State of Power Capability Prediction of Lithium-Ion Battery From the Perspective of Electrochemical Mechanisms Considering Temperature Effect

Abstract

Safe and efficient battery operations necessitate the technique of accurate state of power (SOP) capability prediction for lithium-ion batteries. From the perspective of electrochemical mechanisms, the battery SOP depends strongly on the status of battery electrochemical reaction processes and associated variables, especially the lithium-ion concentrations in solid phases. Battery electrochemical models (EMs) are capable of depicting dynamics inside the battery with high fidelity, but most EMs fail to capture the temperature dependences of model parameters, thereby limiting the model accuracy and their application. This paper proposes an EM-based SOP prediction method considering temperature effect, in which battery peak currents and power capabilities are mainly determined by the intercalated or de-intercalated number of lithium-ions from solid particles, and the temperature dependences of battery EM parameters are investigated to enhance the robustness of the model. With the physical limits of lithium-ion concentrations in solid particles, the grey wolf optimizer (GWO) algorithm is applied to seek the peak currents and power capabilities within different predictive time horizons. The accuracy and robustness of the proposed method are evaluated systematically, and promising SOP prediction results with most of the mean absolute percentage errors of less than 4.0% can be achieved under various temperatures.