Trajectory signal detection of lead-acid battery in solar container communication station

Is there a capacity trajectory prediction method for lead–acid battery?

Conclusions Aiming at the problems of difficulty in health feature extraction and strong nonlinearity of the capacity degradation trajectory of the lead–acid battery, a capacity trajectory prediction method of lead–acid battery, based on drop steep discharge voltage curve and improved Gaussian process regression, is proposed in this paper.

Is the capacity degradation trajectory of a battery linear or nonlinear?

The capacity degradation trajectory of the battery presents strong nonlinear, so the rational quadratic covariance function is selected to map the capacity trajectory nonlinearly, as shown in Equation (12).

Why is a prediction battery state of charge important?

To have a more viable and economical battery for the energy storage system, an accurate prediction battery State of Charge (SOC) is important to help control the battery charging and discharging, to extend the battery lifespan.

Does Gaussian process regression predict battery capacity trajectory?

The prediction results of capacity trajectory for each battery with the improved Gaussian process regression (CG-GPR) model is satisfying, the relative error percentage of most points is within 1%, and the error of a few points is about 2%, and the error of very few points is up to 3%.