Integrated estimation method for health state of retired power lithium battery

Abstract

The invention provides an integrated estimation method for the health state of a retired power lithium battery, and the method comprises the following steps: firstly obtaining an aging feature for health state estimation from an applied combined current pulse test; secondly, using a multi-objective optimization method, optimizing the establishment method of the weak learner from two aspects of thenumber of aging features and the estimation precision; on the basis, using a selection method based on Pareto surface distribution characteristics to further reduce the number of weak learners, meanwhile, the precision of each weak learner is kept, and the overall efficiency of integrated estimation is improved; finally, obtaining each weak learner through training according to an optimal weak learner scheme, and realizing the integrated estimation of the charge state of the retired power lithium battery by adopting a differential evolution algorithm and combining a plurality of weak learnersin a weight mode. Through the design, the data can be actively analyzed, the optimal design of an ensemble learning scheme is automatically realized, and the accuracy and robustness of retired powerlithium battery health state estimation are improved.

Description

technical field [0001] The invention belongs to the technical field of decommissioned power lithium batteries, and relates to an integrated estimation method for the health status of decommissioned power lithium batteries. Background technique [0002] Power lithium batteries have many advantages such as high energy density, low self-discharge rate and no memory effect, and have become the most important energy storage components in current new energy vehicles. The performance of new energy vehicles is closely related to power lithium batteries. During the use of vehicles, lithium batteries will inevitably gradually age. It is generally believed that when the capacity is lower than 80%, the power lithium battery should be decommissioned. However, decommissioned batteries still have potential value for continued application in low-speed vehicles, stationary energy storage and other occasions. Decommissioned power lithium batteries are in the middle and late stages of their l...

AI Technical Summary

Problems solved by technology

However, considering factors such as noise in actual data, the generalization and robustness of data-driven methods still need to be improved. In the practical application of ensemble learning, it has certain advantages in robustness and generalization, but the composition of ensemble Weak learners for learning need both accuracy and diversity

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