Lithium ion battery state-of-charge estimation method based on F-FOMIAEKF under multiple working conditions and wide temperature

By introducing fractional-order model, multi-new information theory and adaptive theory into the SOC estimation method of lithium-ion batteries, the filter update process is optimized, and the problem of insufficient accuracy and stability of SOC estimation in the prior art is solved, and higher SOC estimation accuracy and stability are achieved.

CN119782672AActive Publication Date: 2025-04-08KUNMING UNIV OF SCI & TECH
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Patent Information

Application Number
CN202411940669.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-26
Publication Date
2025-04-08
Estimated Expiration
2044-12-26

AI Technical Summary

Technical Problem

The existing SOC estimation methods for lithium-ion batteries are insufficient in reflecting the complex dynamic behavior of batteries, and have poor estimation accuracy and stability under wide temperature and multiple operating conditions.

Method used

The state of charge estimation method of lithium-ion batteries under multi-condition wide temperatures based on F-FOMIAEKF is adopted. By introducing fractional-order model, multi-new information theory and adaptive theory, the filter update process is optimized, and the accuracy and stability of the estimation is improved by real-time update of noise statistical characteristics.

Benefits of technology

It improves the accuracy and stability of SOC estimation, can effectively estimate SOC under wide temperature and multiple operating conditions, and significantly improves the model's prediction accuracy of the dynamic behavior of the battery.

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Abstract

The invention discloses a lithium ion battery state-of-charge estimation method based on F-FOMIAEKF multi-working-condition wide temperature, and the method comprises the steps: calculating an SOC reference value according to a capacity value at a first working condition wide temperature; after the first working condition wide temperature experiment, constructing a data set containing W groups of data points, and deducing an OCV-SOC relational expression by using a polynomial fitting method; establishing a lithium ion battery fractional order second-order equivalent circuit model and a state-space equation; identifying parameters to be identified in the model; and under different working condition wide temperatures, state variables and observation variables in a state-space equation of the fractional-order second-order equivalent circuit model are estimated by adopting an SOC estimation algorithm according to the identified parameters, and an SOC estimation value is obtained. On one hand, a fractional order model is introduced; on the other hand, a filter updating process in the whole estimation process is optimized by introducing a multi-information theory, and in addition, an estimation algorithm is optimized by updating statistical characteristics of process noise and measurement noise in real time by introducing an adaptive theory, so that the accuracy and the stability of SOC estimation are improved.
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