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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 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]

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]

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]

This paper studies a multilevel multiphase dc–ac conversion system configured by a neutral-point-clamped converter fed by multiple battery packs connected in series. A virtual-vector modulation is selected and a state-of-charge (SoC) balancing control is designed to be able to employ the full battery bank capacity, even under different battery initial SoC values or different battery nominal capacities. The SoC balancing among battery packs is accomplished through the multilevel converter operation in a lossless manner, by simply distributing the dc-to-ac power flow among the batteries according to their SoC. A simple average system model is also presented, which allows performing very fast system simulations over long periods of time and serves as a convenient tool to tune the compensator parameters. The satisfactory performance of the proposed system configuration and control, which can be applied with any number of levels and phases, has been verified through simulations and experiments in a four-level three-phase dc–ac converter fed by three lithium-ion battery packs. The results prove the feasibility and advantages of the proposed system configuration, which can be used to implement conversion systems with different specifications combining several instances of a standard battery pack and a standard power semiconductor device. [ABSTRACT FROM AUTHOR]

This paper proposes a novel error correction grey prediction model for degradation prediction of renewable energy storages. The proposed approach uses an error correction factor ψ to eliminate the inherent error of the original grey model (GM), and at the same time retain the original simplicity and fast prototyping. In addition, due to the uncertainty and complexity of failure mechanisms, a trigonometric residual modification is considered in order to well-describe the influence of operating conditions or cyclic fluctuation on the renewable energy storages. Two experimental case studies, including lithium-ion battery and fuel cell aging tests, are performed to validate the performance of the proposed method. In particular, the accuracy of the proposed method is investigated for different prediction horizon lengths, in order to further demonstrate its effectiveness and robustness. It is worth mentioning that the proposed method can ensure the accuracy of the remaining useful life estimation in the case of long-term forecasting, and thus, the maintenance management and corrective action of renewable energy storages can be scheduled earlier, leading to more effective cost minimization and risk mitigation. [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]

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]

This paper presents a generic electrothermal model for Li-ion battery. The model is developed with the objective to simplify the parameter identification procedure, while representing adequately the thermal effects on the battery performance. Most of the well-accepted electrothermal Li-ion battery models require in-depth and proprietary battery data or dedicated test environments for parameter identification. The dedicated test bench usually involves expensive thermal test chambers, calorimeters, and temperatures sensors, and challenges associated with their installations. This makes the electrical and thermal simulation of Li-ion batteries difficult to achieve. This paper proposes a generic electrothermal model with a simpler parameter identification process. The parameters identification process is solely based on datasheet discharge curves and simple experiments at room temperature. The model is validated experimentally using a 12 V 40 Ah LiFePO4 battery module. The performance of the model is tested with constant current discharges, constant current–constant voltage charges, as well as with a Simplified Federal Urban Driving Schedule dynamic driving cycle, at different operating temperatures. As expected, the simulation results show an error within $\pm$1% and $\pm$1.3% compared to experimental results, for both steady and dynamic states, respectively. [ABSTRACT FROM AUTHOR]

Accurate estimation of power capacity is critical to ensure battery safety margins and optimize energy utilization. Power capacity estimators based on online identified equivalent circuit model have been widely investigated due to the high accuracy and affordable computing cost. However, the impact of noise corruption which is common in practice on such estimators has never been investigated. This paper scrutinizes the effect of noises on model identification, state of charge (SOC) and power capacity estimation. An online model identification method based on adaptive forgetting recursive total least squares (AF-RTLS) is proposed to compensate the noise effect and attenuate the identification bias of model parameters. A Luenberger observer is further used in combination with the AF-RTLS to estimate the SOC in real time. Leveraging the estimated model parameters and SOC, a multiconstraint analytical method is proposed to online estimate the power capacity. Simulation and experimental results verify that the proposed method is superior in terms of estimation accuracy and the robustness to noise corruption. [ABSTRACT FROM AUTHOR]

In this paper, an automotive onboard heater equalizer is proposed to heat low-temperature batteries and balance cell voltages without the requirement of external power supplies. The proposed integrated topology only needs one MOSFET for one cell, resulting in a compact size and low cost, which can be easily applied to electric vehicles. In particular, all MOSFETs are driven by one high-frequency pulsewidth modulation signal and the batteries can be heated internally by the ohmic and electrochemical losses and warmed externally by the switching and conduction losses of MOSFETs, leading to a high heating speed and efficiency. Further, a thermoelectric model for the internal and external combined heating is developed to provide guidance for the optimized design of the proposed heater. In addition, the proposed topology can realize passive balancing of series-connected battery strings at a higher switching frequency and a smaller duty cycle. Experimental results show that the proposed heater, by generating a periodic ramped discharge current with an rms value of 1.8 C at a switching frequency of 150 kHz, can heat the lithium-ion batteries from −20 to 0 °C within 1.9 min, consuming about 5% of the cell energy. [ABSTRACT FROM AUTHOR]

The estimation and prediction of state-of-health (SOH) and state-of-charge (SOC) of Lithium-ion batteries are two main functions of the battery management system (BMS). In order to reduce the computation cost and enable deployment of the BMS on the low-cost hardware, a Lebesgue-sampling-based extended Kalman filter (LS-EKF) is developed to estimate the SOH and SOC. An LS-EKF is able to eliminate unnecessary computations, especially when the states change slowly. In this paper, the SOH is first estimated and the remaining useful life is predicted by the LS-EKF. Then, the estimated SOH is used as the initial battery capacity for SOC estimation and prediction. The SOH and SOC estimation and prediction are calculated repeatedly in the whole battery service life. The proposed method is verified with the application to the capacity degradation of the Lithium-ion battery. The results show that the LS-EKF-based algorithm has a good performance in SOH and SOC estimation and prediction in terms of accuracy and computation cost. [ABSTRACT FROM AUTHOR]

Micro-short-circuit (MSC) is a latent risk in power batteries, which may give rise to thermal runaway and even catastrophic safety hazards. The motivation of this paper is to quantitatively analyze MSC in an initial stage, particularly for lithium-ion batteries. To verify the feasibility of the proposed method, an equivalent MSC experiment is carried out. Based on a cell difference model, the cell state of charge (SOC) differences with the mean SOC for a battery pack are estimated by extended Kalman filter. The evaluated SOC difference can track the actual value well. Furthermore, an MSC diagnostic method is developed by employing recursive least squares filter. The method is demonstrated to examine the short-circuit resistance accurately. The results also show that the proposed method requires low computational load for the SOC difference and short-circuit resistance diagnosis. [ABSTRACT FROM AUTHOR]

The current lithium-ion battery remaining useful life (RUL) prediction techniques are mainly developed dependent on offline training data. The loaded current, temperature, and state of charge of lithium-ion batteries used for electric vehicles (EVs) change dramatically under the working conditions. Therefore, it is difficult to design acceleration aging tests of lithium-ion batteries under similar working conditions as those for EVs and to collect effective offline training data. To address this problem, this paper developed an RUL prediction method based on the Box–Cox transformation (BCT) and Monte Carlo (MC) simulation. This method can be implemented independent of offline training data. In the method, the BCT was used to transform the available capacity data and to construct a linear model between the transformed capacities and cycles. The constructed linear model using the BCT was extrapolated to predict the battery RUL, and the RUL prediction uncertainties were generated using the MC simulation. Experimental results showed that accurate and precise RULs were predicted with errors and standard deviations within, respectively, [-20, 10] cycles and [1.8, 7] cycles. If some offline training data are available, the method can reduce the required online training data and, thus, the acceleration aging test time of lithium-ion batteries. Experimental results showed that the acceleration time of the tested cells can be reduced by 70%–85% based on the developed method, which saved one to three months’ acceleration test time compared to the particle filter method. [ABSTRACT FROM AUTHOR]

State of health (SOH) estimation of lithium-ion batteries is a key but challengeable technique for the application of electric vehicles. Due to the ambiguous aging mechanisms and sensitivity to the applied conditions of lithium-ion batteries, the recognition of aging mechanisms and SOH monitoring of the battery might be difficult. A novel SOH estimation and aging mechanism identification method is presented in this paper. First, considering the dispersion effect, a fractional-order model is constructed, and the parameter identification approach is proposed, and a comparison between integer-order model and fractional-order model has been done from the prospect of predicting accuracy. Then, based on the identified open-circuit voltage, the battery aging mechanism can be analyzed by the means of an incremental capacity analysis method. Moreover, the normalized incremental capacity peak is used to estimate the remaining capacity. Finally, the robustness of the SOH estimation method is validated by batteries aged at different conditions based on the idea of cross validation, and the estimation error of the remaining capacity can be reduced within 3.1%. [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]

This paper provides a theoretic and systematic analysis on the battery state of charge (SOC) estimation errors caused by sensor noises. The considered noises include the bias and the variance of both current and voltage sensors. Specifically, the bias and the variance of the SOC estimation error are derived as explicit functions of sensor noises, battery parameters, and observer tuning parameters. The derivation is performed for the Kalman filter, which is the most commonly used method for SOC estimation, and the least squares method. It is found that the observer parameter tuning is subject to a tradeoff between suppressing the estimation bias and variance. Either one of these two errors becomes dominant under different observer parameter ranges. The fundamental estimation error that cannot be mitigated through observer tuning has also been identified. The results have been validated by both simulation and experiment. The obtained theoretical findings are of great practical significance as they could be used to guide sensor selection and observer design, as well as enable online uncertainty management. [ABSTRACT FROM PUBLISHER]

Traditional fault diagnosis and prognosis (FDP) approaches are based on periodic sampling, i.e., samples are taken and algorithms are executed both in a periodic manner. As the volume of sensor data and complexity of algorithms keep increasing, the bottleneck of FDP is mainly the limited computational resources, which is particularly true for distributed applications where FDP functions are deployed on microcontrollers and embedded systems with limited computation resources. This paper introduces the concept of Lebesgue sampling (LS) in FDP and proposes a LS-based FDP (LS-FDP) framework. In the proposed LS-FDP, a novel diagnostic philosophy of “execution only when necessary” is developed in computation cost reduction. For prognosis, different from traditional approaches in which the prognostic horizon is on the time axis, the proposed approach defines the prognostic horizon on the fault state axis. With a reduced prognostic horizon, the LS-FDP naturally benefits the uncertainty management. The goal of this paper is to create the fundamental knowledge for LS-FDP solutions that are cost efficient, capable for the deployment on systems with limited computation sources, and supportive to the trend of distributed FDP schemes in complex systems. The design and implementation of particle-filter-based LS-FDP are presented with experimental results on lithium-ion batteries to verify the effectiveness of the proposed approaches. [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]

A nondissipative equalization scheme based on fuzzy logic control (FLC) is presented to improve the inconsistency of series-connected Lithium-ion batteries. The two-stage bidirectional equalization circuit with energy transferring inductors is designed to implement the equalization of cell to cell for battery packs, and the equalization circuit paves the way for module-based equalization of hardware. Equalization based on the state of charge (SOC) is proposed in this paper, and the Thevenin equivalent circuit model of the Lithium-ion battery, as well as the extended Kalman filter (EKF) algorithm, is employed for SOC estimation. For effective equalization, the FLC is proposed to reduce energy consumption and equalization time. The FLC strategy is constructed with a set of membership functions to describe the equalization behavior of the cell. A comparison of the proposed FLC with the mean-difference algorithm is carried out to validate the advantages of the proposed scheme. Simulation results show that the standard deviation of final SOC reduces by \text18\text.5\% , and equalization time decreases by \text23\% for FLC compared with the mean-difference algorithm under new European driving cycle working conditions. The energy efficiency improves by \text5\text.54\% compared with the mean-difference algorithm. In addition, the two-stage bidirectional equalization circuit has good performance and improves the inconsistency. [ABSTRACT FROM PUBLISHER]

Fast charging strategies have gained an increasing interest toward the convenience of battery applications but may unduly degrade or damage the batteries. To harness these competing objectives, including safety, lifetime, and charging time, this paper proposes a health-aware fast charging strategy synthesized from electrochemical system modeling and advanced control theory. The battery charging problem is formulated in a linear time-varying model predictive control algorithm. In this algorithm, a control-oriented electrochemical–thermal model is developed to predict the system dynamics. Constraints are explicitly imposed on physically meaningful state variables to protect the battery from hazardous operations. A moving horizon estimation algorithm is employed to monitor battery internal state information. Illustrative results demonstrate that the proposed charging strategy is able to largely reduce the charging time from its benchmarks while ensuring the satisfaction of health-related constraints. [ABSTRACT FROM PUBLISHER]

Accurate remaining useful life (RUL) prediction and state-of-health (SOH) diagnosis are of extreme importance for safety, durability, and cost of energy storage systems based on lithium-ion batteries. It is also a crucial challenge for energy storage systems to predict RUL and diagnose SOH of batteries due to the complicated aging mechanism. In this paper, a novel method for battery RUL prediction and SOH estimation is proposed. First, a novel support vector regression-based battery SOH state-space model is established to simulate the battery aging mechanism, which takes the capacity as the state variable and takes the representative features during a constant-current and constant-voltage protocol as the input variables. The estimated impedance variables are taken as the output due to the correlation between battery capacity and the sum of charge transfer resistance and electrolyte resistance. Second, in order to suppress the measurement noises of current and voltage, a particle filter is employed to estimate the impedance degradation parameters. Furthermore, experiments are conducted to validate the proposed method. The results show that the proposed SOH estimation method can provide an accurate and robustness result. The proposed RUL prediction framework can also ensure an accurate RUL prediction result. [ABSTRACT FROM PUBLISHER]

This paper proposes a new estimator design algorithm for state-of-charge (SoC) indication of lithium-ion batteries. A fractional-order model-based nonlinear estimator is first framed including a Luenberger term and a sliding mode term. The estimator gains are designed by Lyapunov's direct method, providing a guarantee for stability and robustness of the error system under certain assumptions. This generic estimation algorithm is then applied to lithium-ion batteries. A fractional-order circuit model is adopted to predict battery dynamic behaviours. Assumptions based on which the estimation algorithm is developed are justified and remarked. Experiments corresponding to electric vehicle applications are conducted to parameterize the battery model and demonstrate the estimation performance. It shows that the proposed approach is able to estimate SoC with errors less than 0.03 in the presence of initial deviation and persistent noise. Furthermore, the benefits of using the proposed estimator relative to other estimators are calculated over different cycles and conditions. [ABSTRACT FROM PUBLISHER]

Sinusoidal ripple-current charging has previously been reported to increase both charging efficiency and energy efficiency and decrease charging time when used to charge lithium-ion battery cells. In this paper, we show that no such effect exists in lithium-ion battery cells, based on an experimental study of large-size prismatic cells. Additionally, we use a physics-based model to show that no such effect should exist, based on the underlying electrochemical principles. [ABSTRACT FROM PUBLISHER]

This paper presents novel machine-learning-based methods for estimating the state of charge (SoC) of lithium-ion batteries, which use the Gaussian process regression (GPR) framework. The measured battery parameters, such as voltage, current, and temperature, are used as inputs for regular GPR, whereas the SoC estimate at the previous sample is fed back and incorporated into the input vector for recurrent GPR. The proposed methods consist of two parts. In the first part, training is performed wherein the optimal hyperparameters of a chosen kernel function are determined to model data properties. In the second part, online SoC estimation is carried out according to the trained model. One of the practical advantages of a GPR framework is to quantify estimation uncertainty and, hence, to enable reliability assessment of the battery SoC estimate. The performance of the proposed methods is evaluated by using a simulated dataset and two experimental datasets, one with constant and the other with dynamic charge and discharge currents. The simulations and experimental results show the superiority of the proposed methods in comparison to state-of-the-art techniques including a support vector machine, a relevance vector machine, and a neural network. [ABSTRACT FROM PUBLISHER]

In order for the battery management system (BMS) in an electric vehicle to function properly, accurate and robust indication of the energy state of the lithium-ion batteries is necessary. This robustness requires that the energy state can be estimated accurately even when the working conditions of batteries change dramatically. This paper implements battery remaining available energy prediction and state-of-charge (SOC) estimation against testing temperature uncertainties, as well as inaccurate initial SOC values. A double-scale particle filtering method has been developed to estimate or predict the system state and parameters on two different time scales. The developed method considers the slow time-varying characteristics of the battery parameter set and the quick time-varying characteristics of the battery state set. In order to select the preferred battery model, the Akaike information criterion (AIC) is used to make a tradeoff between the model prediction accuracy and complexity. To validate the developed double-scale particle filtering method, two different kinds of lithium-ion batteries were tested at three temperatures. The experimental results show that, with 20% initial SOC deviation, the maximum remaining available energy prediction and SOC estimation errors are both within 2%, even when the wrong temperature is indicated. In this case, the developed double-scale particle filtering method is expected to be robust in practice. [ABSTRACT FROM PUBLISHER]

In this paper, a dynamic model of Li-ion batteries incorporating electrothermal and ageing aspects is proposed for electric vehicle applications. The main goal of the proposed model is to be both simple and sufficiently representative of the physical phenomena occurring in a battery cell. These two features allow for using this model as an evaluation tool of electric vehicle performances under different operational and environmental conditions. The developed model is based on an equivalent circuit diagram coupled with a thermal circuit and a semi-empirical ageing equation. Identification of parameters in the dynamic model is conducted by measurement tests in time domain, which uses a hybrid particle Swarm–Nelder–Mead optimization algorithm to achieve excellent prediction over the whole applicable current and state of charge ranges. The validation results show that the proposed model is able to simulate the dynamic interaction between the battery ageing and the thermal as well as electric behavior with sufficient accuracy in the range tested. [ABSTRACT FROM PUBLISHER]

Lebesgue-sampling-based fault diagnosis and prognosis (LS-FDP) is developed with the advantage of less computation requirement and smaller uncertainty accumulation. Same as other diagnostic and prognostic approaches, the accuracy and precision of LS-FDP are significantly influenced by the parameters and uncertainties in the diagnostic and prognostic models. To improve performance of LS-FDP, this paper introduces an online model parameter adaptation scheme, which is realized by a recursive least square method with a forgetting factor. In addition, uncertainty of remaining useful life (RUL) prediction is managed by adjusting the model noises through a short-term prediction and correction loop. To verify the proposed parameter adaptation and noise adjustment methods, they are designed and implemented in a particle-filtering-based LS-FDP algorithm with applications to Li-ion batteries. Experimental results show that the proposed approach has significant improvement on both battery capacity estimation and RUL prediction. [ABSTRACT FROM PUBLISHER]

Among various active cell balancing circuits, a switched capacitor circuit is promising because it can be implemented with low cost and small size. However, when the switched capacitor is applied in the lithium-ion battery, cell balancing speed is generally slow when the number of batteries is high. Therefore, this paper proposes the chain structure of the switched capacitor to increase balancing speed, particularly among outer cells. In this paper, the cell balancing principle of the conventional switched capacitor is explained, and the reason why slow cell balancing of the switched capacitor is shown in the lithium-ion battery is analyzed. To improve cell balancing speed, two circuits with chain structure are proposed. The balancing performance of the proposed circuits is confirmed by computer simulation, and a comparison between conventional and proposed circuits is presented. The theoretical analysis on the cell balancing speed of conventional structures and the proposed chain structure is also shown in this paper. Experimental tests were carried out to verify the validity of the proposed structures, and the experimental results show an improved balancing performance of the proposed circuit. [ABSTRACT FROM PUBLISHER]

Quality control in the production of automotive Li-ion cells is essential for both safety and economic reasons. At present, as part of the production process, it is common practice to store Li-ion cells for up to two weeks to analyze self-discharge performance and to subject sample cells to months of cycling to assess lifetime performance. This paper presents a new state-of-the-art nondestructive testing technique for automotive scale, Li-ion batteries. Importantly, the test can discriminate between viable and nonviable cells in less than one minute. This is significantly quicker than many industrially applied techniques. The proposed method, developed in partnership with three independent original equipment manufacturer automotive Li-ion cell manufacturers, uses empirical data gathered off-line for benchmarking cell response followed by a unique targeting process to reduce the test time to a level compatible with industrial manufacturing processes. The technique used is a targeted form of electrochemical impedance spectroscopy (EIS) using a commercially available potentiostat with EIS capability. The novel aspect of the research is the treatment of off-line empirical data, the construction of an empirical library database, and the development of a reliable and robust in-line test procedure. For reasons of commercial sensitivity, no knowledge of the underlying chemistry of the cells is available for use. This demonstrates the functionality of the proposed method across a range of different cell technologies and its applicability to multiple battery technologies. [ABSTRACT FROM AUTHOR]

This paper presents a real-time estimation method for the temperature distribution of cylindrical batteries under boundary air cooling. A space-/time-separation-based analytical model is developed using Karhunen–Loève decomposition and Galerkin's method. The model parameters can be identified and optimized using data-based approaches. The developed analytical model demonstrates the robustness to variation of thermal parameters. However, the change of boundary cooling will significantly degrade the performance of the developed analytical model. For the known boundary cooling, the compensation model for cooling effects can be derived to improve the modeling performance. For the unknown boundary cooling in real practice, a dual-extended Kalman filter can be used to simultaneously estimate coupled parameters and convection coefficient in the compensation model. The proposed method can achieve satisfactory performance in the battery duty-cycle experiments. [ABSTRACT FROM PUBLISHER]

Energy storage systems with Lithium-ion batteries require balancing due to individual cells having manufacturing inconsistencies, different self-discharge rates, internal resistances, and temperature variations. Nondissipative redistributive balancing further improves on the pack capacity and efficiency over a dissipative approach where energy is consumed across shunt resistors. This paper presents a high-level fast model predictive control (MPC) in continuous time. The optimization problem uses performance metrics to balance the state of charge (SoC) in the battery pack. It is shown in simulation that MPC achieves a single point convergence of the SoC when compared against a common rule-based algorithm. This improves the efficiency of the power electronics and prolongs the life of each battery cell since frequent switching between charging and discharging of intermediate cells is avoided. Experimental results are presented to show a redistributive battery balancing system that achieves a balanced state in the minimum amount of time by coupling the fast MPC with microcontrollers available on todays market. [ABSTRACT FROM AUTHOR]

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-of-charge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is first built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviors are captured, thus offering a comprehensive description of the battery thermal and electrical behavior. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a metaheuristic algorithm and the least-square approach. Further, time-varying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO$_4$ batteries confirm the efficacy of the proposed method. [ABSTRACT FROM PUBLISHER]

Recent advances in lithium-ion battery modeling suggest unequal but controlled and carefully timed charging of individual cells by reduce degradation. This paper compares anode-side film formation for a standard equalization scheme versus unequal charging through switches that are controlled by deterministic dynamic programming (DDP) and DDP-inspired heuristic algorithms. A static map for film growth rate is derived from a first-principles battery model adopted from the electrochemical engineering literature. Using this map, we consider two cells that are connected in parallel via relay switches. The key results are the following: 1) optimal unequal and delayed charging indeed reduces film buildup, and 2) a near-optimal state feedback controller can be designed from the DDP solution and film growth rate convexity properties. The simulation results indicate that the heuristic state feedback controller achieves near optimal performance relative to the DDP solution, with significant reduction in film growth compared to charging both cells equally, for several film growth models. Moreover, the algorithms achieve similar film reduction values on the full electrochemical model. These results correlate with the convexity properties of the film growth map. Hence, this paper demonstrates that unequal charging may indeed reduce film growth, given certain convexity properties exist, lending promise to the concept for improving battery pack life. [ABSTRACT FROM PUBLISHER]

Based on the fact that a hybrid electric vehicle (HEV) connects a high number of batteries in series to obtain more than approximately 300 V, this paper proposes a modularized charge equalizer for an HEV battery pack. In this paper, the overall battery string is modularized into M*N cells, where M is the number of modules in the string and N is the number of cells in each module. With this modularization, low voltage stress on the electronic devices can be achieved, which means that there is less chance of a failure on the charge equalizer. The power rating selection is one of the most important design issues for a charge equalizer because it is very closely related to equalization time. To solve this problem optimally, this paper presents a power rating design guide. In addition, this paper considers system-level design issues, such as cell voltage acquisition, equalizer control logic, and system-level grounding. The simulation and experimental results are presented to show the usefulness of the optimal power rating selection guide and the low voltage stressed charge equalization process. [ABSTRACT FROM AUTHOR]

In this paper, a soft computing technique for estimating battery state-of-charge of individual batteries in a battery string is proposed. The soft computing approach uses a fusion of a fuzzy neural network (FNN) with B-spline membership functions (BMFs) and a reduced-form genetic algorithm (RGA). The algorithm is employed to tune both control points of the BMFs and the weights of the FNNs. The traditional multiple-input multiple-output FNN (MIMOFNN) cannot directly be used in this paper. The main reason is that there are too many free parameters in the MIMOFNN to be trained if many inputs are required. In this paper, a merged multiple-input single-output (MISO) FNN is proposed and can be trained by the RGA optimization approach. The merged MISO FNN with RGA (FNNRGA) can achieve faster convergence and lower estimation error than neural networks with the back propagation method. From experimental results, the proposed merged MISO FNNRGA is superior, more robust than the traditional method, and the overfitting suppression features are significantly improved. [ABSTRACT FROM AUTHOR]

Lithium (Li)-ion battery cells suffer from significant performance degradation at subzero temperatures. This paper presents a predictive control-based technique that exploits the increased internal resistance of Li-ion cells at subzero temperatures to increase the cell’s temperature until the desired power can be delivered. Specifically, the magnitude of a sequence of bidirectional currents is optimized such as to minimize total energy discharged. The magnitude of current is determined by solving an optimization problem that satisfies the battery manufacturer’s voltage and current constraints. Drawing bidirectional currents necessitates that a temporary energy reservoir for energy shuttling, such as an ultracapacitor or another battery, be available. When compared with the case when no penalty on energy withdrawn is imposed, simulations indicate that reductions of up to 20% in energy dispensed as heat in the battery as well as in the size of external storage elements can be achieved at the expense of longer warm-up operation time. [ABSTRACT FROM PUBLISHER]

This paper presents a hybrid state-of-charge (SOC) and state-of-health (SOH) estimation technique for lithium-ion batteries according to surface temperature variation (STV). The hybrid approach uses an adaptive observer to estimate the SOH while an extended Kalman filter (EKF) is used to predict the SOC. Unlike other estimation methods, the closed-loop estimation strategy takes into account the STV and its stability is guaranteed by Lyapunov direct method. In order to validate the proposed method, experiments have been carried out under different operating temperature conditions and various discharge currents. Results highlight the effectiveness of the approach in estimating SOC and SOH for different aging conditions. [ABSTRACT FROM AUTHOR]

Battery health monitoring and management is of extreme importance for the performance and cost of electric vehicles. This paper is concerned with machine-learning-enabled battery state-of-health (SOH) indication and prognosis. The sample entropy of short voltage sequence is used as an effective signature of capacity loss. Advanced sparse Bayesian predictive modeling (SBPM) methodology is employed to capture the underlying correspondence between the capacity loss and sample entropy. The SBPM-based SOH monitor is compared with a polynomial model developed in our prior work. The proposed approach allows for an analytical integration of temperature effects such that an explicitly temperature-perspective SOH estimator is established, whose performance and complexity is contrasted to the support vector machine (SVM) scheme. The forecast of remaining useful life is also performed via a combination of SBPM and bootstrap sampling concepts. Large amounts of experimental data from multiple lithium-ion battery cells at three different temperatures are deployed for model construction, verification, and comparison. Such a multi-cell setting is more useful and valuable than only considering a single cell (a common scenario). This is the first known application of combined sample entropy and SBPM to battery health prognosis. [ABSTRACT FROM AUTHOR]

This paper is focused on the development of an innovative device, which is based on a bidirectional converter, for the interface to the supply utility grid of combined renewable-energy-source-based generators and electric storage systems. The device can be controlled so as to ease the interface between the low-voltage grid and photovoltaic or wind generators combined with lithium-ion \LiFePO4 batteries, taking into account the requirements of the reference technical standards for users connection and offering different ancillary services. The operational functioning of the device, the architecture, and its electronic components, as well as laboratory and field test activities and results are described. The conversion device has been developed, and the main results that have been achieved are detailed. [ABSTRACT FROM PUBLISHER]

This paper presents an adaptive state of charge (SOC) and state of health (SOH) estimation technique for lithium-ion batteries. The adaptive strategy estimates online parameters of the battery model using a Lyapunov-based adaptation law. Therefore, the adaptive observer stability is guaranteed by Lyapunov's direct method. Since no a priori knowledge of battery parameters is required, accurate estimation is still achieved, although parameters change due to aging or other factors. Unlike other estimation strategies, only battery terminal voltage and current measurements are required. Simulation and experimental results highlight the high SOC and SOH accuracy estimation of the proposed technique. [ABSTRACT FROM PUBLISHER]

This paper presents the functionality of a commercialized fast charger for a lithium-ion electric vehicle propulsion battery. The device is intended to operate in a battery switch station, allowing an up-to 1-h recharge of a 25-kWh depleted battery, removed from a vehicle. The charger is designed as a dual-stage-controlled ac/dc converter. The input stage consists of a three-phase full-bridge diode rectifier combined with a reduced rating shunt active power filter. The input stage creates an uncontrolled pulsating dc bus while complying with the grid codes by regulating the total harmonic distortion and power factor according to the predetermined permissible limits. The output stage is formed by six interleaved groups of two parallel dc–dc converters, fed by the uncontrolled dc bus and performing the battery charging process. The charger is capable of operating in any of the three typical charging modes: constant current, constant voltage, and constant power. Extended simulation and experimental results are shown to demonstrate the functionality of the device. [ABSTRACT FROM PUBLISHER]

In this paper, the ac impedance analysis is used to explore the optimal discharging frequency for a Li-ion battery. Experiments indicate that the optimal discharging frequency is at the minimum ac impedance frequency fZmin for a conventional pulse-current (PC) discharging. In addition, a sinusoidal current (SC) discharging strategy is also proposed to achieve better performances. This SC discharging strategy improves the discharging capacity, discharging efficiency, and rising temperature of the Li-ion battery by about 1.3%, 1.32%, and 41.9%, respectively, as compared with the traditional constant-current discharging. [ABSTRACT FROM PUBLISHER]

Voltage equalization is essential for series-connected energy storage cells, such as lithium-ion cells and supercapacitors (SCs), because it ensures years of service life and maximizes the available energies of cells. Conventional equalizers and multioutput chargers require many switches and/or a multiwinding transformer, which are not desirable in terms of circuit simplicity and extendibility. In this paper, single-switch multioutput chargers based on a combination of an input cell (which generates a square voltage wave) and a voltage multiplier (which produces multiple uniform output voltages) are proposed. A fundamental operation analysis is made, and a dc equivalent circuit is derived to describe their charging and equalization behaviors. Experimental charge tests were performed separately for series-connected SC modules and lithium-ion batteries from voltage-unbalanced conditions. The least charged module/battery was charged preferentially; as a result, all of the modules/batteries were charged to a desired uniform voltage level while eliminating the voltage imbalance. [ABSTRACT FROM PUBLISHER]

In this paper, the sinusoidal-ripple-current (SRC) charging strategy for a Li-ion battery is proposed. The ac-impedance analysis is used to explore the optimal charging frequency. Experiments indicate that the optimal charging performance can be achieved by the proposed SRC with the minimum-ac-impedance frequency fZ\min. Compared with the conventional constant-current constant-voltage charging strategy, the charging time, the charging efficiency, the maximum rising temperature, and the lifetime of the Li-ion battery are improved by about 17%, 1.9%, 45.8%, and 16.1%, respectively. [ABSTRACT FROM PUBLISHER]

Due to their great energy density, high nominal voltage, and lack of memory effect, Li-ion cells play a more crucial role in electric vehicle (EV) applications. Unfortunately, if not correctly handled, this chemistry can quickly bring loss in performance and produce potentially dangerous situations. Battery management systems (BMSs) are a practical way to manage these battery packs and improve their efficiency. In this paper, we propose an original BMS architecture particularly suited for light-EV applications based on the concept of redundant cell, i.e., a technique that dynamically disconnects a cell in the battery pack for optimal balancing purposes. The proposed BMS is very flexible, economical, stable, and space saving. [ABSTRACT FROM AUTHOR]

In order to meet cost targets for hybrid electric (HEV), plug-in hybrid electric (PHEV), and all-electric vehicles (EV), an improvement in the battery life cycle and safety is essential. Recently, lithium batteries, in the form of lithium-ion, lithium-polymer, or lithium iron phosphate have been explored. Despite research initiatives, lithium-based batteries have not yet been able to meet steep energy demands, long lifetime, and low cost of vehicular propulsion applications. One practical approach to improve performance is to use power electronics intensive cell voltage equalizers, in conjunction with on-board energy storage devices. The purpose of this paper is to introduce a simplified control scheme, based on open-circuit voltage estimation, for a novel cell equalizer configuration, with the potential to fulfil expectations of the following: 1) low cost; 2) large currents; and 3) high efficiency. Issues, such as the limitations on maximum and minimum cell voltage, noise, and quantization errors, are explored. Finally, a comprehensive comparison between the theoretical test results and practical equalization test results is presented. [ABSTRACT FROM PUBLISHER]

This paper presents a method for modeling and estimation of the state of charge (SOC) of lithium-ion (Li-Ion) batteries using neural networks (NNs) and the extended Kalman filter (EKF). The NN is trained offline using the data collected from the battery-charging process. This network finds the model needed in the state-space equations of the EKF, where the state variables are the battery terminal voltage at the previous sample and the SOC at the present sample. Furthermore, the covariance matrix for the process noise in the EKF is estimated adaptively. The proposed method is implemented on a Li-Ion battery to estimate online the actual SOC of the battery. Experimental results show a good estimation of the SOC and fast convergence of the EKF state variables. [ABSTRACT FROM PUBLISHER]

Lithium-ion batteries are playing important roles as energy storage solutions for portable devices, automotive electronics, and renewable energy systems. In order to maximize the performance of lithium-ion batteries, an advanced rapid-charging pattern is required. In this paper, a Taguchi-based algorithm is presented. Orthogonal arrays are implemented to determine the optimal rapid-charging pattern for multistage constant-current (CC)-charging method. Experimental results show that the obtained rapid-charging pattern is capable of charging lithium-ion batteries to 75% capacity in 40 min. The obtained pattern also provides 60% more cycle life than the conventional CC–constant-voltage method. [ABSTRACT FROM PUBLISHER]