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High accuracy, high adaptability and low complexity have always been the goals of on-board state of health (SoH) estimation in a well-designed battery management system for electric vehicles. In this article, we proposed a practical SoH estimation method for LiFePO4 batteries based on Gaussian mixture regression (GMR) and incremental capacity (IC) analysis. According to the close correlation between the SoH and the IC curve of LiFePO4 battery, the peak coordinates of the curve are extracted as capacity feature to train an aging model using GMR. The capacity degradation of batteries is well described by multiple Gaussian distributions, and the estimation accuracy of SoH is improved. Furthermore, the coordinates of three peaks in the curve are separately analyzed as capacity feature to enhance the adaptability of the aging model to different charging regions. Finally, the complexity of the proposed method is compared with that of other methods. The results show that taking the coordinates of any peak as the capacity feature, SoH can be accurately estimated based on GMR even for different tested batteries, and the mean absolute error and root mean square error are both less than 1%. [ABSTRACT FROM AUTHOR]

The parameters of a lithium-ion battery are important to construct an effective battery management system. Parameter estimation assisted by the pseudo-two-dimensional (P2D) model is much more cost-effective than direct measurement methods. However, this is a nontrivial task, because the simulation of the P2D model is time-consuming. Alternatively, surrogate models such as reduced-order/data-driven models are often used to accelerate the parameter estimation process. Each category of surrogate models has its own strengths and weaknesses. Traditionally, reduced-order models run faster than data-driven models, while data-driven models are more accurate than reduced-order models. To leverage the complementary advantages of these two kinds of surrogate models, we make an interesting attempt to combine them compactly, thus proposing a two-phase surrogate model-assisted parameter estimation algorithm (TPSMA-PEAL). In the first phase, a fast reduced-order model is designed for parameter prescreening. In the second phase, a high-fidelity data-driven model is developed for fine estimation. In TPSMA-PEAL, except the time-consuming simulation, the other two challenges (i.e., the overfitting problem and the low observability of some parameters) are also considered from the perspective of optimization. Note that TPSMA-PEAL takes advantage of differential evolution and parameter sensitivity analysis to address them. Simulations and experiments verify that TPSMA-PEAL is efficient and accurate. [ABSTRACT FROM AUTHOR]

Accurate and robust state of charge (SOC) estimation of lithium-ion batteries is very important to prolong battery life and prevent catastrophic failures. However, the accuracy of SOC estimation is seriously affected by unknown noise, uncertain interference and temperature. In this article, a novel model fusion method is presented to achieve precise SOC estimation in the case of non-Gaussian noise and outliers. A state-space model of battery system is first developed to conduct SOC estimate. Then, a novel robust kernel Takagi–Sugeno fuzzy method to minimize the mean and variance of model error is developed to characterize the electrical performance of battery. This modeling strategy uses local nonlinear modeling mechanism, which the nonlinear relationship between data can be well represented, thus it can obtain a superior modeling performance. Finally, a multi-innovation unscented Kalman filter (UKF) algorithm considering the historical state information is designed to incorporate with the robust fuzzy model to filter out the noise in the observation and update the SOC estimation. Additional stability analysis shows the convergence of the proposed multi-innovation UKF algorithm. Experiments and verifications show that the presented estimation architecture is effective and has better modeling ability compared with several common methods. [ABSTRACT FROM AUTHOR]

In this article, a computationally efficient state estimation method for lithium-ion (Li-ion) batteries is proposed based on a degradation-conscious high-fidelity electrochemical–thermal model for advanced battery management systems. The computational burden caused by the high-dimensional nonlinear nature of the battery model is effectively eased by adopting an ensemble-based state estimator using the singular evolutive interpolated Kalman filter (SEIKF). Unlike the existing schemes, it shows that the proposed algorithm intrinsically ensures mass conservation without imposing additional constraints, leading to a battery state estimator simple to tune and fast to converge. The model uncertainty caused by battery degradation and the measurement errors are properly addressed by the proposed scheme as it adaptively adjusts the error covariance matrices of the SEIKF. The performance of the proposed adaptive ensemble-based Li-ion battery state estimator is examined by comparing it with some well-established nonlinear estimation techniques that have been used previously for battery electrochemical state estimation, and the results show that excellent performance can be provided in terms of accuracy, computational speed, and robustness. [ABSTRACT FROM AUTHOR]

This article presents a joint state-of-charge (SOC) and state-of-available-power (SOAP) estimation method based on online battery model parameter identification. First, the SOAP of lithium-ion batteries is analyzed thoroughly, and a safe operating area border-based (SOAB-based) SOAP estimation is proposed. Second, based on the adaptive battery-state estimator (ABSE) and improved ABSE, a joint SOC and SOAB-based SOAP estimation method is proposed. The joint estimation results show that the improved ABSE achieves higher accuracy than the ABSE at different battery aging states. The open-loop accuracy evaluation results show that the improved ABSE identifies the battery model parameters more accurately, and the ABSE algorithm error source lies in its identified Rp being much higher than the actual value when the battery is charged/discharged at a high current. The ABSE does not consider the influence of load current on the equivalent circuit model parameters, so it is not suitable for SOAP estimation in theory. The improved ABSE proposed by our team can eliminate this modeling error, identify the battery model parameters, and estimate the SOC and SOAB-based SOAP more accurately. This improved ABSE is an effective algorithm for estimating the battery state when the battery is charged/discharged with a high current. [ABSTRACT FROM AUTHOR]

Real-time electrochemical state information of lithium-ion batteries attributes to a high-fidelity estimation of state-of-charge (SOC) and state-of-health (SOH) in advanced battery management systems. However, the consumption of recyclable lithium ions, loss of the active materials, and the interior resistance increase resulted from the irreversible side reactions cause severe battery performance decay. To maintain accurate battery state estimation over time, a scheme using the reduced-order electrochemical model and the dual nonlinear filters is presented in this article for the reliable co-estimations of cell SOC and SOH. Specifically, the full-order pseudo-two-dimensional model is first simplified with Padé approximation while ensuring precision and observability. Next, the feasibility and performance of SOC estimator are revealed by accessing unmeasurable physical variables, such as the surface and bulk solid-phase concentration. To well reflect battery degradation, three key aging factors including the loss of lithium ions, loss of active materials, and resistance increment, are simultaneously identified, leading to an appreciable precision improvement of SOC estimation online particular for aged cells. Finally, extensive verification experiments are carried out over the cell's lifespan. The results demonstrate the performance of the proposed SOC/SOH co-estimation scheme. [ABSTRACT FROM AUTHOR]

Current pulses are convenient to be actively implemented by a battery management system. However, the short-term features (STF) from current pulses originate from various sensors with uneven qualities, which hinder one powerful and strong learner with STF for the battery state of health (SOH) estimation. This article, thus, proposes an optimized weak learner formulation procedure for lithium-ion battery SOH estimation, which further enables the automatic initialization and integration of the weak learners with STF into an efficient SOH estimation framework. A Pareto front-based selection strategy is designed to select the representative solutions from the nondominated solutions fed by a knee point driven evolutionary algorithm, which guarantees both the diversity and accuracy of the weak learners. Afterward, the weak learners, whose coefficients are obtained by self-adaptive differential evolution, are integrated by a weight-based structure. The proposed method utilizes the weak learners with STF to boost the overall performance of the SOH estimation. The validation of the proposed method is proved by LiFePO4/C batteries under accelerated cycling ageing test including one mission profile providing primary frequency regulation service to the grid and one constant current profile. [ABSTRACT FROM AUTHOR]

In this article, a highly efficient battery-balancing circuit with output voltage regulation for mobile applications is proposed and analyzed. The proposed circuit performs a cell-balancing operation for series-connected multiple-battery cells and voltage regulation operation simultaneously without additional power conversion stages. Therefore, it can be integrated into a battery pack with maximized power density and efficiency. These features are essential for recent low-power applications with multiple batteries, such as wireless/smart speakers, drones, and electronic point-of-sale, that require high power density. The operational principle, electrical modeling, and tradeoff relationship between the system power losses and balancing characteristics are derived, and the optimal design candidates of the proposed topology are discussed in terms of Pareto optimization. The effectiveness of the proposed system is verified with a prototype board using GaN switches, and 96.05% efficiency is achieved with a 1.8 V, 0.5 A load. [ABSTRACT FROM AUTHOR]

Online identification of a battery model can capture the parameter variations in real time, thus, providing an accurate model under various conditions, such as a wide temperature range, different aging degree in electric vehicle application. However, the measurement noise and sampling asynchronization of voltage and current are usually inevitable in the battery management system (BMS), which can lead to a large identification error. In this article, the mechanism of sampling asynchronization and measurement noise is analyzed first, and then the identification sensitivity analyses on noise and sampling asynchronization are carried out. Simulation results indicate that even a small fluctuation as small as 5 mV or the sampling latency between voltage and current within 10 mS can cause relatively large identification errors, especially for polarization parameters. In order to guarantee the online identification accuracy and robustness in the BMS application, an improved robust recursive least-squares (RLS) algorithm with adaptive outlier boundary is proposed to eliminate the input outlier caused by sampling latency and suppress the effect of measurement noise utilizing the bias compensation method. The experimental results demonstrate that the proposed method can provide sufficient accuracy and robustness under noise interference and sampling latency in BMS application. [ABSTRACT FROM AUTHOR]

Safe and effective exploitation of lithium ion (Li-ion) batteries requires advanced battery management systems. This article proposes a computationally efficient, control-oriented model of a Li-ion cell. The model describes the spatial nature of both the chemical species and temperature dynamics in a computationally efficient way. The method takes advantage of the algebraic structure that arises from the distributed nature of the model. We show that, by discretizing the model partial differential equations with a finite difference method, the coupling equations take a semiseparable structure for which an efficient algebra exists. This approach yields an efficient modeling tool that can be employed to design model-based estimation and control algorithms. The proposed model is validated against a high order computational fluid dynamics model showing accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Alternating current (ac) impedance spectra facilitate lithium-ion battery management. Realizing a low-cost and low-complexity onboard impedance measuring system is a vital issue for the management based on the ac impedance. In the article, a novel impedance measuring system combined with a high-power dual active bridge (DAB) converter and distributed sampling units is proposed and verified. The DAB converter is designed to generate the ac disturbance to ensure a quasi-steady measurement of the battery impedance. The distributed signal sampling units simultaneously measure the voltage and current of all the series-connected battery cells in a module to measure their impedance. The measured impedance in a frequency range of 0.1–500 Hz shows the feasibility of the system. The root-mean-square errors of the measured impedance phase from 0.2 to 200 Hz and the magnitude from 0.1 to 500 Hz are less than 6.9% and 4.0%, respectively. The errors in some frequency ranges are slightly larger, which are analyzed. The novelty is reflected in that the system is easily integrated into a bidirectional onboard charger and compatible with the battery management system, thus reducing costs and complexity. It provides a basis for the onboard application of the battery impedance. [ABSTRACT FROM AUTHOR]

For lithium-ion batteries, active balancing can bring advantages compared to passive balancing in terms of lifetime and available capacity. Most known balancing techniques suffer from a low efficiency or high complexity and cost. This article proposes a new technical solution based on a nonisolated dc/dc converter and a low-speed switching matrix to overcome efficiency and power limitations of present balancing methods. The proposed circuit allows balancing current paths which are not possible with previous methods and results in balancing efficiencies of over 90%. The performance comparison is based on batch numerical simulations using calculated and measured efficiency values. The simulation results are compared to the approved active balancing method cell-to-cell and to passive balancing. A study case with eight randomly distributed battery cells shows an improvement in overall balancing efficiency of up to 47.4% and a reduction in balancing time of up to 36.9%. The available capacity increases from 97.2% in a passively balanced system to 99.7% for the new method. A hardware prototype was set up to demonstrate the working principle and to verify the numerical simulations. [ABSTRACT FROM AUTHOR]

The constant-current constant-voltage (CC–CV) charging method is a common approach to charge batteries with a single charger. Recently, with the emergence of high power-density batteries, e.g., supercapacitors, several chargers are typically connected in parallel to provide the required charging power in many applications. However, the classical decentralized control scheme for the multicharger system leads to the current imbalance, which deteriorates the reliability of the charging system. In this article, we extend the CC–CV charging protocol to multicharger systems using a cooperative control method, which can alleviate the current imbalance among chargers effectively. First, the motivation and charging system modeling of this work are provided. Then, a cooperative CC charging protocol and a cooperative CV charging protocol are presented, respectively, and a switching logic is designed to achieve the switching between them based on the supercapacitor voltage. The closed-loop model of the proposed charging system is developed using the block diagram. A laboratory testbed is built to verify the effectiveness of the proposed method. Experiment results show that the proposed method provides a better current balancing and voltage regulation performance when compared with existing decentralized control methods. [ABSTRACT FROM AUTHOR]

In this article, a photovoltaic (PV) reconfigurable grid-tied inverter (RGTI) scheme is proposed. Unlike a conventional grid-tied inverter (GTI) that ceases operation during a power outage, the RGTI is designed to act as a regular GTI in the on-grid mode but it is reconfigured to function as a dc–dc charge-controller that continues operation during a grid outage. During this period, the RGTI is tied to the battery-bank of an external uninterruptible power supply (UPS)-based backup power system to augment it with solar power. Such an operation in off-grid mode without employing high-bandwidth communication with the UPS is challenging, as the RGTI control must not conflict with the battery management system of the UPS. The hardware and control design aspects of this requirement are discussed in this article. A battery emulation control scheme is proposed for the RGTI that facilitates seamless functioning of the RGTI in parallel with the physical UPS battery to reduce its discharge current. A system-level control scheme for overall operation and power management is presented to handle the dynamic variations in solar irradiation and UPS loads during the day, such that the battery discharge burden is minimized. The design and operation of the proposed RGTI system are independent of the external UPS and can be integrated with an UPS supplied by any manufacturer. Experimental results on a 4 kVA hardware setup validate the proposed RGTI concept, its operation, and control. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) battery performance near end of life is limited by mismatched cell degradation, leading to an estimated 5–10% cell capacity variation across the pack. Active cell balancing hardware architectures incorporating a low-voltage (LV) bus supply have been introduced to unlock lost capacity due to cell imbalance at reduced cost, through elimination of the vehicle's 400-to-12 V dc–dc converter. In this article, a hierarchical model-predictive control scheme is applied to a time-shared isolated converter active balancing architecture that incorporates LV bus supply. The proposed controller efficiently divides computation among the battery management system (BMS) hardware components. The energy-buffering capability of the lead-acid battery, which is connected to the LV bus, is used to tradeoff balancing and bus regulation objectives, reducing peak power and improving the system cost-effectiveness. Simultaneous state-of-charge balancing and LV bus regulation is verified in simulation and experiment using real-world drive and LV load data collected from a GM Bolt EV. Similar controller performance compared to a central scheme is achieved in simulation. The experimental setup includes a custom 12S2P, 3.9 kWh, liquid-cooled Lithium Nickel Manganese Cobalt battery module with an embedded BMS. The controller performance is evaluated with an initial maximum state-of-charge imbalance of 6.8%. [ABSTRACT FROM AUTHOR]

A battery/supercapacitor hybrid energy storage system (HESS) is overactuated in the sense that there are two power sources providing a single power output. This feature of HESS is exploited in this article to simultaneously achieve accurate identification of the battery states/parameters and high system efficiency. By actively injecting current signals, the state of charge and state of health, together with other battery parameters, can be identified sequentially. Sufficient richness in the input (i.e., battery current) is necessary to ensure identification accuracy. Since signal richness for identification can be in conflict with efficient operation, a novel model predictive control (MPC) strategy is used to simultaneously consider both objectives to determine the optimal power distribution between supercapacitor and battery. The tradeoff between identification accuracy and system efficiency is investigated. Simulation results show that the proposed MPC can significantly improve identification accuracy at the expense of a slight decrease in system efficiency when compared to the baseline MPC, which does not consider the signal richness. Therefore, it is validated that the proposed MPC can effectively achieve simultaneous identification and efficient operation. [ABSTRACT FROM AUTHOR]

In portable devices, such as notebook computers and tablet personal computers, one or more battery strings with multiple lithium-ion (Li-ion) cells have been used since long. Life cycle and safety of the Li-ion battery strings are crucial aspects. A battery management system (BMS) is required for monitoring the status of each cell and balancing their state of charge (SoC). However, most conventional cell equalizers provide only cell monitoring and equalization functions. In this article, a parallel architecture is presented that not only achieves battery cell equalization but also is capable of providing voltage regulation for point-of-loads (POLs). In the proposed equalizer, several isolated dc–dc converters are constructed. The input terminal of each converter is connected to a battery cell, and the output terminals are connected to one point for the poststage POLs. A battery voltage acquirement system is built for SoC estimation. A central BMS controller is realized using a field-programmable gate array to receive sensed signals and control all the functions. The entire system is verified using the experimental results for a battery string involving three NCR 18650B Li-ion cells connected in series. [ABSTRACT FROM AUTHOR]

Accurate information of battery internal variables is crucial for health-conscious and optimal battery management. Due to lack of measurements, advanced battery management systems rely heavily on estimation algorithms that provide such internal information. Although algorithms for cell-level charge and health estimation have been widely explored in the literature, algorithms for electrode-level quantities are almost nonexistent. The main obstacle in electrode-level estimation is the observability problem where the individual electrode states are not observable from terminal voltage output. However, if available, real-time feedback of electrode-level charge and health can be highly beneficial in maximizing energy utilization and battery life. Motivated by this scenario, in this paper we propose a real-time algorithm that estimates the available charge and health of individual electrodes. We circumvent the aforementioned observability problem by proposing an uncertain model-based cascaded estimation framework. The design and analysis of the proposed scheme are aided by a combination of Lyapunov's stability theory, adaptive observer theory, and interconnected systems theory. Finally, we illustrate the effectiveness of the estimation scheme by performing extensive simulation and experimental studies. [ABSTRACT FROM AUTHOR]

Hardware structure is a basic characteristic of a battery management system (BMS), which influences the overall performance of battery packs. This paper presents a novel BMS architecture based on the power/data time division multiplexing transmission technique. In the proposed system, a common bus is employed to transfer power and data simultaneously, so the system wiring is simplified. The system consists of a pack management unit and several cell management units (CMUs). The circuit of the CMU is compact and achieves conventional management functions like status monitoring and active cell balancing; thus, it can be integrated with battery cell to be an intelligent cell. The timing sequence of the system is presented, and the design considerations are discussed in detail, including terminal resistance selection, maximum node number calculation, and power consumption analysis. Finally, a simulation based on MATLAB is carried out and an experimental prototype system with six nodes is built, which verifies the correctness of the architecture and proves the feasibility in practical applications. [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]

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]

Multifrequency impedance measurements have been recognized as a technique for the monitoring of individual cells in lithium-ion (Li-ion) batteries. However, its practical introduction for battery management has been slow, mainly due to added size and larger operating power requirements. Here, we describe a small, low-power, multifrequency (1–1000 Hz) impedance-based battery management system (BMS) for multicell batteries of varying capacities. This BMS ensures battery safety and efficiency by tracking and acting on emerging mismatches and other electrical and thermal abnormalities in each individual cell without adding cost, volume, weight, and power, compared to conventional BMSs. Multicell batteries face a unique problem that single-cell Li-ion batteries do not: mismatching of one or more cells is detrimental to the safety and efficiency of the entire battery. Thus, cell matching is an important first step required for safe operation of multicell Li-ion batteries. Cell mismatch can occur due to battery overdischarge, overcharge, internal and external shorts, etc., or even when leaving a battery unused for an extended period (calendar aging). Predicting a mismatch is essential for a battery's thermal safety and electrical efficiency. Conventional BMSs typically monitor cell voltages and surface temperature. However, these measurements and related protocols have not succeeded in ensuring battery safety or improving efficiency. Data for batteries with intentionally calendar-aged and overdischarged cells convincingly demonstrate that such BMSs cannot identify cell mismatches and emerging failures. In contrast, the impedance-based BMS, described here, tracks, identifies, and acts on changes in the internal state of each cell continuously in real time, including battery charging, discharging, and at rest. [ABSTRACT FROM PUBLISHER]

State of charge (SOC) estimation is critical to the safe and reliable operation of Li-ion battery packs, which nowadays are becoming increasingly used in electric vehicles (EVs), Hybrid EVs, unmanned aerial vehicles, and smart grid systems. We introduce a new method to perform accurate SOC estimation for Li-ion batteries using a recurrent neural network (RNN) with long short-term memory (LSTM). We showcase the LSTM-RNN's ability to encode dependencies in time and accurately estimate SOC without using any battery models, filters, or inference systems like Kalman filters. In addition, this machine-learning technique, like all others, is capable of generalizing the abstractions it learns during training to other datasets taken under different conditions. Therefore, we exploit this feature by training an LSTM-RNN model over datasets recorded at various ambient temperatures, leading to a single network that can properly estimate SOC at different ambient temperature conditions. The LSTM-RNN achieves a low mean absolute error (MAE) of 0.573% at a fixed ambient temperature and an MAE of 1.606% on a dataset with ambient temperature increasing from 10 to 25 ^\circ C. [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 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 novel error-tolerant iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal battery control and management problems in smart home environments with renewable energy. A main contribution for the iterative ADP algorithm is to implement with the electricity rate, home load demand, and renewable energy as quasi-periodic functions, instead of accurate periodic functions, where the discount factor can adaptively be regulated in each iteration to guarantee the convergence of the iterative value function. A new analysis method is developed to guarantee the iterative value function to converge to a finite neighborhood of the optimal performance index function, in spite of the differences of the electricity rate, the home load demand, and the renewable energy in different periods. Neural networks are employed to approximate the iterative value function and control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Numerical results and comparisons are given to illustrate the performance of the developed algorithm. [ABSTRACT FROM AUTHOR]

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

This paper presents an energy sharing state-of-charge (SOC) balancing control scheme based on a distributed battery energy storage system architecture where the cell balancing system and the dc bus voltage regulation system are combined into a single system. The battery cells are decoupled from one another by connecting each cell with a small lower power dc–dc power converter. The small power converters are utilized to achieve both SOC balancing between the battery cells and dc bus voltage regulation at the same time. The battery cells' SOC imbalance issue is addressed from the root by using the energy sharing concept to automatically adjust the discharge/charge rate of each cell while maintaining a regulated dc bus voltage. Consequently, there is no need to transfer the excess energy between the cells for SOC balancing. The theoretical basis and experimental prototype results are provided to illustrate and validate the proposed energy sharing controller. [ABSTRACT FROM PUBLISHER]

In this paper, a novel iterative $Q$-learning method called “dual iterative $Q$-learning algorithm” is developed to solve the optimal battery management and control problem in smart residential environments. In the developed algorithm, two iterations are introduced, which are internal and external iterations, where internal iteration minimizes the total cost of power loads in each period, and the external iteration makes the iterative $Q$-function converge to the optimum. Based on the dual iterative $Q$-learning algorithm, the convergence property of the iterative $Q$-learning method for the optimal battery management and control problem is proven for the first time, which guarantees that both the iterative $Q$-function and the iterative control law reach the optimum. Implementing the algorithm by neural networks, numerical results and comparisons are given to illustrate the performance of the developed algorithm. [ABSTRACT FROM PUBLISHER]

Computational intelligence techniques are used to approximate the nonlinear operation of \LiFePO4 batteries using rule-based systems. In this paper, rule-based systems are not directly fitted to data, but comprise constructive blocks in a differential-equation-based dynamical model that is numerically integrated to infer battery voltage, charge, and temperature. The design methodology has been validated with three different \ \hspace*-1.8ptLiFePO4 batteries, and the results were found to be more accurate than those of a selection of statistical models and state-of-the-art artificial intelligence techniques. [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]