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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]

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]

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]

Compared with the conventional hard switching battery equalizers, hierarchical battery equalizer based on the bipolar continuous-conduction mode buck–boost units could effectively reduce the switching loss and improve the conversion efficiency. This paper mainly addresses the key technical challenges associated with the optimal design of this battery equalizer. The inductor current is controlled to enter into the negative region. Thus, the mosfet body diode provides a freewheeling path for the inductor current during the dead band. This ensures zero-voltage switching turn-on of both power mosfets in each buck–boost unit without auxiliary snubbers. Therefore, the switching losses are significantly reduced. Meanwhile, the integral of the inductor negative current is precisely controlled to minimize the circulating current. This guarantees both minimized conduction losses and suitable equalization speed. Furthermore, an equalization algorithm, which compensates the battery internal resistance induced state-of-charge error, is proposed to improve the equalization accuracy. The operating principles and design considerations are analyzed in detail. An experimental prototype to balance four series-connected lithium-ion battery cells is implemented. The experimental results validate the design of the equalizer and demonstrate obvious efficiency enhancement over the conventional method. [ABSTRACT FROM AUTHOR]

Microgrid protection schemes require a fast, reliable, and a robust communication system, to adjust relay settings for the appropriate current levels according to the microgrid's operation mode. However, risks of communication link failures and cyber security threats are major challenges for the implementation of protection scheme. This paper presents a co-simulation platform for microgrid based on multiagent system (MAS) when the communication is available in the system. IEC 61850 is used to emulate the proposed protection scheme. The data distribution service (DDS) middleware is used to link between the hardware and software environments. During islanded mode, the system is capable of riding-through communication failures by the aid of a lithium-ion battery. When the communication is attacked, the battery plays an important role and contribute to the fault current for helping the circuit breaker to trip during islanded mode. The design of the control algorithm for the battery's ac/dc converter is developed with single mode operation to eliminate the reliance on communicated control command signals to shift the controller between different modes. [ABSTRACT FROM AUTHOR]

This paper develops a state-of-charge (SOC) estimation model for a lithium-ion battery using an improved extreme learning machine (ELM) algorithm. ELM is suitable for an SOC estimation since the ELM algorithm has fast estimation speed, good generalization performance, and high accuracy. However, the performance of ELM is highly dependent on training accuracy and the number of neurons in a hidden layer. Hence, a gravitational search algorithm (GSA) is applied to improve the ELM computational intelligence by searching for the optimal value hidden layer neurons. The optimal ELM-based GSA model does not require internal battery knowledge and mathematical model for an SOC estimation. The model robustness is validated at different temperatures using different electric vehicle drive cycles. The performance of the ELM-GSA model is verified with two popular neural network methods: back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). The results are evaluated using different error rates and computation costs. The results demonstrate that the ELM-based GSA model offers a higher accuracy and lower SOC error rate than those of BPNN-based GSA and RBFNN-based GSA models. Furthermore, a detailed comparative study between the proposed model and existing SOC strategies is conducted, which also demonstrates the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are characterized by having a very good performance in terms of efficiency, lifetime, and self-discharge, which allowed them to become the major player in the electric vehicle (EV) applications. However, they were not able to totally overcome the EV range anxiety. Thus, a research is carried out nowadays to develop batteries with even higher gravimetric energy density, which should allow a substantial range increase. One of the technologies, which should be able to meet the range requirements is the lithium–sulfur (Li–S) battery. Thanks to the extensive research and development efforts, these cells are close to enter the market, being evaluate in various projects. In this paper, we have proposed an electrical circuit model for a Li–S pouch cell, which was parameterized based on extensive electrochemical impedance spectroscopy measurements. The developed model was verified using static and pulse discharge profiles, showing a good accuracy in predicting the voltage of the tested Li–S battery cell. [ABSTRACT FROM AUTHOR]

This paper is focused on determining the efficiency dependency on the switching frequency for a solid state transformer (SST) with one of the ports connected to an energy storage device (Lithium-Ion battery). Some contributions for measuring the efficiency/losses for different power converter structures for energy storage applications can be found in the literature. However, there are few references which consider the impact of the high frequency behavior of the battery in the complete system performance. Moreover, to the best of the authors’ knowledge, there is no study considering these effects when the energy storage system is directly coupled to an SST port. This research will first obtain parametric high frequency models of the battery cells and modules, the SST, and the power converter based on a vector fitting method in frequency domain. The models will be used to evaluate the system losses depending on the converter switching frequency and the dc-link capacitance. Experimental results confirm the accuracy of the proposed model as well as the battery losses dependency on the switching frequency, the dc-link capacitance and the state of charge. [ABSTRACT FROM AUTHOR]

Due to the inconsistent and varied characteristics of lithium-ion battery (LiB) cells, battery pack modeling remains a challenging problem. To model the operation of each cell in the battery pack, considerable work effort and computation time are needed. This paper proposes a data-driven bias-correction-based LiB modeling method, which can significantly reduce the computation work and remain good model accuracy. [ABSTRACT FROM AUTHOR]

Currently available wireless charging technology for portable electronics demonstrates several shortcomings. For the consumers, the two major shortcomings are limited receiver positioning flexibility and lower efficiency compared to hard-connected charging. For the industry, the major disadvantages are higher cost and complexity in design and control. To overcome these shortcomings and disadvantages, this paper presents a novel control method for unity-gain frequency tracking (UGFT) under coupling coefficient $(k)$ variation in a resonant converter containing a symmetrically implemented series–series (SS) resonant tank. An SS resonant converter employing the UGFT control provides several desirable features: the need for digital communication between the transmitter and the receiver to achieve leakage inductance compensation is eliminated, the design is robust against coupling (k)$ and load (Q)$ variations, the SS resonant converter can operate most of the time at the frequency of highest efficiency (unity-gain frequency ), and the downstream regulator commonly employed in a wireless charging system may be eliminated. Furthermore, in this paper, frequency-domain characteristics of SS resonant tank are explained in detail to illustrate various desirable characteristics. Detailed design criteria for SS resonant tanks are provided to achieve these characteristics. Throughout the analysis, various simulation results are provided to complement the discussion. The proposed UGFT control method is validated through experimental results. [ABSTRACT FROM AUTHOR]

Reusing electric vehicle batteries once retired from the automotive application is stated as one of the possible solutions to reduce electric vehicle costs. Many publications in the literature have analyzed the economic viability of such a solution, and some car manufacturers have recently started running several projects to demonstrate the technical viability of the so-called battery second life. Nevertheless, the degradation behavior of second life batteries remains unknown and represents one of the biggest gaps in the literature. This paper aims at evaluating the effects of lithium-ion nickel manganese cobalt/carbon (NMC/C) battery state of health (SOH) and ageing history over the second life performance on two different applications, a residential demand management application and a power smoothing renewable integration application. The performance and degradation of second life batteries were assessed both at the cell level and at stack level. Homogeneous and heterogeneous stacks were analyzed in order to evaluate the impact of cell-to-cell history and SOH differences over the battery performance and degradation behavior. Results obtained showed a strong influence of the first life battery ageing history upon the second life battery performance and degradation. Thereby, proper monitoring and battery selection appears crucial to certify the technical viability of battery second life. [ABSTRACT FROM PUBLISHER]

This paper deals with the arc flash hazard calculation in large energy storage systems (ESSs), with specific reference to battery energy storage systems (BESSs) and supercapacitor energy storage systems (SESSs). Due to the lack of international harmonized standards and the growing use of large ESSs, the evaluation of arc flash hazard associated with BESS maintenance operation is an interesting subject. In this study, battery circuit models, based on experimental short-circuit tests carried out by TERNA (the Italian transmission system operator) and a simplified supercapacitor circuit model, both coupled with a nonlinear arc resistance model, are used to assess the incident energy related to arc flash in BESSs and SESSs, respectively. Efficient and cost-effective solutions, able to reduce incident energy below the safe value 8 cal/cm2 and based on the installation of fast-acting fuses, are then proposed and discussed. [ABSTRACT FROM PUBLISHER]

With the popularity of electrical vehicles, the lithium-ion battery industry is developing rapidly. To ensure battery safe usage and to reduce its average lifecycle cost, accurate state of charge (SOC) tracking algorithms for real-time implementation are required for different applications. Many SOC estimation methods have been proposed in the literature. However, only a few of them consider the real-time applicability. This paper classifies the recently proposed online SOC estimation methods into five categories. Their principal features are illustrated, and the main pros and cons are provided. The SOC estimation methods are compared and discussed in terms of accuracy, robustness, and computation burden. Afterward, as the most popular type of model-based SOC estimation algorithms, seven nonlinear filters existing in literature are compared in terms of their accuracy and execution time as a reference for online implementation. [ABSTRACT FROM AUTHOR]

The lithium-ion batteries are commonly used in electric vehicle (EV) applications due to their better performances as compared with other batteries. However, lithium-ion battery has some drawbacks such as the overcharged cell which has a risk of explosion, the undercharged cell eventually reduces the life cycle of the battery, and unbalanced charge in series battery gradually reduces overall charge capacity. This paper presents a battery charge equalization algorithm for lithium-ion battery in EV applications to enhance the battery's performance, life cycle, and safety. The algorithm is implemented in series-connected battery cells of 15.5 Ah and 3.7 V nominal each using a battery monitoring integrated circuit for monitoring and equalization of an 8-cell battery pack using a bidirectional flyback dc–dc converter as the channel for charging and discharging of the battery cell. The obtained results show that the developed charge equalization controller algorithm performs well in equalizing both undercharged and overcharged cells, and equalizes the cell within the safety operation range of 3.81 V. To validate the charge equalizer performance, the proposed algorithm outperforms with other studies in terms of balancing, equalization speed, low power loss, and efficiency. Thus, the proposed battery charge equalization algorithm proves an effective and automated system to modularize the battery charge that improves the safety and life cycle of battery. [ABSTRACT FROM PUBLISHER]

Energy storage systems based on Lithium-ion (Li-ion) batteries have been proposed as an environmentally friendly alternative to traditional conventional generating units for providing grid frequency regulation. One major challenge regarding the use of Lithium-ion batteries in such applications is their higher cost—in comparison with other storage technologies or with the traditional frequency regulation methods&#x2014.combined with performance-degradation uncertainties. In order to surpass this challenge and to allow for optimal sizing and proper operation of the battery, accurate knowledge about the lifetime of the Li-ion battery and its degradation behavior is required. Thus, this paper aims to investigate, based on a laboratory developed lifetime model, the degradation behavior of the performance parameters (i.e., capacity and power capability) of a Li-ion battery cell when it is subjected to a field measured mission profile, which is characteristic for the primary frequency regulation service. [ABSTRACT FROM AUTHOR]

In this paper, a novel battery electric vehicle (BEV) concept based on a small fixed and a big swappable Li-ion battery pack is proposed in order to achieve longer range, lower initial purchase priceand lower energy consumption at short ranges. For short ranges, the BEV is only powered by the relatively small-fixed battery pack, without the large swappable battery pack. In this way, the mass of the vehicle is reduced and, therefore, the energy consumed per unit distance is improved. For higher ranges, the BEV is powered by both battery packs. This concept allows the introduction of subscription-based ownership models to distribute the cost of the large battery pack over the vehicle lifetime. A methodology is proposed for the analysis and evaluation of the proposed concept in comparison with a direct owned nonswappable single-pack BEV, proving that significant improvements on city fuel economy (up to 14%) and economic benefits are achievable under several scenarios. These results encourage further study of battery swapping service plans and energy management strategies. [ABSTRACT FROM AUTHOR]

In the last few years, several Li-ion battery technologies have been studied and developed for their use in electric vehicles (EVs). Among them, lithium iron phosphate (LFP) batteries are considered a promising battery technology for EVs, due to their key advantages, such as cycle life, efficiency, and reliability, to name a few. This paper evaluates several commercial LFP batteries, studied under various testing scenarios, including cycle life, energy efficiency, power capability, or internal resistance test. The obtained results are compared with the long-term U.S. Advanced Consortium (USABC) goals, in order to validate the feasibility of this technology for its use in EVs. We found that although the tested LFP batteries successfully met some important USABC goals, there are still several technical challenges to be addressed, such as the increase of the energy density or the reduction of the final cost. [ABSTRACT FROM PUBLISHER]

The development of lifetime estimation models for Lithium-ion battery cells, which are working under highly variable mission profiles characteristic for wind power plant applications, requires a lot of expenditures and time resources. Therefore, batteries have to be tested under accelerated lifetime aging conditions. This paper presents a three-stage methodology used for accelerated lifetime testing of Lithium-ion batteries. The results obtained at the end of the accelerated aging process were used for the parameterization of a performance-degradation lifetime model, which is able to predict both the capacity fade and the power capability decrease of the selected Lithium-ion battery cells. In the proposed methodology both calendar and cycling lifetime tests were considered since both components are influencing the lifetime of Lithium-ion batteries. Furthermore, the proposed methodology was validated by running a verification stage of the lifetime model, where Lithium-ion battery cells were tested at normal operating conditions using an application specific mission profile. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are regarded as the key energy storage technology for both e-mobility and stationary renewable energy storage applications. Nevertheless, Lithium-ion batteries are complex energy storage devices, which are characterized by a complex degradation behavior, which affects both their capacity and internal resistance. This paper investigates, based on extended laboratory calendar ageing tests, the degradation of the internal resistance of a lithium-ion battery. The dependence of the internal resistance increase on the temperature and state-of-charge level has been extensive studied and quantified. Based on the obtained laboratory results, an accurate semiempirical lifetime model, which is able to predict with high accuracy the internal resistance increase of the lithium-ion battery over a wide temperature range and for all state-of-charge levels, was proposed and validated. [ABSTRACT FROM AUTHOR]

In this paper, a method to estimate the capacity of individual lithium ion battery cells during operation is presented. When having two different states of charge of a battery cell as well as the transferred charge between these two states, the capacity of the battery cell can be estimated. The method is described in detail and validated on a battery cell with a current pulse test cycle. It is then applied to a real-life cycle; the accuracy is analyzed and discussed. [ABSTRACT FROM AUTHOR]

A semi-active hybrid energy storage system, consisting of a Li-ion battery pack, dc/dc converter, and Li-ion capacitor pack was developed for a range extended plug-in vehicle. The vehicle has a series-parallel drivetrain with two electric motors, a gas engine, gearbox, and a clutch to allow the engine to run decoupled from the gearbox in range extending mode. The peak dc electrical requirement of the electric drivetrain is about 175 kW, which is similar to the peak power capability of the developed hybrid energy storage system. A model of the prototype hybrid energy storage system, which has the Li-ion capacitor pack connected directly to the motor drive's dc bus and the battery pack connected to the Li-ion capacitor pack via a dc/dc converter, is developed and used to determine the optimal power split between the battery and Li-ion capacitor packs and for tuning the developed real-time control system. The real-time control system is shown through modeling and experimental testing of the full scale hybrid energy storage systems to reduce battery pack losses, increase vehicle range, and to have performance approaching that of the optimal control solution as calculated via dynamic programming. [ABSTRACT FROM AUTHOR]

Lithium-ion battery (LIB) degradation originates from complex mechanisms, usually interacting simultaneously in various degrees of intensity. Due to its complexity, to date, identifying battery aging mechanisms remains challenging. Recent improvements in battery degradation identification have been developed, including validated, in situ incremental capacity (IC) and peak area (PA) analysis. Due to their in situ and non-destructive nature, IC and PA implementation is feasible in on-board battery management systems (BMSs). Despite their advantages, the understanding and applicability of IC and PA techniques is not straightforward, as it requires both electrochemical and material science backgrounds. However, BMS design teams are mainly integrated by electrical engineers and may not include battery scientists. Aiming to bridge gaps in knowledge between electrical engineering and battery science toward battery degradation identification, here we present a systematic approach consisting in a set of lookup tables generated from IC and PA techniques. The lookup tables provide a simple, yet reliable, tool for the evaluation of LIB degradation modes. Various real-life examples of cell degradation are also presented to illustrate and validate the use of the proposed approach. This study exemplifies the use of lookup tables providing a simple, fast, and accurate automated estimation of LIB degradation modes to be implemented in BMSs. [ABSTRACT FROM AUTHOR]

The design of a combined observer for battery state of charge (SoC) and state of health (SoH) using data-driven models is addressed. Knowledge of both SoC and SoH is an important topic in battery management system design for (hybrid) electrical vehicles. The proposed cascaded observer is designed for estimating SoC and SoH during normal vehicle operation based on measured signals, which are directly available in the car. For an SoC estimation, that is accurate in the entire battery lifespan, the underlying battery model has to be adapted based on the estimated SoH. For that purpose, ageing data analysis using data-driven models and the associated model adaptation is discussed. A cascaded observer structure for combined estimation of SoC and SoH is presented and the performance of the proposed concepts is demonstrated by means of battery ageing data covering the entire lifetime of a lithium-ion cell. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are booming all over the world for a low carbon emission and greener environment. The fast charging is an urgent demand for consumers. However, the dramatic temperature rising during high-power charging has a high risk of trigging thermal runaway and other safety issues. This article proposes an external liquid cooling method for lithium-ion battery module with cooling plates and circulating cool equipment. A comprehensive experiment study is carried out on a battery module with up to 4C fast charging, the results show that the three-side cooling plates layout with low coolant temperature provides better temperature control effect, while the coolant velocity and type have very weak impact. Then the simulation model with high precision is built for ultra-fast charging, and a safe charging range under 5C charging is proposed. The safe charging range under 5C can be increased by 50.1%. The environment close to the room temperature demonstrates the best effects of external cooling. For actual applications in the EVs, the circulating cooling equipment outside EVs can be integrated with high-power charging infrastructure and provide a 117 km improvement in mileage under 5C safe charging which indicates a tremendous application prospect. [ABSTRACT FROM AUTHOR]

The Earth orbiting spacecrafts use lithium-ion batteries as an energy storage device. Each battery is made up of several series- and parallel-connected cells. The constraints concerning the cell parameters, obtained by testing, drive the positioning of the cells in the battery. Positioning by cell sorting to meet the required battery performance can be approached through the genetic algorithm (GA). A GA improved cell sorting algorithm (GICSA) is proposed in this work. It sorts the cell parameters randomly with GA to match them within a given set of tight constraints. It sorts different cell types and numbers of cells for different battery configurations. It gives the practically best possible number of matched cells for the battery. [ABSTRACT FROM AUTHOR]

Although several state-of-charge (SOC) estimation methods have been proposed at the battery cell level, limited work has been done to identify the effect of cell aging on SOC estimations. To address this challenge, this article proposes a novel method for estimating the SOC of Lithium-ion (Li-ion) battery cells by accurately modeling the cell aging and degradation information. The proposed method, termed as “NNGP,” is a deep neural network with Gaussian process feedback. The novel Gaussian process feedback helps the NNGP correlate SOC trends over consecutive charge–discharge cycles. Instead of time, the NNGP leverages available energy to correlate these SOC trends. The deep neural network within the NNGP then utilizes this information and other measured inputs to capture long-term cell aging and degradation trends. The NNGP leverages the most recent cell information to adapt its weights and estimate the SOC by employing an adaptive weighted training strategy. In our experiments on four Li-ion battery cells from three publicly available accelerated aging datasets, the NNGP clearly outperforms other benchmarked methods. The NNGP is also shown to be a useful prognostic tool capable of accurately estimating the SOC for up to 25 cycles in the future with an MAE below 3.5%. When tested under dynamic driving conditions and unknown initial SOC, the NNGP is shown to offer considerable improvements over other state-of-art methods. The derivation of the model followed by experimental evaluation is presented. [ABSTRACT FROM AUTHOR]

The estimation of lithium-ion batteries degradation plays an important role for the correct operation of smart grid and electric vehicle applications. In fact, only healthy battery energy storage systems meet minimum performance in terms of supplied voltage and power. Health prognostic is mandatory to ensure safe and reliable operation of batteries, as unsuccessful operation may cause technical/economic detriments or complete failures. The battery remaining useful life (RUL) depends on several random factors and thus it should be probabilistically characterized to allow the application of decision-making processes. In this article, a hybrid methodology is developed to characterize the RUL even with small amount of data, in view of the typical scarcity of data for adequately fitting the RUL probability distributions. Several distributions are considered in the methodology and compared for such purpose: the inverse Burr (IB) distribution, and mixtures of IB with inverse Gaussian and with inverse Weibull distributions. Expectation-maximization algorithms are specifically developed for the parameter estimation of the considered mixture distributions in the proposed methodology. The IB-based distributions are applied on a RUL dataset created by Monte Carlo sampling on an electrochemical battery model fitted upon given charge/discharge cycles. Numerical experiments are reported to assess the proposal with respect to benchmark distributions. [ABSTRACT FROM AUTHOR]

This article presents a noninvasive technical analysis of the degradation of four lithium-ion batteries (LIBs) used in extreme frigid weather. In contrast to other studies in which the batteries were tested in laboratory conditions, the LIBs studied in this article were aged in a real application, more specifically in the WindSled project. In this project, an expedition was made using a zero-emission vehicle drawn by kites, covering more than 2500 km on the East Antarctic Plateau. The study performed in this article aims to quantify the degradation of the LIBs during the expedition. The results show a 5% capacity fade, a 30% increase in the internal resistance and no substantial increase in the impedance of the solid electrolyte interface. Moreover, no evidence of dendrite growth at the anode is inferred by the interpretation of the distribution of relaxation times, incremental capacity analysis, and differential voltage analysis. Based on these results, it can be claimed that the LIBs used in the WindSled Project can successfully operate under –50 °C. Furthermore, since noninvasive techniques were used to characterize the batteries, they can still be used in upcoming expeditions, with subsequent financial and environmental benefits. [ABSTRACT FROM AUTHOR]

Capacitive power transfer (CPT) techniques are attracting widespread attention due to some unique advantages compared to conventional plug-in systems and inductive power transfer techniques. Since Lithium-ion batteries have been widely used in many energy storage applications, a well-compensated CPT charger should provide an efficient charging profile consisting of first constant current (CC) and later constant voltage (CV), while maintaining nearly unity power factor and soft switching of power switches simultaneously. However, many CPT systems can only realize either CC output or CV output with the input zero-phase angle. Thus, a high-performance CPT charger should possess at least one CC frequency and one CV frequency to charge the battery from CC mode to CV mode by simply switching the operating frequency. In this article, an LCLC-LC-compensated CPT charger is systematically analyzed to achieve these characteristics. Design principle of the CPT charger is also derived with arbitrarily given coupler and configurable charging profile, proving enough design freedom. Besides, the sensitivities of input impedance, output current, and output voltage to variations of compensation parameters are also discussed to realize soft switching. Finally, an LCLC-LC-based CPT prototype with 48 V/2 A charging capability is built to verify the analysis. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are the core equipment of electric vehicles (EVs) and they are expected to spread out through smart power systems [e.g., smart grids (SGs)]. Batteries are subject to degradation over time, which gradually reduces the supplied voltage and capacity until they fail to meet minimum requirements. Battery prognostic is mandatory in order to keep track of the degradation over time and to schedule maintenance and replacement before failures, which can cause severe damages and harmful effects on EVs and SGs. However, the estimation of the state of health and the remaining useful life of batteries is not trivial, since neither can be directly measured. In this article, multiple linear regression models are developed within deterministic and probabilistic frameworks to predict the SoH of batteries based on historical charge/discharge data. The models exploit informative data collected for the battery under investigation and stored data of batteries characterized by accelerated degradation tests. Numerical experiments based on actual data are presented to validate the performance of the proposal. [ABSTRACT FROM AUTHOR]

In this article, a reduced-component-count centralized equalization system based on a novel integrated cascade equalization topology is proposed to achieve the equalizing of series-connected battery packs. The proposed converter topology is the integration of the polarity switches and the bidirectional converter in traditional centralized battery equalization systems, and can also be regarded as a cascade structure composed of a bidirectional buck-boost and a bidirectional flyback converter. Compared with existing centralized equalizers, the number of components applied on the proposed scheme is significantly reduced, which obtains higher cost performance and reliability, and is more suitable for the equalization of large-capacity battery packs. Meanwhile, this integrated cascade structure enables the proposed topology to achieve an ultra-high voltage conversion ratio; hence, the efficiency can be significantly enhanced by employing a transformer with a smaller turns ratio and reduced leakage inductance. Furthermore, the operating principles, design considerations, and comparative analysis are presented in detail. Finally, an experimental prototype based on a 13-cell lithium-ion battery pack is established to verify the superior performance of the proposed equalization system. [ABSTRACT FROM AUTHOR]

This article proposes a method to estimate the impedance of lithium-ion cells by using an excitation signal. The injection signal is intended to be delivered to the battery module by the power converter used for the interface of the energy storage and using the current and voltage measurements provided by the module battery management system (BMS). The estimation method is conducted in two steps. During the system commissioning, the impedance is estimated by using a frequency-domain method known as vector fitting. Despite its high accuracy, it has an important computational burden that makes it difficult to use in real-time applications running on the embedded systems often used for battery and converter control. For that reason, during the regular operation, a recursive least squares algorithm is proposed, being the initial estimation given by the frequency-domain method employed during the commissioning process. The discussion includes not only the description and testing of the method but also the effects due to the reduced digital-domain resolution of the acquired data used for the estimation (terminal current and cell voltage), as well as the discretization method used for the digital implementation. The proposal is validated by simulation and by experimental results using the cell measurements provided by a BMS of a commercial module. [ABSTRACT FROM AUTHOR]

This article presents the fuzzy-based charging–discharging control technique of lithium-ion battery storage in microgrid application. Considering available power, load demand, and battery state-of-charge (SOC), the proposed fuzzy-based scheme enables the storage to charge or discharge within the safe operating region. Various controlling techniques have been implemented to evaluate and control the battery performance, which has the limitation of controlling overcharging or overdischarging, complexity in control, and longer charging time. Besides, a fuzzy controller is less complex and faster, as it obviates the extra sensing components, requires no additional deep discharging and overcharging protection, and easy to implement due to the absence of mathematical calculation. The numerical simulations with the load demand and the generations demonstrate the effectiveness of the proposed charging–discharging controller strategy. The efficacy of the proposed controller is tested under certain load variations for real-time application. The obtained experimental result shows that the developed model can control the battery charging–discharging efficiently. Moreover, it is also seen from the output that the battery SOC does not go beyond the limit of the respective safe battery operating region (20%–80%). Thus, the main contribution of this research is to develop an improved fuzzy model and, thus, implement the system for real-time application to control the charging–discharging of the battery. [ABSTRACT FROM AUTHOR]

The most relevant results from the IDR/UPM Institute research on solar cells/panels and Li-ion batteries performance are reviewed in this article. The aim of this article is to present the possibilities of the mathematical procedures developed for space applications, with a view to extend their use to other industrial sectors. The research at the IDR/UPM Institute has been driven by selecting simple tools and procedures to model the behavior of solar cell/panel, since modeling these photovoltaic devices is normally carried out by 1-diode/2-resistor equivalent circuit models, which might represent an unaffordable mathematical challenge for many professionals and technicians in the renewable energy field. Concerning Li-ion batteries, the large experience accumulated during the maintenance of the UPMSat-2 battery resulted in the successful development of new mathematical models to study the performance of batteries. The models developed are based on the amount of discharged energy, measured in W·h, instead of other more traditional parameters such as the state of charge, measured in A·h. [ABSTRACT FROM AUTHOR]

Deep learning has gained much traction in application to state-of-charge (SOC) estimation for Li-ion batteries in electric vehicle applications. However, with the vast selection of architectures and hyperparameter combinations, it remains challenging to design an accurate and robust SOC estimation model with a sufficiently low computation cost. Therefore, this study provides a comparative evaluation among commonly used deep learning models from the recurrent, convolutional, and feedforward architecture benchmarked on an openly available Li-ion battery dataset. To evaluate model robustness and generalization capability, we train and test models on different drive cycles at various temperatures and compute the root mean squared error (RMSE) and mean absolute error metric. To evaluate model computation costs, we run models in real-time and record the model size, floating-point operations per second (FLOPS), and run-time duration per datapoint. This study proposes a two-hidden layer stacked gated recurrent unit model trained with a one-cycle policy learning rate scheduler. The proposed model achieves a minimum RMSE of 0.52% on the train dataset and 0.65% on the test dataset while maintaining a relatively low computation cost. Executing the proposed model in real-time takes up approximately 1 MB in disk space, 300K FLOPS, and 0.03 ms run-time per datapoint. This makes the proposed model feasible to be executed on lightweight battery management system processors. [ABSTRACT FROM AUTHOR]

The state of health (SoH) of electric vehicle (EV) batteries is important for the EV owner and potential buyer of second hand EVs. The incremental capacity analysis (ICA) has by several researchers proven to be a promising SoH estimation method for lithium-ion batteries. However, in order to be practical useable, the method needs to be feasible on a pack or EV level and not only on an individual cell level. Therefore, the purpose of this article is to demonstrate the feasibility of the ICA method on real EVs. Nickel manganese cobalt (NMC) cells used in BMW i3 EVs and lithium manganese oxide (LMO) used in Nissan Leaf EVs have been tested both on the cell level and on car level. The results are consistent and the characteristic peaks and valleys of the ICA on car level match with the same on cell level. A root-mean-square error of 1.33% and 2.92% has been obtained for the SoH estimation of the NMC and LMO type, respectively. It is therefore concluded that the ICA method is also applicable to the car level for battery SoH estimation. [ABSTRACT FROM AUTHOR]

In order to study the unique dynamic behavior of the lithium-ion battery system in the periodic high-rate pulse discharge, a frequency-domain impedance model and a dynamic extraction method of impedance parameters are proposed. Moreover, a 24-kWh lithium battery system is built to carry out the pulse-discharged cycle experiment. The harmonic characteristics in high and low frequency pulse discharge are studied by harmonic analysis method. Besides, the impedance characteristics of the 24-kWh lithium battery system under different state-of-charge and different cycles are analyzed quantitatively. The results show some new characteristics and verify that the proposed frequency-domain impedance model and impedance extraction method are well qualified to analyze the frequency-domain characteristics and dynamic behavior of the periodic pulse-discharged lithium battery system. [ABSTRACT FROM AUTHOR]

As the global environmental pollution and energy crisis become more serious, lithium-ion batteries (LIB) received an increasing research interest due to their low self-discharge rates, long cycle life, and high power density for the transportation applications. Battery management system (BMS) plays an essential part for the LIB system as it can guarantee an efficient and stable operation through LIB state of charge (SOC) estimation. In this article, a nonlinear observer with terminal voltage feedback injection (VFNO) is designed based on the electrochemical single particle (SP) model to monitor the SOC of LIB. The convergence of the SP-VFNO system in consideration of measurement error is proved in terms of Lyapunov stability theory. The current, the terminal voltage, and the SOC reference value are measured in the battery testing system. Besides, the extended Kalman filter (EKF) based on both the SP model and the second-order resistor-capacitor (SORC) model to estimate SOC is adopted for comparison. The experimental results indicate that the proposed SP-VFNO method has superiority with a faster convergence rate and a higher estimation precision, which can help the accurate SOC estimation for BMS in practical application. [ABSTRACT FROM AUTHOR]

A state-of-charge (SOC) balancing method which accounts for state-of-health (SOH) status of battery cells is presented in this article. The data collected from aging experiments conducted in the laboratory indicates that there is correlation between the minimum impedance of the battery and capacity fading which can be used to predict capacity capability of the battery. However, this relationship is complex and nonlinear. In this article, artificial neural network (ANN) is utilized to learn this relationship and an ANN-based capacity estimator is developed to predict available capacity of battery cells. An online impedance measurement method with improved measurement resolution is presented to obtain a more accurate minimum impedance for the ANN-based capacity estimator. The estimated capacity from the ANN capacity estimator is fed to an SOC balancing controller to calculate SOC values for the battery cells. A battery cell with worse health has lower available capacity than a battery cell with better health, and, therefore, its SOC value is adjusted to a smaller value when SOH or capacity estimation functionality is activated. Because of this mechanism and the control principle of the presented SOC balancing controller, the system draws energy at a slower rate from the battery cell(s) with lower SOH and draws energy at a faster rate from the battery cell(s) with higher SOH such that all battery cells reach their end of discharge at the same time. This results in extending operation time of the system, makes best use of energy from every battery cell, and avoids over-discharging battery cells with lower SOH. The presented method is evaluated using results obtained from a laboratory experimental setup. [ABSTRACT FROM AUTHOR]

In this article, we propose a method for extracting, modeling, and predicting the resistance of Lithium-ion batteries directly from the battery dynamic mission profile. While the extraction of the mainly relied on data manipulation and bookkeeping, the modeling and subsequent prediction of the resistance used a log-linear model. It is shown that the estimated log-linear model can be used to create a posterior probability distribution of the age of the battery, given an internal resistance measurement and the state-of-charge (SOC) value at which it was measured. This distribution was used calculate the expected age of the battery, and the expected age was compared to weekly check-ups. At an SOC of 80% a mean absolute error (MAE), between the weekly check-ups and the expected age, of 5.83 weeks [706 full equivalent cycles (FEC)] was achieved. Furthermore, it is shown that by introducing a decision threshold the MAE could be reduced as far as 2.65 weeks (321 FEC). Finally, a method is introduced for handling cases where the SOC was not known exactly. [ABSTRACT FROM AUTHOR]

A novel nondissipative two-stage equalization circuit topology based on the traditional buck–boost circuit is proposed to achieve balancing of series-connected lithium-ion battery packs with higher efficiency and less cost, considering the background on international energy issues and the development trend of battery balancing. The proposed topology achieves high efficient balancing of lithium-ion battery packs without adding additional devices. Detailed illustration of the presented topology, the operation principles and control approaches are described with visualized figures in this article. Then, under the condition of accurate modeling of the lithium-ion battery, relying on the experimental data, the simulation block is built based on MATLAB. Furthermore, the prototype of the proposed topology was designed and manufactured. The effectiveness of the proposed topology is verified by both the simulation and experiment. [ABSTRACT FROM AUTHOR]

An accurate battery model is essential for precise estimation of battery performance indicators, such as state of charge (SoC), state of health (SoH), etc. It helps in determining the optimum energy management and therefore, optimum utilization of the battery capacity. Some of the battery model parameters may change with the variation of environmental conditions, such as operating temperature, and therefore, accurate temperature compensated model estimation is necessary to minimize the error in battery model and SoC estimation. This article presents a novel parameters extraction method for the Thevenin equivalent circuit model of Li-ion batteries considering their resiliency on temperature effect. The proposed approach represents each of the RC parallel circuits of the Thevenin-based equivalent circuit battery model as a first-order linear time-invariant (LTI) system. The resistance and capacitance values for each of the RC circuits in the model have been identified as the parameters of a standard LTI system using the system identification theory and represented them as the temperature-compensated model parameters. The proposed model can be applied to various applications, such as SoC, SoH estimation, and battery energy management. In order to demonstrate the suitability of the proposed modeling approach, the model is augmented with the conventional extended Kalman filter to enable accurate SoC estimation under varying operating temperatures. The accuracy and effectiveness of the proposed modeling approach and its suitability to enhance SoC estimation under a wide operating temperature range ($-$ 5 to 45  $^\circ$ C) have been validated through laboratory experiments in the LabVIEW platform. [ABSTRACT FROM AUTHOR]

The state of charge (SOC) is a crucial parameter of a battery management system for Li-ion batteries. The SOC indicates the amount of charge left in the battery of electric vehicles—akin to the fuel gauge in combustion vehicles. An accurate SOC knowledge contributes largely to the longevity, performance, and reliability of the battery. However, the SOC of Li-ion batteries cannot be easily measured by any apparatus. Furthermore, the SOC can also be influenced by numerous incalculable factors such as battery chemistry, ambient environment, aging factor, etc. In this article, we propose an SOC estimation model for a Li-ion battery using an improved deep neural network (DNN) approach for electric vehicle applications. We found that a DNN with a sufficient number of hidden layers is capable of predicting the SOC of the unseen drive cycles during training. We developed a series of DNN models with a varying number of hidden layers, and its training algorithm was to investigate their respective performance when evaluated on different drive cycles. We observe that the increasing number of hidden layers in the DNN (up to four hidden layers) decreases the error rate and improves SOC estimation. An additional increase in the number of hidden layers beyond that increases the error rate. In this study, we show that a four-hidden-layer DNN trained on Dynamic Stress Test drive cycle is capable of predicting SOC values unexpectedly well of other unseen drive cycles such as Federal Urban Driving Schedule, Beijing Dynamic Stress Test, and Supplemental Federal Test Procedure, respectively. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are gaining importance for a variety of applications due to their price decrease and characteristics improvement. For a proper use of such storage systems, an energy management algorithm (EMA) is required. A number of EMAs, with various characteristics, have been published recently, given the diverse nature of battery problems. The EMA of deterministic battery problems is usually based on an optimization algorithm. The selection of such an algorithm depends on a few problem characteristics, which need to be identified and closely analyzed. The aim of this article is to identify the critical optimization problem parameters that determine the most suitable EMA for a Li-ion battery. With this purpose, the starting point is a detailed model of a Li-ion battery. Three EMAs based on the algorithms used to face deterministic problems, namely dynamic, linear, and quadratic programming, are designed to optimize the energy dispatch of such a battery. Using real irradiation and power price data, the results of these EMAs are compared for various case studies. Given that none of the EMAs achieves the best results for all analyzed cases, the problem parameters that determine the most suitable algorithm are identified to be four, i.e., desired computation intensity, characteristics of the battery aging model, battery energy and power capabilities, and the number of optimization variables, which are determined by the number of energy storage systems, the length of the optimization problem, and the desired time step. [ABSTRACT FROM AUTHOR]

A precise estimation of the state of charge (SoC) of the lithium-ion battery is crucial for reducing range-anxiety and improving the performance of the electric vehicle (EV) battery management system. An accurate estimation of the SoC, however, has remained elusive due to the complex and nonlinear behavior of the battery. In this article, a new mixed estimation model (MEM) for the battery parameters and the SoC estimation is proposed, where the route is specified before the travel. The new MEM uses a combination of a battery power-based method (BPBM), a combined model, and a partial adaptive forgetting factor recursive least-square (PAFF-RLS) SoC calibration algorithm to make use of the best characteristics of each model to determine a better and more accurate SoC estimation. The partial adaptive forgetting factors solves the issue of the different rate changes in the battery parameters and reduces the complexity of the algorithm compared to the fully adaptive recursive models. The BPBM allows various traveling factors to be included in the model, such as the environmental conditions, the effect of auxiliary loads, and the traffic congestion. To verify the validity of the PAFF-RLS algorithm, two laboratory tests using real-time driving cycles have been conducted on a 2012 Nissan Leaf 31.1 Ah Manganese-oxide Li-ion battery cell. The effectiveness of the MEM model has been demonstrated by driving the Nissan Leaf along two selected routes in Australia. The results demonstrate the great accuracy of the proposed method for the SoC estimation, when compared with those from the previous models. [ABSTRACT FROM AUTHOR]

This article proposes an adaptive sensorless measurement technique for internal temperature of lithium-ion (Li-ion) batteries. The proposed technique is based on measuring the impedance phase of the battery in real time by a simple search algorithm. The proposed technique then identifies the zero-crossing frequency at which the phase of the impedance is zero. From the value of the zero-crossing frequency, the cell's internal temperature is estimated. Details on the proposed technique followed by the experimental verification using a 3.8-V/2600-mAh Samsung Li-ion battery cell at temperatures between −20 °C and +50 °C are presented. [ABSTRACT FROM AUTHOR]

This article presents an online electrochemical impe-dance spectroscopy (EIS) technique for a Li-Ion battery. A frequency sweeping method and a precise impedance measurement algorithm are used for the proposed online EIS technique. The proposed algorithm consists of fast Fourier transformations, peak amplitudes, and phase difference estimations. Additional instrumentation with offset clipping and amplifying circuits expand the accuracy of the signal measurement. The offset clipping circuit increases the ripple to measurement voltage ratio without phase delay. The amplifying circuit gives more resolution for the ripple voltage. The proposed instrumentation ensures the precise measurement of ripple voltage and current. The proposed impedance measurement algorithm fulfills the precision level required for industrial Li-Ion battery. To validate the proposed approach, a 40-Ah 13.6 V Li-Ion battery charger was built using a synchronous buck converter and an Opal-RT based controller. The proposed spectroscopy technique will be useful to track and estimate internal electrochemical conditions of a battery. [ABSTRACT FROM AUTHOR]

The implementation of an accurate and low computational demanding state-of-health (SOH) estimation algorithm represents a key challenge for the battery management systems in electric vehicle (EV) applications. In this article, we investigate the suitability of the incremental capacity analysis (ICA) technique for estimating the capacity fade and subsequently the SOH of LMO/NMC-based EV lithium-ion batteries. Based on calendar aging results collected during 11 months of testing, we were able to relate the capacity fade of the studied batteries to the evolution of four metric points, which were obtained using the ICA. Furthermore, the accuracy of the proposed models for capacity fade and SOH estimation was successfully verified considering two different aging conditions. [ABSTRACT FROM AUTHOR]