Internal and external characteristic considered remaining service life prediction method for lithium ion battery

Abstract

The invention relates to an internal and external characteristic considered remaining service life prediction method for a lithium ion battery. The method is characterized by comprising the steps: a mixed kernel correlation vector machine MRVM method based on a linear kernel function, a polynomial kernel function and a Gaussian kernel function is built, and the problem that a single-kernel RVM islow in prediction capability is solved; a whale optimization algorithm IWOA with a self-adaptive inertia weight is used for providing more suitable parameters for the MRVM method; and the IWAO algorithm can expand the particle search range, so that particles obtain a global optimal solution, and the prediction precision is improved. In order to more accurately characterize the health state of thebattery, health factors of internal and external characteristics of the battery are extracted as input of the IWOA-MRVM method, and a prediction result with 95% confidence interval is output. Since the internal and external characteristics of the battery are considered in the charge-discharge cycle process of the lithium ion battery, the remaining service life of the battery can be more accuratelyrepresented.

Description

technical field [0001] The invention belongs to the technical field of batteries, and relates to a method for predicting the remaining service life of a lithium-ion battery based on internal and external characteristics. Background technique [0002] With the increasingly severe energy crisis and environmental pollution, new energy electric vehicles are undoubtedly a new revolution in the automotive industry. Lithium-ion batteries have become one of the main power sources of electric vehicles due to their advantages in high energy density, low self-discharge rate and long cycle life. As lithium-ion batteries are cycled through charge and discharge, their performance degrades. When the capacity value degrades to a failure threshold of 70%-80% of the initial capacity, it can be considered that the battery life has reached the end state. Therefore, research on the prediction of the remaining service life of lithium-ion batteries provides guarantee for the safe operation of el...

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Problems solved by technology

Evaluation only based on the external characteristics of lithium-ion batteries cannot effectively characterize the remaining service life of lithium-ion batteries

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