燃料电池堆和动力电池组都存在“短板效应”现象。保障各节一致性才能减少“短板效应”对燃料电池堆和动力电池组带来的不良影响。一致性检测方法是基础。对于质子交换膜燃料电池堆，各片膜电极参数的不一致是各节电池不一致的根本原因，其制约着电堆的发电性能和使用寿命。传统的膜电极参数检测方法的理论模型和分析过程存在缺陷，造成了检测结果的不准确。目前缺少能够准确表征燃料电池堆内膜电极的检测过程模型和规范化的电化学参数检测方法。本文基于电荷守恒方程建立了燃料电池堆充电过程模型，优化了线性电位扫描法，使扫描速率不再影响检测精度；优化了恒电流充电法的解析过程，使其能够检测膜短路电阻；提出了可检测膜电极参数的氢泵辅助质谱分析方法。其中，恒电流充电法最适合对电堆内膜电极参数进行同步检测。分析了恒电流充电法的设定参数对检测结果的影响规律，给出条件参数的选择依据；开发了120路膜电极检测仪，实现对大功率电堆内膜电极参数的同步检测；提出多片膜电极电化学参数一致性评价指标，对两台商业燃料电池堆进行一致性评价：催化剂活性下降是复合板电堆的性能衰减的主因，而金属板电堆内各片膜电极状态良好。基于膜电极单参数对电池性能的影响规律，建立了膜电极参数表征电池性能的模型公式，利用该模型量化了电池的活化阻抗、传质阻抗和极限电流等参数。因此，可以综合利用恒流充电法、膜电极参数一致性评价指标和燃料电池性能模型来分析电堆性能和一致性变化的原因，为预测燃料电池堆的剩余寿命提供了有力支持。对于锂离子电池组，各节电池的参数一致性影响着电池组的性能和使用寿命。尤其是自放电率，在出厂分组、日常维护及梯次利用筛选等过程中都需要对电池的自放电率进行检测。现有的自放电测量方法耗时较长，影响电池筛选成组的效率。本文基于电荷守恒定律构建了锂离子电池恒电流脉冲激励检测模型，该模型综合考虑了开路电压变化、弛豫效应和自放电现象等多因素的影响，并在电池端电压动态变化过程中捕捉自放电电流。在优化激励模式、脉冲电流和静置时间等参数后，利用该方法对磷酸铁锂电池和三元镍钴锰电池进行自放电检测，其检测结果与传统方法一致。该方法可在短时间内检测到锂离子电池自放电电流、电池充电容量和欧姆内阻等多项电池参数，为锂离子电池筛选和电池组一致性评价打下基础。

There is a phenomenon of ‘Buckets effect’ in both fuel cell stack and power battery pack. Ensuring the consistency of individual cell can reduce the adverse impacts of the ‘Buckets effect’ on the fuel cell stack and the power battery pack, and the consistency detection method is the basis.The voltage inconsistency of single cells in the proton exchange membrane fuel cell (PEMFC) stack restricts the stack’s generating performance and service life, which is due to the inconsistency of the quality and state of each membrane electrode assembly (MEA). The traditional detection methods and their theoretical models have various defects, thus resulting in inaccurate detection results of MEA parameters. At present, there still lack a standardized detection method for MEA parameters detection and an analytical model which expresses the electrochemical process in the PEM fuel cell accurately. Based on the law of charge conservation, a new charging theoretical model of the PEM fuel cell is established firstly, which involves the characterization of electron transfer across the PEM. Based on the model, the linear potential scanning method (LSV) is improved so that the scanning rate no longer affects the detection accuracy; the galvanostatic charging method (GCM) is optimized so that it can detect the short-circuit resistance of PEM; a hydrogen pump assisted mass spectrometry is proposed for MEA parameters identification. Among these methods, the GCM is the most suitable method for detecting the MEA parameters in a fuel cell stack. The influence of the test condition parameters of each step in the GCM on the detection results is then analyzed, which further provides the guidance and reasonable suggestions for the parameter setting of the GCM. A 120 channels’ MEA parameters detector is developed to realize the synchronous detection of MEA parameters in the high power stack. The consistency evaluation index of the MEA parameters is put forward, based on which the MEA parameters detection and consistency evaluation of two commercial fuel cell stacks are carried out. The decrease in the roughness of the catalyst results in the performance deterioration of the stack with composite bipolar plates. However, the MEAs in the stack with metallic bipolar plates keep in good condition. Based on the influence of single MEA parameters on the cell performance, the relationship model between multi-parameters of the MEA and the performance in the whole current densities is established, in which the activation impedance, mass transfer impedance and limiting current of the fuel cell are all quantified. By using the GCM, the consistency index of MEA parameters and the model of fuel cell performance can be used to analyze the reasons for the performance decay and the consistency evolution in a stack, and to provide strong support for predicting the remaining life of the fuel cell stack.The self-discharge of lithium-ion batteries affects the performance and service life of the battery pack, making itself an essential index in the battery test. The self-discharge current of lithium-ion batteries needs to be detected before delivery and in the process of cascade utilization and maintenance. The existing measuring methods of self-discharge take a long time, thus decreasing the efficiency of battery packing greatly.Based on the law of charge conservation, a model for detecting lithium-ion batteries under the pulse current excitation is established, with the change of open-circuit voltage (OCV), the relaxation effect and the self-discharge considered. The self-discharge current of the battery is obtained during the dynamic evolution of the battery’s terminal voltage. After optimizing the excitation mode, the pulse current and the calendar time, the rapid self-discharge detection method be used to detect the self-discharge of the LiFePO4 batteries and the Li(NiCoMn)O2 batteries. The detection results are consistent with those of the traditional method.This method can be used to detect the variety parameters of lithium-ion batteries in a short time, including the self-discharge current, the charging capacity and internal resistance, which lays a foundation for the screening of the high consistency lithium-ion batteries.