Uncertainty factors in lithium‑battery reliability testing refer to various sources that may cause test results to deviate from the “true value” or lead to irreproducibility. Such factors can compromise the reliability and comparability of test data, posing challenges for design validation, quality control, life‑cycle prediction, and safety assessment. The primary uncertainty factors fall into the following categories:

I. Uncertainty and Variability of the Sample Itself

Between batches / In-batch variation:

Minor variations in the source, batch, purity, and physicochemical properties of raw materials (such as the cathode, anode, electrolyte, and separator).

Manufacturing processes (coating, calendering, slitting, winding) / The control accuracy and stability of parameters such as cell stacking, electrolyte injection, formation, and aging.

Changes in the production environment (temperature, humidity, and cleanliness).

Sampling representativeness:

Do test samples drawn from a large batch truly represent the statistical characteristics of the entire population?

Is the sampling method random and unbiased?

Historical Status:

Storage conditions of the sample prior to testing (temperature, humidity, SOC ) and time.

Has the sample undergone preconditioning cycles or other pre-treatments? Is its condition consistent?

Initial performance dispersion:

Even within the same batch, individual cells exhibit inherent variability in initial capacity, internal resistance, voltage plateau, and other parameters.

II. Uncertainty of Test Equipment and Instruments

Measurement precision and accuracy:

The accuracy class, linearity, drift, and calibration status of measurement instruments for key parameters such as voltage, current, temperature, pressure, and time (e.g., data acquisition systems, multimeters, thermocouples, and pressure sensors).

Output control accuracy of test equipment (such as charge–discharge cabinets, temperature chambers, vibration tables, extruders, etc.) (e.g., current) / Voltage control accuracy, temperature uniformity / Stability, force / Displacement control accuracy).

Calibration and Traceability:

Is the equipment calibrated regularly in accordance with national or international standards? Is the calibration certificate still valid?

Can the calibration chain be effectively traced back to the national standard? / International benchmark?

Device resolution and sampling rate:

Is the data acquisition resolution sufficient to capture critical details, such as subtle changes in the voltage plateau?

Is the sampling rate high enough to capture fast transient events, such as the current during a short-circuit moment? / Voltage spikes)?

Equipment Aging and Stability:

Equipment in long-term use may experience performance degradation or drift.

III. Uncertainty of Test Environment Conditions

Environmental Parameter Control:

Temperature: The uniformity of temperature distribution within the temperature chamber, as well as the stability and accuracy of temperature control (within what range of the setpoint?). Temperature fluctuations in the environmental laboratory.

Humidity: The accuracy and uniformity of humidity control in high-humidity testing.

Pressure: The control accuracy of pressure during low-pressure (altitude simulation) testing.

Environmental fluctuations:

The impact of natural fluctuations in temperature and humidity within the environmental test chamber on equipment performance, particularly that of temperature‑controlled chambers.

The impact of external disturbances—such as power supply fluctuations and heat dissipation from other equipment—on the test environment.

IV. Inconsistency in Testing Methods and Procedures

Definition and Execution of Test Procedures:

The test standard or procedure itself may contain ambiguities or incomplete definitions (e.g., “by” 1C Charged to a multiple of 4.2V” -  Constant current? Constant voltage? What is the cutoff current for constant voltage?

Different operators may have varying levels of understanding and adherence to the procedures (e.g., the method of fixture installation, the tightness of wiring, needle insertion, etc.). / (Extrusion speed control).

Are the sequence and intervals of the test steps strictly consistent?

Jigs and Connections:

Is the contact resistance of the test fixtures (charge/discharge fixtures, mechanical test fixtures) stable and consistent?

Are the wiring configuration, length, and wire gauge consistent? Has any additional impedance been introduced?

Is the effect of the fixture on the battery’s thermal dissipation conditions the same?

Test Interruption and Recovery:

Interruptions during testing caused by equipment malfunctions, power outages, or other factors, as well as the methods used to resume testing, may introduce bias.

V. Subjectivity in Data Processing and Analysis

Life‑termination criterion:

Cycle life / Calendar life is typically defined as the number of cycles required for the capacity to decay to a certain percentage of its initial value (e.g., 80% ) as the endpoint. How should the “initial capacity” be determined? Should it be the capacity at the first cycle, the rated capacity, or the capacity after several cycles have reached a steady state? This criterion itself is somewhat arbitrary.

In safety testing, the definition of “failure” (including ignition, explosion, smoking, leakage, and specified temperature rise) may involve subjective judgment or ambiguities in the standard definitions.

Data filtering and selection:

Should “outliers” be removed? And on what criteria? This can affect the statistical properties of the results.

Models and Algorithms:

The choice of life‑prediction model, the parameter‑estimation method, and the assumptions regarding aging mechanisms all influence the uncertainty of the prediction results.

The results vary depending on the internal resistance calculation method used—either the DC internal resistance method or the AC impedance method.

Statistical analysis methods:

The sample size directly affects the confidence interval of statistical results. A small sample size leads to greater uncertainty.

Which statistical method (e.g., mean, median, Weibull distribution fitting) should be used to describe the results?

VI. Subjective Judgment Factors (Especially in Security Testing)

Result determination:

Following safety tests—such as puncture, compression, overcharge, and overdischarge—the determination of whether a battery has “caught fire,” “exploded,” or merely “smoked” can sometimes be subject to the observer’s subjective judgment.

Descriptions and classifications of failure modes may vary from person to person.

How can we reduce the impact of uncertainties?

Strict sample management: Clearly defined sampling protocols and comprehensive documentation of sample history, including batch number, production date, and storage conditions.

Equipment Calibration and Maintenance: Establish a rigorous equipment calibration schedule (in accordance with… ISO/IEC 17025 (Refer to the relevant standards) to ensure that equipment is within its validity period and in good working condition. Perform regular equipment maintenance.

Environmental Control and Monitoring: Ensure that the test environment (temperature and humidity) remains stable, and continuously monitor and record critical environmental parameters—not just the setpoints.

Detailed and standardized test procedures (SOP) : Develop clear, unambiguous operating procedures that specify each step, parameter, and acceptance criterion. Provide rigorous training for operators.

Automation and Consistency Control: Whenever possible, use automated testing equipment to perform critical steps (such as charge–discharge control, nail penetration, etc.). / Extrusion rate), thereby reducing variability introduced by manual operations.

Repeatability and reproducibility: Conduct sufficient replicate measurements (with an adequately large sample size), and, when necessary, perform reproducibility tests across different instruments or laboratories.

Transparency in Data Recording and Analysis: Fully document raw data, test conditions, equipment details, operator information, and any anomalies. Clearly specify the data processing methods and acceptance criteria.

Use reference samples or certified reference materials: Whenever possible, employ reference batteries with known characteristics to monitor the stability of the test system.

Uncertainty Assessment: For critical tests, particularly certification tests, perform and report the evaluation of measurement uncertainty (in accordance with…). GUM Guide).

Summary: Uncertainty is pervasive in lithium‑battery reliability testing, arising from factors such as the test samples, equipment, environmental conditions, operator practices, data analysis, and subjective judgment. Recognizing these sources of uncertainty is the first step toward obtaining reliable, comparable test results. By implementing rigorous quality‑control measures, standardizing operating procedures, ensuring thorough equipment calibration and maintenance, and adopting transparent data‑processing protocols, it is possible to minimize the impact of these uncertainties and enhance the credibility and value of the test outcomes.