Lithium ion battery remaining capacity and sensor deviation cooperative estimation method based on neural network and unscented Kalman filter

Abstract

The invention discloses a lithium ion battery remaining capacity and sensor deviation cooperative estimation method based on a neural network and an unscented Kalman filter, and the method comprises the following steps: S1, carrying out the charging and discharging experiment of a lithium ion battery, and collecting sample data which comprises training data and test data; S2, determining input andoutput variables of a neural network, and establishing an RBFNN model of the SOC; S3, performing parameter learning on the established RBFNN based on the training data set to obtain an accurate RBFNNmodel; S4, performing independent precision inspection on the established RBFNN by using the test data; S5, setting the SOC to be in an internal state, designing an RBFNN-UKF, and achieving the real-time estimation of the SOC under the condition that the initial SOC is uncertain; S6, setting the sensor deviation to be in an extended state, designing an extended RBFNN-UKF on the basis of the original RBFNN-UKF, and achieving the cooperative estimation of the SOC and the unknown sensor deviation. The method can achieve the cooperative estimation of the SOC and sensor deviation, is high in convergence speed, is high in precision, and is small in error.

Description

technical field [0001] The invention relates to the field of electric vehicle batteries, in particular to a method for cooperatively estimating the remaining power of lithium electronic batteries and sensor deviations based on a neural network and an unscented Kalman filter. Background technique [0002] The rapid development of the automobile industry has indirectly led to a large amount of energy consumption and the deterioration of the environment. Therefore, battery-powered electric vehicles have gained widespread attention in the new energy vehicle industry due to their advantages of low energy consumption, zero emissions, and high cost performance. Due to the advantages of high energy density, low self-discharge, fast charging, and long life, lithium-ion batteries are widely used in portable appliances such as laptops, cameras, and mobile communications, and thus become the first choice for new energy electric vehicles. [0003] The battery management system is one of...

AI Technical Summary

Problems solved by technology

The shortcomings of the existing methods are as follows: 1. The ampere-hour integration method needs to know the initial value of the SOC, and at the same time as an open-loop algorithm, due to uncertain interference such as temperature and current measurement deviation, there will be an error accumulation effect in the SOC calculation process; 2. The open-loop voltage method can only estimate the SOC when the battery is in an open circuit and under long-term static conditions, and cannot be estimated online, and the static process generally takes several to ten hours; Training; 4. The traditional Kalman filter algorithm is only suitable for linear systems, so it is not suitable for lithium-ion batteries with highly nonlinear systems

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