Battery SOC robustness evaluation method based on PLSTM sequence mapping

Abstract

The invention discloses a battery SOC robustness evaluation method based on PLSTM sequence mapping. The battery SOC robustness evaluation method comprises the following specific steps: step 1, normalizing training and test data; 2, slicing a sliding window sequence; step 3, constructing and training an auto-encoder based on PLSTM; step 4, constructing and optimizing a sequence mapping model basedon PLSTM; and step 5, using the test data to complete SOC evaluation. On the basis of a basic LSTM unit, process information is introduced into input and gating, the PLSTM is provided, the sequence mapping model is constructed and trained on the basis of pre-training of the auto-encoder on the basis of the PLSTM unit, and SOC robustness evaluation is completed. According to the evaluation model, the association between the battery state and SOC can be learned, and the influence of the charging and discharging process on the SOC can be learned, so that high-accuracy SOC evaluation can be completed under the adverse conditions of variable sampling frequency, position load profile and the like, and the evaluation model has relatively high practical engineering application value.

Description

technical field [0001] The invention relates to the field of battery charge state evaluation, in particular to a battery SOC robust evaluation method based on PLSTM sequence mapping. Background technique [0002] With the use of fossil fuels, human beings have been able to achieve large-scale industrial production, but they have also brought great negative impacts on the environment. The energy crisis and environmental pollution have brought severe challenges to human beings. Therefore, clean energy represented by lithium batteries has been greatly developed in recent years. The state-of-charge (SOC) of the battery represents the ratio between the current available capacity of the battery and its rated capacity. Accurate SOC evaluation plays a key role in mastering the remaining available capacity of the battery. important functions. Therefore, it is necessary to develop an accurate battery SOC evaluation method. [0003] The existing SOC evaluation methods can be divided...

AI Technical Summary

Problems solved by technology

Existing methods only consider the state information during the charging and discharging process of the battery, and establish the mapping relationship between the battery state parameters and the SOC. This type of method ignores the dynamic characteristics of the battery. The complete information in the input cannot be fully learned; or only the process information is considered. On the one hand, this type of method requires high accuracy of the initial value setting, and on the other hand, there is no correction of the time accumulation effect of the state information, which will give the SOC estimation Bring cumulative error and affect the estimation accuracy

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