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The electrification of transportation is experiencing rapid development. Electric bicycles (e-bikes) are commonly employed as convenient modes of transportation. Thanks to the advantages of long life and high energy density, lithium-ion batteries (LIBs) are widely used in e-bikes. In certain business models, e-bikes can utilize rental LIBs, which are centrally managed at charging stations. The low-temperature charging and discharging performance of the LIB system poses a significant challenge during usage. Among various heating methods, alternating current (AC) heating has garnered attention due to its high efficiency and has been applied to quickly warm up the LIB system. To address this issue, an AC heating model was established to determine the appropriate frequency and magnitude of the current, and a prototype AC heating system for the LIB modules used in e-bikes was designed. A full-bridge topology system model was established, and an experimental platform was constructed to test the effectiveness of the proposed AC heating topology and thermoelectric model under different AC heating frequencies and currents. The results show that the proposed AC heating system can heat an 18650 battery module within 20 min. Under an ambient temperature of −20 °C, using a 10 A, a 100 Hz excitation current achieves a heating rate of 1.3 °C per minute, with minimum power losses. The prototype also has a fast response time of only 70 ms. Finally, the strategies of LIB heating and insulation are proposed for the scenario of a battery swapping station. This research holds great significance in resolving the problem of low-temperature heating for e-bikes in cold regions. [ABSTRACT FROM AUTHOR]

Bidirectional pulsed current (BPC) heating has proven to be an effective method for internal heating. However, current research has primarily focused on the impact of symmetrical BPC on battery heat generation, while neglecting the influence of different BPC parameters. To address this gap, this paper investigates the effects of various BPC parameters on battery heat generation. Initially, an electro-thermal coupled model of the battery is constructed based on the results of electrochemical impedance spectroscopy (EIS) tests conducted at different temperatures and amplitudes at 20% state of charge (SOC). The validation results of the model demonstrate that the absolute errors of voltage and temperature are generally less than 50 mV and 1.2 °C. Subsequently, the influence of BPC parameters on battery heat generation is examined under different terminal voltage constraints, temperatures, and frequencies. The findings at 20% SOC reveal that symmetrical BPC does not consistently correspond to the maximum heating power. The proportion of charge time and discharge time in one cycle, corresponding to the maximum heating power, varies depending on the charge and discharge cut-off voltages. Moreover, these variations differ across frequencies and temperatures. When the terminal voltage is constrained between 3 V and 4.2 V, the maximum heat power corresponds to a discharge time share of 0.55 in one cycle. In conclusion, the results underscore the complex relationship between BPC parameters and battery heat generation, which can further enhance our understanding of effective heating strategies for batteries. [ABSTRACT FROM AUTHOR]

Models play a crucial role in explaining internal processes, estimating states, and managing lithium-ion batteries. Electrochemical models can effectively illustrate the battery's mechanism; however, their complexity renders them unsuitable for onboard use in electric vehicles. On the other hand, equivalent circuit models (ECMs) utilize a simple set of circuit elements to simulate voltage–current characteristics. This approach is less complex and easier to implement. However, most ECMs do not currently account for the nonlinear impact of operating conditions on battery impedance, making it difficult to obtain accurate wideband impedance characteristics of the battery when used in online applications. This article delves into the intrinsic mechanism of batteries and discusses the influence of nonstationary conditions on impedance. An ECM designed for non-steady state conditions is presented. Online adaptive adjustment of model parameters is achieved using the forgetting factor recursive least squares (FFRLS) algorithm and varied parameters approach (VPA) algorithm. Experimental results demonstrate the impressive performance of the model and parameter identification method, enabling the accurate acquisition of online impedance. [ABSTRACT FROM AUTHOR]

Lithium plating during the fast charging process significantly decreases the health and safety of lithium-ion batteries (LIBs). Hence, detecting the onset of lithium plating is essential to realize the highly effective and low-damage fast charging. However, an online lithium plating onset detection method that can be used in constant current (CC) and multiage constant current (MCC) fast charging under different working conditions without interrupting the charging process of LIBs has not been reported yet. To fill this gap, a novel method based on dynamic impedance measurement has been proposed. The abnormal drop of dynamic impedance at 1 Hz during the charging processes was treated as a universal feature of lithium plating onset. A method based on real-time impedance feedback and a long short-term memory (LSTM) was first proposed to identify this feature. It was validated in various CC and MCC charging rates at different temperatures and battery types. With the cross-validation of the voltage relaxation method, an error of <2.5% SOC in detecting the lithium plating onset was achieved. Besides, the proposed method effectively optimizes the misidentification and the time delay in detection. For practical applications, the method's accuracy and robustness were additionally validated in a rapid prototyping system. The method proposed in this paper solves the complex problem of lithium plating detection during the CC and MCC charging procedures, which is essential for improving the health and safety of LIBs under fast charging conditions. • Real-time EIS is measured without interrupting the fast-charging process. • The real part of impedance at 1 Hz indicates the onset of lithium plating. • Novel lithium plating detection is realized by combining impedance and LSTM-RNN. • The proposed method is applicable to CC and MCC fast charging conditions. • The proposed method is validated in a rapid prototype. [ABSTRACT FROM AUTHOR]

Alternating current (AC) impedance is an important and promising feature for lithium‐ion battery state estimation and prediction. Herein, a new battery capacity estimation method using AC impedance with Gaussian process regression (GPR) is proposed. A bunch of high‐energy 18 650‐type batteries with a nominal capacity of 3.5 Ah are cycled at 25, 35, and 45 °C until the capacity drops below 2.6 Ah. Two single‐frequency points are found which are highly correlated with the battery residual capacity regardless of the cycling temperatures. Machine learning methods are used to probe the battery capacity with the real and imaginary impedance of two single‐frequency points. The best model achieves a test root‐mean‐squared error of 0.5% (17.36 mAh) with GPR. This work provides a new perspective to predicting the complex dynamical behavior of batteries by combining electrochemical impedance with data‐driven methods. [ABSTRACT FROM AUTHOR]

The thermal safety variations of lithium-ion batteries during operational usage pose a significant threat to the safe application of electric vehicles. This work initially investigates the battery thermal safety evolution mechanism under different degradation paths. Lithium plating is identified as the critical common degradation mechanism leading to the decline of battery thermal safety through multi-angle characterization analysis. However, lithium plating generated by different formation mechanism has different impact on the battery thermal stability. The most severe degradation of battery thermal safety is caused by lithium plating which is induced by the limited lithium intercalation rate under low-temperature cycling and high-rate cycling. Further, the mechanism of internal degradation on the battery thermal safety is elucidated through multi-scale thermal test analysis. It is indicated that lithium plating and transition metal dissolution are crucial factors leading to the decreased thermal stability of the anode and cathode respectively, which consequently results in a significant reduction of T 1 and T 2. Furthermore, based on the principle of internal degradation synchronously affecting electrochemical characteristics and thermal stability characteristics, the mapping relationship between the observation indicators of electrochemical characteristics and the characterization parameters of thermal safety is established, realizing the non-destructive assessment of battery thermal safety during the whole lifecycle. [Display omitted] • Battery thermal safety evolution under different degradation paths is revealed. • Lithium plating is the common critical degradation mechanism. • Multiscale thermal tests reveal the thermal stability evolution of cell components. • Non-destructive assessment method of battery thermal safety is established. [ABSTRACT FROM AUTHOR]

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

Summary: Life prediction facilitates efficient management and timely maintenance of lithium‐ion batteries. Challenges are still faced in eliminating the effects of battery temperature or state of charge (SOC) on the life indicator to form a life prediction method for complex onboard working conditions. To fulfill the research gap, this paper focuses on three novelties about the life indicator, effect elimination, and life prediction method. First, impedance spectra at different temperatures, SOC, and aging cycles are comprehensively studied by experiments. By fitting the spectra with an equivalent circuit model, changes of ohmic resistance, solid electrolyte interphase resistance, and charge transfer resistance (CTR) are analyzed in detail. CTR is determined as a novel life indicator, and an empirical model describing the changing trend of CTR with aging cycles is established. Second, a multi‐factor coupled CTR model is applied to eliminate the strong effects of temperature and SOC during the prediction. Third, the tracking of the effects and the changing trend of the CTR with the aging cycles form a composite life prediction method with dual time scales. The results show that the battery life can be accurately predicted and the errors converge to within ±5% even though the indicator CTR is obtained at different temperatures and SOC. With this method, life prediction no longer depends on the indicator obtained in a specific state. It has great potential to broaden the implementation of life prediction for onboard conditions. [ABSTRACT FROM AUTHOR]

Lithium‐ion battery cells are usually connected in series or parallel to form modules to meet power and energy requirements for specific applications. Inconsistency of the cells' performance, i.e., capacity and internal resistance, is initially formed during production. Then the inconsistency evolves in the lifespan. Herein, the performance and inconsistency evolution of the series and parallel modules for the design and management guide are investigated. A quantitative analysis of the performance and inconsistency is made. The performance and inconsistency evolution are investigated experimentally. Based on it, the trend judgment of the inconsistency evolution is derived. It is found that the cell performance in series modules is self‐divergent, especially in the later stage of the aging process. And the divergence becomes more severe with an unreasonable equalization, such as the traditional cell voltage equalization. Equalization of the cell performance is needed to cut off the vicious circle. For the parallel module, self‐convergence of the performance exists. The better‐performing cells in the module withstand a larger current, making their performance decay faster and finally becoming consistent with the cells with worse initial performance. Meanwhile, the parallel module performance also decays significantly. Therefore, maintaining the cells' consistency is still necessary for the parallel modules. [ABSTRACT FROM AUTHOR]

Summary: The nickel‐rich lithium‐ion batteries (LIBs) are widely used as the energy source of new energy vehicles. But the relatively poor safety performance results in serious accidents, which attracts more and more attention of researchers. In this study, the thermal runaway (TR) features of a pouch cell consisted of LiNi0.5Mn0.3Co0.2O2/graphite are investigated by an extended volume‐accelerating rate calorimeter combined with resistance monitoring, voltage monitoring, and video imaging technologies. Based on these techniques, the TR process is divided into five representative stages, that is, the start of self‐heating, the venting of flammable gas, the volatilization of electrolyte, the melt of separator, and the internal short circuit of LIBs. Besides, a lumped‐parameter thermal model with high precision is developed to predict and explain the TR process. The heat generated by the decomposition of solid electrolyte interface (SEI), the reaction of anode‐electrolyte, the multiple phase transition of the cathode is considered as the main heating source of TR. The resistance shows a higher sensitivity to the SEI decomposition than temperature does. The venting process along with the evaporation of electrolytes provides significant opportunities to obstruct the TR. The interval time between voltage drop and temperature rise is 6.9 seconds, which is beneficial for the early warning of TR. [ABSTRACT FROM AUTHOR]

To reveal the impact of alternating current (AC) amplitude on impedance, this study investigates the electrochemical impedance with different AC amplitudes for a lithium-ion battery (NCA vs. graphite) and half cells under different states of charge (SOCs), at room and low temperatures. To determine the relationship of different polarization processes between the full cell and half cells, the symmetric cells and the distribution of relaxation times (DRT) are utilized for electrochemical impedance spectroscopy (EIS) analysis. The experimental results indicate that the medium- and low-frequency impedance arcs gradually shrink with the increase of the AC amplitude at low temperatures. DRT focuses on the anode solid electrolyte interphase (SEI), cathode electrolyte interphase (CEI), and charge transfer processes. It is proved that the impedance arc shrinkage is determined by the nonlinear relationship that can be described by the Butler-Volmer equation between current and resistances of the SEI and the charge transfer processes. When the AC amplitude increases to a certain extent, lithium plating also causes impedance arc shrinkage. Moreover, the impedance arc shrinkage of the full cell is mainly affected by the NCA cathode under low SOC. At medium and high SOCs, it is determined jointly by the NCA cathode and graphite anode. [Display omitted] • The impedances of full and half cells at different AC amplitudes are studied. • The impedance arcs shrink with the increase of the AC amplitude at low temperatures. • The charge transfer and SEI resistances become smaller is the reason. • The Butler-Volmer equation can be used to describe the changes of polarization resistances. [ABSTRACT FROM AUTHOR]

Early internal short circuit (ISC) diagnosis is critical for a battery management system (BMS) to prevent the thermal runaway of lithium-ion batteries. However, it is difficult to be diagnosed owing to few obvious electric and thermoelectric characteristics in the early stage of ISC. In this study, a novel ISC diagnosis method based on the incremental capacity (IC) curves is proposed. Different charging rates on four ISC situations are carried out on a cell to verify the proposed method. The leakage current of the ISC battery can be obtained by the area difference between the normal cell and the ISC cell, and it can be converted into the ISC resistance. The experiments of different initial charging states of charge (SOC) in a series-connected battery pack are conducted to verify the method in the real EVs working environment. The diagnosis results of the battery cell and battery pack indicate the proposed method is feasible and effective to quantitatively diagnose the ISC. • A novel internal short circuit diagnosis method based on the peak area of the IC curve is proposed. • The battery cell and series-connected battery pack charging experiments are designed and conducted. • The influence of internal short circuit on IC curves is revealed. • The experimental results show that the proposed approach is feasible and effective. [ABSTRACT FROM AUTHOR]

Low temperatures seriously affect the performance of lithium-ion batteries. This study proposes a non-destructive low-temperature bidirectional pulse current (BPC) heating method. Different from existing heating approaches, this method not only optimizes heating frequency and amplitude but also considers the optimization of the charge/discharge pulse duration ratio. To optimize the BPC heating strategy, a precise electro-thermal coupled model is established, and a neural network is employed to delineate the relationship among model parameters, temperature, and state of charge (SOC). Additionally, the interplay between the impedance of the graphite anode and that of the full cell is analyzed by constructing a three-electrode battery. Then, a novel full-cell-oriented lithium plating criterion is introduced. Finally, based on the constructed electro-thermal coupled model, lithium plating criterion, and terminal voltage constraint, a novel non-destructive BPC heating method is proposed. The results show a significant improvement in heating efficiency compared to conventional BPC heating. Especially for high SOCs, the heating power is increased at least 8 times. When the battery SOC is below 40 %, the average heating rate from −10 °C to 10 °C is 11.28 °C/min. Even at 90 % SOC, the heating rate remains at 2.88 °C/min. Furthermore, the capacity and impedance of a battery at 50 % SOC exhibit no significant changes after 60 heating cycles using the optimal BPC heating strategy at 100 Hz. These findings show that the optimized method proposed in this study has high heating efficiency and no damage to the battery. [Display omitted] • The kinetic processes of the graphite and full cell are compared. • A novel full-cell-oriented lithium plating criterion is introduced. • The heating power is studied for different BPC parameters. • A novel non-destructive BPC heating method is developed. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel electro-thermal coupled model is proposed. • A three-electrode battery is constructed for study. • A low-temperature charging framework is developed. This paper proposes a novel framework for low-temperature fast charging of lithium-ion batteries (LIBs) without lithium plating. The framework includes three key components: modeling, constraints, and strategy design. In the modeling phase, a new electro-thermal coupled model is introduced, which integrates both frequency-domain and time-domain electro-thermal coupled models. They optimize the bidirectional pulsed current (BPC) heating parameters and charging current, respectively. Terminal voltage boundaries and lithium plating serve as constraints. Terminal voltage is confined within upper and lower limits to prevent battery overcharge and overdischarge. Lithium plating avoidance entails setting a minimum frequency for BPC heating. During charging, lithium plating is mitigated by maintaining negative potential above 0 V. To obtain calculated negative electrode potential, a three-electrode battery is employed to calibrate the electro-thermal coupled model parameters. Subsequently, a novel low-temperature fast charging strategy is devised. This strategy enables intelligent switching between BPC heating and charging, while also synergistically optimizing BPC heating parameters and charging current. The strategy also achieves optimization of both charging speed and energy consumption. Charging the battery SOC from 0.2 to 0.9 in 42 min at −10 °C, without triggering lithium plating, is feasible with this proposed strategy. Compared to strategies focusing solely on current amplitude optimization, heating followed by charging, and traditional methods, this heating strategy exhibits the highest charging speed. [ABSTRACT FROM AUTHOR]

The degradation mode is of great significance for reducing the complexity of research on the aging mechanisms of lithium-ion batteries. Previous studies have grouped the aging mechanisms into three degradation modes: conductivity loss (CL), loss of lithium inventory (LLI) and loss of active material (LAM). Combined with electrochemical impedance spectroscopy (EIS), degradation modes can be identified and quantified non-destructively. This paper aims to extend the application of this method to more operating conditions and explore the impact of external factors on the quantitative results. Here, we design a quantification method using two equivalent circuit models to cope with the different trends of impedance spectra during the aging process. Under four conditions, the changing trends of the quantitative values of the three degradation modes are explored and the effects of the state of charge (SoC) and excitation current during EIS measurement are statistically analyzed. It is verified by experiments that LLI and LAM are the most critical aging mechanisms under various conditions. The selection of SoC has a significant effect on the quantitative results, but the influence of the excitation current is not obvious. [ABSTRACT FROM AUTHOR]

Accurate state of charge (SOC) estimation helps achieve efficient battery management, which is essential for transportation electrification. Significantly different from existing data-driven estimation methods only considering battery electrical information, this study proposes a mechanics-based battery SOC estimation using deep learning techniques. First, an experimental setup for measuring pouch-type battery stress is designed, followed by constructing four typical operating conditions to establish a sophisticated battery dataset and investigate the primary relationship between battery stress and SOC. Then, a data-driven estimation model composed of long short-term memory neural network is investigated to achieve the interlink between battery external measurements and internal states, in which the battery voltage, current, and stress sequences within a sliding window length are fed into the deep learning model. Quantitative experimental results demonstrate that the proposed mechanics-based battery SOC estimation approach can achieve acceptable accuracy and is adaptable to different training and operating conditions, as well as ensure the estimation performance under limited data length. Moreover, the proposed method has also been proven robust to the interference of battery measurement noise compared to the traditional estimation method. • A battery state of charge estimation method using mechanical information is proposed. • The proposed method adopts the deep learning technique to achieve the estimation. • Battery stress is highly correlated to the state of charging through correlation analysis. • The proposed method's estimation accuracy, efficiency, and robustness are discussed. [ABSTRACT FROM AUTHOR]

Large-format prismatic Li-ion batteries (LIBs) are prominent energy storage devices in electric transportation applications. However, large-format LIB induces severe thermal runaway (TR) disasters. Battery failure commonly initiates from a local point of one jelly roll and then propagates to the whole cell, called thermal runaway front (TRF) propagation. This study investigates the TRF propagation mechanism of multi-jelly roll-based LIBs through experiments, modeling, and theoretical analysis for thermal runaway propagation (TRP) mitigation. Experiments prove that battery venting changes along the jelly roll-safety valve directions during the TRF boundary movement. Besides, TRF propagation speed is found to be accelerated inside each cell (from 3.6 to 10.6 mm/s) during TRP, driven by a significant temperature gradient, chemical reactions, and gas flow along the TRP direction. The in-cell TRF acceleration behavior is more noticeable for batteries with more jelly rolls. The TRF speed-jelly roll index equations are proposed to reveal the propagation acceleration principle mathematically. Furthermore, a thermal-physical model is developed to precisely simulate in-cell TRF propagation behavior, which is validated by experimental data. Moreover, the TRF boundary temperature equation and "No TRP" judging criteria are proposed through theoretical analysis. This study proposes promising strategies for potential TRP suppression, contributing to future safe battery system design. [ABSTRACT FROM AUTHOR]

• An in-depth analysis of feature extraction from EIS is provided for SOH estimation. • Three features in SOH estimation are constructed using knowledge of battery EIS. • The Gaussian process regression is employed to realize the battery SOH estimation. • A comprehensive assessment of EIS features in SOH estimation is carried on. • The proposed SOH estimation is adaptive to different aging conditions. Battery state of health (SOH) estimation is a critical but challenging demand in advanced battery management technologies. As an essential parameter, battery impedance contains valuable electrochemical information reflecting battery SOH. This study investigates a systematic comparative study of three categories of features extracted from battery electrochemical impedance spectroscopy (EIS) in SOH estimation. The three representative features are broadband EIS feature, model parameter feature, and fixed-frequency impedance feature. Based on the deduced EIS features, a machine learning technique using Gaussian process regression is adopted to estimate battery SOH. The battery aging and electrochemical tests for commercial 18650-type batteries are performed, in which the constant and dynamic discharging conditions are considered during battery aging. The battery life-cycle capacity and EIS data are collected for the machine learning model. The performance of the constructed features is investigated and comprehensively compared in terms of estimation accuracy, certainty, and efficiency. Experimental results highlight that using the fixed-frequency impedance feature can realize outstanding performance in battery SOH estimation. The average of the maximum absolute errors for different cells under different aging conditions is within 2.2%. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries behave nonlinear degradation during long-term usage. Prediction of the nonlinear degradation is of guiding significance in taking proactive measures to prolong battery life and ensure battery safety. In this study, a new nonlinear degradation knee-point prediction method is proposed utilizing relaxation voltage as the feature sequence, and it is the first attempt with the joint prediction of the knee-point and remaining useful life. A remaining useful life prediction framework integrating degradation features of the knee-point is established, which leads to stable improvements in the accuracy of remaining useful life prediction. Through transfer learning, the proposed joint prediction method is validated on different battery datasets, obtaining mean absolute errors within 26 cycles for the knee-point and remaining useful life prediction, with root mean square errors below 28 cycles. The predicted results can serve as evaluation indicators for various application scenarios, including battery design, ability evaluation, and functionality enhancement. • Relaxation voltage is used for battery knee-point prediction. • Joint prediction of knee-point and remaining useful life is achieved. • Applicability of the prediction method is verified by transfer learning. [ABSTRACT FROM AUTHOR]

Accurate state of health (SOH) estimation is vital to ensure safe, reliable, and efficient operation of lithium-ion batteries. Timescale information on internal kinetic processes is closely related to battery health and can be efficiently identified from broadband impedance by the distribution of relaxation times (DRT). Despite its bright prospects for onboard scenarios, the application of impedance-based timescale information in SOH estimation has rarely been explored. Motivated by this, this work proposes a novel SOH estimation method utilizing impedance-based timescale information and ensemble learning. Adaptive SOH estimation over a wide state of charge (SOC) range is achieved without knowledge of usage history or SOC. Specifically, timescale identification of 584 impedance spectra is conducted via DRT. Based on the extracted timescale features, an ensemble learning technique with a base learner of the regression tree is employed to estimate SOH. The average of the mean absolute errors at different SOCs can be within 1.87 % for all cells under different cyclic conditions. The minimum redundancy maximum relevance algorithm assists in the comparative investigation into SOH estimation using different feature combinations. This work exhibits excellent applications of impedance-based timescale information in SOH estimation and can provide a fresh viewpoint in promoting the health management development. [Display omitted] • Up to 584 impedance spectra for all cells under different conditions are collected. • Impedance-based timescale information is applied in SOH estimation. • Ensemble learning assists in the adaptive SOH estimation over a wide SOC range. • MRMR algorithm assists in the comparative study of different feature combinations. [ABSTRACT FROM AUTHOR]

Thermal runaway propagation (TRP) is the most challenging safety issue of lithium-ion battery systems. An early warning signal is promising for providing time to mitigate or prevent TRP and further disasters timely. This study investigates the strain characteristics of different format prismatic batteries during the TR/TRP processes, dividing the strain-changing trend into three stages according to the complex chemical composition interactions and TR features. Experimental results prove that the signal provides more than a 500s interval for a timely solution from the battery management system. Besides, the maximum strain increment (Δ ε max)/capacity(Q)- Q equalization is quantitively analyzed. Moreover, Δ ε max - Q and R I - Q equalizations are proposed to reveal the TR mechanical feature and guide the TR warning threshold definition of battery management systems (BMSs). Furthermore, the strain-changing trend and warning effect is also proved in TRP tests. TRP strain mechanism is unlocked from the mechanical deformation perspective. Battery deformation is opposite to the TRP direction, which guides accident analysis. This study proposes a cheap and reliable warning signal for an in-line configuration battery system, with only one strain gauge attached to the surface center of the first/last battery casing, which has more potential to guarantee the active safety of battery systems. • Mechanical strain is proposed as a reliable early warning signal for battery failure. • Thermal runaway (propagation) strain changing mechanism is investigated. • Maximum strain increment-capacity equation is proposed for prismatic batteries. • Strain increasing rate-capacity equation is proposed for warning threshold definition. • Thermal runaway propagation deformation feature is revealed for accident survey. [ABSTRACT FROM AUTHOR]

Irreversible lithium plating, as one of the unwanted side reactions, has a high risk of accelerating degradation to destroy the electrochemical performance of lithium-ion batteries (LIBs). Presently, most of the existing irreversible lithium plating detection methods are qualitative or post-mortem quantitative, and there is still a lack of an effective irreversible lithium plating in-situ quantitative detection method. Here, an in-situ quantitative irreversible lithium plating detection method within the full-lifespan of LIBs is proposed. Multi-battery parallel aging experiments are designed for the two abuse scenarios of low-temperature and high-current. By using scanning electron microscope (SEM), inductively coupled plasma-mass (ICP), and Argon-CP technology, the qualitative evolution and the quantitative detection method of irreversible lithium plating are explored. In order to make the proposed detection method non-destructive, two in-situ factors are analyzed to establish the mapping relationship with the measured irreversible lithium plating. It makes it possible that without post-mortem, the irreversible lithium plating, for the first time, is measured quantitatively only by the in-situ factors extracted from the cycle data. Our work provides a possibility for quantitative and onboard detection of irreversible lithium plating, which is of great significance for the development of battery prognostics and health management (PHM) and echelon utilization. [Display omitted] • The problem of discontinuous aging information caused by post-mortem is solved. • The evolution process of irreversible lithium plating is clearly explored. • The evolution process of SEI film thickness is clearly explored. • In-situ quantitative detection of irreversible lithium plating is proposed. • An online full-lifespan irreversible lithium plating detection framework is built. [ABSTRACT FROM AUTHOR]

Occurrence of lithium plating on the anode is a severe side reaction in the lithium-ion batteries, which brings cell capacity degradation and reduces the cell safety. This paper focuses on 37Ah commercial lithium-ion batteries and clarifies the evolution of lithium plating during long-term low temperature (−10 °C) cycling. The tested cells are analyzed at different degradation stages, named "fixed-point analysis", to evaluate the evolution process of lithium plating. It is found that the capacity fade and the internal resistance increase exhibit a decelerated trend with a turning point around 450 cycles. The loss of cyclable lithium caused by lithium plating is deemed to be the main reason behind the battery degradation. Post-mortem analysis including scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS) is conduced to reveal the mechanism. The thickness and morphology change of plated lithium, and the plated lithium covered by solid electrolyte interface film, are disclosed by the surface and cross-section SEM images from the fixed-point analysis. XPS analysis further reveals the composition of solid electrolyte interface film, and the plated lithium existing. In a nutshell, lithium plating is inhomogeneous and has high spatial dependence. • Long-term cycling for large-capacity lithium-ion batteries at −10 °C are performed. • Capacity degradation and internal resistance increase exhibit a decelerated trend. • Post-mortem analysis including SEM and XPS is performed to reveal the mechanism. • The evolution process of lithium plating is evaluated by fixed-point analysis. • Lithium plating is obviously inhomogeneous and has high spatial dependence. [ABSTRACT FROM AUTHOR]

18650-type cells with 2.5 Ah capacity are cycled at both 25 °C and 0 °C separately, and at 25 °C two charging protocols (constant current, and constant current-constant voltage charge) are used. Differential voltage analysis (dV/dQ) and alternating current (AC) impedance are mainly used to investigate battery degradation mechanisms quantitatively. The dV/dQ suggests that active cathode loss and loss of lithium inventory (LLI) are the dominating degradation factors. Significant microcracks are observed in the fatigued cathode particles from the scanning electron microscopy (SEM) images. Crystal structure parameters of selected fatigued batteries at fully charged state are determined by in situ high-resolution neutron powder diffraction. Obvious increases of ohmic resistance and solid electrolyte interphase (SEI) resistance occur when the battery capacity fade falls beneath 20%. Continuous charge transfer resistance and Warburg impedance coefficient (W.eff) increase are observed in the course of cycling. Correlation analysis is performed to bridge the gap between material loss as well as LLI and impedance increase. The increase of the charge transfer resistance is related to both active cathode loss and LLI, and a functional relationship is revealed between LLI and W.eff regardless of the used cycling protocols. • 18650-type Cells are cycled at 0 °C and 25 °C using two charging protocols. • Main degradation factors are loss of lithium inventory (LLI) and active cathode loss. • Neutron powder diffraction and post-mortem analysis are done for deep understanding. • Correlations between material loss and impedance parameters are revealed. • Warburg impedance coefficient could be correlated to LLI in the course of cycling. [ABSTRACT FROM AUTHOR]