A novel equivalent modeling method combined with the splice-electrochemical polarization model and prior generalized inverse least-square parameter identification for UAV lithium-ion batteries

The accuracy of lithium-ion battery state estimation is critical to the safety of unmanned aerial vehicles (UAVs). In this paper, aiming at the high-fidelity modeling of the UAV lithium-ion battery, a splice-electrochemical polarization model (S-EPM) for UAV lithium-ion battery is constructed by combining the traditional electrochemical model with the equivalent circuit model, which greatly improved the accuracy of the battery modeling. In addition, a novel prior generalized inverse least-squares algorithm is proposed. Also, based on this algorithm, the full-parameter identification and multicondition error analysis of the S-EPM are realized based on this algorithm. Finally, a targeted complex discharge rate test and a full-function charge-discharge test were designed to further verify the applicability of the S-EPM to complex conditions. The experimental results show that the voltage error of the model under each working condition is 5.50 and 3.0 mV, and the maximum percentage error ratio is 0.20% and 0.07%. This experiment can provide a theoretical basis for the combination of the electrochemical model and equivalent circuit model and the accurate estimation of internal state variables of lithium-ion batteries.

Automated summary

This paper proposes a splice-electrochemical polarization model (S-EPM) for UAV lithium-ion battery and achieves full-parameter identification and multicondition error analysis using a novel prior generalized inverse least-squares algorithm. Experimental results show that the model has high accuracy under complex conditions.