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Improved storage battery SOC estimation method based on consistency of unit cell

A single battery and battery pack technology, applied in neural learning methods, secondary battery repair/maintenance, biological neural network models, etc., can solve problems such as unrealistic parameter identification

Inactive Publication Date: 2014-03-05
上海松岳电源科技有限公司
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Problems solved by technology

[0003] However, due to the size and cost constraints of the vehicle controller, it is not realistic to perform parameter identification for each single battery

Method used

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  • Improved storage battery SOC estimation method based on consistency of unit cell
  • Improved storage battery SOC estimation method based on consistency of unit cell
  • Improved storage battery SOC estimation method based on consistency of unit cell

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Embodiment

[0032] An improved method for battery pack SOC estimation based on the consistency of a single battery, the method includes an offline learning modeling process based on a fuzzy rule base of an adaptive neural network and an online SOC estimation correction process based on a fuzzy rule base, and the specific steps are as follows:

[0033] Firstly, a fuzzy rule base with optimized structure and parameters is constructed by using the training data set of the battery pack model, the SOC prediction data based on consistency correction and the adaptive neural network;

[0034] Then transplant the fuzzy rule library after off-line learning into the fuzzy inference engine of the embedded controller of BMS, and carry out online correction on the battery pack SOC estimation.

[0035] Such as figure 1 Shown is the battery pack model with distributed parameter characteristics used to obtain the training data set. The battery pack described in the model is composed of single battery mod...

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Abstract

The invention relates to an improved storage battery SOC (State Of Charge) estimation method based on the consistency of a unit cell. In the method, the training data set of a storage battery, SOC predicted data corrected on the basis of consistency, and a self-adoption neural network are use for building a fuzzy rule base with an optimized structure and parameters; and then the fuzzy rule base after off-line learning is implanted into the fuzzy inference machine of an embedded controller of BMS (Battery Management System), so as to carry out on-line correction to storage battery SOC estimation. Compared with the prior art, the invention has the advantage that SOC differences in all unit cells inside the battery can be factually reflected on entire SOC estimation.

Description

technical field [0001] The invention relates to an improved method for estimating the SOC of a battery pack, in particular to an improved method for estimating the SOC of a battery pack based on the consistency of a single battery. Background technique [0002] When the traditional battery management system (Battery Management System, BMS) estimates the internal state of the battery, especially the state of charge (State of charge, SOC), it often regards the entire battery pack as a whole to estimate. In fact, due to There are inconsistencies between the individual cells in the battery pack, so it is unreasonable to replace each individual cell with the overall state for management. For example, when the state of charge of a certain battery in the battery pack is 20%, and the state of charge of another battery is 80%, it is obvious that one of the two batteries is close to overdischarge and the other is close to overcharge. The system's estimate of the state of the battery ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H01M10/42G06N3/08
CPCY02E60/12Y02E60/10
Inventor 戴海峰魏学哲孙泽昌王佳元
Owner 上海松岳电源科技有限公司
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