State estimation for advanced battery management: Key challenges and future trends

Hu, Xiaosong; Feng, Fei; Liu, Kailong; Zhang, Lei; Xie, Jiale; Liu, Bo

doi:10.1016/j.rser.2019.109334

Cited by 465 publications

(143 citation statements)

References 95 publications

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“…In this study, The SRCKF algorithm is adopted to estimate the SOC, of which the general process is summarized in Table 2, where n is the state dimension, and m denotes the total number Energies 2020, 13, 1410 9 of 15 of volume points, which number is twice those of the state dimension. The sample [1] indicates a complete set of fully symmetric points, of which the set of points is obtained through the complete permutation of elements of the n-dimensional unit vector e = [1, 0 · · · 0] T and the alteration of the element symbol. [1] g represents that the point is centered at the gth point of [1].x k andẑ k are the predicted state and measurement, respectively.…”

Section: The Soc Estimation Algorithmmentioning

confidence: 99%

“…The sample [1] indicates a complete set of fully symmetric points, of which the set of points is obtained through the complete permutation of elements of the n-dimensional unit vector e = [1, 0 · · · 0] T and the alteration of the element symbol. [1] g represents that the point is centered at the gth point of [1].x k andẑ k are the predicted state and measurement, respectively. S Q,k−1 and S R,k denote the square-roots of the process noise covariance matrix Q k−1 and the measurement noise covariance matrix R k , respectively.…”

Section: The Soc Estimation Algorithmmentioning

confidence: 99%

“…Nowadays, energy crises and environmental damage have become the main concerns of society, and require being tackled with high attention [1]. Transportation electrification provides a possible manner to reduce emissions and dependence upon fossil fuels.…”

Section: Introductionmentioning

confidence: 99%

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Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

et al. 2020

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In this paper, a co-estimation scheme of the state of charge (SOC) and available capacity is proposed for lithium-ion batteries based on the adaptive model-based algorithm. A three-dimensional response surface (TDRS) in terms of the open circuit voltage, the SOC and the available capacity in the scope of whole lifespan, is constructed to describe the capacity attenuation, and the battery available capacity is identified by a genetic algorithm (GA), together with the parameters related to SOC. The square root cubature Kalman filter (SRCKF) is employed to estimate the SOC with the consideration of capacity degradation. The experimental results demonstrate the effectiveness and feasibility of the co-estimation scheme.Energies 2020, 13, 1410 2 of 15 initial values, and the latter is not suitable for online estimation, as it usually costs long shelving time to acquire the OCV value. With the development of computation technologies and machine learning, a variety of artificial intelligence-based, data-driven methods, such as neural networks [6] and support vector machines [7], are proposed for SOC estimation by establishing black-box models. Data-driven methods feature a strong nonlinear mapping capability with high accuracy; however, these approaches show high complexity, and require a considerable amount of training data. Alternatively, model-based methods have been widely investigated and applied for SOC estimation, thanks to the capabilities of online application, high precision and the independence of initial values. Conventional modeling manners mainly include electrochemical models and the equivalent circuit models (ECMs). Compared with complicated electrochemical models, ECM is commonly used to describe the electrical behavior of batteries, and subsequently to estimate the SOC due to its simplification and preferable precision. Yanwen Li et al. proposed a multi-model probability fusion algorithm to describe the battery's electrical characteristics, and subsequently estimate the SOC [8]. In model-based methods, the combination of the battery model and the intelligent filtering algorithm is a hotspot in SOC estimation research. The frequently used filtering algorithms include Kalman filtering (KF) [9], the H-infinity filter (HIF) [10], particle filter (PF) [11], and their various extensions. In particular, the extended KF (EKF) is widely employed to execute SOC estimation using a first-order Taylor expansion on the basis of the battery's nonlinear model [12]. Nonetheless, the second and higher order expansion is usually neglected, thus leading to slow a convergence rate, and even divergence. The unscented KF (UKF) is exploited to estimate battery SOC, based on the recursive unscented transformation to approximate the nonlinear observation without Taylor polynomial expansions [13]. The UKF shows better estimation precision and robustness than the EKF in strong, nonlinear systems [14]. On the basis of the radial-spherical cubature criterion, the cubature Kalman filter (CKF) leverages a set of volume points to appro...

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Section: The Soc Estimation Algorithmmentioning

confidence: 99%

Section: The Soc Estimation Algorithmmentioning

confidence: 99%

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Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

et al. 2020

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“…Sensor faults in LIBS mainly include the voltage sensor fault, current sensor fault, and temperature sensor fault. The current sensor fault affects the accuracy of state of charge (SOC) estimation [14] and multi-state estimation [15], [16]. The estimated SOC and temperature measurements are used to update the battery model parameters in real-time for high-accuracy prediction [17], [18].…”

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confidence: 99%

Advanced Fault Diagnosis for Lithium-Ion Battery Systems

Hu¹,

Zhang²,

Liu³

et al. 2020

Preprint

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Lithium-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles and smart grids. However, various faults in a lithium-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This paper provides a comprehensive review of fault mechanisms, fault features, and fault diagnosis of various faults in LIBS, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed. IntroductionAs one of the most promising energy storage systems, lithium-ion batteries have been widely used in various applications, such as electric vehicles (EVs) and smart grids. Currently, lithium-ion batteries have become the mainstream energy storage solution, owing to their inherent benefits such as high energy density, high power density, and long lifespan. However, the potential r isks, due to the abusive operating conditions and harsh environment, pose a huge challenge to the safety of Li-ion battery systems (LIBSs). A real-time effective battery management system (BMS) is critical to ensure the safety of LIBSs. BMS has several functionalities, such as state of charge monitoring, thermal management, charging management, and equalization management. It also tracks the health status and monitors the potential faults of LIBS. Without suitable diagnostics and fault handling, a minor fault could eventually lead to severe damages of LIBS [1]. The importance of the fault diagnostics and fault handling has been demonstrated repeatedly in several severe incidents recently [2]- [4].There are different fault modes in the LIBS, and fault mechanisms are usually very complex. From a control perspective, these fault modes can be divided into battery fault, sensor fault, and actuator fault. The battery faults, which include overcharge , overdischarge, overheat, external short circuit (ESC), internal short circuit (ISC), electrolyte le akage, battery swelling, battery accelerated degradation, and thermal runaway (TR), are the most critical faults in the LIBS. These faults are also intertwine d. Overcharge and overdischarge could lead to various undesirable battery side reactions, resultin g in accelerated degradation. These side reactions and gases generated by the chain reactions during TR may eventually cause the battery swelling. Such a swellin g along with mechanical damage may, in turn, lead to electrolyte leakage. The ISC is typically caused by separator failure due to manufacturing defects, overheat, mechanical collisions, or penetration by metal dendrites or mechanical punctures. Fortunatel y, the Joule heat generated by ISC develops into TR only when the equivalent ISC resistance reac hes a very low level [5]. Abnormal heat generation occurs under various conditions, such as side reactions during overcharge/over -di...

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“…Dubarry et al 27,28 have conducted both statistical and electrochemical analyses to characterize cell-to-cell variations, and found that the cell capacity, resistance and rate capability can be regarded as three independent parameters for cell variations evaluation. Furthermore, as cell parameters show different evolution trends during aging, online algorithms are required to evaluate cell-to-cell variation during aging process 15 . Kum developed an overall cell selection framework with significant improvement in fabricating a module/ pack with high homogeneity using second-life cells 21 .…”

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confidence: 99%

A Facile Approach to High Precision Detection of Cell-to-Cell Variation for Li-ion Batteries

Xie

Ren

Wang

et al. 2020

Sci Rep

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over the past decade, it has been repeatedly demonstrated that homogeneity in electrochemical performance of lithium-ion cells plays a major role in determining the life and safety of lithium-ion battery modules or packs. Generally, the homogeneity of a battery pack is evaluated by characterizing the cells individually in terms of capacity, mass, impedance. particularly, high quality electrochemical data heavily relies on the availability of high precision current source to minimize the discrepancy induced by the channel-to-channel variation. Here, a facile and precise measurement method is reported for screening cell-to-cell variations, in which voltage is the only indicator parameter independent of high precision current source. in detail, by connecting the cells in series (ciS), the measurement error of electrochemical data caused by stability and discrepancy of current sources among different charge/discharge equipment can be effectively avoided. The findings of this work showed that the cell-to-cell variations can be simply and sensitively detected with CiS configuration. for example, the relative standard deviation, which is the evaluation criterion of battery homogeneity, was 2.14% based on CiS while it was 0.43% based on individual measurements. The simple and precise ciS measurement is promising for evaluation of cell quality or module integration quality. in addition, this work can also provide a solid foundation for the development of detection algorithms for battery management systems to rapidly monitor battery homogeneity.Lithium ion batteries (LIBs) have to be integrated into modules and packs for large-scale applications such as electric vehicles (EVs) and stationary energy storage systems 1-7 . However, a reliable and long-lasting power system is determined mostly by the cell homogeneity rather than the performance of individual cells 8,9 . The cell-to-cell variations in terms of capacity and impedance will subject individual cells to different levels of state of charge (SOC), current, temperature and aging, which in turn would accelerate the degradation of electrochemical performance or even lead to safety performance of the power system 2,10-13 . For instance, a weak cell with a smaller capacity than the average in a series-connected module will be repeatedly overcharged (over-discharged) during charging (discharging) process if the charge/discharge cutoff condition is determined according to the total voltage 14 . The weaker cell will then have faster decay than others and will be susceptible to failure under harsh conditions. The available energy of the module will also be limited by the weakest cell 15 . Therefore, there is an urgent need to understand and minimize the cell-to-cell variations in a battery module.There are different factors leading to variations among cells 11 , including manufacturing factors (manufacturing tolerances, quality control and process design), and environmental factors such as temperature gradient within the battery pack 16 . Rumpf et al. 12 has evaluated the cel...

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State estimation for advanced battery management: Key challenges and future trends

Cited by 465 publications

References 95 publications

Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

Advanced Fault Diagnosis for Lithium-Ion Battery Systems

A Facile Approach to High Precision Detection of Cell-to-Cell Variation for Li-ion Batteries

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