Identification and Error Analysis of Lithium-Ion Battery Oriented to Cloud Data Application Scenario

Abstract

The label-less characteristics of real vehicle data make engineering modeling and capacity identification of lithium-ion batteries face great challenges. Different from ideal laboratory data, the raw data collected from vehicle driving cycles have a great adverse impact on effective modeling and capacity identification of lithium-ion batteries due to the randomness and unpredictability of vehicle driving conditions, sampling frequency, sampling resolution, data loss, and other factors. Therefore, data cleaning and optimization is processed and the capacity of a battery pack is identified subsequently in combination with the improved two-point method. The current available capacity is obtained by a Fuzzy Kalman filter optimization capacity estimation curve, making use of the charging and discharging data segments. This algorithm is integrated into a new energy big data cloud platform. The results show that the identification algorithm of capacity is applied successfully from academic to engineering fields by charge and discharge mutual verification, and that life expectancy meets the engineering requirements.