Electric vehicle lithium ion battery health state estimation method based on AdaBoost-CBP neural network

Abstract

The invention provides an electric vehicle lithium ion battery health state estimation method based on an AdaBoost-CBP neural network. Due to the fact that the discharge voltage, the discharge currentand the cyclic charge and discharge frequency are obvious in change trend in the battery using process, the three parameters are adopted as input data of SOH estimation, and the battery capacity is adopted as an output parameter. Because the battery data has noise and presents a nonlinear change characteristic, an extended Kalman filtering algorithm is adopted to carry out denoising. Aiming at the problem that the BP neural network is easy to fall into local optimum, a fractional calculus theory is adopted to optimize a gradient descent method. Finally, the fractional order BP neural networkis used as a weak learner; the fitting capability of the learners is enhanced by utilizing the self-adaptive enhancement performance of the AdaBoost algorithm, and each round of weak learners are integrated to obtain the strong learners, so that the diversity of the learners is improved, the performance advantage complementation of the learners under different working condition data is realized, and the estimation precision is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of electric vehicle batteries, and relates to a method for estimating the state of health of a lithium-ion battery, in particular to a method for estimating the state of health of a lithium-ion battery for an electric vehicle based on an AdaBoost-CBP neural network. Background technique [0002] Battery State of Health (SOH) is an important indicator to measure battery health, aging and remaining life, so it is extremely important for the normal driving and safety of electric vehicles. However, the health status of the battery cannot be directly measured, and can only be estimated through parameters such as the voltage, current, and temperature of the battery that can be directly measured. [0003] Due to the complex and changeable working conditions of electric vehicles during driving, their measurement parameters show complex, changeable and nonlinear changing trends due to their own characteristics and th...

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Problems solved by technology

[0004] The object of the present invention is to propose a kind of fractional order BP neural network method based on self-adaptive strengthening algorithm (AdaBoost) improvement, to solve the problem of electric vehicle lithium-ion battery SOH estimation

For the problem that a single model is difficult to accurately describe the dynamic trend of vehicle data under different working conditions, resulting in inaccurate estimation, the basic CBP model is used as a weak learner, and the AdaBoost algorithm is used to perform multiple rounds of iterations to improve the model's accuracy of data under different working conditions. The learning ability of changing rules, and the weighted average method is proposed to integrate each round of weak learners, which can improve the diversity of learning algorithms and make each learner complement each other in the learning performance of different working conditions, thereby improving the health status of lithium batteries The estimated accuracy of

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