Battery control method and system

By establishing the battery electromotive force hysteresis model and using the Kalman filtering algorithm, the problem of low SOC accuracy of aging batteries is solved, and the battery pack energy utilization rate and cycle life are improved.

CN119029350BActive Publication Date: 2025-04-04DONGGUAN GUANGJIN POWER TECH CO LTD
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Patent Information

Application Number
CN202411116258.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-08-14
Publication Date
2025-04-04
Estimated Expiration
2044-08-14

AI Technical Summary

Technical Problem

In the prior art, the SOH and SOC estimation accuracy of aged batteries is not high, resulting in low battery pack energy utilization and inconsistent SOCs in a single unit, affecting the control performance of the battery management system and the vehicle range.

Method used

By establishing a battery electromotive force hysteresis model and combining Kalman filtering algorithm, the online estimation of the open circuit voltage of the battery and the accurate estimation of SOC are achieved, the discretosis of the single SOC is reduced, and the series-parallel structure and equalization strategy of the battery pack are optimized.

Benefits of technology

It improves the energy utilization rate of the battery pack, extends the cycle life of the battery pack, reduces the aging acceleration factor, and ensures efficient control of the battery management system.

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Abstract

The present application discloses a battery control method and system, which relates to the field of battery evaluation, including: obtaining the charge and discharge data and operation data of multiple batteries to form an initial data set; dividing the preprocessed data set according to the charge and discharge cycle to construct a time series sample set; constructing a battery electromotive force hysteresis model based on a long short-term memory (LSTM) neural network; obtaining the historical charge and discharge current and temperature data of the target battery within a certain time range as input, and using the trained battery electromotive force hysteresis model to predict the battery electromotive force value at the current moment; estimating the charge state estimate value according to the battery state; transmitting the charge state estimate value at the current moment to the battery management system, and the battery management system adjusts the charge and discharge power limit and balancing control parameters of the target battery based on the charge state estimate value. In view of the low accuracy of the health state estimation of aging batteries in the prior art, the present application improves the accuracy by characterizing the dynamic response characteristics of the battery electromotive force to the charge and discharge current.
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