Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation

In this work, a coupled electrochemical-thermal model of the power lithium manganate battery under discharging process is established and verified, and its maximum surface temperature errors at the test point under 0.5C (C is discharging rate) and 1.0 C are 1.08 K and 0.95 K, respectively. Moreover, the SOC values of the established model under different discharging conditions are estimated by the improved functional link neural network (FLNN) model. The results show that the improved functional link neural network model is of higher SOC estimation accuracy, namely its maximum estimation errors is 1.82 % and the maximum average errors is 0.98 %, respectively. And SOC estimation results indicate that battery should be properly heated and convection heat transfer coefficient should be suitably reduced at lower ambient temperature during the initial working phase of the battery, SOC value deviation from the new battery increase due to battery aging which increases the SOC estimation error and it should be taken as an important factor to improve the SOC estimation accuracy. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The article establishes a coupled electrochemical-thermal model to describe the surface temperature of a power lithium manganate battery during the discharging process, and estimates the SOC values under different discharging conditions using an improved functional link neural network model. The results indicate that the improved neural network model has higher SOC estimation accuracy.