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The article focuses on a technology developed by Stanford University associate professor Yi Cui and his team which turns a standard office paper into a battery that creates energy source for portable electronics such as laptops and hand held devices. Yi Cui and his team discovered that carbon-nanotube ink infused paper, used as electrodes, offered performance similar to the much-heavier metal electrodes found on lithium-ion batteries. The team is trying to make their technology commercially relevant, like improving their battery's performance.

In this paper, we examine several different types of miniaturized plasma sources that have been developed for nonpropulsion applications, but could be useful for the advancement of electric propulsion. With the same or similar physical principles and often similar design solutions, such sources suggest useful pathways for modernization and integration of presently available well established as well as emerging miniaturized plasma sources into space thruster systems. Features related to miniaturization and optimization of the technological plasma sources will provide useful insights for consideration by the electric propulsion specialists. It is not the aim of this paper to show an entire spectrum of technological microplasma systems, but rather to outline possible future trends and perspectives for the miniaturized technological plasmas in relation to space micropropulsion systems. [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]

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]

Lithium-ion (Li-ion) batteries have been used in a wide variety of applications, ranging from portable electronics to electric vehicles. During repetitive charging and discharging, a battery's capacity fades due to electrochemical reactions such as solid electrolyte interphase growth. Li-ion batteries reach an end-of-life (EOL) point, after which using them is not recommended. However, some unhealthy batteries reach their EOL sooner than expected. A qualification test is usually conducted to evaluate the reliability of Li-ion batteries and classify unhealthy batteries, but this test requires several months. This paper develops a data-driven method to reduce the qualification time by detecting anomalies before EOL. This method detects an anomaly in the capacity fade curve of unhealthy batteries based on their capacity fade trend. Since the developed method detects anomalies of unhealthy batteries before EOL, the method is effective in reducing the time for the qualification test of Li-ion batteries. [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]

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 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]

Electric vehicles based on high-energy lithium-ion batteries often exhibit a substantial loss in performance at subzero temperatures: Due to slower electrochemical kinetics, the internal resistances of the batteries rise and diminish available power and capacity. Hybrid energy storage systems (HESSs) can be used to overcome these weaknesses. In this paper, the performance of two HESSs, combining a high-energy lithium-ion battery with either a high-power lithium-ion battery or a lithium-ion capacitor, has been investigated experimentally for a driving scenario at various temperatures. Both configurations enable driving at -20 °C, which was not possible without hybridization. The HESS using the high-power lithium-ion battery provides a substantially higher driving range due to its higher energy density. An analysis of different operating strategies has helped to maximize the driving range: Discharging the high-energy battery with a constant current and keeping the high-power cell at a higher state of charge (SoC) extend the driving duration, as the requested driving power can still be provided at a lower SoC of the high-energy battery. In addition to the HESSs, two energy storage systems without hybridization, consisting of different generations of high-energy lithium-ion cells, have been examined to disclose improvements in battery technology. These improvements narrow the benefits of HESSs, as the high-energy batteries have become less reliant on the support of an additional high-power device. Although HESSs lose importance for current lithium-ion battery systems, they can be a valuable option for next-generation lithium batteries, which are expected to provide higher energy densities but exhibit reduced rate capability. [ABSTRACT FROM AUTHOR]

State of charge (SOC) estimation of lithium-ion batteries has been extensively studied and the estimation accuracy was mainly investigated through the development of various battery models and dynamic estimation algorithms. All battery models, however, contain inherent model bias due to the simplifications and assumptions, which cannot be effectively addressed through the development of various algorithms such as Kalman filtering (KF) or particle filtering (PF). Consequently, as observed in some study, battery SOC estimation using a typical extended KF in fact is not very accurate where the error could range from 5% to 10% or even more depending on the battery characteristics. This paper proposes bias characterization of the battery model, so that accuracy of the baseline model could be significantly improved and eventually SOC estimation could be much more accurate than the one only using the baseline model. This paper reports great potential for improving battery SOC estimation with the bias characterization and proposes two methods for actual bias modeling. In particular, the polynomial regression model and the Gaussian process regression model are proposed to examine the effects of the two methods on bias modeling and SOC estimation using a typical battery circuit model. Results are demonstrated in both simulation and lab testing using three battery charging/discharging profiles with the cross-validation technique. [ABSTRACT FROM AUTHOR]

This paper addresses a wearable piezoelectric energy harvester that combines a PVDF piezoelectric charge generator with an a-Si:H dual-gate thin-film transistor as a rectifier. An analytical model and equivalent circuit elaborate their working principle and device physics. A preliminary experimental study proves the device concept and demonstrates that such a harvester is capable of generating microwatt-range power with a simple 90° finger bending action. Coupled with low-cost and largearea fabrication processes, a pixelated energy-harvesting array can be potentially achievable, making it a promising alternative to energy sources for wearable electronics. [ABSTRACT FROM AUTHOR]

Accurate and reliable capacity estimation for lithium-ion batteries adapting to uncertain environment plays a significant role in the fields of electric vehicles and renewable energy systems. This paper proposes a novel capacity fusion estimation approach based on three-parameter capacity fade model and constant current charging curves using dual unscented Kalman filters. In view of the battery cycle aging problem under variable temperature situation, a three-parameter capacity fade model is introduced to continuously estimate the battery cell capacity for unknown changes of ambient temperature. Nevertheless, the model parameters may mismatch in practical applications, so the first unscented Kalman filter views the discrete capacity based on constant current charging curves as observations to periodically update the parameters. Thereafter, the second one performs a fusion operation depending on the capacity estimated by constant current charging curves and the three-parameter capacity fade model with the corrected parameters, so as to improve the capacity estimation accuracy online. The results show that the fusion estimation errors of capacity are controlled within 2% by updating the model parameters, and the robustness of the proposed method is also validated. [ABSTRACT FROM AUTHOR]

In this paper, a lithium ion cobalt polymer battery cell is modeled using 3-D finite elements (3DFE) for the study of its performance under a wide range of operating scenarios. The electrochemistry is modeled and coupled to an electromagnetic model to forecast the cell voltage overpotential as a result of charging and discharging under six current densities. To study this phenomenon, light ( $C$ /10), medium ( $C$ /2), and high ( $1C$ ) charging and discharging currents are imposed at the battery terminals and compared with experimental results. The electric field inside the electrodes and electrolyte is analyzed at each case highlighting regions where loss occurs. A magnetic field analysis depicts the total electrochemical generation of current densities inside the cell while identifying how the development of gradient currents occur, a mechanism which can shorten the life span of the battery. This paper distinguishes where 3DFE analysis battery models can be useful in studying how lithium ion batteries will behave given a particular loading or a charging profile before placing them in service. [ABSTRACT FROM AUTHOR]

As an important part of prognostics and health management, accurate remaining useful life (RUL) prediction for lithium (Li)-ion batteries can provide helpful reference for when to maintain the batteries in advance. This paper presents a novel method to predict the RUL of Li-ion batteries. This method is based on the framework of improved particle learning (PL). The PL framework can prevent particle degeneracy by resampling state particles first with considering the current measurement information and then propagating them. Meanwhile, PL is improved by adjusting the number of particles at each iteration adaptively to reduce the running time of the algorithm, which makes it suitable for online application. Furthermore, the kernel smoothing algorithm is fused into PL to keep the variance of parameter particles invariant during recursive propagation with the battery prediction model. This entire method is referred to as PLKS in this paper. The model can then be updated by the proposed method when new measurements are obtained. Future capacities are iteratively predicted with the updated prediction model until the predefined threshold value is triggered. The RUL is calculated according to these predicted capacities and the predefined threshold value. A series of case studies that demonstrate the proposed method is presented in the experiment. [ABSTRACT FROM AUTHOR]

The U.S. Naval Research Laboratory is developing a rep-rate charger for a 60-kJ capacitor module. The charger is lithium-ion battery powered and must charge up the capacitive load 50 times over a 5-min period with roughly 1 s between each charge cycle. This paper examines some aspects of the design: lithium-ion battery energy storage, hard switched dc–dc converter, high-voltage rectifier, and capacitive load with pulsed power switching. The lithium-ion batteries are arranged in 12, 16-cell, 50 V modules that are connected in series to form a roughly 600 V prime power supply. A hard switched dc–dc converter with full-bridge rectifier is used to charge the capacitor and a pulse forming network is used to discharge the capacitor into the load. This paper will discuss the system design and available test data. [ABSTRACT FROM PUBLISHER]

This paper presents a comprehensive survey of machine learning, deep learning, and digital twin technology methods for predicting and managing the battery state of health in electric vehicles. Battery state of health estimation is essential for optimizing the battery usage, performance, safety, and cost-effectiveness of electric vehicles. Estimating the state of health of a battery is a complex undertaking due to its dependency on multiple factors. These factors include battery characteristics such as type, chemistry, size, temperature, current, voltage, impedance, cycle number, and driving pattern. There are drawbacks to traditional methods, such as experimental and model-based approaches, in terms of accuracy, complexity, expense, and viability for real-time applications. By employing a variety of algorithms to discover the nonlinear and dynamic link between the battery parameters and the state of health, data-driven techniques like machine learning, deep learning, and data-driven digital twin technologies can get beyond these restrictions. Data-driven methods can also incorporate physics and domain knowledge to improve the explainability and interpretability of the results. This paper reviews the latest advancements and challenges of using data-driven techniques for battery state of health estimation and management in electric vehicles. The paper also discusses the future directions and opportunities for further research and development in this field. The survey scope spans publications from the year 2021 to 2023.

This paper presents the numerical simulation results of characteristics and efficiency of separation of uranium ion fluxes and its fission products in the process of the plasma separation. The movement of beams of heavy (238 u) and light (160 u) single-charged ions in vacuum and in helium buffer plasma has been investigated at a pressure of 10 mtorr, magnetic fields 0.8–1.2 kG and electrical potentials up to 1 kV. The interaction between the ion beams and their interaction with the buffer plasma has been taken into account. The numerical simulation of plasma has been conducted in a drift–diffusion approximation. It has been shown that the presence of buffer plasma in the area of ion fluxes and occurring compensation of their space charge can greatly improve the separation efficiency compared to the separation of ion fluxes in vacuum. [ABSTRACT FROM AUTHOR]

Existing charging techniques for lithium-ion batteries use a largely open-loop approach, where the charge profile is predecided based on a priori knowledge of cell parameters. There is a need for closed-loop charging techniques that use instantaneous cell voltage and/or temperature to modulate the charging current magnitude. This paper addresses this gap by proposing a constant-temperature constant-voltage (CT-CV) charging technique, considering cell temperature as a key degradation metric. The proposed CT-CV charging scheme employs a simple and easy-to-implement proportional-integral-derivative (PID) controller aided by a feed-forward term. The charging current is dynamically adjusted in response to the battery temperature, which indirectly reflects its aging and thermal environment. As per experimental results, the proposed method achieves 20% faster charging with the same total temperature rise as constant-current constant-voltage (CC-CV) technique. Alternatively, it causes 20% lower cell temperature rise for given total charge time. It can easily accommodate applications that demand even faster charging by simply raising the set temperature. This paper establishes the benefits of the proposed CT-CV charging at cell level and raises the possibility of extending it to the pack level by integrating it with battery management systems. [ABSTRACT FROM AUTHOR]

This paper proposes data transmission method between primary and secondary of the flyback converter without additional communication circuit while simultaneously transferring power. In some application such as a battery charger, the data exchanges between the primary and secondary sides are necessary. In the conventional system, an additional line or wireless communication modules is used for data exchanges, thereby increasing the system and connector size. The proposed system, in comparison, does not use additional signal transceiver but instead exchanges data by simply alternating operation mode of the flyback converter, thus adding communication function while not increasing the volume of the terminal and overall system. The waveform of transformer voltage is used to count the number of resonant pulses, which is used for decoding and encoding the data packet. Bidirectional communication between primary and secondary sides is possible while power is transferred to the output using an appropriate communication protocol. This paper proposes data transmission method for both single output and the multioutput cases. Also, both half-duplex and full-duplex communication using the proposed method is explained. The experimental results are presented to verify the performance of the proposed communication method. [ABSTRACT FROM AUTHOR]

This paper investigates the role of artificial neural networks in enhancing the accuracy of instantaneous power estimation of electric vehicles’ batteries. In electric vehicles, a battery is used as a main or complementary bidirectional power source. To optimize the energy management of the vehicle, the power sourced or sinked by the battery must be estimated in real time under any condition. The power of the battery is a function of many variables including the current, the state of charge, the ambient temperature, and the state of health. This paper evaluates some existing equivalent circuit models for estimating the instantaneous power of electric vehicles' batteries and proposes new artificial neural network-based models to enhance the power estimation accuracy. The experimental data obtained by performing standardized electric vehicle tests using a 3.6-V/16.5-Ah lithium-ion battery cell and a 12.8-V/150-Ah lithium-ion battery pack are presented and used for models' evaluation. [ABSTRACT FROM AUTHOR]

A grid-tied microgrid (MG) with a battery energy storage system (BESS) is studied in this paper. The energy storage efficiencies of the BESS are considered to optimize the operational cost of the MG. Two quadratic functions are verified and utilized to formulate the efficiencies of BESS in both charge and discharge process. Afterward, constraints of MG power scheme are investigated based on aforementioned equations. Furthermore, the 24-h ahead forecasting data of photovoltaic (PV) generation and loads demand are also utilized during MG modeling. To minimize the operational electricity cost of the MG in the next 24 h, a nonlinear programming with discontinuous derivatives solver is applied based on the proposed constraints. Additionally, to balance the power flow of MG and reduce the effects of the forecasting error of PV generation, a two steps MG management strategy is therefore developed based on the scheduled power. Experiments are conducted to verify the relationship between battery energy storage efficiency and charging/discharging current of the lithium-ion battery. Moreover, the proposed energy management strategy is validated by the hardware-in-the-loop experiments for real-time MG operation. [ABSTRACT FROM AUTHOR]

A plug-in hybrid electric vehicle (PHEV) uses an internal combustion engine to extend its cruising range, and to produce the electric power needed to be supplied to its electric motor when the charge level of the vehicle's battery becomes low and reaches a predetermined state of charge (SOC). This paper provides a better solution by replacing the internal combustion engine of a PHEV with a small-size photovoltaic (PV) module located on the roof of the PHEV, and a micro wind turbine located in front of the PHEV, behind the condenser of the air conditioning system. Thus, this study proposes a novel battery/PV/wind hybrid power source to be utilized in PHEVs. The power source equipped with vehicle-to-grid (V2G) technology is composed of a 19.2-kWh Lithium (Li)-ion battery used as the main energy storage device, and a PV module and a wind energy conversion system. A prototype of the battery/PV/wind hybrid power source has been constructed and utilized in a PHEV. Experimental verifications are presented that demonstrate utilizing the PV module and micro wind turbine adds 19.6 km to the cruising range of a PHEV with the weight of 1880 kg during two sunny days, and provides higher power efficiency (91.2%) and speed (121 km/h). Highly accurate dc-link voltage regulation and producing an appropriate three-phase stator current for the traction motor by using pulse width modulation technique are the other contributions of this paper. [ABSTRACT FROM AUTHOR]

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

This paper proposes a flexible data-driven method for online estimating the State of Health (SOH) of Li-ion batteries in both charging and discharging modes. Based on comprehensive battery aging data analytics, a novel health indicator called voltage variance during equal time interval (VVETI) is extracted. The VVETI can be extracted during either charging or discharging mode. For online applications, the indicator is derived from a small segment of charge/discharging curves. Unlike existing methods, the indicator can be extracted in various voltage ranges which is highly flexible for application. Then, a hierarchical ensemble model of extreme learning machine (ELM) is proposed as the machine learning engine for accurate SOH estimation. The proposed method is tested on three open datasets and reports a very high accuracy (average RMSE below 0.5% under appropriate voltage ranges). [ABSTRACT FROM AUTHOR]

A hybrid method for the prediction of the remaining useful life (RUL) of Lithium-ion batteries considering error-correction is proposed in respect of capacity diving phenomenon in later capacity degradation. First, an improved empirical capacity degradation model is proposed based on our previous work, with the analytic expression further revised in this paper to enhance its fitting accuracy, parameter identifiability, and applicability to tracking algorithm. Unscented particle filter (UPF) algorithm is then implemented to obtain prognostic results with original error series. Next, to enhance the quality of original error data by reducing the local uncertainty, complete ensemble empirical mode decomposition (CEEMD) algorithm is utilized to reconstruct error series. The fundamental error evolution information is retained by selecting relatively highly correlated intrinsic mode functions (IMFs) of the decomposition results of original error series. Finally, after employing Gaussian process regression (GPR) algorithm, the prognostic error to correct the UPF-based prognostic result is obtained from the reconstructed error series. RUL prediction experiments for batteries with different working conditions have been conducted to verify the improved performance of the proposed model and the hybrid method, with mean absolute percentage errors of battery capacity degradation predicted less than 0.4%. [ABSTRACT FROM AUTHOR]

Generalized spatial modulation (GenSM) aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) has recently received substantial academic attention. However, due to the insufficient exploitation of the transmitter's knowledge of the channel state information, the achievable rates of state-of-the-art GenSM-aided mmWave MIMO systems are far from being optimal. Against this background, a novel analog precoding scheme is proposed in this paper to improve the spectral efficiency (SE) of conventional GenSM-aided mmWave MIMOs. More specifically, we firstly manage to lower-bound the achievable SE of GenSM-aided mmWave MIMO with a closed-form expression. Second, by exploiting this lower bound as a cost function, a low-complexity iterative algorithm is proposed to design the analog precoder for SE maximization. Finally, numerical simulations are conducted to substantiate the superior performance of the proposed design with respect to state-of-the-art GenSM-aided mmWave MIMO schemes. [ABSTRACT FROM PUBLISHER]

This paper presents the wideband system identification (WSI) technique, i.e., an online method to identify power impedances over a wide frequency range from which the corresponding parametric impedance can be calculated online as well. The WSI technique exploits an existing custom 25-kW power electronic converter on the top of its power conversion function, which serves as power amplifier of an existing power hardware-in-the-loop (PHiL) simulation setup. The PHiL simulation technique allows connecting a real device under test (DUT) with the real-time simulated rest of system (ROS) at power level. An interface algorithm (IA) on simulation side and a power amplifier (the 25-kW power electronic converter) connect ROS and DUT. This paper shows the impact of the uncertainties in the WSI chain on the accuracy of the impedance identification and highlights how the WSI technique can be combined with the damping impedance method IA to improve both accuracy and stability of the PHiL test bench. The application of the method is illustrated for the scenario of a PHiL test of a dc microgrid with a passive load. [ABSTRACT FROM PUBLISHER]

This paper presents a method for an online real-time electrochemical impedance spectroscopy (EIS) measurement of batteries using closed-loop control of power converter. Unlike the previously proposed method which allows the measurement of the ac impedance for a single frequency, the presented method in this paper allows for obtaining the EIS for a spectrum of frequencies by using the information included in a single perturbation cycle, or a few cycles of perturbation to obtain a more accurate EIS with a very wide frequency range. This will result in faster EIS measurement for a spectrum of frequencies and under the same battery operating conditions. The presented method utilizes closed-loop control operation for the EIS measurement functionality, which allows for better control of the output voltage and for upgrading the concept to be able to achieve no added perturbation ripple at the output of the system. The presented online real-time EIS measurement method utilizes a power converter with closed-loop control in order to create an output voltage step-function perturbation at a given frequency to generate battery voltage and current responses. By applying Fourier analysis to these responses, an EIS can be obtained for a range of frequencies equal or higher than the perturbation frequency of the step function. In addition, this paper presents a method to eliminate the added perturbation ripple when two or more power converters are used. The theoretical basis and experimental prototype results are provided to illustrate and validate the presented method. [ABSTRACT FROM PUBLISHER]

Lithium-ion batteries have been widely applied in energy conversion sectors, where effective future ageing prediction is crucial to guarantee their safety and performance. Due to the highly nonlinear ageing behaviours, developing a reliable method that could not only consider the knee point effect but also predict the future ageing trajectory with uncertainty quantification poses a formidable task. This paper derives a machine learning solution, based on the migrated Gaussian process regression (GPR), for predicting future battery two-stage ageing trajectory. Specifically, a base model is first offline identified from the easier collected accelerated-speed ageing data, through which the long life ageing information can be effectively learned. With this base model, a migrated mean function is then designed and coupled within the GPR framework for battery ageing predictions. Experimental data from three different batteries are applied for model validation and performance evaluation. Results indicate that the proposed solution leads to effective improvements in prediction accuracy and uncertainty quantification for both cases of training before and after the knee point. This is the first time to couple migration concept within GPR, paving the way to reduce experimental cost and predict battery future two-stage ageing trajectory with only a few (first 30%) data available. [ABSTRACT FROM AUTHOR]

Long horizon state of health (SOH) monitoring and remaining useful life (RUL) prediction are of industrial value in prognostic and health management (PHM) of lithium-ion batteries (LIBs) to ensure their reliable functionality by early detection. Machine Learning, as a data-driven health diagnostic technique, has been widely utilized in solitary and hybrid structures. However, an accurate SOH estimation and RUL prediction method with less computational burden are highly desirable for the online state prediction in an electric vehicle application. This paper evaluates nonlinear autoregressive with external input (NARX) recurrent neural network (RNN) and time delay neural network (TDNN) in their prediction precision using the NASA dataset. The superior method, NARXRNN, is employed for two different datasets to estimate the battery's SOH and predict its RUL on a broad horizon. The results reveal the outstanding performance by presenting the root mean square error within 3% and mean absolute error within 2% for unseen data. Therefore, this method is capable to accurately predict the SOH of LIBS from historical data at low computational complexity. It is a promising model for long horizon SOH and RUL prediction and practical for online applications. [ABSTRACT FROM AUTHOR]

This paper presents an energy management approach for a hybrid energy system comprised of a photovoltaic (PV) array and a polymer electrolyte membrane fuel cell (PEMFC). A single storage device, i.e., a Li-ion battery module, is used in the proposed structure. Linear proportional-integral (PI) and nonlinear flatness-based controllers for dc bus stabilization for power plants are compared. To verify the control approaches, a hardware system is realized with analog circuits for the PV, FC, and battery current control loops (inner controller loops) and with numerical calculation (dSPACE) for the external energy control loops. Experimental results with small-scale devices [namely, a PV array (800 W, 31 A), a PEMFC (1200 W, 46 A), and a Li-ion battery module (11.6 Ah, 24 V)] illustrate the excellent energy management scheme during load cycles, and the nonlinear differential flatness-based control was determined to provide improved dc bus regulation relative to a classical linear PI control method. [ABSTRACT FROM PUBLISHER]

In this paper, an artificial neural network (ANN) based approach is proposed to estimate the capacity fade in lithium-ion (Li-ion) batteries for electric vehicles (EVs). Besides its robustness, stability, and high accuracy, the proposed technique can significantly improve the state-of-charge (SOC) estimation accuracy over the lifespan of the battery, which leads to more reliable battery operation and prolonged lifetime. In addition, the proposed technique allows accurate prediction of the battery remaining service time. Two identical 3.6-V/16.5-Ah Li-ion battery cells were repeatedly cycled with constant current and dynamic stress test current profiles at room temperature, and their discharge capacities were recorded. The proposed technique shows that very accurate SOC estimation results can be obtained provided enough training data are used to train the ANN models. Model derivation and experimental verification are presented in this paper. [ABSTRACT FROM AUTHOR]

This paper studies the characteristics of battery packs with parallel-connected lithium-ion battery (LiB) cells. To investigate the influence of the cell inconsistency problem in parallel-connected cells, a group of different degraded LiB cells were selected to build various battery packs and test them using a battery test bench. The physical model was developed to simulate the operation of the parallel-connected packs. The experimental results and simulation indicate that, with different degraded cells in parallel, there could be capacity loss and large difference in discharge current, which may cause further accelerated degradation and a more serious inconsistency problem. [ABSTRACT FROM PUBLISHER]

This paper presents a wireless power transfer (WPT) system using a bowl-shaped transmitting (Tx) coil and its application for charging small electronic devices such as hearing aids and wearable electronic devices. The receiving (Rx) electronic devices are placed in the Tx coil and charged effectively with spatial freedom. The WPT system includes three coils for power transfer: a source coil, a Tx resonant coil, and a parallel-resonance Rx coil. The proposed Tx resonant coil is bowl shaped and made of spiral and conical-shaped helical coils in series. A compact box-like Rx coil with ferrite sheets in the box is designed to improve magnetic coupling. To analyze the proposed system, an equivalent circuit of the system is presented. Magnetic fields of the bowl-shaped Tx coil, as well as the mutual inductance between the Tx and Rx coils are derived. For the verification of theoretical analyses and calculations, a wireless charging system for hearing aids is fabricated. It is shown that the proposed system can uniformly charge a Li-ion battery with 30% overall efficiency in the parallel arrangement. Furthermore, even in the perpendicular arrangement, the battery can be charged, except for a narrow dead zone near the center of the Tx coil. [ABSTRACT FROM PUBLISHER]

This paper presents a simple online impedance measurement method for electrochemical batteries, including lithium-ion, lead-acid, and nickel–metal-hydride chemistries. By using the proposed online impedance measurement method, there is no need to disconnect the battery from the system or to interrupt system operation, and there is no need to add ac signal injection circuits, costly response measurement, and analysis circuits/devices. In practical battery-powered systems, a power converter is usually used to interface the battery with the load for voltage/current regulation purposes. In this paper, through the control of the power converter and duty-cycle perturbation, the ac impedance of the battery can be determined. The proposed method provides a low-cost and practical solution for the online measurement of the ac impedance of batteries. Moreover, the proposed method can be either continuously or periodically performed without interrupting the normal operation of the battery system and the power converter. In addition, this paper provides an example where the obtained impedance data are utilized for online state-of-charge estimation of lithium-ion batteries. The proposed online impedance measurement method is validated by experiments conducted on a 2.6-Ah 18650-size lithium-ion battery interfaced to the load via a bidirectional dc–dc boost/buck converter. [ABSTRACT FROM PUBLISHER]

Numerous research efforts have been undertaken to improve the feasibility of fielding compact pulsed power systems for a wide variety of applications. Each of these research efforts focuses on specific areas of pulsed power, but few papers have focused on the performance of a grid independent prime power source. This critical component is often overlooked in mobile pulsed power systems. Though not within the pulsed power community, a great deal of work has been performed by those in the electrochemical energy storage community to improve the energy density, power density, working voltage, and cycle life of electrochemical cells. This paper has helped to develop new energy storage technologies for applications such as consumer electronics, electric vehicles, and electrical grid energy storage. However, these electrochemical cells were not developed with pulsed power applications in mind. For that reason, a large amount of research is needed to understand how to effectively use electrochemical cells in pulsed power applications. In this paper, a number of different energy storage chemistries, including lithium-ion batteries, lead acid batteries, and bi-polar nickel metal hydride batteries, have been evaluated for use in pulsed power applications as a result of their high power and energy densities. [ABSTRACT FROM PUBLISHER]

This paper proposes a battery charger (BC) for electric vehicles based on sinusoidal-ripple-current (SRC) method. The SRC method is used as an advanced charging-discharging method. On the one hand, the sinusoidal current variations alternately change output active power of the BC, negatively affect AC-side terminal current of the BC, and therefore, cause a power quality problem. On the other hand, the output active and reactive powers must be regulated at constant values without any variations for participation in vehicle-to-grid technology. Thus, a particular topology is suggested and then a control system is precisely designed in order to implement SRC charging-discharging method and adjust the output active and reactive powers for vehicle-to-grid technology without the power quality problem. SRC method can also be used in online electrochemical impedance spectroscopy (EIS) for state of charge and state of health estimation in battery management. The proposed control system can also generate the SRC signals, suitable for online EIS, in a controlled way unlike the recently proposed BCs. The simulation and experimental results demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

Lithium-ion rechargeable batteries are widely used in various electronic products and equipment due to their immense benefits in power supplying. The exact remaining useful life (RUL) prediction of lithium-ion batteries has shown excellent achievements in preventing severe economic and security consequences incurred in failing to provide necessary power levels. Recently, the nonlinear-drifted fractional Brownian motion made quite a splash in RUL prediction, since its first hitting time distribution can be approximated by weak convergence theorem and time-space transformation. However, the previous RUL prediction methods based on fractional Brownian motion only considered current state measurement. In this paper, a prediction framework based on nonlinear-drifted fractional Brownian motion with multiple hidden state variables is put forward to estimate RUL. Specifically, all the parameters of nonlinear function are defined as specific hidden state variables of lithium-ion battery degradation model, and all the state measurements are used to posteriorly estimate the distribution of the multiple hidden state variables by unscented particle filter algorithm. Four sets of lithium-ion battery degradation data provided by NASA Ames Research Center are used to validate the proposed prediction framework. According to comparison study with other methods, the proposed prediction framework demonstrates greater precision in the RUL prediction. [ABSTRACT FROM AUTHOR]

This paper investigates the real-time estimation on the state-of-charge (SoC) and state-of-health (SoH) of lithium-ion (Li-ion) batteries for the purpose of achieving reliable, safe, and efficient use of batteries. Three terminal sliding-mode observers (TSMOs) are designed; each observer is used to estimate one variable of a Li-ion cell for developing a real-time SoC estimation algorithm. To estimate the SoH, two additional TSMOs are subsequently presented. Finally, a set of complete estimation algorithms for SoC and SoH are formulated. The output injection signals of the proposed TSMOs are designed to be continuous. This can attenuate chattering that exists in the traditional sliding-mode observers and simplify the estimation algorithms. The main advantage of the proposed algorithms is eliminating the low-pass filter in the estimation algorithms. Therefore, higher estimation accuracy and faster response speed are obtained. The proposed methods are tested and evaluated using the acquired dynamic stress test and federal urban driving schedule test data, which demonstrate the effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

The thermal effect has a significant impact on the performance of a lithium-ion battery. Thus, modeling the thermal process, which always involves unknown boundary heat exchange, is significant to battery management. Two critical issues should be addressed for the thermal process modeling: 1) the nominal model, which is constructed offline, can be updated efficiently to compensate for any online disturbances; and 2) the influence of previous and recent spatiotemporal dynamics may be varying and should be handled properly. Bearing these in mind, in this paper, a spatial correlation-based incremental learning technique is designed for spatiotemporal modeling. First, the incremental learning technique is developed to update the dominant spatial basis functions of the nominal model, which is constructed by a time/space separation-based method. Then, a forgetting factor is incorporated into the incremental learning technique to handle time-varying dynamics. Additionally, the popular approximator, that is, the radial basis function neural network, is utilized to identify the low-dimensional temporal model. Simulations and experiments on a pouch type battery with boundary heat exchange have demonstrated the accuracy and efficiency of the proposed modeling method. [ABSTRACT FROM AUTHOR]

This paper devises an innovative mesoscale electrothermal model for Li-ion batteries. This model manipulates the mesoscale calculation grid in finite element analysis as independent small cell sandwiches and establishes a lumped equivalent circuit model for each cell sandwich. Then, such electrical models are arranged in parallel to form a multilayer equivalent circuit to simulate electrical characteristics of a whole battery, through capturing the current and terminal voltage of each constituent cell sandwich. This modeling idea overcomes the entrenched disadvantage of heat generation models with lumped parameters, i.e., the unavailability of heat generation distribution inside a battery. Besides the current and terminal voltage, the temperature and state of charge dependent open-circuit voltage and entropy coefficient are incorporated into a Newman's heat generation model to estimate the heat generated in the calculation grid. The battery temperature distribution is eventually derived by solving the heat conduction equation with thermal conductivity as a function of the battery temperature. We leverage the developed electrothermal model to track the temperature evolution of an 18 650 Li-ion battery at different ambient temperatures and discharge rates, for the first time. Experimental results demonstrate that the electrothermal model can precisely emulate the battery thermal dynamics with an average error of 0.72 °C. Moreover, a comparative study shows that the proposed model outperforms common resistance-based thermal models that do not consider the heat generation distribution and the interdependence between the battery temperature and thermal conductivity. [ABSTRACT FROM AUTHOR]

This paper proposes a highly effective voltage cell equalization method for lithium-ion (Li-ion) battery management systems (BMSs) for several applications, such as nearly zero energy buildings, microgrids, and electric vehicles. The concept of the suggested method is the proper control of the charging current of each cell of a battery stack through the suitable regulation of the gate source of each cell's parallel-connected mosfet. Thus, the mosfet can act as a controllable current source for providing the proper equalization current according to the specific needs of each battery cell. The correct voltage level of the gate-source of each mosfet is determined by a cell equalization controller through the comparison of the performance of each cell of a battery stack with the healthier cell, which is detected by an aging estimation algorithm based on the evaluation of the internal resistor and the capacity fade of each cell. Therefore, smoother and faster cell equalization is achieved compared to the conventional dissipative method. The practicality and the high effectiveness of the proposed cell equalization technique have been experimentally validated. Several experimental results are presented to demonstrate the operational improvements of the proposed cell equalization technique against the conventional one. [ABSTRACT FROM AUTHOR]

Lithium iron phosphate battery packs are widely employed for energy storage in electrified vehicles and power grids. However, their flat voltage curves rendering the weakly observable state of charge are a critical stumbling block for charge equalization management. This paper focuses on the real-time active balancing of series-connected lithium iron phosphate batteries. In the absence of accurate in situ state information in the voltage plateau, a balancing current ratio (BCR) based algorithm is proposed for battery balancing. Then, BCR-based and voltage-based algorithms are fused, responsible for the balancing task within and beyond the voltage plateau, respectively. The balancing process is formulated as a batch-based run-to-run control problem, as the first time in the research area of battery management. The control algorithm acts in two timescales, including timewise control within each batch run and batchwise control at the end of each batch. Hardware-in-the-loop experiments demonstrate that the proposed balancing algorithm is able to release 97.1% of the theoretical capacity and can improve the capacity utilization by 5.7% from its benchmarking algorithm. Furthermore, the proposed algorithm can be coded in C language with the binary code in 118 328 bytes only and, thus, is readily implementable in real time. [ABSTRACT FROM AUTHOR]

In this paper, a battery balancing circuit is proposed for the series-connected lithium-ion battery cells based on the principle of synchronous rectification. The proposed balancing circuit, also referred to as an equalizer, mainly includes a buck–boost converter (BBC), a multiport half-bridge converter (MHBC), and a driving circuit. The MHBC is coupled with a multiwinding transformer. Compared with the conventional congeneric methods, the number of transformer windings is almost reduced by half, leading to a more compact size and lower cost. Moreover, the secondary mosfets of the MHBC are synchronously driven by the primary half-bridge converter (HBC) without the need of additional isolated mosfet gate drivers and power supplies, dramatically reducing the circuit cost and complexity. In addition, the proposed equalizer can realize the module-to-multi-cell (M2MC) equalization under severe imbalance conditions to improve the balancing speed, and work at the any-cell-to-any-cell (AC2AC) self-balancing mode under slight imbalance conditions to improve the balancing efficiency and effectiveness, which achieves a good trade-off between balancing speed and effectiveness. The expressions of the equalization current and efficiency are derived and verified by experimental results. The proposed equalizer is experimentally evaluated for a string with 12 series-connected lithium-ion cells. [ABSTRACT FROM AUTHOR]

Conventional energy generation sources mainly provide energy supply to remote areas nowadays. However, because of growing concerns over greenhouse gas emissions, the integration of renewable energy sources is mandatory to meet power demands and reduce climatic effects. The advancements in renewable generation sources and battery storage systems pave the way for microgrids (MGs). As a result, MGs are becoming a viable solution for power supply shortage problems in remote-area applications, such as oceanic islands. In this paper, an islanded MG, which consists of PV system, tidal turbine (TT), diesel generator (DG), and Li-ion battery, is considered for Ouessant island in Brittany region in France. The economic operation of the MG is achieved by including battery degradation cost, levelized costs of energy of the PV system and TT, operating and emission costs of DG, and network constraints. The developed model leads to a non-linear and non-convex problem, which unfortunately can converge to a local optimum solution. The problem has, therefore, been relaxed and converted to a convex second-order cone model to achieve an optimal decision strategy for islanded MG operations with a global or near-global solution. Numerical simulations are carried out to prove the effectiveness of the proposed strategy in reducing the operating and emission costs of the islanded MG. It is shown that the developed convex energy management system formulation has an optimality gap of less than 1% with reduced computational cost. [ABSTRACT FROM AUTHOR]

In this paper, an effective and robust algorithm is developed for on-board detection of battery anomaly caused by short circuit (SC). The proposed algorithm uses the battery-terminal voltage and current information measured using battery-management system during standard uses. The proposed method is purely a noninvasive, software-based solution and does not need any additional hardware. It extracts a set of designed features from the recorded data and stores their statistics as normal operating behavior for the initial five charge–discharge cycles. After the initial characterization of the battery, the likelihood of the features of being normal are evaluated for the subsequent cycles. If a feature is likely to be healthy, then the statistics for that feature get updated with the current value. However, if the feature is not likely to be healthy, then the battery is considered to be faulty with respect to that particular feature. If more than $\text{50}\%$ of the features identify the battery as faulty, then the overall battery status is classified as faulty. A mobile application is developed to log the smartphone's battery data during charging and discharging for testing. The faulty data are generated by connecting SC resistances of various values to the battery terminals, which emulates the SC condition in the battery. The data logged in the smartphones are transferred to a PC for analysis using the proposed algorithm. $\text{100}\%$ detection accuracy is achieved for SC resistance values less than $\text{200}\,\Omega$. [ABSTRACT FROM AUTHOR]