Exploring impedance spectrum for lithium-ion batteries diagnosis and prognosis:A comprehensive review

Lithium-ion batteries have extensive usage in various energy storage needs,owing to their notable benefits of high energy density and long lifespan.The monitoring of battery states and failure identification are indispensable for guaranteeing the secure and optimal functionality of the batteries.The impedance spectrum has garnered growing interest due to its ability to provide a valuable understanding of material characteristics and electrochemical processes.To inspire further progress in the investigation and application of the battery impedance spectrum,this paper provides a comprehensive review of the determination and utilization of the impedance spectrum.The sources of impedance inaccuracies are systematically analyzed in terms of frequency response characteristics.The applicability of utilizing diverse impedance features for the diagnosis and prognosis of batteries is further elaborated.Finally,challenges and prospects for future research are discussed.

Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

Reservoir heterogeneity analysis using multi-directional textural attributes from deep learning-based enhanced acoustic impedance inversion:A study from Poseidon,NW shelf Australia

Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.

Transverse mode-coupling instability with longitudinal impedance

Transverse mode-coupling instability(TMCI)is a dangerous transverse single-bunch instability that can lead to severe par-ticle loss.The mechanism of TMCI can be explained by the coupling of transverse coherent oscillation modes owing to the transverse short-range wakefield(i.e.,the transverse broadband impedance).Recent studies on future circular colliders,e.g.,FCC-ee,showed that the threshold of TMCI decreased significantly when both longitudinal and transverse impedances were included.We performed computations for the circular electron-positron collider(CEPC)and observed a similar phenom-enon.Systematic studies on the influence of longitudinal impedance on the TMCI threshold were conducted.We concluded that the imaginary part of the longitudinal impedance,which caused a reduction in the incoherent synchrotron tune,was the primary reason for the reduction in the TMCI threshold.Additionally,the real part of the longitudinal impedance assists in increasing the TMCI threshold.

Novel method for identifying the stages of discharge underwater based on impedance change characteristic

It is difficult to determine the discharge stages in a fixed time of repetitive discharge underwater due to the arc formation process being susceptible to external environmental influences. This paper proposes a novel underwater discharge stage identification method based on the Strong Tracking Filter(STF) and impedance change characteristics. The time-varying equivalent circuit model of the discharge underwater is established based on the plasma theory analysis of the impedance change characteristics and mechanism of the discharge process. The STF is used to reduce the randomness of the impedance of repeated discharges underwater, and then the universal identification resistance data is obtained. Based on the resistance variation characteristics of the discriminating resistance of the pre-breakdown, main, and oscillatory discharge stages, the threshold values for determining the discharge stage are obtained. These include the threshold values for the resistance variation rate(K) and the moment(t).Experimental and error analysis results demonstrate the efficacy of this innovative method in discharge stage determination, with a maximum mean square deviation of Scrless than 1.761.

Construction of a broadband impedance spectrum and synchronous DC voltammetry measurement system for solar cells

The current impedance spectroscopy measurement techniques face difficulties in diagnosing solar cell faults due to issues such as cost,complexity,and accuracy.Therefore,a novel system was developed for precise broadband impedance spectrum measurement of solar cells,which was composed of an oscilloscope,a signal generator,and a sampling resistor.The results demonstrate concurrent accurate measurement of the impedance spectrum(50 Hz-0.1 MHz)and direct current voltametric characteristics.Comparative analysis with Keithley 2450 data yields a global relative error of approximately 6.70%,affirming the accuracy.Among excitation signals(sine,square,triangle,pulse waves),sine wave input yields the most accurate data,with a root mean square error of approximately 13.3016 and a global relative error of approximately 4.25%compared to theoretical data.Elevating reference resistance expands the half circle in the impedance spectrum.Proximity of reference resistance to that of the solar cell enhances the accuracy by mitigating line resistance influence.Measurement error is lower in high-frequency regions due to a higher signal-to-noise ratio.

3D electromagnetic simulation of the coupling characteristics and double-stub Ferrite tuners impedance matching for EAST ICRH four-strap antenna

A program developed with COMSOL software integrates EAST four-strap antenna coupling with the double-stub Ferrite tuners(FT)impedance matching,obtaining physical quantities crucial for predicting the overall performance of the ion cyclotron resonance heating(ICRH)antenna and matching system.These quantities encompass S-matrix,port complex impedance,reflection coefficients,electric field and voltage distribution,and optimal matching settings.In this study,we explore the relationship between S-matrix,reflection coefficients,port complex impedance,and frequency.Then,we analyze the impact of Faraday screens placement position and transparency,the distance from the Faraday screen(FS)to the current straps(CS),the relative distance between ports,and the characteristic impedance of the transmission line on the coupling characteristic impedance of the EAST ICRH system.Finally,we simulate the electric field distribution and voltage distribution of the EAST ICRH system for plasma heating with double-stub FT impedance matching.Using optimized parameters,the coupling power of the ICRH system can be approximately doubled.The results present herein may offer guidance for the design of high-power,long-pulse operation ICRH antenna systems.

Predicting dynamic compressive strength of frozen-thawed rocks by characteristic impedance and data-driven methods

In cold regions,the dynamic compressive strength(DCS)of rock damaged by freeze-thaw weathering significantly influences the stability of rock engineering.Nevertheless,testing the dynamic strength under freeze-thaw weathering conditions is often both time-consuming and expensive.Therefore,this study considers the effect of characteristic impedance on DCS and aims to quickly determine the DCS of frozen-thawed rocks through the application of machine-learning techniques.Initially,a database of DCS for frozen-thawed rocks,comprising 216 rock specimens,was compiled.Three external load parameters(freeze-thaw cycle number,confining pressure,and impact pressure)and two rock parameters(characteristic impedance and porosity)were selected as input variables,with DCS as the predicted target.This research optimized the kernel scale,penalty factor,and insensitive loss coefficient of the support vector regression(SVR)model using five swarm intelligent optimization algorithms,leading to the development of five hybrid models.In addition,a statistical DCS prediction equation using multiple linear regression techniques was developed.The performance of the prediction models was comprehensively evaluated using two error indexes and two trend indexes.A sensitivity analysis based on the cosine amplitude method has also been conducted.The results demonstrate that the proposed hybrid SVR-based models consistently provided accurate DCS predictions.Among these models,the SVR model optimized with the chameleon swarm algorithm exhibited the best performance,with metrics indicating its effectiveness,including root mean square error(RMSE)﹦3.9675,mean absolute error(MAE)﹦2.9673,coefficient of determination(R^(2))﹦0.98631,and variance accounted for(VAF)﹦98.634.This suggests that the chameleon swarm algorithm yielded the most optimal results for enhancing SVR models.Notably,impact pressure and characteristic impedance emerged as the two most influential parameters in DCS prediction.This research is anticipated to serve as a reliable reference for estimating the DCS of rocks subjected to freeze-thaw weathering.

Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy: Robustness improvement and evaluation

Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.

Mean nocturnal baseline impedance in gastro-esophageal reflux disease diagnosis:Should we strictly follow the Lyon 2 Consensus?

Clinical practice guidelines drive clinical practice and clinicians rely to them when trying to answer their most common questions.One of the most important position papers in the field of gastro-esophageal reflux disease(GERD)is the one produced by the Lyon Consensus.Recently an updated second version has been released.Mean nocturnal baseline impedance(MNBI)was proposed by the first Consensus to act as supportive evidence for GERD diagnosis.Originally a cut-off of 2292 Ohms was proposed,a value revised in the second edition.The updated Consensus recommended that an MNBI<1500 Ohms strongly suggests GERD while a value>2500 Ohms can be used to refute GERD.The proposed cut-offs move in the correct direction by diminishing the original cut-off,nevertheless they arise from a study of normal subjects where cut-offs were provided by measuring the mean value±2SD and not in symptomatic patients.However,data exist that even symptomatic patients with inconclusive disease or reflux hypersensitivity(RH)show lower MNBI values in comparison to normal subjects or patients with functional heartburn(FH).Moreover,according to the data,MNBI,even among symptomatic patients,is affected by age and body mass index.Also,various studies have proposed different cut-offs by using receiver operating characteristic curve analysis even lower than the one proposed.Finally,no information is given for patients submitted to on-proton pump inhibitors pH-impedance studies even if new and extremely important data now exist.Therefore,even if MNBI is an extremely important tool when trying to approach patients with reflux symptoms and could distinguish conclusive GERD from RH or FH,its values should be interpreted with caution.

Solid-state impedance spectroscopy studies of dielectric properties and relaxation processes in Na_(2)O–V_(2)O_(5)–Nb_(2)O_(5)–P_(2)O_(5) glass

Solid-state impedance spectroscopy(SS-IS)was used to investigate the influence of structural modifications resulting from the addition of Nb2O5 on the dielectric properties and relaxation processes in the quaternary mixed glass former(MGF)system 35Na_(2)O–10V_(2)O_(5)–(55-x)P_(2)O_(5)–xNb_(2)O_(5)(x=0–40,mol%).The dielectric parameters,including the dielectric strength and dielectric loss,are determined from the frequency and temperature-dependent complex permittivity data,revealing a significant dependence on the Nb2O5 content.The transition from a predominantly phosphate glass network(x<10,region I)to a mixed niobate–phosphate glass net-work(10≤x≤20,region II)leads to an increase in the dielectric parameters,which correlates with the observed trend in the direct-cur-rent(DC)conductivity.In the predominantly niobate network(x≥25,region III),the highly polarizable nature of Nb5+ions leads to a fur-ther increase in the dielectric permittivity and dielectric strength.This is particularly evident in Nb-40 glass-ceramic,which contains Na_(13)Nb_(35)O_(94) crystalline phase with a tungsten bronze structure and exhibits the highest dielectric permittivity of 61.81 and the lowest loss factor of 0.032 at 303 K and 10 kHz.The relaxation studies,analyzed through modulus formalism and complex impedance data,show that DC conductivity and relaxation processes are governed by the same mechanism,attributed to ionic conductivity.In contrast to glasses with a single peak in frequency dependence of imaginary part of electrical modulus,M″(ω),Nb-40 glass-ceramic exhibits two distinct contributions with similar relaxation times.The high-frequency peak indicates bulk ionic conductivity,while the additional low-fre-quency peak is associated with the grain boundary effect,confirmed by the electrical equivalent circuit(EEC)modelling.The scaling characteristics of permittivity and conductivity spectra,along with the electrical modulus,validate time-temperature superposition and demonstrate a strong correlation with composition and modification of the glass structure upon Nb_(2)O_(5) incorporation.

Improved reservoir characterization by means of supervised machine learning and model-based seismic impedance inversion in the Penobscot field,Scotian Basin

The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field,Scotian Basin.An integrated study of instantaneous frequency,P-impedance,volume of clay and neutron-porosity attributes,and structural framework was done to unravel the Late Cretaceous depositional system and reservoir facies distribution patterns within the study area.Fault strikes were found in the EW and NEE-SWW directions indicating the dominant course of tectonic activities during the Late Cretaceous period in the region.P-impedance was estimated using model-based seismic inversion.Petrophysical properties such as the neutron porosity(NPHI)and volume of clay(VCL)were estimated using the multilayer perceptron neural network with high accuracy.Comparatively,a combination of low instantaneous frequency(15-30 Hz),moderate to high impedance(7000-9500 gm/cc*m/s),low neutron porosity(27%-40%)and low volume of clay(40%-60%),suggests fair-to-good sandstone development in the Dawson Canyon Formation.After calibration with the welllog data,it is found that further lowering in these attribute responses signifies the clean sandstone facies possibly containing hydrocarbons.The present study suggests that the shale lithofacies dominates the Late Cretaceous deposition(Dawson Canyon Formation)in the Penobscot field,Scotian Basin.Major faults and overlying shale facies provide structural and stratigraphic seals and act as a suitable hydrocarbon entrapment mechanism in the Dawson Canyon Formation's reservoirs.The present research advocates the integrated analysis of multi-attributes estimated using different methods to minimize the risk involved in hydrocarbon exploration.

Statistical Inversion Based on Nonlinear Weighted Anisotropic Total Variational Model and Its Application in Electrical Impedance Tomography

Electrical impedance tomography (EIT) aims to reconstruct the conductivity distribution using the boundary measured voltage potential. Traditional regularization based method would suffer from error propagation due to the iteration process. The statistical inverse problem method uses statistical inference to estimate unknown parameters. In this article, we develop a nonlinear weighted anisotropic total variation (NWATV) prior density function based on the recently proposed NWATV regularization method. We calculate the corresponding posterior density function, i.e., the solution of the EIT inverse problem in the statistical sense, via a modified Markov chain Monte Carlo (MCMC) sampling. We do numerical experiment to validate the proposed approach.

Investigation into Impedance Measurements for Rapid Capacity Estimation of Lithium-ion Batteries in Electric Vehicles

With the dramatic increase in electric vehicles(EVs)globally,the demand for lithium-ion batteries has grown dramatically,resulting in many batteries being retired in the future.Developing a rapid and robust capacity estimation method is a challenging work to recognize the battery aging level on service and provide regroup strategy of the retied batteries in secondary use.There are still limitations on the current rapid battery capacity estimation methods,such as direct current internal resistance(DCIR)and electrochemical impedance spectroscopy(EIS),in terms of efficiency and robustness.To address the challenges,this paper proposes an improved version of DCIR,named pulse impedance technique(PIT),for rapid battery capacity estimation with more robustness.First,PIT is carried out based on the transient current excitation and dynamic voltage measurement using the high sampling frequency,in which the coherence analysis is used to guide the selection of a reliable frequency band.The battery impedance can be extracted in a wide range of frequency bands compared to the traditional DCIR method,which obtains more information on the battery capacity evaluation.Second,various statistical variables are used to extract aging features,and Pearson correlation analysis is applied to determine the highly correlated features.Then a linear regression model is developed to map the relationship between extracted features and battery capacity.To validate the performance of the proposed method,the experimental system is designed to conduct comparative studies between PIT and EIS based on the two 18650 batteries connected in series.The results reveal that the proposed PIT can provide comparative indicators to EIS,which contributes higher estimation accuracy of the proposed PIT method than EIS technology with lower time and cost.

基于电化学阻抗谱的锂电池过充电阻抗特性与检测方法研究

目前以锂电池为主的电化学储能单元及系统应用日益广泛,而锂电池在实际使用中频发因过充电滥用引发电池故障的情况,因此实际电池的过充电状态准确检测一直是该领域的难点和瓶颈问题。针对此,该文采用电化学阻抗谱技术对单体电池过充电行为及过程开展检测研究,在实验室设计并制定电池过充电模拟循环实验,利用弛豫时间分布法对锂电池阻抗特性进行分析;在获得电池阻抗特性的基础上,对电池弛豫时间分布曲线进行解析;最后筛选阻抗特征参量为模型输入量,构建支持向量机模型进行电池过充电检测。结果表明,弛豫时间分布曲线中的极化峰P1对应锂离子在固态电解质界面(solid electrolyte interphase,SEI)膜中的扩散过程、极化峰P2对应电子在正极材料中的扩散过程、极化峰P3对应锂离子在电极界面的氧化还原反应。过充电会导致电池欧姆内阻、SEI膜内阻与电荷转移电阻的增长速率最大为正常循环的266%、360%和182%,其中固态电解质界面SEI膜内阻为主要因素。电化学阻抗谱的阻抗特征参量以及支持向量机模型可以用于锂电池过充电检测,估计精度达93.24%。不仅可掌握电池的运行状态,还可对过充电进行有效辨识。

高渗透率下基于并网逆变器阻抗重塑的锁相环设计方法

针对锁相环、电网阻抗与并网逆变器相互耦合所引发的系统稳定性下降问题。首先,建立考虑电网阻抗的锁相环控制结构模型,通过分析锁相环闭环传递函数可知,电网阻抗会使锁相环系统产生右半平面闭环极点,严重影响锁相环与逆变器系统的稳定性。其次,通过分析逆变器系统输出阻抗,说明锁相环所引入的负阻抗是逆变器系统稳定裕度下降的主要原因。鉴于此,该文提出一种新型锁相环设计方法,理论分析表明,所提方法既能够保证高渗透率下锁相环具有高鲁棒性,又能够对逆变器系统输出阻抗进行重塑,有效拓宽系统对电网阻抗的适应范围。最后,通过仿真与实验验证所提新型锁相环设计方法的有效性。

基于阻抗在线测量的锂离子电池过放电诱发内短路识别研究

锂离子电池的过放电行为可诱发内短路进而可能导致热失控。由于单体电池一致性差异和过放电导致的内短路初期电、热特征不明显,使得电压和温度等常规物理参数难以可靠实现故障预警,而阻抗可以反映电池内部信息,对故障状态具有较好的指示能力。该文通过分析锂离子电池过放电诱发内短路引起阻抗变化的机理,基于设计的阻抗在线测量装置,确定用于监测电池内短路故障的特征阻抗频率为70 Hz,获取过放电诱发内短路过程中特征频率下动态阻抗及阻抗变化率,提出一种基于动态阻抗特征的内短路在线识别方法并验证了方法的可靠性。实验结果表明,锂离子电池放电过程中动态阻抗半正弦变化特征可提前约144 s实现过放电预警,动态阻抗针状变化特征可提前约152 s实现内短路故障预警,动态阻抗明显回升特征可作为内短路发生的标志。此外,阻抗变化率特征有助于实现锂离子电池故障识别及预警,该方法在锂离子电池故障在线快速诊断中具有重要的应用潜力。

弱电网下基于电网电压前馈的并网逆变器阻抗重塑控制策略

弱电网下,锁相环在基波以上频段引入的负阻特性,会降低系统输出阻抗的相位,严重影响系统稳定性。鉴于此,该文通过系统控制框图等效变换,推导得到可以消除锁相环负阻特性的电网电压前馈函数,进而提出一种基于电网电压前馈控制的并网逆变器阻抗重塑控制策略。同时,为了提高该策略的普适性,该文利用函数近似和多目标约束为电网电压前馈函数的优化提供两种设计方法,并从系统基波电流跟踪性能、并网功率因数等方面进行误差分析,从而证明其函数优化设计方法的可行性。理论分析表明,优化后的电网电压前馈控制策略,可以有效地对系统输出阻抗相位特性进行重塑,很大程度上拓宽了系统对电网阻抗的适应范围。最后,通过仿真与实验验证所提控制策略的有效性。

基于等效序阻抗差异特征的低频输电线路差动保护优化方案

低频输电线路故障时,两侧短路电流均由模块化多电平矩阵变换器提供,两侧短路电流相角均受控且幅值相当,导致差动保护灵敏度严重下降。分析了低频输电系统定电压控制侧和功率控制侧的等效正、负序阻抗特征,指出因控制策略不同,系统故障时两侧等效正、负序阻抗特征存在明显差异。分析了低频输电线路区内和区外故障两侧保护装置测量到的等效序阻抗之间的差异性和相似性,基于此特征构建了两侧等效序阻抗差异指标,提出了基于等效序阻抗差异指标的差动保护制动系数优化方法。仿真结果表明,所提方案能够显著提升低频输电线路区内故障时差动保护的动作速度和动作灵敏性。

V_(1)导联r'波振幅结合阻抗变化评价左束支区域起搏电极植入深度的研究

目的:探讨左束支区域起搏时,V_(1)导联r’波振幅结合阻抗变化判断电极植入的合适深度。方法:纳入2019年1月1日至2021年12月31日于南昌大学第二附属医院就诊的心脏结构正常、有起搏器植入指征并行左束支区域起搏的住院患者78例,收集患者基线数据、术中数据和影像学资料,以及术后3、6、9和12个月的随访情况。将起搏时V_(1)导联r’波振幅、阻抗、电极植入深度进行线性相关性及回归分析。结果:78例患者中70例(89.7%)起搏时V_(1)导联QRS波末端出现r’波,8例(10.3%)QRS波表现为r S、RS型或终末端无r’波。线性相关性分析显示,r’波振幅与电极植入深度呈正相关(r=0.424,P<0.01),与阻抗呈负相关(r=-0.256,P=0.03);电极植入深度与阻抗无明显相关性(r=-0.132,P=0.27)。回归分析显示,电极植入深度是r’波振幅的重要影响因素(回归系数=0.056,P=0.000)。结合建立的回归模型和阻抗大小显示,V_(1)导联r’波振幅在0.24~0.69 mV范围内,阻抗在648.30~828.90Ω之间,电极植入深度6~11 mm最合适,穿孔风险小,且能较大概率成功夺获左束支,起搏参数满意,起搏QRS波时限较窄。在术中、术后48 h及12个月随访期间内,患者均未出现电极穿孔、血栓栓塞、心脏填塞、感染、导线脱位等并发症。结论:左束支区域起搏是一种安全可行的起搏方式。在左束支区域起搏时,V_(1)导联r’波振幅在一定合适范围内,并结合阻抗变化可指导左束支区域起搏,有助于降低电极穿孔的风险。