The actual capacity of a battery is an essential indicator for calculating both the state of health and the remaining electric driving range. Numerous model-based techniques employing adaptive filters have been proposed for the online capacity estimation. However, in these filter-based methods, the impacts of filter configurations and the algorithm effectiveness at various aging stages have not yet been fully investigated. To address this gap and to evaluate the performance of three most popular algorithms, i.e. the extended Kalman filter, the particle filter, and the least-squares-based filter, they are coupled with an SOC estimator in dual frameworks. The characterization and accelerated aging tests have been carried out on a lithium-ion battery. After investigating the possible impacts from the configurations, the tracking accuracy, the robustness against the uncertainty of the initial capacity and the long-term performance of the three algorithms are compared. Furthermore, their computational efforts are extensively assessed regarding complexity, simulation runtime as well as compiled code size utilizing an automotive prototype hardware. The results show that the extended Kalman filter is the least sensitive to model degradation with the lowest computational effort; the particle filter shows the fastest convergence speed but has the highest computational effort; and the least-squares-based filter has an intermediate behavior in both long-term performance and computational effort.