基于加速寿命试验的锂离子电池可靠性分析方法

为了有效地提高锂离子电池寿命评估的准确性,延长储能系统在配电网中运行年限,文章提出了基于加速寿命试验的锂离子电池可靠性分析方法。综合考虑不同放电深度对锂离子电池寿命影响,建立了锂离子电池的寿命衰退模型;确定了荷电状态(SOC)与健康度(SOH)的关联特性关系;提出了基于逆幂率方程的储能系统加速寿命试验方法;基于情景分析法对锂离子电池的可靠性进行了分析。研究结果表明,文章所提出的试验方法能够准确地对不同运行状态下的锂离子电池储能系统进行可靠性评估,保证储能系统并网运行过程中的调控准确性。     

Measurements and application of thermal characteristics of soft-packed NCM lithium-ion power battery

锂离子电池的极化内阻是不可逆热测试的关键参数。为了更准确地计算极化内阻,针对三元软包锂离子动力电池,进行了HPPC测试、熵热系数测试、充放电温升测试,采用两种方法对极化内阻进行了计算,一种是通过电压变化量除以电流得到,另一种是通过建立二阶RC模型,结合HPPC测试工况辨识得到。根据两种方法得到的极化内阻,结合Bernardi生热速率模型公式对电池进行了1C充电和0.5C、1C、2C放电下的温度场仿真,并与红外热成像仪记录到的温度分布进行了对比。结果表明：根据二阶RC模型得到的极化内阻进行的仿真与实验数据吻合较好,说明利用二阶RC模型得到的极化内阻更加适用于电池持续充放电过程中的热分析。模型很好地模拟了电池不同充放电倍率下的温度场信息,对电池热分析及热管理可起到指导作用。     

Lithium-ion battery SOC/SOH adaptive estimation via simplified single particle model

Compared with battery Equivalent Circuit Models (ECM), Single Particle Model (SPM) has more appropriate physics representation and higher accuracy theoretically. However, SPM-based parameter estimation performance is restricted by the SPM model complexities. In this paper, a simplified SPM and its corresponding adaptive State of Charge (SOC)/State of Health (SOH) estimation scheme are studied. First, the SPM is simplified from Partial Differential Equation (PDE) to Ordinary Differential Equation (ODE) for a trade-off between model complexity and consistency. Second, an adaptive model observer is proposed to estimate battery parameters, which include a SOC state implying normalized lithium-ion concentration, and a SOH parameter implying the maximum lithium-ion surface concentration, both in the solid surface phase. Because the ODE-based adaptive parameter estimation is capable of avoiding complex identification procedures, this new approach can be implemented in practical applications with high accuracy. Through massive simulation scenarios, the proposed SPM model is validated based on comparison between ODE SPM and PDE SPM, as well as Benchmark Validation. Finally, both simulation and experiment demonstrate the effectiveness of the simplified SPM and the superiority of the proposed SOC/SOH estimation scheme.     

Review of lithium-ion battery state of charge estimation

The technology deployed for lithium-ion battery state of charge (SOC) estimation is an important part of the design of electric vehicle battery management systems. Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries, thereby improving discharge efficiency and extending cycle life. In this study, the key lithium-ion battery SOC estimation technologies are summarized. First, the research status of lithium-ion battery modeling is introduced. Second, the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed. Third, the development status and advantages and disadvantages of SOC estimation methods are summarized. Finally, the current research problems and prospects for development trends are summarized.     

Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II: Modelling

A common way to model lithium-ion batteries is to apply equivalent circuit (EC) models. In this work two different EC models are build up and parameterized for a commercial 6.5 Ah high-power lithium-ion cell. Measured impedance spectroscopy data depending on temperature and state of charge (SOC) are used for parameter estimation.The first EC model consists of an ohmic resistor (R), an inductor (L) and three RC-elements (a parallel connection of a capacitor (C) and a resistor). The second EC model consists of one R, one L, two Zarc elements and a Warburg element. The estimated parameters were used to develop two empirical electrical cell models which are able to predict the voltage of the cells depending on current, temperature and SOC. Hereby the internal cell resistance Ri is based on the EC models and a Butler-Volmer adjustment. Both approaches were validated by current profiles, which cover typical automotive applications to prove the model performance at low temperatures and high dynamic operation. An accurate voltage prediction could be realized with both EC models. The second, more complex, model is able to predict cell voltage more precisely, but at the expense of up to four times higher computational effort.     

Towards real-time (milliseconds) parameter estimation of lithium-ion batteries using reformulated physics-based models

In this paper, transport and kinetic parameters of lithium-ion batteries are estimated using a rigorous porous electrode theory based model. The rigorous model used in this investigation is reformulated using advanced mathematical techniques. Since batteries and other electrochemical devices are used in hybrid environments, which include devices with time constants less than a second (like supercapacitor), we need to develop parameter estimation codes with computation time less than a second or a few milliseconds. In this investigation, the computation time for parameter estimation measures between 100 and 300 ms since a reformulated battery model is devised especially for these purposes. Obtaining the numerical solution for battery model equations is very difficult towards the end of discharge and is usually neglected for parameter estimation purposes. However, in this paper the estimation takes into account the entire discharge data ranging from an initial potential of 4.2 V to a cut-off potential of 2.5 V. It is found from this investigation that the reformulated lithium-ion battery model is efficient and accurate in estimating parameters.     

Patent technology development trend analysis of lithium-ion battery energy storage system

锂离子电池是目前最具发展前景的高效二次电池和发展最快的化学储能电源，已成为各国竞相竞争的技术热点。专利技术能够反映某一技术领域创新发展的现状和历程，已成为开展竞争情报的重要信息源。本文从专利角度出发，通过对锂离子电池储能系统领域专利的分析，发现和了解锂离子储能系统的发展态势，以期为我国政府与企业的战略决策提供参考与借鉴。     

Model-based distinction and quantification of capacity loss and rate capability fade in Li-ion batteries

This work is focused on an easy-to-handle approach for estimating the residue power and capacity of a lithium-ion cell during operation. For this purpose, an earlier presented lumped parameter electrochemical battery model is employed. By means of the parameters accounting for the cathode capacity and the electrolyte conductivity, the cell degradation is successfully reproduced. Moreover, the method enables the distinction of capacity fade due to impedance rise and due to active material loss. High discharge rates together with the correlated self-heating of the cell enable a model-based quantification of SEI and electrolyte contributions to the overpotential.     

基于PCA-GPR的锂离子电池剩余使用寿命预测

从充电过程中的电压-容量曲线中提取出一个与电池寿命高度相关健康因子（HI）。然后利用主成分分析（PCA）对影响电池寿命的多维因素进行分析和降维,结合高斯过程回归（GPR）机器学习方法提出一个基于PCA-GPR的锂离子电池剩余使用寿命预测模型。最后进行锂离子电池剩余使用寿命预测并与PCA-BP神经网络、PCA-支持向量机（SVM）模型进行比较。结果表明,利用该文提出的HI及预测模型可有效提高锂离子电池剩余使用寿命预测精度,其中通过贝叶斯优化器优化后的PCA-GPR模型的预测效果最佳。     

Integrated data envelopment-thermoexergetic optimization framework for multicomponent distillation system with multiexergetic response in the robust parameter design procedures

Integrated exergetic, data envelopment analysis (DEA)-robust parameter design framework is proposed to optimize and select thermo-feasible multicomponent distillation system which is a herculean task through reported exergy analysis techniques. It attempts to reduce the thermodynamic computational search for optimal sequence and to handle the effects of variations. Conventional exergetic analysis obtained the thermo-responses, variations are smoothened by robust signal-to-noise ratio procedures, variable return to scale (VRS) partitioning is imposed prior to the efficiency determination by the facet VRS model and penalization coefficient model is employed to select the optimum. The numerical illustration reveals its specificity and provides vivid and panoramic insight into actual efficient systems and those that need improvement. This study shows that exergetic analysis when integrated with DEA and robust parameter procedures can interrelate adequately, efficiently and effectively to optimize multicomponent distillation process and can be applied for optimizing energy-intensive systems especially those with multi-response processes.     

误差平方和目标函数在参数率定过程中遇到的问题

目标函数是参数估计最重要的信息来源,针对目前大多以误差平方和目标函数为参数率定方法,通过对误差平方和目标函数的结构及单参数、多参数模型进行实例计算分析,分别发现该方法遇到不同样本所对应的参数局部优值不同、不同样本组合的参数信息不合理、甚至会提供错误参数估计信息等问题,提出了选取合适目标函数的建议,为提高参数率定的精度和效果提供依据。     

A normal-gamma-based adaptive dual unscented Kalman filter for battery parameters and state-of-charge estimation with heavy-tailed measurement noise

This study simultaneously considers the state-of-charge (SOC) estimation and model parameter identification of lithium-ion batteries with outliers in measurements. Conventional Kalman-type filters may degrade performance in this case since they assume Gaussian-distributed measurement noise. To improve the SOC estimation accuracy under this condition, a robust normal-gamma (NG)-based adaptive dual unscented Kalman filter (NG-ADUKF) is proposed. First, by modeling the joint distribution of the state and auxiliary variables of the measurement noise as the NG distribution, the unscented Kalman filter (UKF) is integrated with the NG filter to deal with the heavy-tailed measurement noise. Second, the online parameter identification and SOC estimation are realized simultaneously by alternatively using two NG-based adaptive UKFs. The performance of the proposed algorithm is validated by the New European Driving Cycle and Urban Dynamometer Driving Schedule tests. Experimental results show that the proposed NG-ADUKF algorithm has more accurate SOC estimations compared with the dual UKF (DUKF) and the variational Bayes-based adaptive DUKF (VB-ADUKF) in the case of mistuning and outliers. Moreover, the proposed method is more computationally efficient than VB-ADUKF.     

均匀设计在地裂缝参数敏感性分析中的应用

为研究参数不确定性对于地裂缝扩张及发展的影响,利用FLAC~(3D)建模,模拟在地裂缝两侧土体不同时抽取地下水情况下地裂缝变化情况。以地裂缝两侧水平位移、垂直位移、地裂缝活动量为参数敏感性分析的目标,并通过均匀设计法对断裂粘聚力、断裂内摩擦角等5个参数进行了多次交叉运算,快速准确地判断了参数敏感度大小。研究表明,参数变化对地裂缝活动量及上盘垂直位移影响较大;内摩擦角为影响地裂缝最敏感的因素。研究结果为今后地裂缝的治理提供了参考依据。     

Parameter estimation of an electrochemistry‐based lithium‐ion battery model using a two‐step procedure and a parameter sensitivity analysis

Lithium‐ion batteries are indispensable in various applications owing to their high specific energy and long service life. Lithium‐ion battery models are used for investigating the behavior of the battery and enabling power control in applications. The Doyle‐Fuller‐Newman (DFN) model is a popular electrochemistry‐based model, which characterizes the dynamics in the battery through diffusions in solid and electrolyte and predicts current/voltage response. However, the DFN model contains a large number of parameters that need to be estimated to obtain an accurate battery model. In this paper, a computationally feasible two‐step estimation approach is proposed that only uses voltage and current measurements of the battery under consideration. In the two‐step procedure, the parameters are divided into 2 groups. The first group contains thermodynamic parameters, which are estimated using low‐current discharges, while the second group contains kinetic parameters, which are estimated using a well‐designed highly‐dynamic pulse (dis‐)charge current. A parameter sensitivity analysis is done to find a subset of parameters that can be reliably estimated using current and voltage measurements only. Experimental data are collected for 12 Ah nickel cobalt aluminum pouch lithium‐ion cell. The voltage predictions of the identified model are compared with several experimental data sets to validate the model. A root mean square error between model predictions and experimental data smaller than 16 mV is achieved.     

Discrimination of Li-ion batteries based on Hamming network using discharging-charging voltage pattern recognition for improved state-of-charge estimation

Differences in electrochemical characteristics among Li-ion batteries and factors such as temperature and ageing result in erroneous state-of-charge (SoC) estimation when using the existing extended Kalman filter (EKF) algorithm. This study presents an application of the Hamming neural network to the identification of suitable battery model parameters for improved SoC estimation. The discharging-charging voltage (DCV) patterns of ten fresh Li-ion batteries are measured, together with the battery parameters, as representative patterns. Through statistical analysis, the Hamming network is applied for identification of the representative DCV pattern that matches most closely of the pattern of the arbitrary battery to be measured. Model parameters of the representative battery are then applied to estimate the SoC of the arbitrary battery using the EKF. This avoids the need for repeated parameter measurement. Using model parameters selected by the proposed method, all SoC estimates (off-line and on-line) based on the EKF are within ±5% of the values estimated by ampere-hour counting.     

Propagation of the photothermal waves in a semiconducting medium under L-S theory

The model of the equations of generalized thermoelasticity based on the Lord–Shulman theory with one relaxation time is used to study the photothermal waves in a semiconducting medium. The exact expressions for the displacement components, temperature, carrier density, and stress components are obtained using normal mode analysis. Numerically simulated results are obtained and presented graphically for silicon to depict the effect of time parameter on the different physical quantities.     

A new parameter extraction method for accurate modeling of PEM fuel cells

In this paper, a new parameter extraction method for accurate modeling of proton exchange membrane (PEM) fuel cell systems is presented. The main difficulty in obtaining an accurate PEM fuel cell dynamical model is the lack of manufacturer information about the exact values of the parameters needed for the model. In order to obtain a realistic dynamic model of the PEM system, the electrochemical considerations of the system are incorporated into the model. Although many models have been reported in the literature, the parameter extraction issue has been neglected. However, model parameters must be precisely identified in order to obtain accurate simulation results. The main contribution of the present work is the application of the simulated annealing (SA) optimization algorithm as a method for identification of PEM fuel cell model parameter identification. The major advantage of SA is its ability to avoid becoming trapped in local minimum, as well as its flexibility and robustness. The parameter extraction and performance validation are carried out by comparing experimental and simulated results. The good agreement observed confirms the usefulness of the proposed extraction approach together with adopted PEM fuel cell model as an efficient tool to help design of power fuel cell power systems. Copyright © 2009 John Wiley & Sons, Ltd.     

Performance analysis of a micro-combined heating and power system with PEM fuel cell as a prime mover for a typical household in North China

Proton exchange membrane (PEM) fuel cells produce a large amount of waste heat while generating electricity through electrochemical reactions, making them suitable for driving combined heating and power (CHP) systems. According to the hourly thermal and electric loads in a typical North China household, a 2-kW PEM fuel cell-based micro-CHP system with a lithium-ion battery energy storage system is proposed in this paper. The thermal and economic performances of the micro-CHP system with a lithium-ion battery (CHPWB) and a CHP system without a lithium-ion battery (CHPWOB) are comparatively analyzed by developing a thermal and economic performance analysis model on the MATLAB/Simulink platform. The thermal-load-following strategy is adopted during the design and simulation process. The results indicate that the storage capacity of the lithium-ion battery decreases by 6.6% after one cycle. The lithium-ion battery can be charged by the fuel cell stack during off-peak hours or using commercial electricity, and the charging cycle of the system is one week long. The average total efficiency of the CHPWB system can reach 81.24% with considering the energy loss in each conversion process, which is 11.02% higher than that of the CHPWOB system. The daily hydrogen consumption of the CHPWB system can be reduced by 14.47% compared with the CHPWOB system under the same operating conditions, and the average daily costs can be reduced by 8.4% and 9.5% when the lifespan is 10 and 15 years, respectively.     

The lattice Boltzmann simulation of electrolyte displacement in battery injection process#br#

锂离子动力电池注液工艺中,电解液的驱替效率直接影响后续工艺中固体电解质界面膜（SEI）的成型质量。采用随机生长四参数法,对锂离子电池多孔电极微观结构进行了重构;基于改进的伪势格子Boltzmann模型研究电池注液工艺中的非混相驱替;仿真分析了锂离子电池多孔电极结构的壁面润湿性和孔隙率对驱替效率的影响。研究表明,多孔电极的润湿特性对驱替效率有显著影响,随着接触角增大而驱替效率降低;在单一孔隙率的多孔介质中,随着孔隙率增加而驱替效率增高,被驱替相残留越少,驱替效果越好;但当电解液流经负极和隔膜交界面处时,因两侧润湿特性和孔隙率有差异,会使得电解液在两侧的驱替效率不同,孔隙率大的一侧在垂直流动方向会产生渗流,降低另一侧的驱替效率,产生气泡,影响到SEI膜的成型。