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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

This paper proposes a novel emulation system for emulating the power system in the fuel cell electric vehicle. The major purpose of the proposed emulation system is to test the dc–dc converter between the fuel cell and high-voltage powertrain. The bidirectional buck–boost type dc–dc converter is applied in the emulation system as the basic elements. A modularized approach is presented for easily scaling up and down the power level of the emulation system. Meanwhile, an energy loop structure is implemented in the proposed emulation system for power capability boosting purpose. Because of the proposed energy loop configuration, the emulation system offers long-term running capability with high power rating without the grid connection. A neutral current control mechanism is applied in this paper to further enhance the long-term testing capability as well as extending the lifespan for the battery packages. The simulation studies are proposed in this paper to validate the control mechanisms of the emulation system. A 10 kW hardware prototype has been built and the experimental tests are presented for validating the functionalities of the emulation system. [ABSTRACT FROM AUTHOR]

In this paper, the damping ability of point of common coupling (PCC) voltage feedforward is analyzed in order to improve the stability of current control, in which interference with an LCL filter of a battery energy storage system (BESS) and synchronous inductance of a diesel generator is considered in a stand-alone microgrid. A BESS can be charged by a diesel generator when the state of charge of the battery bank is significantly insufficient and where the BESS operates with constant current constant voltage control and the diesel generator provides constant voltage constant frequency control. In this case, large synchronous when the diesel generator is in a steady state may severely cause the instability of current control. The stability analysis should be required to guarantee the paralleled operation between BESS and diesel generator. An equivalent circuit model of the diesel generator is needed to perform these processes. To bring availability of analysis, this paper considers a simple equivalent circuit model considering synchronous inductance in the steady state, and it has very large inductance corresponding 1 per unit. The resonance issue is attenuated by the damping ability of PCC voltage feedforward where it is possible to improve the stability by appropriately adjusting the gain of the voltage feedforward. The effects of the synchronous inductance and the feedforward loop are analyzed in the s-domain and z-domain. The gain of the voltage feedforward is determined to be 0.2 and 0.4 based on stability analysis. To verify the validity of the damping effect of PCC voltage feedforward, a demonstration site is constructed and the stability is verified through simulation and experiment with a full model, and total harmonic distortion of current is improved to 2.44%. [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]

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 fuel efficiencies of Hybrid Electric Vehicles (HEVs) depend heavily on the control concepts, especially on energy management strategies, because the system efficiency of the vehicle is determined by the exploitation of the electric energy. Control concepts based on optimal control theories like Dynamic Programming (DP) or Pontryagin's Minimum Principle (PMP) have been widely studied over the last two decades, and it has been proven that these control concepts provide near-optimal solutions if future driving conditions are given prior to actual driving. However, it is difficult to apply to real vehicles unless the future driving conditions are known. In this paper, a well-known control strategy based on PMP has been developed, which needs only current driving conditions. The significance of the study is that the control concept introduced herein was evaluated in the IEEE VTS Challenge 2018 competition. It produced the best results in terms of the total energy consumption, where 52 controllers had been competing to minimize the energy consumption in unknown driving cycles. Although the control system was designed from traditional PMP-based control, it becomes a practicable and effective solution by using an adaptive concept for balancing State Of Charge (SOC) of a battery. This study is an extended version of the study presented during IEEE VPPC 2018 and provides the development process of the control concept used in the controller during the IEEE VTS Challenge 2018. [ABSTRACT FROM AUTHOR]

An additive white Gaussian noise energy-harvesting channel with an infinite-sized battery is considered. The energy arrival process is modeled as a sequence of independent and identically distributed random variables. The channel capacity $\frac {1}{2}\log (1+P)$ is achievable by the so-called best-effort and save-and-transmit schemes where $P$ denotes the battery recharge rate. This paper analyzes the save-and-transmit scheme whose transmit power is strictly less than $P$ and the best-effort scheme as a special case of save-and-transmit without a saving phase. In the finite blocklength regime, we obtain new non-asymptotic achievable rates for these schemes that approach the capacity with gaps vanishing at rates proportional to $1/\sqrt {n}$ and $({(\log n)/n})^{1/2}$ respectively where $n$ denotes the blocklength. The proof technique involves analyzing the escape probability of a Markov process. When $P$ is sufficiently large, we show that allowing the transmit power to back off from $P$ can improve the performance for save-and-transmit. The results are extended to a block energy arrival model where the length of each energy block $L$ grows sublinearly in $n$. We show that the save-and-transmit and best-effort schemes achieve coding rates that approach the capacity with gaps vanishing at rates proportional to $\sqrt {L/n}$ and $({\max \{\log n, L\}/n})^{1/2}$ , respectively. [ABSTRACT FROM AUTHOR]

Active cell balancing is the process of equalizing the State-of-Charge (SoC) of all cells in a series-connected battery pack via charge transfers. While several balancing architectures and equalization strategies exist in the literature, optimization of the circuit components in a balancing architecture has not been sufficiently studied so far. Intuitively, one would select components with low parasitic resistances to minimize the energy dissipation. However, as will be shown in this paper, this might not be an optimal choice in most situations. In this paper, we propose an optimization framework that performs an efficient design space exploration to identify the energy-efficient circuit configuration of a balancing architecture. Moreover, for the first time, we propose a methodology to obtain the optimal operating frequency of an active cell balancing architecture and a control circuit to maintain the operation at the optimal frequency. For this purpose, we derive a closed-form analytical model of the charge transfer process with which we obtain the performance metrics required for our framework. Case studies show that such optimally dimensioned active cell balancing architectures operated at the optimal frequency reduce the energy dissipation by approximately 49% compared to the intuitive approach of selecting components with least parasitic resistances. [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]

A decentralized improved I-V droop control strategy for battery-supercapacitor (SC) hybrid energy storage system (HESS) is proposed in this paper. The dynamic power sharing between battery and SC is realized by replacing the constant droop coefficient in I-V droop control with virtual impedance, i.e. virtual inductance for battery side converter and virtual resistance for SC side converter. Besides, by injecting the virtual inductance in the battery side converter, negligible DC bus voltage deviation can be achieved without extra voltage compensator. Moreover, the state-of-charge (SoC) recovery is also considered to extend the service life of the HESS. Furthermore, in the proposed regulated power system, since the power allocation, DC bus stability and SoC recovery are decoupled from each other, the design of control parameters is simple. The corresponding design guideline is demonstrated in this paper. Finally, to verify the accuracy and feasibility of the theoretical analyses, hardware in the loop simulations have been conducted. [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]

Industrial Wireless Sensor Networks (IWSNs) allow the use of battery-operated nodes to provide easy deployment, even in harsh environments, and are low maintenance. The optimal network design can be difficult because several constraints and requirements must be considered among all the power consumption. For this reason, specific approaches, also based on soft computing methods, can be applied in such a way as to further decrease the energy consumption in IWSNs. To this end, this paper proposes a fuzzy logic based mechanism that, according to the battery level and the ratio of throughput to workload, defines the sleeping time of sensor devices in an IWSN based on IEEE 802.15.4 protocol. A particle swarm optimization (PSO) algorithm is introduced to obtain the optimal values and parameters of the proposed fuzzy logic controller (FLC), i.e., optimizing the membership functions, by varying their range, to achieve the best results regarding the battery life of sensor nodes. This paper provides a detailed description of the FLC configuration, a logical analysis of the PSO algorithm for the derivation of best performance conditions values, and simulative assessments obtained through MATLAB simulations. [ABSTRACT FROM AUTHOR]

This paper presents a computation-efficient stochastic dynamic programming algorithm for solving energy storage price arbitrage considering variable charge and discharge efficiencies. We formulate the price arbitrage problem using stochastic dynamic programming and model real-time prices as a Markov process. Then we propose an analytical solution algorithm using a piecewise linear approximation of the value-to-go function. Our solution algorithm achieves extreme computation performance and solves the proposed arbitrage problem for one operating day in less than one second on a personal computer. We demonstrate our approach using historical price data from four price zones in New York Independent System Operator, with case studies comparing the performance of different stochastic models and storage settings. Our results show that the proposed method captures 50% to 90% of arbitrage profit compared to perfect price forecasts. In particular, our method captures more than 80% of arbitrage profit in three out of the four price zones when considering batteries with more than two-hour duration and realistic degradation cost. [ABSTRACT FROM AUTHOR]

The paper develops two computationally efficient models, namely a detailed equivalent model and an average-value model, with provisions for representation of the critical state of converter blocking, for modular multilevel converters with embedded storage. These models are indispensable in the simulation of dc faults. The developed detailed equivalent model provides accuracy that matches electromagnetic transient (EMT) simulations, with much reduced computational burden; the developed average-value model represents the averaged behavior of the converter by neglecting switching transients. Both models are extensively evaluated in the context of an HVDC system against benchmark results form a detailed switching model developed in PSCAD/EMTDC. The results confirm the validity and accuracy of the models for steady state, transient, and faulted operating conditions including when the converter is blocked in response to a dc fault. [ABSTRACT FROM AUTHOR]

With the widespread application of lithium-ion battery packs, it calls for efficient balancing methods to improve the performance of these battery systems. The relevant research has focused on the design of equalization circuits and control strategies while neglecting quantitative analysis and comparison of battery active equalization structures from the systematic level. In this paper, we propose an improved system-theoretic modeling approach for active equalization structures that takes into account the battery's constraints, including equalization current rate and SOC operation range. Accordingly, the mathematical models of various active equalization structures are derived that can describe their equalization dynamic behavior. Then, the analytical algorithms are developed to estimate the balancing time of active equalization structures online. Finally, extensive numerical simulations are conducted to evaluate and compare the average performance of these equalization systems in term of equalization time. It then comes up with a comprehensive comparison among them. Consequently, the Multicell-to-Multicell (MC2MC) balancing structure benefits the fast balancing speed and the lowest sensitive to initial batteries’ SOCs. The global structure has the lowest implementation cost and the series structure benefits the simplest control. Moreover, the module-based structures have faster balancing speed when each module contains about 4–6 batteries. [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]

Regarding the continuing increase of anthropogenic carbon emissions in the power system with growing energy consumption, researchers have focused on managing the demand side to achieve low-carbon transition. However, such a low-carbon transition cannot be achieved effectively without the proper incentive scheme. Moreover, it is desired to improve the computation quality of carbon tracing tools and extend their applications by state-of-art techniques. To fulfil these existing research gaps, this paper proposes a low-carbon optimal scheduling model with demand response (DR) based carbon intensity control. It aims to reduce the dependence of customers on energy sources with high carbon intensities while decreasing reasonable energy consumption. Likewise, a data-driven approach conducted with the Bayesian interfere regression is proposed to carry out the carbon emission flow (CEF) model to cope with the drawbacks of the conventional emission calculation. Moreover, the storage emission and relevant dynamic impacts to the system are fully considered. The proposed data-driven approach is tested on a series of IEEE systems with different sizes, its high accuracy and efficiency are proved by the simulation results. Further, simulation results indicate that the proposed scheduling model can effectively achieve carbon emission mitigation. [ABSTRACT FROM AUTHOR]

Over the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids. [ABSTRACT FROM AUTHOR]

Behind-the-meter battery energy storage is used to mitigate non-dispatchability of customer-owned renewable sources. While serving their owners’ objectives, their underutilized capacities can be used to provide grid services to justify their cost. However, the integration of these resources extends the size and complexity of the network operation problem, imposing a processing burden on the central control system. In this paper, an agent-based distributed control framework is proposed that enables utilizing of the underutilized behind-the-meter capacities. In this framework, while the prosumer agents initially schedule their resources for daily cost management, a local customer aggregator agent performs day-ahead active and reactive power control on the underutilized behind-the-meter batteries to provide multiple grid services. A novel grouping algorithm is developed based on voltage sensitivity and a modified $k$ -means clustering method to assign customer groups to the local agents and establish the network subsystems. Mixed-integer linear programming is used to model and solve multiple optimization problems. The proposed framework is applied to the 123-bus distribution network. The simulation results show its effectiveness in terms of complexity and optimality, compared to the central control approach in allocating the unused behind-the-meter resources for two common industry needs: network bottleneck congestion relief and grid-edge voltage support. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization methodology for sizing and operating battery energy storage systems (BESS) in distribution networks. A BESS optimal operation for both frequency regulation and energy arbitrage, constrained by battery state-of-charge (SoC) requirements, is considered in the proposed optimization algorithm. We use utility historical data as input in a case study on a real-world distribution circuit. The results indicate that a BESS generally brings high profits by participating in the frequency regulation market and should be sized to its highest allowable power capacity rather than its energy capacity. [ABSTRACT FROM AUTHOR]

Modular multilevel converters (MMCs) have been recognized as the most attractive technology for high voltage direct current (HVDC) applications during the last years. Their advantages include the ability to freely control the internally stored energy in the distributed submodules (SMs) and to independently control AC and DC powers. Recent studies showed that an MMC can take advantage of this degree of freedom to supply ancillary services with low energy requirements such as frequency response or power oscillation damping by connecting energy storage systems (ESSs) to its SM capacitors. This paper proposes a modular DC-DC converter as an interface between the energy storage element and the SM capacitor. As it can be built with switches allowing a high switching frequency, it is shown that the volume of the passive components can be reduced in comparison with interface converters known in the literature. The proposed control strategy avoids to transfer the harmonics of a SM to the ESS that prevents to drastically oversize the system. The proposed control method also provides an appropriate energy exchange without affecting the control philosophy of an MMC and it addresses balancing issues. Simulations and a downscaled prototype confirm the relevance of the proposed control. As the proposed interface converter is able to track the power reference with fast dynamics, it provides the same system-level advantages as other interface converters known in the literature but with a reduced volume. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to review the recent literature regarding the effects of low temperatures on Lithium ion (Li-ion) batteries for electric vehicle, plug-in hybrid electric vehicle, and hybrid electric vehicle applications. Special consideration is given to nine major effects that directly or indirectly impact the battery use at low temperatures and it is presented that they are correlated to each other. The main discussions in this paper are capacity loss, power loss, life degradation, safety hazard, unbalanced capacity, charging difficulty, thermal management system complexity, battery model and state estimation method complexity, and incremental cost. The effects correlations and possible solutions are explained to provide a detailed, yet broader understanding of the Li-ion batteries low-temperature operating scenarios. [ABSTRACT FROM AUTHOR]

Although there have been significant advancements in low-power remote sensors in recent years, the challenge of sensor availability and data reliability in remote monitoring applications still persists. The fault and failure of sensors will affect the reliability of the monitored data and subsequently the adverse effect will inevitably propagate itself to the data analytics stage. There are many existing solutions focusing on improving sensor nodes to enhance data reliability and couple it with various energy harvesting techniques to prolong the availability of sensor nodes. This paper presents a complementary solution to these existing solutions by analyzing the correlation between data from different sensor nodes using cloud computing resources. The discovered relationship between the sensor nodes can then be used to improve data reliability and availability of sensor nodes. Performance evaluations using real data sets show that there are indeed relationships between the collected data, and through these discovered relationships the fault detection and fault masking methods outperform conventional approaches such as autoregressive-integrated moving average. In addition, this paper also proposes an approach to extend operation of sensor nodes duration through the discovered relationships, with experiments showing promising results. [ABSTRACT FROM AUTHOR]

In order to improve the driving range and reduce the cost of battery electric vehicles through a higher efficiency, this paper proposes to adopt multilevel converters using low-voltage Si mosfets in the electric powertrains. A multilevel Si mosfet inverter, a conventional insulated-gate bipolar transistor (IGBT) inverter, and a SiC mosfet inverter are modeled and compared using a reference vehicle over various driving cycles. The costs of the three solutions are also compared. It is shown that the multilevel Si mosfet inverter has a rather high efficiency and realizes the lowest cost among the three solutions even when the worst case of cost is considered. Sensitivity analysis also shows that the multilevel Si mosfet inverter is suitable for a wide range of vehicle concepts in addition to the reference vehicle. Moreover, the multilevel topology also features lower electromagnetic interference and provides modularity. Therefore, Si mosfet-based multilevel inverters are proved in this paper to be an appropriate option to improve the efficiency and reduce the cost of electric powertrains. [ABSTRACT FROM AUTHOR]

This paper presents a novel platform for accurate mathematical modeling of the propulsion system of electric cars. It provides, for the first time, a hardware-in-the-loop (HIL) real-time experimental verification case study of a General Motor Chevrolet Volt for power, control systems, and the mechanical systems. The contribution of this paper can be categorized into three approaches: First, each component of the powertrain is accurately modeled taking transient dynamics of all components of the electric vehicle into consideration. Further, a PSIM simulation platform is consequently developed to demonstrate the validity of this mathematical modeling. Finally, the Typhoon HIL is used to provide the experimental verification of the proposed model in real time, which precisely validates the viability of the model. HIL technology is used to prototype and test the proposed control system while simulating the power circuit on the HIL module platform. The permanent magnet synchronous motor and the power electronics hardware components are simulated in real time at which the parameters can be changed during the simulation. However, the control algorithm is generated as a C code and downloaded to the TI controller that exists on a digital signal processing board. The results from the simulation based on PSIM environment and hardware validations using HIL are in agreement, which validates the developed model. The performance has been investigated under different load operating conditions in real time to verify its robustness. The case study can be extended for any electric car as it provides a generic platform for modeling any propulsion system. [ABSTRACT FROM AUTHOR]

This paper presents a novel power flow control strategy, combined with an economic analysis, for an energy management system (EMS) involving a hybrid energy storage. The EMS operates a remote microgrid and directs the power flow to either batteries or supercapacitors to increase the life of the batteries. This paper demonstrates how the use of supercapacitors increases the lifetime of the batteries and ultimately how it affects the economics of the system. The proposed EMS controller also compensates for the 120-Hz ripple on the dc bus. Modeling, simulations, and experimental verification are presented together with the procedure to perform the assessment of the battery lifetime, according to the tuning parameters of the controller. [ABSTRACT FROM AUTHOR]

This paper presents a new security-constrained multiobjective optimal dispatch (SC-MOOD) framework for an economic and reliable operation of microgrids. The framework is developed based on a computationally effective multiobjective optimization technique, Pareto concavity elimination transformation (PaCcET). The new method considers grid steady-state and dynamic constraints while solving the optimal dispatch in both grid-connected and islanded modes. The constraints consist of power balance, voltage magnitude, line flows, power generation, frequency, and voltage transients. The proposed framework finds the most economic operating solutions to not only minimize the generation cost but also minimize the reliability cost. In this paper, the PaCcET is utilized to solve the SC-MOOD. The PaCcET uses an extraordinary transformation to first transfer all the points from a multiobjective space to a transformed objective space. It then solves a linear combination of transformed objectives using a single-objective optimizer to find all the nondominated points of the original multiobjective space. The performance of the new framework is verified using the simulation results in a microgrid with several distributed energy resources in both grid-connected and islanded modes. The results are then compared with two multiobjective optimization techniques, nondominated sorting genetic algorithm II and multiobjective particle swarm optimization, as two well-known benchmarks. [ABSTRACT FROM AUTHOR]

The electric vehicle battery life is generally affected by its rapid utilization, accumulated heat and overall energy throughput. Inclusion of ultracapacitor along with battery provides enhanced flexibility in operating and utilizing the battery more adequately. In such dual storage systems, the energy management scheme plays an important role in determining the overall system efficiency. In this paper, a new concept to manage the state of charge (SOC) of ultracapacitor is proposed. The aim of the proposed approach is, based on the vehicle velocity at acceleration, to regulate the amount of energy that ultracapacitor should support in order to ensure its availability for an extended period. A two-stage artificial neural network based strategy is developed to achieve the aforementioned ultracapacitor SOC control. The battery energy throughput and its temperature rise during a complete battery discharge are taken as key parameters for the evaluation. It is shown in the paper that with the proposed approach, the energy storage system efficiency and battery life can be improved significantly. Finally, the applicability of the proposed concept is demonstrated experimentally. [ABSTRACT FROM AUTHOR]

Internet of Things devices, such as video-based detectors or road side units are being deployed in emerging applications like sustainable and intelligent transportation systems. Oftentimes, stringent operation and energy cost constraints are exerted on this type of applications, necessitating a hybrid supply of renewable and grid energy. The key issue of a cost-constrained hybrid of renewable and grid power is its uncertainty in energy availability. The characteristic of approximate computation that accepts an approximate result when energy is limited and executes more computations yielding better results if more energy is available, can be exploited to intelligently handle the uncertainty. In this paper, we first propose an energy-adaptive task allocation scheme that optimally assigns real-time approximate-computation tasks to individual processors and subsequently enables a matching of the cost-constrained hybrid supply of energy with the energy demand of the resultant task schedule. We then present a quality of service (QoS)-driven task scheduling scheme that determines the optional execution cycles of tasks on individual processors for optimization of system QoS. A dynamic task scheduling scheme is also designed to adapt at runtime the task execution to the varying amount of the available energy. Simulation results show that our schemes can reduce system energy consumption by up to 29% and improve system QoS by up to 108% as compared to benchmarking algorithms. [ABSTRACT FROM AUTHOR]

Inrush currents in transformers can have very disruptive effects, such as voltage sags, false tripping of the protective devices, and mechanical stresses in the transformer windings. This paper shows that there are operating situations that may cause a transformer to draw abnormally high inrush currents. Examples include the normal operation of offline uninterruptible power-supply (UPS) systems, interruptions, voltage sags, and notching. These conditions may produce inrush-like currents of more than twice the value of the “normal” maximum inrush caused by energizing at voltage zero-crossing. For this condition, the term “phase-hop” is used in this paper. Laboratory experiments were performed on four different transformers (1 kVA) with varied characteristics and show the impact of phase-hop in the magnitude of inrush currents. The experiments are also used to validate the Electromagnetic Transients Program model used for the analysis of multiple cases. In addition, the behavior of the magnetic flux in a transformer under phase-hop is investigated and compared with different operating conditions using finite elements. The results of this paper have implications in transformer design and in the operation and design of UPS systems to prevent the damaging effects of phase-hop. [ABSTRACT FROM AUTHOR]

Active balancing architectures effectively increase the efficiency of large battery packs by equalizing charge between cells. For this purpose, a balancing circuit and appropriate control scheme have to be designed to enable the charge transfer via energy storage elements such as inductors. Using a manual approach to design balancing architectures can be tedious and error-prone, resulting in potentially suboptimal solutions. As a remedy, this paper presents an automatic synthesis of balancing circuits and their corresponding control, optimizing the number of required metal-oxide-semiconductor field-effect transistors, and necessary control signals. The proposed synthesis combines a satisfiability solver to explore the search space with a graph-based verification that iteratively excludes infeasible solutions until the optimal architectures are obtained. The experimental results are carried out for three given template circuits and two signal templates. The synthesis results in architectures that are superior in terms of all design objectives in comparison to solutions from literature that result from a manual design approach. [ABSTRACT FROM PUBLISHER]

This paper considers a parallel hybrid electric truck where the tractive force delivered to the wheels can be provided by a conventional internal combustion engine (ICE) and/or a motor generator (MG). This hybrid truck is equipped with a clutch system that is capable of decoupling the ICE and the MG from the drivetrain to improve the fuel reduction performance, compared with a hybrid truck without a separate clutch for the MG. In the considered hybrid truck, the energy management strategy utilizes the battery to reduce the fuel consumption and the associated emissions. As the battery life is limited, the vehicle owner will suffer from extra costs for battery replacement. Henceforth, it is necessary to guarantee sufficient battery life. This paper develops a quasi-static battery cycle-life model and formulates a model-based integrated energy management (IEM) strategy. This IEM strategy optimizes the power split (between the ICE and the MG) and the operation of the clutch system to minimize the hybrid truck fuel consumption while guaranteeing the requested battery life. The problem of fuel minimization with battery life guarantee is analytically solved using optimal control theory. Mathematical and physical arguments are used to explain the compromise between fuel reduction and battery life preservation. Simulation results demonstrate that the IEM strategy will guarantee the requested battery life for different driving cycles while allowing appropriate hybrid powertrain operations for fuel minimization. Moreover, the results also show the fuel reduction improvement from decoupling the MG from the drivetrain using the clutch system. [ABSTRACT FROM PUBLISHER]

This paper deals with real-time scheduling of homogeneous multicore platforms powered by a battery to be periodically recharged. A system is composed of reconfigurable real-time dependent and periodic tasks to be assigned to different cores interconnected by a network-on-chip (NoC). The system is subject to reconfigurations, which are automatic operations allowing the addition and/or removal of tasks as well as their exchanged messages on the NoC. Consequently, any reconfiguration can violate real-time and energy constraints on cores as well as the NoC when the energy is unavailable until the next recharge. A novel periodic task model based on elastic coefficients and a new scheduling strategy are proposed to compute useful temporal parameters allowing for tasks and messages to meet the related constraints while controlling the communication cost on the NoC. This strategy is compared with an integer linear programming-based optimal solution, and a tool named OptimalMappingTasks is developed to run different simulations that prove the originality of this paper’s contribution. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic valuation framework to determine the opportunity value of battery capacity degradation in grid applications based on the internal degradation mechanism and utilization scenarios. The proposed framework follows a dynamic programming approach and includes a piecewise linear value function approximation solution that solves the optimization problem over a long planning horizon. The paper provides two case studies on price arbitrage and frequency regulation using real market and system data to demonstrate the broad applicability of the proposed framework. Results show that the battery lifetime value is critically dependent on both the external market environment and its internal state of health. On the grid service side, results show that second-life batteries can provide more than 50% of the value compared to new batteries, and frequency regulation provides two times more revenue than price arbitrage throughout the battery lifetime. [ABSTRACT FROM AUTHOR]

Smart buildings form an integral part of the smart grid that lowers electricity cost. This objective can be achieved by using suitable enhanced algorithms and techniques. The motivation of this paper is the request for an economical decision-making system from a commercial company in Singapore. This paper presents an economical building management system (EBMS) concept inspired by the building management system and machine learning. EBMS improves the decision-making process through rule-based algorithm within the system. Machine learning algorithm is included as part of EBMS to enhance the computational calculation for the electricity distribution. It includes the function of self-making decision for purchasing electricity based on forecasting system for electricity price or real-time electricity price data when facility managers are not present. EBMS uses multiagent system to communicate, interact, and negotiate with multiple agents for energy supply and demand in the building. Simulation studies have shown the potential of EBMS concept to provide a cost reduction solution up to 61.42% savings for smart buildings. Better electricity purchasing contract option can be decided based on the forecasting system for building power consumption. [ABSTRACT FROM AUTHOR]

In this paper, the modeling of storage elements and a unified theory liberated of the I–V characteristics of a specific technology is developed. First, the definition of an ideal storage element with limited energy capacity is presented, then the semiideal storage properties are discussed. The addition of power conservative two-port network denoted POPI (Pout = Pin) is added in order to define a flexible storage element in terms of characteristics. The paper demonstrates the theory by examples of storage elements integrated in power electronics systems. The ideas and methodology presented in this work can be applied to power electronics applications as well as power systems, smart grids, and microgrids, where storage is an important factor. [ABSTRACT FROM AUTHOR]