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State of charge estimation for lithium-ion pouch batteries based on stress measurement

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Abstract

State of charge (SOC) estimation is one of the important tasks of battery management system (BMS). Being different from other researches, a novel method of SOC estimation for pouch lithium-ion battery cells based on stress measurement is proposed. With a comprehensive experimental study, we find that, the stress of the battery during charge/discharge is composed of the static stress and the dynamic stress. The static stress, which is the measured stress in equilibrium state, corresponds to SOC, this phenomenon facilitates the design of our stress-based SOC estimation. The dynamic stress, on the other hand, is influenced by multiple factors including charge accumulation or depletion, current and historical operation, thus a multiple regression model of the dynamic stress is established. Based on the relationship between static stress and SOC, as well as the dynamic stress modeling, the SOC estimation method is founded. Experimental results show that the stress-based method performs well with a good accuracy, and this method offers a novel perspective for SOC estimation.

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... In this regard, Popp et al. [17] proposed an interesting review of the mechanical methods for state determination of LIB. In literature, mechanical measurements have been carried out with optical displacement sensors [18][19][20], strain gauges [21,21,22], internal pressure sensors [23], pressure sensors [24][25][26][27][28][29][30], dial indicator [31][32][33][34][35][36][37], load cells [38][39][40][41][42], fiber optics [43][44][45][46] and eddy current sensors [47][48][49]. ...
... Figueroa et al. [40] compared the SOC estimated with the Kalman filter based on voltage and swelling or voltage alone, showing that the inclusion of voltage improved the estimation, especially in those cases, such as lithium iron phosphate (LFP), where voltage is flat with respect to SOC. Dai et al. [42] found a mathematical correlation between stress exerted by pouch battery surface over a constraint and SOC, which is at the basis of their estimation algorithm. Finally, Rente et al. [46] estimated SOC with a dynamic time-warping technique, based on strain and temperature measurements. ...
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Lithium-ion batteries undergo structural deformation during operation because of the electrochemical-induced strain caused by the insertion of lithium ions inside the active material of the electrodes. In this work, the mechanical characteristic, i.e. thickness change, of batteries with different chemistries (lithium iron phosphate and lithium cobalt oxide) and formats (prismatic and pouch) is measured in-operando and with different current rates. A dedicated test bench is built to carry out the measurements with contactless triangulation optical sensors, sensing the displacement of the battery surfaces. The results are critically discussed, justifying the trajectory of the thickness change observed experimentally with the mechanical properties of the electrode's active materials. Furthermore, the results obtained with the batteries of different chemistries are compared, discussing the reasons underlying these differences. Statistical considerations on the results, such as repeatability errors and cell-to-cell differences, are provided as well. The mechanical characteristic appears as a three-stage linear curve, with two deflection points between the linear sections, and proportional to the state of charge of the battery. In fact, the mechanical characteristic tells the amount of lithium ions stored in each electrode, which together with the stoichiometric range defining the electrodes balancing, is closely related to the state of charge. Then, these measurements reveal an interesting tool for evaluating the state of charge. The two deflection points of the mechanical characteristic are linked to graphite stage changes. Then, these measurements carry the same information about battery degradation as the peaks in differential voltage analysis. The advantage is that the deflection points remain visible at any current rate, but the peaks in the differential voltage analysis vanish at high current rates. Then, these preliminary studies of the mechanical characteristic evidence it can become a promising alternative tool for evaluating the state of health as well.
... (SoC) [8,[10][11][12][13]. To compensate for the impact of SoC, continuous pressure measurements or the incorporation of additional SoC information are essential. ...
... As either graphite particles or cathode active material would expand/shrink during lithium intercalation/deintercalation [29,[34][35][36] and the relative amount of intercalated lithium affects their elastic properties [34,37], the loss of active lithium causes variations in particle volumes and elastic properties. Secondly, due to the expansion/shrinkage of active particles during lithium intercalation/deintercalation, the binder that connects the particles suffers cyclic loading [11,36]. Demirocak et al. [38] conducted nanoindentation on the cathodes of LFP prismatic cells and found that the cathode solid frame's modulus degradation due to cycling can be up to 76.1% under 90% capacity retention and 2 C-rate. ...
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... (SoC) [8,[10][11][12][13]. To compensate for the impact of SoC, continuous pressure measurements or the incorporation of additional SoC information are essential. ...
... As either graphite particles or cathode active material would expand/shrink during lithium intercalation/deintercalation [29,[34][35][36] and the relative amount of intercalated lithium affects their elastic properties [34,37], the loss of active lithium causes variations in particle volumes and elastic properties. Secondly, due to the expansion/shrinkage of active particles during lithium intercalation/deintercalation, the binder that connects the particles suffers cyclic loading [11,36]. Demirocak et al. [38] conducted nanoindentation on the cathodes of LFP prismatic cells and found that the cathode solid frame's modulus degradation due to cycling can be up to 76.1% under 90% capacity retention and 2 C-rate. ...
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Electric vehicles (EVs) have become a popular mode of transportation, with their performance depending on the ageing of the Li-ion batteries used to power them. However, it can be challenging and time-consuming to determine the capacity retention of a battery in service. A rapid and reliable testing method for state of health (SoH) determination is desired. Ultra-sonic testing techniques are promising due to their efficient, portable, and non-destructive features. In this study, we demonstrate that ultrasonic speed decreases with the degradation of the capacity of an LFP prismatic cell. We explain this correlation through numerical simulation, which describes wave propagation in porous media. We propose that the reduction of binder stiffness can be a primary cause of the change in ultrasonic speed during battery ageing. This work brings new insights into ultrasonic SoH estimation techniques .
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Rechargeable batteries have recently experienced increases in productivity and the economy, solidifying their dominance in energy-intensive cultures. Regular performance monitoring is required to lessen the adverse environmental effects of batteries in the face of increased demand. The distinctive features of lithium-ion batteries (LIBs) make them an ideal choice for energy storage. Battery management systems (BMSs) are needed to make sure that LIB systems are safe and operate effectively. Critical problems in the existing structure and operation of BMSs are their limited data storage capacity and weak computational power. This paper studies the idea and architecture of cloud-based smart BMSs and offers some viewpoints on their performance, usability, and advantages for upcoming battery applications. While some of the benefits of sensors have been recognized for more than a hundred years, the combination of diverse sensing technologies with novel battery platforms has the potential to revolutionize the sector by changing how both new and old lithium-ion devices are used. This paper also highlights current advances and their associated benefits focusing on electrochemical, mechanical, acoustic, and optical sensors that can potentially boost battery sustainability and longevity.
... In [36] the stresses due to thermal and chemical expansion are examined at the level of a battery, specifically a spirally-wound cylindrical roll. Finally, another fertile area is mechanics at the level of entire packs where a common aim is to make use of stress/strain measurements that can be taken in-situ to estimate states of charge or health [37,38]. Whilst all these investigations are highly valuable, a complete theory of LIB mechanics can only be achieved if phenomena occurring at these vastly different lengthscales can be reconciled. ...
Preprint
Full-text available
This paper explores the mechanical behaviour of the composite materials used in modern lithium-ion battery electrodes. These contain relatively high modulus active particle inclusions within a two-component matrix of liquid electrolyte which penetrates the pore space within a viscoelastic polymer binder. Deformations are driven by a combination of (i) swelling/contraction of the electrode particles in response to lithium insertion/extraction, (ii) swelling of the binder as it absorbs electrolyte, (iii) external loading and (iv) flow of the electrolyte within the pores. We derive the macroscale response of the composite using systematic multiple scales homomgenisation by exploiting the disparity in lengthscales associated with the size of an electrode particle and the electrode as a whole. The resulting effective model accurately replicates the behaviour of the original model (as is demonstrated by a series of relevant case studies) but, crucially, is markedly {simpler and hence} cheaper to solve. This is significant practical value because it facilitates low-cost, realistic computations of the mechanical states of battery electrodes, thereby allowing model-assisted development of battery designs that are better able to withstand the mechanical abuse encountered in practice and ultimately paving the way for longer-lasting batteries.
... There are a few studies that have focused on short-term observations of pressure increase or the mechanical response and relaxation during a single cycle [26,[33][34][35]. The studies exploring the pressure evolution in batteries under cycle aging are relatively rare [30,32,[36][37][38][39][40][41]. The investigations are often focused on trying to correlate the state of health (SOH) and state of charge (SOC) with the external mechanical pressure of the battery. ...
Article
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Understanding the behavior of pressure increases in lithium-ion (Li-ion) cells is essential for prolonging the lifespan of Li-ion battery cells and minimizing the safety risks associated with cell aging. This work investigates the effects of C-rates and temperature on pressure behavior in commercial lithium cobalt oxide (LCO)/graphite pouch cells. The battery is volumetrically constrained, and the mechanical pressure response is measured using a force gauge as the battery is cycled. The effect of the C-rate (1C, 2C, and 3C) and ambient temperature (10 °C, 25 °C, and 40 °C) on the increase in battery pressure is investigated. By analyzing the change in the minimum, maximum, and pressure difference per cycle, we identify and discuss the effects of different factors (i.e., SEI layer damage, electrolyte decomposition, lithium plating) on the pressure behavior. Operating at high C-rates or low temperatures rapidly increases the residual pressure as the battery is cycled. The results suggest that lithium plating is predominantly responsible for battery expansion and pressure increase during the cycle aging of Li-ion cells rather than electrolyte decomposition. Electrochemical impedance spectroscopy (EIS) measurements can support our conclusions. Postmortem analysis of the aged cells was performed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) to confirm the occurrence of lithium plating and film growth on the anodes of the aged cells. This study demonstrates that pressure measurements can provide insights into the aging mechanisms of Li-ion batteries and can be used as a reliable predictor of battery degradation.
... They proposed a warning method based on multiple gases, demonstrating its rapid and reliable warning capability through validation. Regarding pressure detection, Dai et al. 24 conducted constant current charging experiments on lithium batteries at different charging rates, finding that stress on the battery surface under intermittent charging was lower than that under continuous charging. Jia et al. 25 experimentally investigated battery expansion behavior during TR of LiFePO4 batteries under four preload forces, establishing a hazard assessment model for TR under preload force and analyzing the relationship between preload force and TR risk. ...
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Overcharging of lithium-ion batteries may lead to severe thermal runaway (TR) incidents, resulting in significant economic losses and safety hazards. Therefore, it is crucial to research early warning methods for TR behavior in overcharged lithium batteries. This study initially conducted overcharging experiments on LiFePO4 battery packs under different initial charging states and charging rates, analyzing variations in temperature, voltage, and inter-group pressure during overcharging. The TR process was divided into three stages: non-overcharged, early, and middle. Based on this, temperature change rate, pressure change rate, and voltage were extracted as input feature parameters, and the Mean Shift algorithm was employed for stage identification and classification of overcharging experiments on LiFePO4 battery packs. According to experimental results, the algorithm achieved an accuracy of over 96% in stage identification and classification of TR in overcharged lithium batteries, accurately determining the current stage of TR and providing a reliable and effective solution for preventing TR in overcharged lithium batteries.
... At present, the research on electrochemical and thermal models of lithium-ion batteries focuses on simplifying electrochemical models, including constructing reduced-order models to reduce computational costs while ensuring model accuracy [11][12][13][14] and analyzing the applicability of different types of electrochemical models [15,16]. On the other hand, we focus on the specific application of electrochemical thermal coupling models in lithium-ion batteries, including the research and design of battery thermal management systems based on electrochemical thermal coupling models [17][18][19], the analysis and estimation of the internal state of charge of batteries [20][21][22], and the analysis of battery aging and capacity degradation behaviors including the side reactions of the SEI film inside lithium-ion batteries [23,24]. In addition, electrochemical thermal coupling models are often used to analyze and study the electrochemical and thermal characteristics of lithium-ion batteries. ...
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Accurate battery models are of great significance for the optimization design and management of lithium-ion batteries. This study uses a pseudo-two-dimensional electrochemical model combined with a three-dimensional thermal model to describe the electrodynamics and thermodynamics of commercial LIBs and adopts the concept of variable solid-state diffusion in the electrochemical model to improve the fitting ability of the model. Compared with the discharge curve without the VSSD concept, the progressiveness of the model is verified. On the other hand, by comparing the temperature distribution of batteries with different negative electrode thicknesses, it is found that the battery temperature decreases with the increase in battery thickness. At the same time, with the increase in active material volume fraction, the gradient of electrochemical performance is greater, and the heat generation rate is higher. This model can be used for online management of batteries, such as estimating charging status and internal temperature, and further constructing a lithium battery electrochemical capacity degradation model based on the VSSD concept to study the aging behavior of lithium batteries.
... [21] Considering the good correspondence between the mechanical stress and the SOC, some attempts have been made in the SOC estimation. [22][23][24] Nevertheless, the application of stress measurements in the SOH estimation has not been well developed. Based on the incremental capacity analysis (ICA) method, Samad et al. used mechanical force instead of voltage to derive the IC curves. ...
Article
Full-text available
As one of the key parameters to characterize the life of lithium‐ion batteries, the state of health (SOH) is of great importance in ensuring the reliability and safety of the battery system. Considering the complexity of practical application scenarios, a novel method based on mechanical‐electrical feature extraction and machine learning is proposed to accurately estimate the SOH. A series of degradation experiments are designed to generate battery aging datasets, including the stress and voltage changes. Health features are directly extracted from the stress‐voltage profile and the mechanical‐electrical health feature factors are obtained through correlation analysis. The long short‐term memory (LSTM) network is introduced to map the relationship between mechanical‐electrical responses and the SOH, where the health feature factors are selected as input vectors. The effectiveness of the proposed method is demonstrated by battery datasets under different conditions, from which the estimated errors are less than 1.5 %. This work demonstrates that the analysis and utilization of mechanical‐electrical parameters can not only realize accurate SOH estimation, but also provide a broader field for battery energy management.
... et al.[33] designed a stress evolution experiment platform under constant displacement conditions for LIBs and proposed a SOC estimator based on stress characteristics. The results demonstrated that the proposed SOC estimator exhibits high response speed and estimation accuracy.Digital twinning and cloud platform technologies are pivotal for the ...
... The volume change of the electrodes have a measurable impact on the volume change of the entire battery: the battery generally swells during charge and shrinks during discharge, as the volume change of the anode (graphite in commercial batteries) is greater than the cathode. The battery volume change is detected with different methodologies and sensors: displacement with laser triangulation sensors (Davide Clerici, Mocera and Somà, 2022) and touch probes (Rieger et al., 2016;Mohtat et al., 2021), pressure with load cells (Mohan et al., 2014;Dai et al., 2017;Figueroa-Santos, Siegel and Stefanopoulou, 2020;Perez Estevez et al., 2023), strain with fibre optics (Rente et al., 2021;Peng et al., 2022) and strain gauges (Willenberg et al., 2020). Figure 1. ...
... However, embedding sensors inside the battery requires complex processes and incurs higher costs. One-dimensional expansion measurement devices and fiber-optic sensors have been used to measure the expansion and strain signals on the surface of pouch-type lithium-ion [6,7]. Relevant studies have shown that the deformation signals on the battery surface can still reflect the internal deformation information. ...
Article
Full-text available
In order to ensure the safe operation of lithium-ion batteries, real-time monitoring of battery status is necessary. The surface strain signal of lithium-ion batteries has the potential to evaluate the battery’s state, but it is significantly affected by temperature. Generally, measuring the battery surface temperature and the thermal expansion coefficient can be performed to quantify and eliminate the influence of temperature on strain, but this increases the cost and complexity of strain measurement. This article proposes a method that eliminates the need to measure the battery temperature and material parameters. By simultaneously measuring the circumferential and axial strains on the battery surface and calculating their difference, the influence of temperature on strain can be minimized. Furthermore, the effectiveness of the proposed method is experimentally tested. Results demonstrate that after applying temperature compensation to commercial lithium-ion batteries, the influence of temperature on strain can be reduced from 16.4 ppm/°C to 1.7 ppm/°C. The strain no longer exhibits sensitivity to current, making it more suitable for evaluating the state of lithium-ion batteries.
... Single point displacement sensor [11][12][13], strain sensor [14,15], digital image correlation (DIC) [16][17][18] and multidimensional laser scanning [19] were the commonly used methods for the displacement/deformation detection. On the other hand, the force was measured under constrained conditions [20,21]. The results show that the swelling behavior has a strong relationship with the electrochemical loadings and states, i.e., charge rate, state of charge (SOC), and state of health (SOH) [20,22], while the electrochemical performance is also related to the swelling behavior of LIB [23][24][25]. ...
Article
The swelling of lithium-ion batteries (LIBs) is one of the responsible reasons to cause capacity degradation and safety problems. Quantification of the swelling force and the corresponding strain is a critical problem in exploring the complex electro-mechanical behaviors in batteries. Though in the current open literature, a few models are available to describe the swelling of the component materials in a battery. A physics-based detailed model linking the component material scale to the cell scale is still lacking. Herein, we develop a fully detailed three-dimensional swelling mechanical model considering the actual structure of the battery. After rigorous validation by the experiment, we leverage the model to investigate a complicated coupled boundary, i.e., plate-constrained swelling. The swelling force and uneven stress distribution of each component are analyzed among various influential factors. The results provide fundamental insights into the application of LIB.
... Reference [50] found that the thickness of a pouch cell changed by as much as 4% of the total cell thickness during charging, while a fully charged prismatic cell with a hard shell also resulted in a 1.5% increase in cell thickness [51]. The reversible strain change in the LIB is directly related to the SOC, which makes it possible to estimate the SOC of the LIB by mechanical measurement [52,53]. A relatively common and simple method is to measure the strain by detecting the total volume change of the battery with a strain gauge [54]. ...
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New energy storage devices such as batteries and supercapacitors are widely used in various fields because of their irreplaceable excellent characteristics. Because there are relatively few monitoring parameters and limited understanding of their operation, they present problems in accurately predicting their state and controlling operation, such as state of charge, state of health, and early failure indicators. Poor monitoring can seriously affect the performance of energy storage devices. Therefore, to maximize the effciency of new energy storage devices without damaging the equipment, it is important to make full use of sensing systems to accurately monitor important parameters such as voltage, current, temperature, and strain. These are highly related to their states. Hence, this paper reviews the sensing methods and divides them into two categories: embedded and non-embedded sensors. A variety of measurement methods used to measure the above parameters of various new energy storage devices such as batteries and supercapacitors are systematically summarized. The methods with different innovative points are listed, their advantages and disadvantages are summarized, and the application of optical fiber sensors is emphasized. Finally, the challenges and prospects for these studies are described. The intent is to encourage researchers in relevant fields to study the early warning of safety accidents from the root causes.
... The current research has proved that the expansion force of the battery can be used to characterize its internal electrochemical mechanism and apply the thermal management system [255][256][257][258][259][260][261]. Cai et al. [262] studied the expansion force of the battery under the condition of ISC and established a gas model of the early side reaction of TR, which could accurately capture the expansion force of the battery in the early stage of TR. ...
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Lithium-ion batteries (LIBs) are booming in the field of energy storage due to their advantages of high specific energy, long service life and so on. However, thermal runaway (TR) accidents caused by the unreasonable use or misuse of LIBs have seriously restricted the large-scale application of LIBs. Avoiding TR through advance warning has been becoming an increasing focus of research by scholars. In view of this, we provide a comprehensive review of TR warnings for LIBs. In this paper, an analysis of the existing monitoring parameters of the TR process is presented, and the sensitivity and robustness of multiple warning methods for the same characteristic parameters are compared. Subsequently, this is followed by a presentation of early warning applications in portable devices, electric vehicles and energy storage systems. Finally, combining the existing warning methods with the system's operational data, the future warning methods are envisioned.
... The internal and ambient temperature fields of the battery may also exhibit inhomogeneities, due to the inconsistency between internal heat generation and external heat dissipation [14]. As a result of the structure-specific nature of large-size batteries, inhomogeneities in current density, SOC, and temperature, as described above [15], will further affect the homogeneity of mechanical deformation under charge and discharge conditions of large-size Li-ion batteries [16] and lead to the problem of inhomogeneity in the pressure fields within the electrode surface [17]. In general, during the charging and discharging process, physical fields, including pressure, current density, and temperature, will be distributed inhomogeneously. ...
Article
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... Therefore, it closely relates to the SOH of cells, following a linear relationship with SOH when the SEI growth is the dominating degradation mechanism [280]. Several papers even proposed to estimate SOH of cells based on the measured external pressure [281][282][283]. ...
Article
There are abundant electrochemical-mechanical coupled behaviors in lithium-ion battery (LIB) cells on the mesoscale or macroscale level, such as electrode delamination, pore closure, and gas formation. These behaviors are part of the reasons that the excellent performance of LIBs in the lab/material scale fail to transfer to the industrial scale. This paper aims to systematically review these behaviors by utilizing the ‘mechanical origins – structural changes – electrochemical changes – performance’ logic. We first introduce the mechanical origins i.e., the external pressure and internal deformation, based on the different stages of battery life cycle, i.e., manufacture and operation. The response of the batteries due to the two mechanical origins are determined by the mechanical constitutive relation of battery components. The resulting structural changes are ascribed to size and distribution of pores and particles of the battery components, the contact states between different components. The electrochemical changes are divided into ionic/electrical impedance and lifespan. We have summarized massive experimental observations and modelling efforts and the influencing factors in each section. We also clarify the range of external pressure and internal deformation under which the proposed structural and electrochemical changes are likely to take effects. Lastly, we apply the logic to the next generation lithium metal-based solid-state battery. This review will provide useful guidelines to the design and manufacture of lithium-based rechargeable batteries and promote the development of the electric vehicle industry.
... But the battery volume changes during charging and discharging gives new indicators [14][15][16][17][18][19][20] When LIBs fully charge, the lithium-ions in the graphite layer can create a volumetric increase in thickness of up to 10.4% [21,22] The cathode material used is volumetrically stable and its change in thickness is negligible [23]. Based on these concepts, the strain method was proposed to study the effect of electrode volume change [21,[23][24][25][26] The rectangular rosette strain gauge and a pair of piezoelectric transducers were also used, and given the cell deformation mechanism and electrodes structure information during the cycling [27]. ...
Article
Full-text available
Most battery management systems make decisions based on voltage, current and temperature. Based on volume change, the resistance strain method is proposed and used to obtain more information about battery status. By comparing circumferential and axial strain, it is found that the strain mainly originates from the electrode volume change and SEI formation. The intercalation information of lithium-ion in graphite can be obtained by comparing the circumferential strain curve with the interpolation curve of graphite layer spacing, observed by X-ray diffraction. Rapid increase in circumferential strain is observed during charge when SOC > 92% owing to SEI formation. The difference in strain values along the axial direction is mainly caused by the polarization effects. The strain is buffered by the presence of flexible components, which delays strain inflection points and causes the discharge strain curve to show a monotonic decrease. The optimal location for strain measurement is also proposed. As a result of the rapid rise of strain at the end of charge, this method can be used to optimize the battery and battery management system design. Graphical abstract
... A stress sensor either based on the piezoelectricity or multi-beam optical path only measures stress at the macroscopic scale, while lithium dendrites grow at a much finer length scale. 33,34 Stress can also be measured using X-ray diffraction (XRD). 35 However, stress mapping is almost impossible to achieve with XRD. ...
Article
Solid-state lithium-metal batteries possess intrinsic advantages in terms of both safety and energy density. However, the fundamental origin of electrochemical lithium deposition heterogeneity in solid-state batteries is much less understood than that in lithium-metal batteries using a liquid electrolyte, partly due to the difficulties of directly mapping lithium-deposition reaction fronts and the associated changes in local stress of the solid-state electrolyte. Here, we trace the evolution of three-dimensional microscopic stress and demonstrate that the stress distribution is rather broad in a garnet solid-state electrolyte during processing and battery cycling using confocal Raman spectroscopy. We further discuss the effect of local stress variations on the overpotential of lithium deposition as the most likely origin of lithium-deposition heterogeneity in garnet systems. The ex situ stress-mapping tool developed in this work provides a strong basis for understanding the electromechanical effects, a prerequisite to fully unlock the potential of solid-state lithium-metal batteries.
... 20 The estimation method was also studied by using the stress measurement. 21 The electrochemical model-based estimation algorithm was also investigated. 22 The improved SOC estimation method was conducted by considering the current dependence on the internal resistance. ...
... Capacity is closely related to the prediction of SOH, SOC and other states, and they can all characterize the battery usage status. Dai et al. [22] proposed a multiple regression model combined with stress effects to calculate SOC accurately. Moreover, the standard Kalman filter and improved Kalman filter have been used with a support vector machine for SOC and SOH estimation [23]. ...
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Predicting discharge capacities of Lithium-ion batteries (LIBs) is essential for safe operation of the battery in Electric Vehicles (EVs). In this paper, a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) based deep learning is proposed to estimate the discharge capacity of LIBs. The parameters such as the voltage, current, temperature and charge/discharge capacity are recorded from a Battery Management System (BMS) at various stages of the charge-discharge cycles. Data was recorded keeping the stress constant because this parameter couldn't be controlled. Two different sets of data were obtained at two magnitudes of stress values. The experiments conducted to collect the data was recorded in cycles, where each cycle was divided into 7 steps. Each testing cycle comprises of charging, discharging, rest and cross validation test. The initial layers are convolutional layers that helps in feature extraction followed by a Long Short Term Memory (LSTM) layer. The evaluation model was done using multiple train test split method. The lower values of weighted mean squared error (MSE) obtained suggests that discharge capacity estimation using CNN-LSTM is a reliable method when compared to the conventional voltage-based method. The CNN-LSTM program can further be compiled in BMS in EVs to obtain real time status for State of Charge (SOC) and State of Health (SOH) values.
... Researchers have found that the thickness of pouch cell during charging changes up to 4% of total cell thickness [202], and a complete charge of a hard cased prismatic cell also causes a cell thickness increase by 1.5% [203]. The reversible stress/stain change of LIB is linked directly to the SOC, which opens the possibility to estimate the SOC of LIB via mechanical measurement [204]. ...
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Lithium-ion batteries (LIBs) has seen widespread applications in a variety of fields like the renewable penetration, electrified transportation, and portable electronics. A reliable battery management system (BMS) is critical to fulfill the expectations on the reliability, efficiency and longevity of LIB systems. Recent research progresses have witnessed the emerging technique of smart battery and the associated management system, which can potentially overcome the deficiencies met by traditional BMSs. Motivated by this, this paper reviews the research progresses on the smart cell and smart battery system from multiple aspects, including the system design, sensing techniques, and the potential innovation of system integration. The transition from conventional LIB system towards higher smartness and the incurred advantages/challenges are overviewed. Special focuses are given to the existing and emerging cell-level, multi-dimensional (electro-mechanical-thermal) sensing techniques that may play critical roles in the management of smart cells. The mitigation from external to internal embedded sensing and its potential benefits/impacts to the performance of battery system, as well as the general requirement for sensor design and integration, are discussed systematically. Considering the design innovation and data transmission burden, the potential change of system-level smart battery integration is further discussed as an open outlook.
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Multi-dimensional sensing is the key characteristic of next generation smart batteries. But the existing researches on multi-sensor fusion methods haven't focused on algorithm mechanism, the global optimal solution has not been built, the superiority has not been proved theoretically. For the first time, this research built the global optimal structure of multi-sensor fusion state estimation algorithm. Specifically, the state of charge (SoC) estimation problem of lithium iron phosphate (LFP) batteries is studied, cooperating with voltage signal, expansion force (EF) signal is introduced. Firstly, a normalization algorithm is introduced to overcome the drift of EF under different cycles and different pre-tightening force. Secondly, the non-monotonic relationship of LFP battery's EF-SoC curve is addressed with a forced monotone transformation method in the transition areas. Then the global optimal multi-sensor fusion method is built, theoretical reductions are carried out to prove the higher precision of multi-sensor fusion methods compared to single-signal methods. Experiments are conducted to verify the effectiveness of the methods, even under the most serious situations, the fusion methods exhibit powerful correction ability, and the root mean square error can be controlled within 3 %. Moreover, the proposed algorithms shows strong tolerance to error sources.
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The flat open-circuit voltage (OCV) curve of LiFePO4 (LFP) batteries poses a significant challenge to state of charge (SOC) estimation. To solve this problem, this paper proposes a data-driven SOC estimation method based on multi-dimensional features, especially incorporating force signals. The significant force variation at the middle SOC region section compensates for the flat OCV problem. A long short-term memory (LSTM) neural network model is established to estimate SOC. Battery voltage, current, temperature, and force data sampled only in 5 s are taken as input. The proposed method is validated under different dynamic testing profiles and different temperatures. Experimental results indicate that this method can highly improve SOC estimation accuracy in the middle SOC region, with less than 0.5% root mean square errors and less than 2.5% maximum errors. The validation results at different temperatures also maintain high accuracy with the same model, showing strong robustness and excellent generalization performance. Additionally, the model training process of this method only takes 1.5 h, and the online estimation time is less than 1 s, considerably reducing time cost.
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Sulfide‐based all‐solid‐state batteries (ASSBs) are one of the most promising energy storage devices due to their high energy density and good safety. However, due to the volume (stress) changes of the solid active materials during the charging and discharging process, the generation and evolution of electrochemomechanical stresses are becoming serious and unavoidable problems during the operation of all‐solid‐state batteries due to the lack of a liquid electrolyte to partially buffer the stress generated in the electrodes. To understand these electrochemo‐mechanical effects, including the origins and evolution of mechanical or internal stresses, it is necessary to develop some highly sensitive probing techniques to measure them precisely and bridge the relationship between the electrochemical reaction process and internal stress evolution. Herein, recent progress on uncovering the origins of the internal stresses, the working principle and experimental devices for stress measurement, and the application of those stress‐measuring techniques in the study of electrochemical reactions in sulfide‐based ASSBs are briefly summarized and overviewed. The investigation of precise and operando monitoring techniques and strategies for suppressing or relaxing these electrochemomechanical stresses will be an important direction in future solid‐state batteries.
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Accurate state-of-charge (SOC) estimation is critical to the energy management of lithium-ion batteries, which are increasingly employed in electric vehicles (EVs). In this work, a novel SOC estimation method of a battery based on voltage and stress measurements is proposed with the utilization of machine learning techniques. To evaluate the necessity of stress measurements for SOC estimation, a series of stress characteristic experiments are designed to probe the relationship between mechanical responses and the SOC. On this basis, the stress is taken as an auxiliary for the SOC estimation. Then, a typical long short-term memory (LSTM) network is introduced to map battery measurement signals directly to the battery SOC, where the voltage, stress, and current are selected as input vectors. The proposed method is evaluated under constant current (CC), dynamic stress test (DST), and urban dynamometer driving schedule (UDDS) conditions. Experimental results show that the maximum absolute error (MAE) and root mean square error (RMSE) can be bounded within 0.34 % and 0.45 %, which demonstrates that the voltage-stress-based method can achieve good SOC estimation accuracy. Therefore, the proposed method provides new insight into the SOC estimation of pouch lithium-ion batteries.
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Accurate monitoring and prediction of the operating status of Li-ion batteries (LiBs) are essential for enhancing the longevity and safety of LiB-powered applications. In contrast to conventional battery management strategies that rely solely on voltage, current, and temperature at module level, we present a smart Li-ion cell with an integrated fiber Bragg grating (FBG) optical fiber sensor that enables simultaneous measurement of temperature, force, and displacement at the cell level with a simple beam structure. The Li-ion smart battery sensor scheme realizes the synchronous monitoring of battery mechanical, electrical and thermal multi-physics parameters. We demonstrate that monitoring force is beneficial for enhancing cell life and safety. Specifically, the evolution of peak force upon cycling correlates linearly with the capacity fade, making the force signal a useful state-of-health indicator. Further, the change in cell force is tens of seconds earlier than the change in cell temperature under nail penetration and thermal abuse tests, exhibiting enormous potential for early detection of battery safety incidents, using the Li-ion smart battery scheme, we realize the quantitative description of the evolution of battery structure. By the Li-ion smart battery, it has the ability to improve the quality, reliability and service life of the battery.
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The performance of state of charge (SOC) estimation can be improved by using multi-dimensional signals. However, conventional battery models developed for SOC estimation have difficulties in simulating the coupling relationship between the mechanical and electrical characteristics. In this paper, an electromechanical coupling model (EmCM) of a lithium-ion pouch battery module is established for SOC estimation in real-time. To achieve the closed-loop SOC estimation based on force signal feedback, the stack pressure is chosen as the model output. The current and SOC are set as the model input and a state variable, respectively. On this basis, a novel SOC estimation method through current and stack pressure is proposed. The model parameters are identified by a genetic algorithm, and SOC estimation is performed using the Extended Kalman filter algorithm. The experiment results indicate that the proposed EmCM can depict the stack pressure variations with high accuracy. The SOC estimation error can be controlled within ±2.8% for both Li [NiCoMn]O2 cells and LiFePO4 cells.
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With zero excess lithium, anode‐free lithium metal batteries (AFLMBs) can deliver much higher energy density than that of traditional lithium metal batteries. However, AFLMBs are prone to suffer from rapid capacity loss and short life. Monitoring and analyzing the capacity decay of AFLMBs are of great importance for their future applications. It is known that the capacity fade mainly comes from the formation of solid electrolyte interphase species and dead lithium, which leads to irreversible volume expansion. Therefore, monitoring and distinguishing the irreversible volume expansion or reversible volume expansion are the key points to analyze the capacity fade of AFLMBs. Herein, an applicable technique based on optical fiber sensors to characterize and quantize the volume change of AFLMBs is developed. By attaching fiber Bragg grating (FBG) sensors onto the surface of the multilayered anode‐free pouch cells, the strain evolution of the cells is successfully monitored and correlated with their electrochemical properties. It is found that the decline of surface strain fluctuation amplitude caused by the loss of active lithium is the leading indicator of battery failure. The proposed sensing technique has excellent multiplexing capability that can be considered as an elementary unit for capacity fade analysis in next‐generation battery management system.
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State of health (SOH) estimation of lithium-ion batteries is widely concerned. Currently, electric vehicles are rarely complete discharging in practical application, which remains lots of electricity and reduces constant current charging time. Therefore, this phenomenon hinders the applications of many traditional methods that require a complete constant current charging process. In this paper, we put forward a data-driven and model fusion method for SOH estimation based on constant voltage charging process (CVCP). Firstly, an improved equivalent circuit model (IECM) is established based on the current-time data of the CVCP. Secondly, Pearson correlation coefficient describes the strong mapping relationship between model parameters and SOH, so the model parameters are used as health indicators. Then, SOH prediction model is established by back propagation neural network whose model parameters are optimized by improved particle swarm optimization algorithm. Thirdly, considering time-consuming problem, a new scheme based on the incomplete CVCP that combine time constants prediction model and SOH prediction model is adopted. Finally, comparative results show that proposed IECM has the higher current estimation accuracy than traditional equivalent circuit models for different batteries. The SOH maximum errors of proposed method in different temperatures and data lengths are both within 2%.
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Lithium batteries are prone to swelling during overcharge, which leads to a huge external force in modules and poses a potential threat to electric vehicles. Therefore, it is extremely important to understand the swelling mechanism of lithium batteries in‐depth. In this paper, expansion force evolution during overcharge‐induced thermal runaway process for large format lithium‐ion battery with Li(Ni0.5Co0.2Mn0.3)O2 cathode is investigated. The rupture of the battery shell due to huge swelling is proved as the trigger of the thermal runaway event. In detail, the expansion force increases dramatically with the state of charge, which can be up to 3400 N. And it is a highly dynamic quantity with four obvious inflection points during overcharge, indicating a strong relationship with failure mechanisms. The inherent failure mechanism behind expansion force is investigated using both in‐situ and ex‐situ measurement techniques. The results revealing that the early abnormal expansion of the battery is caused by lithium dendrite and lithium plating. The rapid expansion mainly caused by CO, which is produced by incomplete electrolyte oxidation, leads to the rupture of the battery shell and thermal runaway events. This paper aims to unravel the mystery of lithium battery swelling during overcharge and provide valuable guidance to mitigate the overcharge risk induced by battery swelling. Huge swelling ruptures battery case and triggers thermal runaway event. Expansion force mechanism is investigated using the in‐situ and ex‐situ method. Expansion force can reflect failure mechanisms with higher sensitivity and faster responsibility. Incomplete electrolyte oxidation contributes most to huge battery swelling. Provide valuable guidance for battery and BMS design.
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The safety of lithium-ion batteries (LIBs) remains the focus of engineering and scientific attention as its bending failure cannot be monitored by conventional means such as voltage and temperature due to its special failure mechanism. Therefore, it is of great significance to study the mechanical properties and failure mechanisms of LIBs during bending failure. In this paper, 18,650 LIBs are taken as the research object, and an in-situ experimental platform based on acoustic emission (AE) and three-dimensional digital image correlation (3D-DIC) methods is built to study the mechanical properties and failure mechanisms of LIBs in different states of charge (SOC) under bending loads. The results show that the bending modulus and stiffness of the battery increase with the increase of SOC. The main types of damage and the expansion process of the battery during the bending failure process are also obtained. The research results provide a theoretical basis for research on the bending failure of LIBs. Meanwhile, the experimental platform built in this article solves the failure of previous monitoring methods, including deformation and temperature during the bending failure of LIBs. In addition, this paper provides a new method for in-situ monitoring of LIBs.
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State of Health (SOH) is critical for ensuring the safety and reliability of lithium-ion batteries. Incremental capacity analysis (ICA) method based on measurement data obtained during constant current (CC) charging is used for SOH estimation in the paper. Firstly, to improve the accuracy of SOH estimation in practical application, an improved feature extraction framework is proposed. It mainly includes three stages: data acquisition, data preprocessing and health indication generation. For depressing the noise, two methods are put forward. One is to reconstruct the charging voltage curve in the data preprocessing stage to avoid finding the wrong maximum of the IC curve. The other is to dispose of the outlier feature in the health indication generation stage. Secondly, a health indicator that can be used to characterize the fading of the batteries is proposed. It includes four features and they are the maximum value of the IC curve, the corresponding voltage, the energy and the capacity of a constant current (CC) charging interval determined by the maximum value of the IC curve. Finally, a support vector regression (SVR) model is built to connect the health indicator and the SOH of the battery. The experimental results show that the voltage curve reconstruction and the outlier feature disposition can weaken the influence aroused by the noise and the proposed health indicator can predict the SOH of the batteries with high precision.
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The stack level mechanical pressure will be inevitably generated among the battery cells, which dramatically affects the battery properties. To accurately characterize the battery performance with such aspects, an equivalent mechanical model with full consideration of stack pressure is proposed in this paper. Consisting of a small amount of simple mechanical components, the proposed model is simple but accurate, which is suitable for real time applications. By measuring the stack pressure, the battery thickness can be predicted by the proposed model, and accordingly, the battery states can be estimated. So the proposed equivalent mechanical model provides a potential use of the force signal based battery state estimation. The characteristics of the battery thickness variations caused by the applied force are first analyzed and the equivalent mechanical model is proposed. Furthermore, the coupling of stack pressure and the battery thickness is incorporated to the proposed model. Then a reliable parameter identification method is proposed to obtain the model parameters under different conditions. On this basis, the accuracy and the adaptability of the proposed model are experimentally validated.
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The expansion of battery material during lithium intercalation is a concern for the cycle life and performance of lithium ion batteries. In this paper, electrode expansion is quantified from in situ neutron images taken during cycling of pouch cells with lithium iron phosphate positive and graphite negative electrodes. Apart from confirming the overall expansion as a function of state of charge and the correlation with graphite transitions that have been observed in previous dilatometer experiments we show the spatial distribution of the expansion along the individual electrodes of the pouch cell. The experiments were performed on two cells with different electrode areas during low and high c-rate operation. The measurements show how charging straightened the cell layers that were slightly curved by handling of the pouch cell during setup of the experiment. Subsequent high charging rate, that exceeded the suggested operating voltage limits, was shown to have a strong influence on the observed expansion. Specifically, during high-rate cycling, the battery showed a much larger and irreversible expansion of around 1.5% which was correlated with a 4% loss in capacity over 21 cycles.
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The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, and cell balancing are functionalities that have been implemented in BMS. As an electrochemical product, a battery acts differently under different operational and environmental conditions. The uncertainty of a battery's performance poses a challenge to the implementation of these functions. This paper addresses concerns for current BMSs. State evaluation of a battery, including state of charge, state of health, and state of life, is a critical task for a BMS. Through reviewing the latest methodologies for the state evaluation of batteries, the future challenges for BMSs are presented and possible solutions are proposed as well.
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Compared with other commonly used batteries, lithium-ion batteries are featured by high energy density, high power density, long service life and environmental friendliness and thus have found wide application in the area of consumer electronics. However, lithium-ion batteries for vehicles have high capacity and large serial-parallel numbers, which, coupled with such problems as safety, durability, uniformity and cost, imposes limitations on the wide application of lithium-ion batteries in the vehicle. The narrow area in which lithium-ion batteries operate with safety and reliability necessitates the effective control and management of battery management system. This present paper, through the analysis of literature and in combination with our practical experience, gives a brief introduction to the composition of the battery management system (BMS) and its key issues such as battery cell voltage measurement, battery states estimation, battery uniformity and equalization, battery fault diagnosis and so on, in the hope of providing some inspirations to the design and research of the battery management system.
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An innovative method is applied to investigate anode swelling during electrochemical processes in commercial lithium-ion batteries. Cathode surface is partially covered with a piece of paste to block the transportation of lithium ion from active material during charging/discharging, and the corresponding part on the anode film shows no formation of Li-graphite compounds during different electrochemical processes, which is confirmed by XRD analysis. The increases of anode thickness within and outside lithiated zone are measured, and defined as electrochemical swelling and physical swelling respectively. The microscopic lattice expansion of graphite due to lithiation process correlates to mesoscopic electrochemical swelling synchronically, while physical swelling tends to decrease steadily with time. The relationship among the microscopic stress due to lithium-ion intercalation, the mesoscopic stress resulting in anode swelling, and the macroscopic rippling of pouch cell after a large number of cycle test, is analyzed and correlated in terms of stress evolution across different scales, and suggestions for solving anode swelling are provided.
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The SOC (state of charge) is the most important index of the battery management systems. However, it cannot be measured directly with sensors and must be estimated with mathematical techniques. An accurate battery model is crucial to exactly estimate the SOC. In order to improve the model accuracy, this paper presents an improved parameter identification method. Firstly, the concept of polarization depth is proposed based on the analysis of polarization characteristics of the lithium-ion batteries. Then, the nonlinear least square technique is applied to determine the model parameters according to data collected from pulsed discharge experiments. The results show that the proposed method can reduce the model error as compared with the conventional approach. Furthermore, a nonlinear observer presented in the previous work is utilized to verify the validity of the proposed parameter identification method in SOC estimation. Finally, experiments with different levels of discharge current are carried out to investigate the influence of polarization depth on SOC estimation. Experimental results show that the proposed method can improve the SOC estimation accuracy as compared with the conventional approach, especially under the conditions of large discharge current.
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A coupled electrochemical-mechanical model is developed and applied to predict transient three-dimensional stress fields within reconstructed LixCoO2 cathode particles from commercial Li-ion batteries. The reconstructed particle geometries are derived from focused-ion-beam-scanning-electron-microscopy (FIB-SEM) experiments. The study uses three individual particles, representing typical sizes and shapes. The mechanical model incorporates measured anisotropic strain within the LixCoO2 lattice and includes strains due to phase transformations. The stresses are generally found to be compressive in the particle interiors and tensile near the surfaces. Small-scale surface morphology, high Li concentration gradients, and phase transformations are found to have a major influence on the stresses, with particularly high tensile stresses near small protuberances and concave notch-like features on the electrode surfaces. The study considers 1C and 5C discharge rates. The qualitative behaviors are similar at different discharge rates, but the stress magnitudes are higher at higher discharge rates.
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Despite the fundamental importance of state of health (SOH) and state of charge (SOC) measurement to lithium-ion battery systems, the determination of these parameters is challenging and remains an area of active research. Here we propose a novel method of SOH/SOC determination using mechanical measurements. We present the results of long term aging studies in which we observe stack stress to be linearly related to cell SOH for cells aged with different cycling parameters. The observed increases in stack stress are attributed to irreversible volumetric expansion of the electrodes. We discuss the use of stress measurements for SOC determination, which offers the advantage of being more sensitive to SOC than voltage as well as the ability to measure SOC in the presence of self discharge. Finally we present a simple model to explain the linear nature of the observed stress-SOH relationship. The inherent simplicity of the mechanical measurements and their relationships to SOH and SOC presented in this paper offer potential utility for the improvement of existing battery management systems.
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Charging Li-ion cells at low temperatures and/or high currents can cause the lithium ions to deposit on the anode surface rather than intercalating into the graphite lattice. This effect, named lithium plating, may severely affect the performance, reliability and safety of the cell. Because metallic lithium at the anode consumes more space than intercalated into the graphite, the cell volume increases in the case of lithium plating. An experimental setup for precise measuring the thickness of pouch cells with a resolution of 1 μm was built. The cell thickness as a function of its charge under standard cycling conditions was recorded as a reference. In the case of lithium plating an additional increase of the thickness was observed. This can be used as an indicator for the non-destructive detection of lithium plating in pouch cells. Compared with the standard capacity based methods this procedure allows a higher sensitivity and gives results more quickly.
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Structural stability and mechanical integrity of electrode materials during lithiation/delithiation influence the performance of Li-ion batteries. Significant dimensional and volume changes are associated with variations in lattice parameters and transformations of crystalline/amorphous phases that occur during electrochemical cycling. These phenomena, which occur during Li-intercalation/deintercalation, Li-alloying/dealloying and conversion reactions, result in deformations and stress generation in the active cathode and anode materials. Such stresses can cause fragmentation, disintegration, fracturing, and loss in contact between current collectors and the active electrode materials, all of which can also expose fresh surfaces to the electrolyte. These degradation processes ultimately lead to capacity fade with electrochemical cycling for nearly all electrode materials, and are some of the major causes for the eventual failure of a Li-ion cell. Furthermore, severe stresses have made it nearly impossible to use higher capacity anode materials (e.g., Si, Sn) in practical batteries and also limit the ‘usable’ capacity of the present cathode materials (e.g., LiCoO2, LiMn2O4) to nearly half the theoretical capacity. Against this backdrop, this review presents an overview of the causes and the relative magnitudes of stresses in the various electrode materials, highlights some of the more recent discoveries concerning the causes (such as stress development due to passivation layer formation), introduces the recently developed techniques for in-situ observations of lithiation induced deformations and measurement of stresses, analyses the strategies adopted for addressing the stress-related issues, and raises various issues that still need to be addressed to overcome the stress related problems that are some of the major bottlenecks towards the development of new high-capacity electrode materials for Li-ion batteries.
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An accurate SoC (state of charge) and SoP (state of power capability) joint estimator is the most significant techniques for electric vehicles. This paper makes two contributions to the existing literature. (1) A data-driven parameter identification method has been proposed for accurately capturing the real-time characteristic of the battery through the recursive least square algorithm, where the parameter of the battery model is updated with the real-time measurements of battery current and voltage at each sampling interval. (2) An adaptive extended Kalman filter algorithm based multi-state joint estimator has been developed in accordance with the relationship of the battery SoC and its power capability. Note that the SoC and SoP can be predicted accurately against the degradation and various operating environments of the battery through the data-driven parameter identification method. The robustness of the proposed data-driven joint estimator has been verified by different degradation states of lithium-ion polymer battery cells. The result indicates that the estimation errors of voltage and SoC are less than 1% even if given a large erroneous initial state of joint estimator, which makes the SoP estimate more accurate and reliable for the electric vehicles application.
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A model is developed to study the stress generation in a spherical particle subjected to lithium insertion. The model accounts for both the plastic deformation and the coexistence of lithium-poor and lithium-rich phases with a sharp and curved phase boundary. Such two-phase and inelastic deformation characteristics often arise during lithiation of crystalline particles with high capacity. A flexible sigmoid function is used to create the lithium profile with a step-like change in lithium concentration, mimicking a sharp phase boundary that separates a pristine core and a lithiated shell in the particle. The mechanics results, obtained by an analytic formulation and finite difference calculations, show the development of tensile hoop stress in the surface layer of the lithiated shell. This hoop tension provides the driving force of surface cracking, as observed by in situ transmission electron microscopy. The two-phase lithiation model is further compared with the single-phase one, which assumes a gradual and smooth variation in radial lithium distributions, and thus predicts only hoop compression in the surface layer of the particle. Furthermore, the effect of dilatational vs. unidirectional lithiation strains in the two-phase model is studied, thereby underscoring the critical role of anisotropy of lithiation strain in controlling stress generation in high-capacity electrodes for lithium ion batteries.
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Mechanical stress is generated in electrode particles of a Lithium ion Polymer Battery (LiPB) during charge and discharge. The stress can cause cracks and fractures in the solid particles over time when cells are cycled, which leads to a disorder and a fracture of the electrodes. In order to understand the mechanism, a stress model for a pouch type high power LiPB is developed based on electrochemical and thermal model, where the stress induced by the ion concentration in the electrode particles is considered. The stress in the particles causes changes in the electrode volumes and leads to changes in the thickness of a battery cell, which is measured using a device designed with two linear voltage displacement transducers (LVDT). This model is validated against experimental data obtained from a pouch cell. Analysis shows that the magnitude of stress depends upon locations and C rate, while the thickness of a single cell is mainly affected by the state of charge (SOC), but not C rate.
Article
Dilatometer was fabricated and applied to measure change in electrode thickness of the graphite-negative electrode for lithium-ion batteries. Anomalous expansion was observed in the initial part of the first reduction, i.e. so-called irreversible capacity region in voltages of 1.1–0.5V versus Li. During further reduction to 0V versus Li, the thickness of electrode increased monotonously as the lithium-intercalation reaction proceeded. The observed change in electrode thickness was larger than that of 10% expected from XRD data, due mainly to the expansion at the initial part of the first reduction. One-to-one correspondence between the electrochemical and dilatometric data was observed. The acoustic events occurring inside the Li/graphite cells were also measured. The effect of electrolyte on the anomalous expansion was also examined by dilatometry in relation to the irreversible capacity for the first reduction of graphite, and the significance of introducing the dilatometry together with acoustic emission technique to insertion materials research for advanced lithium-ion batteries was discussed.
Article
Because structural change in lithium cobalt oxide (LiCoO2) cathode is primarily responsible for the performance degradation of lithium-ion cells in simulated satellite operation, it is important to investigate the operating-condition effect on cell-volume change. In this work, we used in situ strain- gauge measurement to probe the total volume change during charging and discharging of five 50 Ah- class lithium-ion cells with graphite anodes and LiCoO2 cathodes. Some interesting phenomena concerning the correlation of the taper voltage with the strain change at the end of the charge were found in the strain trend curve. To explain these phenomena, we examined the strain change of a commercial 0.65 Ah- class lithium- ion polymer cell with the same electrodes as a function of taper voltage by using in situ load- cell measurement and were able to deduce that the cell-volume change during charging correlated to the structure transition of the LiCoO2 cathode from the initial hexagonal phase (H1) to a new hexagonal phase (H2) at a taper voltage near 4.00 V. We conclude that the taper voltage should be maintained below 4.00 V to maximize the cycle life of lithium- ion cells with graphite anodes and LiCoO2 cathodes during practical satellite operation. (c) 2006 The Electrochemical Society.
Article
Since battery performance is the main factor affecting on-orbit satellite operation, it is very important to understand the degradation mechanism of battery performance and operating conditions. In particular, a lithium (Li) ion cell used as a satellite power source may be subjected to high charge and discharge rates, and the cell volume change during the charge-discharge cycle might cause the mechanical degradation of the electrode. In this work, we used two in situ methods, load cell measurement and X-ray observation, to investigate the cell volume change of a commercial Li-ion polymer cell with charging and discharging An excess cell volume change that was delayed with respect to the change induced by the state of charge was observed at high charge and discharge rates. We attributed this phenomenon to the slow diffusion of Li ions in the electrode active materials and slow structural change in the outer region of the electrode particle. Furthermore, we deduced that the outer region of the electrode particle, rather than the inner region, was mainly responsible for the excess cell volume change. (C) 2004 The Electrochemical Society.
Article
During cycles, the battery thickness changes for the three reasons—(i) expansion and contraction of host materials due to lithium intercalation, (ii) electrode volume increase caused by irreversible reaction deposits, and (iii) dead volume and pressure changes within the cell case depending on battery structure and construction. In this study we have identified and quantified those three reasons and related thickness increases employing commercially available thin prismatic lithium ion and polymer cells.
Article
Battery management systems in hybrid-electric-vehicle battery packs must estimate values descriptive of the pack’s present operating condition. These include: battery state-of-charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack.In a series of three papers, we propose methods, based on extended Kalman filtering (EKF), that are able to accomplish these goals for a lithium ion polymer battery pack. We expect that they will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results.This third paper concludes the series by presenting five additional applications where either an EKF or results from EKF may be used in typical BMS algorithms: initializing state estimates after the vehicle has been idle for some time; estimating state-of-charge with dynamic error bounds on the estimate; estimating pack available dis/charge power; tracking changing pack parameters (including power fade and capacity fade) as the pack ages, and therefore providing a quantitative estimate of state-of-health; and determining which cells must be equalized. Results from pack tests are presented.
Article
We have previously described algorithms for a battery management system (BMS) that uses Kalman filtering (KF) techniques to estimate such quantities as: cell self-discharge rate, state-of-charge, nominal capacity, resistance, and others. Since the dynamics of electrochemical cells are not linear, we used a nonlinear extension to the original KF called the extended Kalman filter (EKF).Now, we introduce an alternative nonlinear Kalman filtering technique known as “sigma-point Kalman filtering” (SPKF), which has some theoretical advantages that manifest themselves in more accurate predictions. The computational complexity of SPKF is of the same order as EKF, so the gains are made at little or no additional cost.This paper is the second in a two-part series. The first paper explored the theoretical background to the Kalman filter, the extended Kalman filter, and the sigma-point Kalman filter. It explained why the SPKF is often superior to the EKF and applied SPKF to estimate the state of a third-generation prototype lithium-ion polymer battery (LiPB) cell in dynamic conditions, including the state-of-charge of the cell.In this paper, we first investigate the use of the SPKF method to estimate battery parameters. A numerically efficient “square-root sigma-point Kalman filter” (SR-SPKF) is introduced for this purpose. Additionally, we discuss two SPKF-based methods for simultaneous estimation of both the quickly time-varying state and slowly time-varying parameters. Results are presented for a battery pack based on a fourth-generation prototype LiPB cell, and some limitations of the current approach, based on the probability density functions of estimation error, are also discussed.
Article
We have previously described algorithms for a battery management system (BMS) that uses Kalman filtering (KF) techniques to estimate such quantities as: cell self-discharge rate, state-of-charge (SOC), nominal capacity, resistance, and others. Since the dynamics of electrochemical cells are not linear, we used a non-linear extension to the original KF called the extended Kalman filter (EKF).We were able to achieve very good estimates of SOC and other states and parameters using EKF. However, some applications e.g., that of the battery-management-system (BMS) of a hybrid-electric-vehicle (HEV) can require even more accurate estimates than these. To see how to improve on EKF, we must examine the mathematical foundation of that algorithm in more detail than we presented in the prior work to discover the assumptions that are made in its derivation. Since these suppositions are not met exactly in BMS application, we explore an alternative non-linear Kalman filtering techniques known as “sigma-point Kalman filtering” (SPKF), which has some theoretical advantages that manifest themselves in more accurate predictions. The computational complexity of SPKF is of the same order as EKF, so the gains are made at little or no additional cost.The SPKF method as applied to BMS algorithms is presented here in a series of two papers. This first paper is devoted primarily to deriving the EKF and SPKF algorithms using the framework of sequential probabilistic inference. This is done to show that the two algorithms, which at first may look quite different, are actually very similar in most respects; also, we discover why we might expect the SPKF to outperform EKF in non-linear estimation applications. Results are presented for a battery pack based on a third-generation prototype LiPB cell, and compared with prior results using EKF. As expected, SPKF outperforms EKF, both in its estimate of SOC and in its estimate of the error bounds thereof. The second paper presents some more advanced algorithms for simultaneous state and parameter estimation, and gives results for a fourth-generation prototype LiPB cell.
Article
A mathematical model that calculates volume expansion and contraction and concentration and stress profiles during lithium insertion into and extraction from a spherical particle of electrode material has been developed. The maximum stress in the particle has been determined as a function of dimensionless current, which includes the charge rate, particle size, and diffusion coefficient. The effects of pressure-driven diffusion and nonideal interactions between the lithium and host material have also been described. The model predicts that carbonaceous particles will fracture in high-power applications such as hybrid-electric vehicle batteries.
The molecular dynamics simulation of molar volume and elastic properties in lithium-ion battery cathode
  • Xiang Ji
  • Guo Changsheng
Xiang Ji, Guo Changsheng. The molecular dynamics simulation of molar volume and elastic properties in lithium-ion battery cathode. Chin Q Mech 2014;35:171e9.