An insight into the errors and uncertainty of the lithium-ion battery characterisation experiments

TLDR: Control measures to remove or minimize the contributions from the sources identified can reduce the error and uncertainty within battery experimental results to around 0.6%, from the figure of around 4.0%.

Abstract: Errors and uncertainty within the experimental results have long-term implications in lithium-ion battery research. Experimental directly feed into the development of different battery models, thus having a direct impact on the accuracy of the models, which are commonly employed to forecast short to long term battery performance. The estimations made by such models underpin the design of key functions within the BMS, such as state of charge and state of health estimation. Therefore, erroneous experimental results could evolve into a much larger issue such as the early retirement of a battery pack from the end-use application. For original equipment manufacturers (OEM), such as automotive OEMs this may have a significant impact, e.g. high warranty returns and damage to the brand. Although occasionally reported in published results, currently, little research exists within the literature to systematically define the error and uncertainty of battery experimental results. This article focuses on the fundamental sources of error and uncertainty from experimental setup and procedure and suggests control measures to remove or minimize the contributions from the sources identified. Our research shows that by implementing the control measures proposed, the error and uncertainty can be reduced to around 0.6%, from the figure of around 4.0%.

Figures

Figure 5: 1C discharge profile and voltage relaxation afterwards for different sensor locations.

Table 1: Internal resistance calculated from all 8 cells

Figure 3: Discharge power versus internal resistance as carried out by 4 testers (R) on 8 test cells. Marker with the solid filling shows average value.

Table 3: A summary of the governing equations for the electrochemical battery model

Figure 1: Pulse power test profile for a 20Ah test cell, charge profile is truncated for cell safety.

Table 4: Capacity degradation results from calendar ageing test and the estimation by the electrochemical model.

Frequently asked questions

Q1. What have the authors contributed in "An insight into the errors and uncertainty of the lithium-ion battery characterisation experiments" ?

This article focuses on the fundamental sources of error and uncertainty from experimental setup and procedure and suggests control measures to remove or minimize the contributions from the sources identified. 

Q2. What is the key to fine tuning the P2D degradation model?

Measurement of battery performance and the degradation characteristics under different operating conditions are key in fine tuning the P2D based degradation model. 

Q3. What is the main concern when designing the experiment?

Within all research, ensuring experiments give accurate, repeatable, reproducible results should be a primary concern when designing the experiment. 

Q4. What is the main problem of electrochemical modellers?

Electrochemical modellers are often faced with uncertainty and unavailability of chemical data during the parameterisation process and the model development. 

Q5. What are the two types of errors in li-ion battery experiments?

In li-ion battery experiments the sources of error can be broadly categorised into two types: environmental errors and procedural errors. 

Q6. What are the sources of error in li-ion battery experiments?

Environmental errors include ambient temperature and humidity conditions, equipment accuracy and resolution, manufacturing tolerances on battery samples and equipment used. 

Q7. What are the types of errors that can be controlled to a limited degree within known bounds?

Environmental errors are those sources of error that are systematic to multiple experiments and can be controlled to a limited degree within known bounds. 

Q8. What can be the impact of omission of this information on the reproducibility of results?

While an individual experimenter may produce repeatable results within their experiment, the omission of this information can have an impact on the reproducibility of results. 

Q9. What is the effect of the gold plating on the lug?

The gold plating reduced the connection resistance, thus the heat generation and protects the lug from corrosion, which avoids the need for cleaning before every time they are being used; justifying the additional cost. 

Q10. How much has the new design negatively impacted the accuracy of the test results?

The introduction of a new cable design and experimental rig has negatively impacted accuracy by 0.5%, however this has dramatically improved the repeatability and reproducibility of experimental test results with a seven-fold reduction in variation from 4.0% to 0.6% (Table 1 and 2). 

Q11. What is the way to assess the accuracy of a large volume of experimental data?

When a large volumes of experimental data need to be assessed for their correctness quickly, and detailed analysis and consideration of their experimental error is time consuming to perform across all data. 

Q12. How can environmental errors be identified before running an experiment?

Environmental errors can be identified before running an experiment, here it was shown an environmental error of 1.1 % from cell-to-cell variation and chamber variation combined was expected.