Improved Multiple Feature-Electrochemical Thermal Coupling Modeling of Lithium-Ion Batteries at Low-Temperature with Real-Time Coefficient Correction

Abstract

Monitoring various internal parameters plays a core role in ensuring the safety of lithium-ion batteries in power supply applications. It also influences the sustainability effect and online state of charge prediction. An improved multiple feature-electrochemical thermal coupling modeling method is proposed considering low-temperature performance degradation for the complete characteristic expression of multi-dimensional information. This is to obtain the parameter influence mechanism with a multi-variable coupling relationship. An optimized decoupled deviation strategy is constructed for accurate state of charge prediction with real-time correction of time-varying current and temperature effects. The innovative decoupling method is combined with the functional relationships of state of charge and open-circuit voltage to capture energy management effectively. Then, an adaptive equivalent-prediction model is constructed using the state-space equation and iterative feedback correction, making the proposed model adaptive to fractional calculation. The maximum state of charge estimation errors of the proposed method are 4.57% and 0.223% under the Beijing bus dynamic stress test and dynamic stress test conditions, respectively. The improved multiple feature-electrochemical thermal coupling modeling realizes the effective correction of the current and temperature variations with noise influencing coefficient, and provides an efficient state of charge prediction method adaptive to complex conditions.

State of Health Estimation of Lithium-Ion Battery for Electric Vehicle Based on VMD-DBO-SVR Model

“…By integrating the thermal model and enhanced ohmic resistance dynamics, the changes in battery performance can be monitored more accurately. Wang et al 12 proposed an improved multi-feature electrochemical thermal coupling modeling method that considers low-temperature performance degradation. By optimizing the decoupling strategy, the current and temperature changes affected by noise are effectively corrected, achieving efficient charge state prediction.…”