Lead-acid storage battery SOH estimation method based on SA and ANN algorithms

Abstract

The invention discloses a lead-acid storage battery SOH estimation method based on SA and ANN algorithms. The method comprises the steps: S1, carrying out cyclic charging and discharging experiment ona lead-acid storage battery, and recording the one-to-one correspondence between the actual capacity and the number of cycles of the cyclic charging and discharging experiment, and the time of constant-voltage charging and constant-current charging in a charging stage of each experiment; S2, based on the test result of the step S1, determining an influence factor with the highest SOH associationdegree with the lead-acid storage battery, and establishing an artificial neural network regression model; and S3, training weights and bias values in the artificial neural network regression model through an improved simulated annealing algorithm, establishing a new regression model, and estimating the battery capacity data points of the SOH of the lead-acid storage battery by using the new regression model. According to the invention, the artificial neural network model ANN is adopted, the method has the advantages of being good in nonlinear approximation capacity and generalization performance, small in number of training samples and high in fitting precision, the number of model parameters is small, and the calculation speed is high.

Description

technical field [0001] The present invention relates to the technical field of prediction and assessment of the state of health of a lead-acid battery, in particular to a method for estimating the SOH of a lead-acid battery based on a simulated annealing algorithm (Simulated Annealing, SA) and an artificial neural network (Artificial Neural Networks, ANN) algorithm. Background technique [0002] With the improvement of substation intelligence, the substation DC power system bears the important responsibility of supporting the stable operation of equipment in the substation. Lead-acid batteries are low in price, high in specific power, relatively complete in technology, and high in recycling rate. They are the core components of substation DC power supply systems. In addition, lead-acid batteries are also widely used in large-scale photovoltaic power plants, energy storage equipment, power and start-stop equipment and other fields. [0003] The State of Health (SOH) of a lea...

AI Technical Summary

Problems solved by technology

[0004] However, there are few studies on SOH estimation of lead-acid batteries at present, mainly including model-based methods and data-driven methods

The former needs to establish a physical and chemical model of the lead-acid battery. Since many reaction mechanisms have not been mastered, the estimation accuracy and generalization ability of the model are poor.

The latter mainly includes machine learning methods, such as Relevance

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