Lithium ion battery state-of-health prediction method based on ISSA coupled DELM

Abstract

The invention discloses a lithium ion battery state-of-health prediction method based on ISSA coupled DELM. The method employs a DELM network prediction battery state-of-health module and an ISSA optimization DELM network parameter module to realize battery state-of-health prediction, wherein a DELM network comprises two ELM-AE structures. According to the lithium ion battery state-of-health prediction method, 30% of excellent sparrows are used as elite sparrows, and a search space of an SSA algorithm is further expanded by solving reverse solutions of the sparrows; a Cauchy-Gaussian mutation operator is adopted to relocate the position of the optimal sparrow, so that the whole population moves to the vicinity of an optimal solution as far as possible, and the algorithm is prevented from falling into local optimum; the optimal hidden layer weight and bias of the DELM network are solved based on the improved SSA algorithm, and the prediction precision of the DELM network is further improved; and an ISSA-DELM lithium ion battery SOH estimation model is high in prediction precision and can be used for accurately predicting the state of health of a lithium ion battery under the condition of random discharge.

Description

technical field [0001] The invention belongs to the technical field of lithium ion battery health management, and in particular relates to a lithium ion battery health state prediction method based on ISSA coupling DELM. Background technique [0002] Lithium-ion batteries have become one of the most popular and widely used energy storage methods due to their light weight, high charging efficiency, long life, low maintenance cost, and environmental protection. State of Health (SOH) is a standard for measuring battery life. Accurate monitoring of SOH is crucial to improving the performance of battery energy storage systems and realizing timely maintenance of equipment. In most existing studies, standard charge-discharge patterns and many assumptions are considered to accelerate the battery aging process. However, this model and assumptions do not reflect the actual operating conditions of the battery. In addition, the actual capacity of the battery is closely related to the ...

AI Technical Summary

Problems solved by technology

However, like other optimization algorithms, the SSA algorithm still has the problem of low computational efficiency in the later stage of iteration, and it is easy to fall into local optimum problems. In this way, when the SSA algorithm is used to solve the problem of DELM predicting the SOH of lithium-ion batteries, the prediction effect of battery SOH will be unsatisfactory.

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