Abstract
An equivalent circuit model of dual polarization (DP) of lithium battery was established according to the application characteristics of lithium battery under the standby condition of 5G base station. On the basis of the model, recursive least square method with forgetting factor (RLS) was used to identify the model parameters. Finally, the Unscented Kalman filtering (UKF) was used to estimate the SOC of lithium battery in real time with the identified model parameters. The simulation and experimental results showed that the combined estimation using recursive least square method with forgetting factor (RLS) and UKF could greatly improve the estimation accuracy of lithium battery SOC, reduce the estimation error, and further verify the accuracy and effectiveness of the whole modeling.
Highlights
With the vigorous promotion of 5G technology in China, energy storage technology under the background of new 5G infrastructure is becoming more and more important
The essence of parameter identification by recursive least square method is to calculate the error between the system output and the expectation at this time based on the result identified at the previous time, and correct the error and newly observed data to get the corrected estimate result at the current time
In order to verify the accuracy of the Unscented Kalman filtering (UKF) estimation of the lithium battery SOC, before the test, the capacity of the lithium battery is calibrated to determine that the battery is fully charged with an initial capacity of 20.508Ah, and the hybrid pulse power characteristic (HPPC) pulse discharge test is carried out on the lithium battery with 0.5C (10 A) current
Summary
With the vigorous promotion of 5G technology in China, energy storage technology under the background of new 5G infrastructure is becoming more and more important. Due to the wide distribution of 5G micro base stations, the power system is difficult to meet its requirements, so many base stations have been used energy storage systems to ensure continuous and stable power transmission. The energy storage system realizes peak-shaving and valley-filling of base stations and reduces base station construction and operating costs. Lithium battery is popular with base station batteries because of its low installation cost and long service life, and has been widely used in 5G base station energy storage systems [1][2][3]. The accurate estimation of the SOC of the lithium battery has become one of the key factors to ensure the reliability and stability of the lithium battery and the entire energy storage system. The accuracy of the algorithm is verified through simulation and experiments
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