Power battery health state and residual life prediction method based on big data

Abstract

The invention discloses a power battery health state and residual life prediction method based on big data, and the method comprises the steps: 1, obtaining a large amount of real-time driving condition data from an electric vehicle, and carrying out the data cleaning and filling; 2, extracting four working condition characteristic values including temperature, speed, current and mileage value by processing the discharge data to express equivalent cycle index increase; 3, processing charging data to obtain an accurate battery average capacity curve; 4, based on a battery charging and discharging characteristic experiment, obtaining a relation between SOH and equivalent cycle times, and establishing a battery health state evaluation model based on driving condition-charging calculation; and 5, constructing an integrated neural network machine learning model by taking the characteristic working condition as input and the difference of the equivalent cycle times as output, thereby realizing accurate estimation and prediction of the SOH of the battery.

Description

technical field [0001] The invention is applied in the field of electric vehicles, and is specifically a method for predicting the state of health of a power battery based on big data, which is suitable for accurate estimation of the state of health of a battery vehicle. Background technique [0002] In recent years, with the rapid development of lithium-ion battery technology, the electric vehicle industry has gradually entered a new stage. Battery state of health (SOH, State Of Health) estimation, as one of the key technologies in the battery management system (BMS, Battery Management System), plays a vital role in the mileage and life prediction of electric vehicles. However, since the battery is a highly nonlinear electrochemical system, the identification and estimation of its internal state is still a huge problem. [0003] Since the SOH cannot be directly measured, in order to accurately measure the SOH of the battery, a large number of SOH estimation methods have be...

AI Technical Summary

Problems solved by technology

The model-based estimation method simulates the internal working principle of the battery by establishing an equivalent circuit, and combines corresponding algorithms, such as particle filter, Kalman filter, sliding mode observer, etc., to estimate SOH, but this type of method relies too much on model accuracy, and The algorithm design is more complex

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