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An electric vehicle battery pack which is a gathering of battery modules which subsequently comprised of the battery cell is a primary source of control transmission for an Electric Vehicle (EV). The inappropriate design of the battery enclosure will cause many genuine issues, such as cracking, causing noise, or battery harm. At the same time, the weight of the battery enclosure is huge; in order to get better the driving range of the electric vehicle and diminish the influence of the battery on the vehicle dynamic performance and acceleration performance, it is essential to carry out the lightweight design of the battery enclosure. This paper reviews the multi-material battery enclosure design optimization, the multi- technologies, and a proficient Battery Management System (BMS) for compact battery pack design used to lightweight battery pack enclosure design; the multi-objective optimization approach for distinctive parameters of battery pack enclosure design optimization by diverse manufacturing techniques.

This paper deals with the simulation verification of balancing systems with different numbers of batteries. There is a simulation model of the battery with adjustable inputs and a simulation model of flyback converter. These components are interconnected and controlled externally to ensure balancing of individual cells. The batteries are connected in series and a flyback converter is connected to each battery. The outputs of these inverters are connected to the output of all the batteries. So, one can say that this balancing system is designed to lighten the charger. The results of the simulations show the voltage, current and State of Charge (SoC) of the batteries. Simulation models are presented in diagrams. These models were created in the PLECS environment.

In order to guarantee adequate operating conditions in an energy storage system (SAE), extending its useful life, and offering safety to the user, a device known as the battery management system (BMS) is used. Most devices currently sold are restricted to operating characteristics of lithium battery technologies, which are different in different ways from the operation of other types of batteries that are studied and developed in laboratories. The restriction of use in other technologies could be circumvented through open platforms, allowing modifications in the BMS in order to adapt to the application technology. This adaptive characteristic is rarely found in commercialized devices, being fundamental when the objective of the BMS is linked to academic research that requires experimental steps. Therefore, this work proposes a low cost adaptive open source BMS prototype, capable of monitoring the variables of voltage, current, temperature and state of charge for a battery with up to 10 cells in series. The development includes hardware and software for the basic functioning of BMS functions. The proposed BMS was developed based on two cell technologies with: 18650 lithium ions, and sodium nickel chloride. The versatility of BMS in operating on these two technologies aims to demonstrate the adaptive capacity of the system. For remote monitoring, an interface was developed using the Node-RED and IBM Watson tools.

Electric vehicles (EVs) are clean substitutes for conventional vehicles. Battery management system (BMS) is the intelligence behind the EV battery packs. One of the key functions of BMS is cell bal...

Lithium-Ion battery packs are an essential component for electric vehicles (EVs). These packs are configured from hundreds of series and parallel connected cells to provide the necessary power and energy for the vehicle. An accurate, adaptable battery management system (BMS) is essential to monitor and control such a large number of cells. Series and parallel connected cells also experience different production and operational conditions, which makes it challenging for the BMS to ensure the safe operation of each individual cell. The main functions of the BMS include battery state estimation, cell balancing, thermal management, and fault diagnosis. Robust estimation of the state of charge (SOC) is crucial for providing the driver with an accurate indication of the remaining range. This paper presents the state of art of battery pack SOC estimation methods along with the impact of cell inconsistency on pack performance and SOC estimation. Cell balancing methods, which are necessary due to cell inconsistencies, are discussed as well. Four categories of pack SOC estimation methods are presented, including individual cell, lumped cell, reference cell, and mean cell and difference estimation methods. The SOC estimation methods are compared in terms of algorithm type, computational load, and engineering effort to help practitioners decide which method best fits their application.

The State of Charge (SoC), State of Health (SoH), and State of available Power (SoaP) of Lithium-Ion Batteries (LiB) are critical quantities which cannot be measured but must be estimated by Battery Management Systems (BMS) instead. A technique known as Electrochemical Impedance Spectroscopy (EIS) provides a non-destructive way of measuring battery impedance, offering detailed insight into the battery state of operation. Several challenges must be solved in to utilize EIS as part of the BMS, including the defining of operating conditions at which to perform the sensitive EIS measurement. In laboratory conditions, several hours of rest are used to ensure a stable response of the battery, but such rest times are impractical for BMS applications. This paper proposes a methodology combining drift compensation and a Voigt-circuit used as a filter to obtain valid EIS data with short rest times under different operating conditions. Extensive tests were conducted on lithium nickel manganese cobalt (NMC) batteries to evaluate the methodology and show how the different operating conditions impact the rest time required for valid EIS measurement. The results show that rest times between 5 and 30 minutes can be used to obtain useful EIS data for a wide range of operating conditions.

The prognostics and health management (PHM) plays the main role to handle the risk of failure before its occurrence. Next, it has a broad spectrum of applications including utility networks, energy storage systems (ESS), etc. However, an accurate capacity estimation of batteries in ESS is mandatory for their safe operations and decision making policy. ESS comprises of different storage mechanisms such as batteries, capacitors, etc. Consequently, the measurement of different charging profiles (CPs) has a strong relation to battery capacity. These profiles include temperature (T), voltage (V), and current (I) where the CPs patterns vary as the battery ages with cycles. Consequently, estimating a battery capacity, the conventional methods practice single channel charging profile (SCCP) and hop multiple channel CPs (MCCPs) that cause incorrect battery health estimation. To tackle these issues, this article proposes MCCPs based battery management system (BMS) to estimate batteries health/capacity through the deep learning (DL) concept where the patterns in these CPs are changed as the battery ages with time and cycles. Thus, we deeply investigate both machine learning (ML) and DL based methods to provide a concrete comparative analysis of our method. The adaptive boosting (AB) and support vector regression (SVR) are widely compared with long short-term memory (LSTM), multi-layer perceptron (MLP), bi-directional LSTM (BiLSTM), and convolutional neural network (CNN) to attain the appropriate approach for battery capacity and state of health (SOH) estimation. These approaches have a high learning capability of inter-relation between the battery capacity and variation in CPs patterns. To validate and verify the proposed technique, we use NASA battery dataset and experimentally prove that BiLSTM outperforms all the approaches and obtains the smallest error values for MAE, MSE, RMSE, and MAPE using MCCPs compared to SCCP.

This paper presents a reduced-order electrochemical battery model designed for the online implementation of battery control systems. The model is based on porous-electrode and concentrated-solution theory frameworks and is able to predict voltage as well as the internal electrochemical variables of a battery. The reduction of the model leads to a physics-based one-dimensional discrete-time state-space reduced-order model (ROM), which is especially beneficial for online systems. Models optimized around different operational setpoints are combined to predict cell variables over a wide range of temperatures and state of charges (SOCs) using the output-blending method. A sigma-point Kalman filter is further used to manage inaccuracies generated by the reduction process and experimental-related issues such as measurement error (noise) in the current and voltage sensors. The state-estimation accuracies are measured against a full-order model (FOM) developed in COMSOL. The whole system is able to track the internal variables of the cell, as well as the cell voltage and SOC with very high accuracy, demonstrating its suitability for an online battery control system.

While lithium–sulfur batteries are a promising next-generation chemistry devices due to their high theoretical energy density, commercialization has been slow due to low coulombic efficiency and poor cycle life. This review explores the ways in which continuum modeling contributes to the understanding of lithium–sulfur (LiS) battery mechanisms and cell-level performance through the lens of micro- and macroscale phenomena. We examine different approaches to modeling important physical phenomena such as reaction mechanisms, cathode microstructure, shuttling, nucleation and precipitation, and transport limitations. This paper also emphasizes the significance and challenge of connecting typical modeling parameters and assumptions to systems-level metrics of a standard state-of-art high performing lithium–sulfur cell. Particularly important, the considerations for high energy density cells and the areas where continuum models can facilitate better collaboration are discussed. We also summarize a few selected works to highlight experimentally-driven modeling, use of electroanalytical techniques, and parameter identification approaches to enable model-based design and advanced battery management systems.

Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.

Nowadays, the use of electric vehicles is increasing leading to a growing demand for more efficient use of lithium-ion batteries. The state-of-charge (SOC) has been estimated in previous studies to optimize energy management of batteries. For more efficient battery utilization, detecting degradation is important. However, it is difficult for conventional methods to distinguish the effect of the model parameters including different time constants. Identifying model parameters of multiple RC parallel branches, which represent the impedance of wider frequency ranges, is a necessary requirement to detect the degradation of parts. In this study, we present a method for estimating the model parameters of multiple RC parallel branches. We designed the Markov Chain Monte Carlo algorithm by setting a search range limit and moving window, which enable estimation of the model parameters of parallel branches of different time constants. Through validation of the algorithm based on simulation, the model parameters of a third-order circuit were estimated to be within the error range of 15.2 %. In addition, impedance was calculated from the estimated model parameters in the test using a real battery dataset. The error of impedance was less than 10 % from 0.01 to 100 Hz which was sufficiently low to monitor the change of the parameters owing to degradation. As the impedance in the high-frequency band above 0.1 Hz is more likely to change because of degradation, the proposed method can be used to monitor the model parameters that change as a result of degradation.

A large proportion of electric vehicle accidents are attributed to lithium-ion battery failure recently, which demands the time-efficient diagnosis and safety warning in advance of severe fault occurrence to ensure reliable operation of electric vehicles. However, serious battery system faults are often not caused by easily-observed cell state inconsistency, but derived from a certain cell failure with precursory signals untended, or occasional abuse, thus eventually thermal runaway. In this paper, a signal-based fault diagnosis method is presented, including signal analysis to eliminate the impact of state inconsistency on time-series feature extraction, feature fusion, and dimensionality reduction by manifold learning, with clustering-based outlier detection to identify abnormal signal features. The challenges in threshold determination of fused features can be effectively resolved by supplementary correction to largely reduce the amount of false alarms. Compared with the judgments from actual battery management systems, and other signal-based methods with single features, earlier detections can be achieved with robustness, verified by real-world pre-fault operation data of electric vehicles that suffered thermal runaway.

In recent years, the pervasive use of lithium ion (Li-ion) batteries in applications such as cell phones, laptop computers, electric vehicles, and grid energy storage systems has prompted the development of specialized battery management systems (BMS). The primary goal of a BMS is to maintain a reliable and safe battery power source while maximizing the calendar life and performance of the cells. To maintain safe operation, a BMS should be programmed to minimize degradation and prevent damage to a Li-ion cell, which can lead to thermal runaway. Cell damage can occur over time if a BMS is not properly configured to avoid overcharging and discharging. To prevent cell damage, efficient and accurate cell charging cycle characteristics algorithms must be employed. In this paper, computationally efficient and accurate ensemble learning algorithms capable of detecting Li-ion cell charging irregularities are described. Additionally, it is shown using machine and deep learning that it is possible to accurately and efficiently detect when a cell has experienced thermal and electrical stress due to cell overcharging by measuring charging cycle divergence.

A proper battery management system (BMS) plays a vital role in ensuring the safety and reliability of electric vehicles (EVs) and other electronic products. Accurate State-of-Health (SOH) estimation of Lithium-ion (Li-ion) batteries is a key factor in a BMS. It is difficult to determine SOH because of the complexity of the electrochemical reactions within the battery. To improve the accuracy of SOH estimation, a dynamic spatial-temporal attention-based gated recurrent unit (DSTA-GRU) model is proposed in this paper. First, we extract six features from the battery's charging and discharging processes that can reflect the aging degree of the battery to some extent. Second, this paper proposes a model to combine spatial attention and temporal attention that can not only consider the effects of states at different time step on the results, but also consider the effects of different features in the space domain. Third, the proposed model is trained and tested on NASA battery datasets and compared with other conventional models. Experiments carried on these data sets demonstrate that our model achieves higher accuracy than other conventional models.

The use of energy storage systems in standalone photovoltaic installations is essential to supply energy demands, independently of solar generation. Accurate prediction of the battery state is critical for the safe, durable, and reliable operation of systems in this type of installations. In this study, an installation located in the area of Aragon (Spain) has been considered. Two methods, based on different types of Recurrent Neural Networks (RNN), are proposed to predict the battery voltage of the installation two days ahead. Specifically, the Nonlinear Auto Regressive with Exogenous Input (NARX) network and the Long Short-Term Memory (LSTM) network are studied and compared. The implemented algorithms process battery voltage, temperature and current waveforms; and rely on the selection of different future scenarios based on weather forecasting to estimate the future voltage of the battery. The proposed methodology is capable of predicting the voltage with a Root Mean Squared Error (RMSE) error of 1.2 V for batteries of 48 V, in critical situations where the installation is running out of energy. The study contributes to the ongoing research of developing preventive control systems that help reduce costs and improve the performance of remote energy storage systems based on renewable energies with a positive outcome.

Battery management systems are used to monitor power batteries in electric vehicles and solar power systems. They control the charge and discharge, identity and correct many problems in these batteries. Nowadays, measurements on each cell or stack of cells, which compose the battery, are necessary to ensure the safety and integrity of the system, because the battery cells degrade unequally since they are not built identically. A new methodology is presented in this work, for identification cells with finished life cycle in batteries parallel cells associations, without using temperature measurements, cell monitoring and State of Health estimators, through Support Vector Machines and Wavelet Transform. The proposed method opens possibilities for a new strategy of battery management in electric vehicles, inserting Battery Management System in the charging stations and allowing more sophisticated diagnoses.

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.

The Li-ion battery has emerged as the most prominent energy storage device and been widely applied in electric vehicles (EVs). To ensure the safety and performance of batteries, a battery management system (BMS) is incorporated in the EVs. However, how to predict and verify the BMS reliability remains an issue. We conduct the accelerated degradation test on the BMS and develop a semi-parametric framework to predict the reliability. A new measure based on similarity is further proposed for model comparison. A real data set of the BMS is used to verify the validity of the proposed method. A comparison with the other four models shows that our approach can generate a more similar trend to the original data, with 39.23% improvement compared with the best-performed model based on the proposed measure. Besides, the proposed model gives a prediction of lower bound of the reliable lifetime as 10.8 years, which is more reasonable compared with the results (i.e., around one year) given by the conventional models.

For safe and efficient operation of electric vehicles (EVs), battery management system is essential. Nevertheless, a challenge lying in battery management systems is how to obtain an algorithm for state of charge (SOC) estimation that has both high accuracy and low computational cost. For this purpose, the battery parameters and SOC joint estimation algorithm based on bias compensation least squares and alternate (BCLS-ALT) algorithm are proposed in this paper. The battery model parameters are identified online using the bias compensation least squares (BCLS), while the SOC is estimated applying the alternate (ALT) algorithm, which can switch the computational logic between H-infinity filter (HIF) and ampere-hour integral (AHI) to improve the computational efficiency and accuracy. The experimental results show that the accuracy of the SOC estimated by the BCLS-ALT algorithm is the highest, and the computational efficiency is also high, with the switching threshold SOCALT being set to 25%. Despite the 20% initial error and the 10% current drift, the proposed BCLS-ALT algorithm can obtain high accuracy and robustness of SOC estimation under different ambient temperatures and dynamic load profiles.

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.

In this Letter, a battery state of health monitoring technique suitable for battery management systems is presented. This method analyses switching and battery transitions in the battery management system topologies to distinguish any early degraded battery cell in the battery strings. The proposed technique processes the voltage of the system, captured by an already employed sensor during the transition of the states. To verify the performance of the proposed technique, some experiments have been carried out on a typical charge balancing circuit.

The lithium-ion battery plays a crucial role in the power supply of the electric vehicles (EVs). Battery remaining useful life (RUL) is critically vital to ensure the vehicles’ safety and reliability. Due to the complicated aging mechanism, predicting RUL for the battery management systems (BMSs) is challenging. In this article, a novel degradation indicator was constructed using the information extracted from the discharge voltage. The indicator reflected the complete and effective energy information from the voltage signals to reveal battery degradation characteristics. Additionally, an innovative fractional grey model (FRGM) unscented particle filter (UPF) framework was developed for RUL prediction in this article. To improve the accuracy and traceability of prediction, the framework adopted a novel FRGM to update the state transition equation in UPF. Meanwhile, the UPF was employed to extrapolate trends of the indicator and achieve the RUL prediction. The performances of FRGM-UPF with the degradation indicator were synthetically verified by the data from various types of batteries under different aging tests. The experimental results indicated that the proposed method could achieve precise prediction results and had a wide range of practicability and universality. The developed technologies could be incorporated with the other control algorithms for application in BMS of EVs.

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.

Success of electric mobility and connected future depends on advanced high capacity Lithium-ion batteries and a tailored battery management system that keeps track of battery health and safety to optimize the performance. The drive for advanced batteries is being pursued on one front via the development of novel chemistry, for example, blended composite cathodes to achieve enhanced performance and life. On the other front, the need for optimal battery performance via operational control viz. a robust Battery Management System (BMS) that accurately predicts state-of-charge (SOC), state-of-power (SOP) and state-of-health (SOH) in a computationally efficient way, has been lacking in many ways due to the reliance on simple and fast-to-compute models such as equivalent circuit models (ECM) that lack the accuracy needed for large battery packs. This paper reports an on-board reduced-order electrochemical thermal model (ROTM) based SOC and voltage estimation for a 12S1P configuration composite-cathode battery pack and demonstrates its practicality by implementing it on four different micro-controller units (MCU) (ATmega:2560&328, Infineon:TC275&TC297). We show that on-board ROTM based SOC and voltage prediction have better accuracy compared to the conventional ECM based methods under both static and dynamic load conditions.

Rechargeable batteries are critical components in many electrical systems nowadays. One has to ensure reliable diagnosis and assessment of the installed batteries for smooth and safe operations. Assessment of the remaining capacity of a battery is crucial diagnostic information. A battery management system (BMS) needs to reliably report the ability of the battery to supply power or the lack thereof. If the BMS fails to do so at an early stage, this may compromise the health of the entire electric system. When a battery nears a region where the battery state-of-charge (SOC) is low, there is a risk of an abrupt drop in the terminal voltage. An early detection of such a region is crucial; otherwise, the BMS may not have enough time to react. To address this issue, our work provides a novel supervised learning approach towards an early detection of Li-ion battery terminal voltage collapse. No knowledge of initial SOC or battery model parameters is required. This is particularly important as batteries lose their capacity to store charge over time. The efficacy of the proposed approach is demonstrated by an early and accurate detection of terminal voltage collapse over a set of discharge tests conducted using multiple batteries.

This paper proposes an online autonomous specific gravity measurement strategy for lead–acid battery applications. The main objective of this strategy is to achieve the intelligent and high-precision measurements. In general, the electricity of a lead-acid battery is related to the state-of-charge (SOC), which can be obtained by gauging the specific gravity. However, the traditional process of capturing the lead–acid battery data is relatively complex and time consuming. Moreover, it is difficult to directly transfer the specific gravity data as electronic signals for the battery management system (BMS) by using traditional methods. In view of this, an autonomous strategy with online measurement feature is proposed to gauge the specific gravity. In addition, the liquid level sensing concept combined with the ultrasonic sensor module is adopted. To realize the proposed strategy, a hydrometer integrated with the liquid level sensor is designed and implemented. By utilizing the proposed strategy and developed mathematical equations, the traditional visual inspection data could be transferred as electronic data and combined with the BMS. In other words, the intelligent online real-time monitoring measurement can be achieved whereas the human resources cost can be reduced. Moreover, environmental factors are also taken into account in the proposed strategy to precisely capture the specific gravity under different operating conditions. Finally, experimental results demonstrate the performance and feasibility of the proposed strategy.

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

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

Summary Lithium-ion batteries have been widely used in many important applications. However, there are still many challenges facing lithium-ion batteries, one of them being degradation. Battery degradation is a complex problem, which involves many electrochemical side reactions in anode, electrolyte, and cathode. Operating conditions affect degradation significantly and therefore the battery lifetime. It is of extreme importance to achieve accurate predictions of the remaining battery lifetime under various operating conditions. This is essential for the battery management system to ensure reliable operation and timely maintenance and is also critical for battery second-life applications. After introducing the degradation mechanisms, this paper provides a timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches. The details, advantages, and limitations of these approaches are presented, analyzed, and compared. Future trends are presented, and key challenges and opportunities are discussed.

The equivalent circuit model for utility-scale battery energy storage systems (BESS) is beneficial for multiple applications including performance evaluation, safety assessments, and the development of accurate models for simulation studies. This paper evaluates and compares the performance of utility-scale equivalent circuit models developed at multiple sub-component levels, i.e. at the rack, module, and cell levels. This type of modeling is used to demonstrate that the equivalent circuit model for a reference cell, module, or rack of a BESS can be scaled to represent the entire battery system provided that the battery management system (BMS) is active and functional. Contrary to the rapid pulse discharge cycles employed in conventional cell parameter estimation approaches, the study proposes a new charge/discharge cycle for identifying the equivalent circuit parameters for utility-scale battery systems using equipment readily available at installation sites without the need for laboratory setups. Furthermore, a sensitivity analysis for classifying and quantifying the effect of each equivalent circuit parameter on the performance of the proposed battery system model was executed. The measurements and simulations are conducted for a 1MW/2MWh BESS testing facility located at the Louisville Gas and Electric and Kentucky Utilities (LG&E and KU) E.W. Brown generating plant. The results indicate that for the example utility-scale battery setup with an active BMS, the equivalent circuit model of either the cell, module, or rack can be scaled to represent the battery system with less than 1% average voltage error.

The lithium-ion batteries are recurrently used in a variety of applications. To assure an effective battery utilization and longer life the Battery Management Systems (BMSs) are employed. Recent BMSs are becoming sophisticated and causes a higher consumption overhead on the battery. To enhance the BMS power efficiency, this work exploits the input signal non-stationary nature. The idea is to employ event-driven sensing and processing. In contrast to the traditional counterparts, the battery cells parameters like voltages and currents are no more captured periodically but are acquired based on events. It results in significant real-time data compression. Afterward, this non-uniformly partitioned information is employed by a novel event-driven Coulomb counting algorithm for a real-time determination of the State of Charge (SoC). The estimated SoCis calibrated by using an original event-driven Open Circuit Voltage (OCV) to SoC curve relation. The devised system comparison is made with the traditional counterparts. Results demonstrate a more than third-orderof magnitude outperformance of the proposed system in terms of compression gain and computational efficiency while assuring an analogous SoC estimation precision.

The thermal dynamics of Li-ion batteries are very complicated, and the battery temperature is spatially distributed imbalanced from the battery interior to the surface. The thermal dynamics are commonly modeled by partial differential equations (PDEs); however, parameter identification for PDEs is difficult and time consuming. In this work, a Karhunen-Loeve Galerkin method is proposed to obtain a simple but effective low-order model for the distributed thermal dynamics of Li-ion batteries. The Karhunen-Loeve decomposition method is applied to capture the most representative spatial modes of the dynamics, while the Galerkin method is used to obtain the corresponding temporal modes. All the uncertain physical parameters in the temporal modes are identified by the Levenberg-Marquardt algorithm. The updated temporal modes synthesized with spatial modes can offer a fast estimation of the temperature distribution on the battery surface and thus has the potential to provide distributed temperature prediction for the battery management system. The proposed modeling scheme is tested on a 60Ah Li-ion battery cell, and the simulation result shows an excellent match in the temperature distribution and a faster computing speed than the rigorous physical model.

Load currents of batteries undergo real-time change depending on the operations of battery-powered devices, such as acceleration, deceleration, and sudden braking of electric vehicles. With the variation in current magnitude, parameters in an equivalent circuit model of the battery change depending on its chemical characteristics. Therefore, accurately identifying model parameters offline for various sizes and patterns of load currents with few experiments is difficult. Besides, there is a model uncertainty due to the difference between the linearity of the model structure and the nonlinearity of the real battery; hence, an experimentally obtained open-circuit voltage (OCV)-state-of-charge (SOC) curve has difficulty in providing an accurate OCV value for the model. Consequently, these difficulties lead to inaccuracies in the model and SOC estimation. In this article, novel methods are proposed to reduce these inaccuracies under time-varying current conditions, where the magnitude of the load current changes in real time. These are (1) an OCV-SOC curve estimation for reducing the model uncertainty and (2) a current-adaptive extended Kalman filter algorithm for decreasing the effect of inaccurately identified model parameters. The experimental results show that the maximum-absolute-errors of the proposed SOC estimation were reduced to

Remaining useful life (RUL) prediction of lithium-ion batteries can reduce the risk of battery failure by predicting the end of life. In this paper, we propose novel RUL prediction techniques based on long short-term memory (LSTM). To estimate RUL even in the presence of capacity regeneration phenomenon, we consider multiple measurable data from battery management system such as voltage, current and temperature charging profiles whose patterns vary as aging. Unlike the traditional LSTM prediction that matches input layer with output layer as one-to-one structure, we leverage many-to-one structure to be flexible for various input types and to substantially reduce the number of parameters for better generalization. Using the NASA lithium-ion battery datasets, we verify the accuracy of the proposed LSTM-based RUL prediction. The experimental results show that the proposed single-channel LSTM model improves the mean absolute percentage error (MAPE) by 39.2% compared to the baseline LSTM model. Furthermore, the proposed multi-channel LSTM model significantly improves the MAPE, e.g., by 63.7% compared to the baseline; the proposed model achieves 0.47–1.88% of MAPE while the state-of-the-art baseline LSTM shows 0.6–6.45% of MAPE.

Crucial factors with respect to modern autonomous vehicles include reliability and design. Researchers and engineers strive to increase the number of vehicles over the latest possibilities both in industrial and in military applications. A number of modern batteries are available on the market for the electric autonomous vehicles. The authors suggest the use of test chambers to investigate optimal battery use and performance in vehicles. The results of the theoretical research suggest that the use of test chambers during battery management system development is necessary.

Due to the increasing demand of electrical vehicles (EVs), prognostics of the battery state is of paramount importance. The nonlinearity of the signal (e.g. voltage) results in the complexity of analyzing the degradation of the battery. Aging characteristics extracted from the voltage, current, and temperature when the battery is fully charged/discharged were commonly used by previous researchers to determine the battery state. The drawbacks of the previous prediction algorithms are insufficient or irrelevant features to explicitly model the battery aging and the use of fully charged/discharged datasets, which might result in poor prediction accuracy. Therefore, this study proposes a feature selection technique to adequately select optimum statistical feature subset and the use of partial charge/discharge data to determine the battery remaining useful life (RUL) using Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM). The proposed approach demonstrated exceptional RUL prediction results, with the root mean square error (RMSE) of 0.00286 and mean average error (MAE) of 0.00222 using partial discharge data. The proposed method shows prediction improvement in comparison with the use of full data and state-of-the-art outcomes from previous studies of the same open data from the National Aeronautics and Space Administration (NASA) prognostic battery data sets.

State of charge (SOC) estimations are an important part of lithium-ion battery management systems. Aiming at existing SOC estimation algorithms based on neural networks, the voltage increment is proposed in this paper as a new input feature for estimation of the SOC of lithium-ion batteries. In this method, the port voltage, current and voltage increment are taken as inputs and the current SOC is used as output to train a neural network. Different from the adaptive filtering algorithm, which requires complex equivalent circuit parameter identification, this algorithm uses the voltage increment instead of the open circuit voltage (OCV); hence, the complexity of the SOC estimation algorithm is reduced, and the problem of inaccurate estimation caused by neural network algorithms without considering the internal structure of the battery is avoided. The experimental results show that compared with the traditional neural network algorithm, the neural network SOC estimation algorithm based on the voltage increment could improve the accuracy of SOC estimation.

The battery in the electric vehicles provides the electrical energy necessary to power all electrical and electronic components and main-drive electric motor. So, an accurate estimation of discharge capacity to predict the battery’s end of life is of paramount importance and critical for safe and efficient energy utilization, especially for battery management systems. The resistor–capacitor (RC) equivalent circuit model is commonly used in the literature to model battery. However, a battery is a chemical energy storage system, and then the RC model will therefore be extremely sensitive to the presence of vagueness of information due to that some parameters cannot be directly accessed using sensors. In this paper, we propose a new design methodology for estimating simultaneously the model and the discharge capacity of a Nickel–Metal Hydride (Ni–MH) battery. A modified fuzzy c-regression model algorithm is used to construct a prediction model for a small Ni–MH battery pack. Then, the model, so developed, is used to estimate the discharge capacity of the battery and to predict its remaining useful life. The validity of the proposed method is experimentally verified. According to experimental results, the proposed method can achieve satisfactory results with no more than a 2% error rate for the training and test data sets.

The state of charge (SOC) of lithium-ion batteries (LIBs) is an important evaluation index in battery management system of energy storage systems. However, it is always a challenging task to accurately estimate SOC of LIBs, because of the existence of nonlinear characteristics and significant temperature effects. To improve the SOC estimation accuracy and robustness, a model-based estimation approach for SOC and impedance of LIBs is proposed against uncertain loading profiles and ambient temperatures. First an auto regressive exogenous model for online model order determining and parameters identification is established to monitor parameters variations based on numerical subspace state space system identification method. Second, a sequential Monte Carlo filter is employed to overcome the nonlinear and non-Gaussian error distribution state estimation problems caused by complicated open-circuit voltage characteristics. Finally, evaluation of the adaptability and generality of the proposed method are verified by different LIBs under different operating conditions. Experimental results indicate that the proposed method shows great performance, whose estimation value converges to real SOC within an error of $\pm \text{3}\%$ , and the battery model can simulate battery dynamics robustly with high accuracy.

An accurate prediction of the state of charge (SOC) of lithium-ion battery provides crucial information for the Battery Management System (BMS). In this paper, a simplified uneven electrochemical m...

A Battery Management System (BMS) can prolong the life of the battery but it depends on the accuracy of the adopted scheme. Different techniques have been developed to enhance the BMS by monitoring the State of Health (SOH) of the battery. In this paper, the detection of battery voltage is analyzed by using the cycle counting method, which is a conventional technique and compared with Artificial Neural Network (ANN), a heuristic method. The advantage of the proposed ANN method is that SOH can be monitored without disconnecting the battery from the load. Also, the sampling data to the ANN are derived from various techniques including Open Circuit Voltage (OCV) method, Ambient temperature measurement, and valley point detection. A feed-forward backpropagation algorithm is used to achieve the purpose of real-time monitoring of the LAB. The results show that the precise estimation of SOH can be obtained by Feed-Forward Neural Network (FFNN) when trained with more sampling data.

In this work the authors investigate the different parts and functions offered by Battery Management Systems (BMS) specifically designed for secondary/rechargeable lithium batteries. Compared to other chemistries, lithium batteries offer high energy density and cell voltage, which makes them the most attractive choice for electronic devices including EV and RES. However, lithium technology is vulnerable and highly susceptible to catastrophic failures which result in fire. Hence, the use of electronic safety designs is a must. BMS are responsible for the monitoring of the battery state, ensuring operation within safe limits. BMS offer multiple functionalities with the state of charge (SoC) estimation being the most challenging hence the most studied by engineers. All estimation methodologies and algorithms have pros and cons, which best suits the application that is developed for. This research concludes that according to designers, the optimum BMS provides battery packs with the needed protection, good functioning conditions and accurate prediction for the battery's state including charge and life. Finally, this research presents and validates an SoC algorithm based on the reformulated Peukert's equation which is also valid for variable load and multi-pulsing scenarios with an accuracy exceeding 95%.

To fulfill a reliable battery management system, a precise state of charge (SOC) estimation method for a battery energy storage system should be developed. This study makes two contributions to the battery management system. First, a combined electro-thermal battery model is proposed. To identify the electrical and thermal battery parameters, constant current -constant voltage (CC-CV) charge, constant current (CC) discharge, and pulse discharge tests should be performed on the lithium-ion battery cells and each of the above experiments, battery SOC level should be estimated precisely. The second study of this research is the development of the SOC level estimation method by using time series forecasting algorithms. In this study, six kinds of models are used in realtime, and each of the models is evaluated with the performance indices and the computational time, and finally, forecast diagrams are graphically represented for each of the experiments.

The key for decentralized battery systems is a robust and communication-less control strategy for autonomous power sharing of parallel-connected DC-DC converters. Battery systems improve the reliability and quality of power supply in renewable energy systems and enable power supply for off-grid, mobile applications, including islanded grids, home storage, and electric vehicles. In many cases, components with different elec trical properties require different voltage levels. An adaptation is consequently essential and is normally implemented in DC grids for the batteries via bidirectional DC-DC converters. The power flow in both directions can thus be ensured. To achieve a power distribution in parallel connected DC-DC converters, a droop control in the form of a virtual internal resistor can be used. This paper presents a novel approach of a DC DC converter with a digitally parameterizable droop resistor, whose voltage regulation is based on an analog operational amplifier circuit to ensure low delays and robustness. The droop resistor is adjusted with a microcontroller, which offers the possibility to apply a higher-level control for load sharing via an interface. Mathematical correlations are used to clearly define the parameters of the control. Furthermore, the circuit was completely simulated and tested in the hardware setup. The shown results verify the functionality and indicate only minor deviations. Therefore, this circuit is important for future use in distributed battery systems.

The state-of-charge (SOC), state-of-health (SOH) estimation and prediction of lithium-ion batteries remaining useful life (RUL) are critical for the safety and reliability of battery management systems (BMS) in electric vehicles (EVs). In this paper, two different methods are used to estimate how much memory is needed to evaluate the SOC and their performances regarding tracking accuracy, memory volume and computational complexity. The SOC and SOH cannot be directly measured and estimation is influenced by many factors, such us battery aging, ambient temperature and the current rate. The complex interrelationship of these factors causes the difficulties in the pursuit of a precise SOC estimation method. The important issue of this study is to obtain the SOC estimation for a specific Li-ion battery based on the behaviour of the battery during operation. The analyzed Li-ion battery is part of a micro-grid (MG) belonging to the Advanced Electrical Systems Research Centre within the Research and Development Institute of Transilvania University of Brasov, which provides around 20 kWh storage capacity. Through simulations, the analyzed SOC estimation method is verified to demonstrate the computational complexity and accuracy.