Lithium ion battery residual life prediction method based on fusion of improved particle filtering and double-exponential recession empirical physical model

Abstract

The invention discloses a lithium ion battery residual life prediction method based on fusion of improved particle filtering and a double-exponential decay empirical physical model. Aiming at the problem that the precision of a method based on data driving seriously depends on the perfection accuracy degree of a model architecture, the lithium ion battery residual life prediction method adopts a nonlinear least square method to carry out parameter identification on a double-exponential model, utilizes methods such as analogue simulation and test measurement to verify a specific research objectbattery and optimize an empirical model, meanwhile, adopts a statistical correlation coefficient theory to improve a resampling strategy, utilizes a path similarity degree threshold value to correctthe particle weight again, and abandons state smooth estimation to solve the problem of particle degradation in a standard PF algorithm. Based on this, a complete set of lithium ion battery remaininglife prediction systematic research method integrating an improved particle filtering algorithm based on a correlation coefficient theory and a parameter identification double-exponential recession empirical model with scientific and accurate architecture is proposed, and high-precision and high-timeliness prediction of battery health management is fully realized.

Description

technical field [0001] The invention relates to the technical field of battery health management and state of charge prediction, in particular to a systematic research method for lithium-ion battery remaining life prediction based on the combination of statistical correlation theory improved PF algorithm and parameter optimization identification double-exponential decay model. Background technique [0002] With the aggravation of the world resource crisis and environmental pollution, the design and manufacture of electric vehicles have attracted the attention of governments and enterprises around the world. Lithium-ion batteries are an important power source for electric vehicle systems, and their performance is a key factor restricting the development of electric vehicles. Battery state of charge (State of Charge, SOC) prediction is the core task in the battery pack management system, which directly affects battery reliability, safety and service life. At the same time, ac...

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Problems solved by technology

[0004] In order to solve the problem that the particle filter algorithm used in the RUL prediction of the existing lithium-ion battery relies heavily on the accuracy of the research object model architecture and its own algorithm defects (particle degradation phenomenon), thereby affecting the accuracy, effectiveness and stability of the RUL prediction, a method is proposed. Research method for lithium-ion battery remaining life prediction based on Pearson correlation coefficient theory improved resampling strategy particle filter algorithm and nonlinear least squares method to optimize identification parameters of double exponential capacity fading model

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