turn, facilitates breaking the original problem into multiple problems with significantly reduced dimensions. Furthermore, some widely used special cases in which the algorithm takes simpler structures are characterized, and the application to the energy harvesting systems is discussed. Numerical evaluations are provided to assess the key properties of the optimal resource allocation structure and to compare the performance with the generic convex optimization algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the capacity of a point-to-point, full-duplex (FD) wirelessly powered communication system impaired by self-interference. This system is comprised of an energy transmitter (ET) and an energy harvesting user (EHU), which operates in the FD mode. The ET transmits energy toward the EHU. The EHU harvests this energy and uses it to transmit information back to the ET. As a result of the FD mode, both nodes are affected by self-interference. The self-interference has different effects at the two nodes. In particular, the self-interference impairs the decoding of the received information signal at the ET, whereas it serves as an additional source of energy at the EHU. In this paper, we derive the capacity of the adopted system model assuming a processing cost at the EHU and an additive white Gaussian noise channel with block fading. Thereby, we show that the capacity achieving scheme is relatively simple and therefore applicable to devices with limited resources. Moreover, our numerical results show significant improvements in terms of data rate when the capacity achieving strategy is employed compared to half-duplex transmission, even for very high self-interference at the ET. Moreover, we show the positive effects of the self-interference at the EHU, as well as the crippling effect of the processing cost. [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]

Environmental concerns with gasoline vehicles have led to increased attention to electric vehicles in recent years. Plug-in hybrid electric vehicles (PHEV) use both electricity and gasoline to propel the vehicle, and is being recognized as a potential alternative to conventional vehicles. PHEVs offer opportunity to use electric energy generated by renewable resources and significantly reduce greenhouse gas emissions. The electric energy requirement of PHEV can, however, cause negative impacts on the power system reliability, especially when the size of a PHEV fleet is relatively large. This paper presents the development of a probabilistic model considering the driving distance, charging times, charging locations, battery state of charge, and charging requirements of a PHEV. A methodology using hybrid analytical and Monte Carlo simulation approach is presented to evaluate the reliability of a power system integrated with PHEVs, considering the important PHEV characteristics, charging scenarios, and power system parameters. Studies are presented on the IEEE-reliability test system to illustrate the impact of PHEV penetration in a power system. Based on the study results, the methods of augmenting system reliability through controlled PHEV charging are presented in this paper. [ABSTRACT FROM PUBLISHER]

This brief paper examines the problem of optimizing lithium-ion battery management online, in a health-conscious manner. This is a computationally intensive problem. Previous work by the authors addresses this challenge by exploiting the differential flatness of Fick’s law of diffusion to improve computational efficiency, but is limited by the fact that the dynamics of a full battery cell are not differentially flat, even when the individual battery electrode dynamics are. The brief paper addresses this challenge by extending the application of differential flatness to a full single particle model. In particular, we use the conservation of charge to express the flat output trajectory of one electrode as an affine function of the other electrode’s flat output trajectory. In this way, we enforce differential flatness for the full battery model. This makes it possible to express the battery charge/discharge trajectory in terms of one flat output trajectory. We optimize this trajectory using a pseudospectral method. This reduces the computational cost of the optimization by about a factor of 5 compared with pseudospectral optimization alone. In addition, the robustness of the nonlinear model predictive control strategy is demonstrated in simulation by adding state-of-health parameter uncertainties. [ABSTRACT FROM AUTHOR]

This paper presents an offline state-of-health (SoH) estimation based on charge transfer resistance for high-power lithium-ion (Li-ion) batteries, such as lithium iron phosphate (LFP) batteries. As shown in the experimental results, the charge transfer resistance has a great aging change with battery degradation and good abilities against state-of-charge (SoC) drift and external resistance variation in the impedance parameter set of a single-time-constant equivalent circuit model (ECM), including ohmic resistance, charge transfer resistance, double-layer capacitance, and time constant, for SoH estimation. A fast and efficient three-point (TP) impedance extraction method is also proposed in this paper for accurately extracting the charge transfer resistance in offline SoH estimation. The results of long-term cycling test demonstrate that the TP impedance extraction method can successfully indicate the SoH of LFP batteries with low estimation error of 6.1%. [ABSTRACT FROM PUBLISHER]

The increasing prevalence of technologies such as advanced metering and controls and continuously variable power electronic devices are enabling a radical shift in the way frequency regulation is performed in the bulk power system. This is a welcome development in light of the increase of unpredictable and variable generation. The idea of active participation of loads in frequency markets is not new, but the rapidly changing landscape of the power grid requires new techniques for successful integration of new types of resources; this paper works towards that end. Previously, it has been shown that residential HVAC systems can be aggregated and used to provide frequency regulation by utilizing their thermal energy capacity and flexibility of energy consumption. The virtual battery model—a first-order linear dynamical model—was analytically shown to be an accurate and simple model to capture the flexibility of residential HVAC systems. This paper presents a technique for creating the same battery-type models for many other types of systems, which can be much more complex. Our technique is based on stress testing detailed software models of physical systems. A realistic case study involving the terminal building of a small airport is presented as evidence of the effectiveness of the proposed techniques. [ABSTRACT FROM PUBLISHER]

The reliable operation of battery management systems depends critically on the accurate estimation of the state of charge (SOC) and characterizing parameters of a battery system. SOC estimation employs models that must be robust against variations in battery cell electrochemical features, aging, and operating conditions. This paper reveals that commonly used SOC estimation schemes are fundamentally flawed in providing the robustness of SOC estimation against model uncertainties. Parameter estimation methodologies and adaptive SOC estimation design are introduced in this paper to enhance SOC estimation accuracy and robustness. By a scrutiny of the impact of parameter variations on SOC estimation accuracy, the SOC–open-circuit-voltage mapping is identified to be the most critical function that must be accurately established. Identification algorithms are introduced, and their convergence properties are established. The integration of the identification algorithms and SOC estimation schemes lead to an adaptive SOC estimation framework that is superior over the existing methods in providing much improved accuracy and robustness. Experimental studies are conducted to validate the algorithms. [ABSTRACT FROM PUBLISHER]

Integration of an energy storage system (ESS) into a distribution network not only affects the supply reliability of the customer, but also has distinct reliability implications and consequences to the utility. The reliability value associated with an ESS highly depends on the ownership, market, and regulatory structures. This paper presents a probabilistic framework to evaluate the reliability value of ESS to the distribution system considering aforementioned factors. In this regard, a new probabilistic reliability model of ESS is developed and integrated into a sequential Monte Carlo based simulation framework. The developed ESS model consists of the Markov-based component model and the mixed integer linear programming based formulation of operating strategies that incorporate different scenarios of ownership, market structures, and the ESS characteristics. The reliability/financial risk performance of the distribution system operator (DSO) with ESS under quality regulations are quantified. Furthermore, the developed ESS model is utilized to explore the prospect of investor-owned ESS providing supply recovery and distribution grid capacity services to the DSO. Case studies are conducted on a test distribution network to show the effectiveness of the proposed model. Finally, the paper presents discussions on important considerations for efficient utilization of ESS in active distribution systems. [ABSTRACT FROM AUTHOR]

Battery safety is one of the most crucial issues in the utilization of lithium-ion batteries (LiBs) for all-climate electric vehicles. Short circuit, overcharge, and overheat are three common field failures of LiBs. In this paper, online fault diagnosis for external short circuit (ESC) of LiB packs is investigated. The experiments are carried out to obtain and compare ESC characteristics of 18650-type NMC battery pack and single cell. Based on the analysis of experimental results, a two-step equivalent circuit model is established to describe the ESC process and an online model-based scheme is proposed to diagnose ESC faults of battery packs. The proposed scheme is evaluated by experimental data. The results show that it can effectively diagnose ESC faults in 3.5 s after their occurrences with the terminal voltage error less than 25 mV. The proposed scheme has shown great generalization ability. ESC faults of battery packs under different number of cells connected in series and unavailable current information can also be diagnosed at the terminal voltage error less than 48 and 60 mV, respectively. [ABSTRACT FROM AUTHOR]

The energy management (EM) and driving speed co-optimization of a series hybrid electric vehicle for minimizing fuel consumption is addressed in this paper on the basis of a suitably modeled series powertrain architecture. This paper proposes a novel strategy that finds the optimal driving speed simultaneously with the energy source power split for the drive mission specified in terms of the road geometry and travel time. Such a combined optimization task is formulated as an optimal control problem (OCP) that is solved by an indirect optimal control method, based on Pontryagin's minimum principle. The optimization scheme is tested under a rural drive mission by extensive comparisons with conventional methods that deal with either speed optimization only or EM strategies with given driving cycles. The comparative results show the superior performance of the proposed method and provide further insight into efficient driving. [ABSTRACT FROM AUTHOR]