###### Abstract
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{Test Method} & Advantages & High-precision \\ \cline{2-3}  & Disadvantages & High-cost \\  & & Long time-consuming \\ \cline{2-3}  & Accuracy & Good \\ \cline{2-3}  & Robustness & Poor \\ \hline \multirow{6}{*}{Parameter-Based Method} & Advantages & Simple \\  & & Low computational-burden \\  & & High real-time \\ \cline{2-3}  & & Sensitive to external environments, such as \\  & & working environment and aging \\ \cline{2-3}  & Disadvantages & Require regular calibration of parameters \\  & & Require precise equipment \\ \cline{2-3}  & Accuracy & Poor \\ \cline{2-3}  & Robustness & Good \\ \hline \multirow{6}{*}{Ampere-Hour Integral Method} & Advantages & Simple \\  & & Low computational-burden \\  & & High real-time \\ \cline{2-3}  & & Depends on the exact initial value \\ \cline{2-3}  & Disadvantages & Open-loop control \\ \cline{2-3}  & Influenced by current drift, noise, and aging \\ \cline{2-3}  & Accuracy & General \\ \cline{2-3}  & Robustness & Good \\ \hline \multirow{6}{*}{Model-BasedMethod} & Advantages & High-precision \\  & Advantages & Closed-loop control \\ \cline{2-3}  & High real-time \\ \cline{2-3}  & Well-adapted & \\ \cline{2-3}  & Disadvantages & Require an accurate battery model \\ \cline{2-3}  & & High computational complexity \\ \cline{2-3}  & Accuracy & Good \\ \cline{2-3}  & Robustness & Good \\ \hline \multirow{6}{*}{Data-Driven Method} & Advantages & High precision \\ \cline{2-3}  & & Excellent nonlinearity \\ \cline{2-3}  & Disadvantages & High computational complexity \\ \cline{1-1} \cline{2-3}  & Influence by data \\ \cline{1-1} \cline{2-3}  & Accuracy & Good \\ \cline{1-1} \cline{2-3}  & Robustness & Poor \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The common prediction methods for battery capacity. a destructive operation for primary lithium batteries. Hence, this method can be used to verify the accuracy of the primary lithium battery capacity estimation model. Due to the chemical characteristics, such as the degree of active materials degradation, internal resistance, voltage response, current response, etc., will change with charge capacity, the mapping of capacity vs. parameters can be obtained. Hence, the parameter-based method is proposed to estimate battery capacity. [PERSON] first discovered that the open circuit voltage (OCV) monotonically varied with the charge capacity of lithium-ion battery and established a relationship between them by combining linear function, power function, and logarithmic function [3]. [PERSON] et al. [4] and [PERSON] et al. [5] have also experimentally established a one-to-one mapping table of OCV and the charge capacity for lithium-ion batteries. However, the primary lithium battery discharge voltage profile is quite flat [6; 7], making platform voltage vs. charge capacity methods meaningless. Moreover, OCV measurement requires a long rest to depolarize, which cannot be applied in real-time. [PERSON] et al. take entropy '\(\Delta\)S' and enthalpy '\(\Delta\)H' as features to assess charge capacity of Li/MnO\({}_{2}\) primary batteries [6]. If this method is translated into an online form, an embedded chip needs to be equipped to collect voltage and temperature data and convert them into thermodynamic data. It may increase equipment weight and computational-burden on the engineering. To sum up, the parameter-based method is more suitable for the laboratory. Moreover, for some primary lithium batteries, it is quite difficult to find significant capacity characterization parameters. The ampere-hour integral method is based on ampere-hour counting or the coulomb counting algorithm. Due to a good balance of precision and complexity, this method is preferred in various battery management systems (BMS). Moreover, it is suitable for different battery systems.