A lithium battery state estimation method for distributed energy storage system

Abstract

The invention discloses a lithium battery state estimation method used in a distributed energy storage system, belonging to the application field of lithium ion batteries. The method includes using the gated cyclic unit cyclic neural network to establish a deep learning-based lithium battery health state estimation model; using the second-order equivalent circuit model of the lithium battery combined with the extended Kalman filter algorithm to estimate the distributed energy storage unit management system in real time. Lithium battery state of charge; through information interaction, the battery capacity and lithium battery state of health estimation model in the lithium battery state of charge estimation process are updated synchronously. On the premise of not increasing the system hardware cost, the present invention provides accurate lithium battery capacity for SOC estimation through information interaction, and at the same time provides training samples for the big data SOH estimation model; and further improves the relationship between the SOC and SOH of the lithium battery in the system The estimation accuracy is convenient for the subsequent effective completion of energy management and scheduling of the system.

Description

technical field [0001] The invention relates to the application field of lithium ion batteries, in particular to a lithium battery state estimation method for a distributed energy storage system. Background technique [0002] In recent years, with the rapid development of new energy vehicles, lithium-ion battery technology has also been significantly improved. Its energy density has increased year by year, but its price has continued to decline. It is a potential electric energy storage component in the future. At present, large-scale lithium battery energy storage applications still have certain difficulties, and face a series of challenges in terms of safety and cost. The joint scheduling of distributed energy storage units is an important means to give full play to the application value of distributed energy storage in power systems. [0003] In this application scenario, accurate state of charge (State of Charge, SOC) and state of health (State of Health, SOH) of lithiu...

AI Technical Summary

Problems solved by technology

At present, the estimation of the SOC and SOH of the lithium battery by the energy storage unit only depends on the battery management system (Battery Management System, BMS) equipped with each distributed energy storage unit, and the equipped BMS is limited by the cost, and the processor does not have Powerful computing power can only run relatively low-complexity algorithms, resulting in limited accuracy of BMS in estimating the SOC and SOH of lithium batteries

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