Internal short circuits and thermal runaway in lithium-ion batteries (LIBs) are mainly caused by deformation-induced failures in their internal components. Understanding the mechanisms of mechanical failure in the internal materials is of much importance for the design of LIB pack safety. In this work, the constitutive behaviors and deformation-induced failures of these component materials were tested and simulated. The stress-strain constitutive models of the anode/cathode and the separator under uniaxial tensile and compressive loads were proposed, and maximum tensile strain failure criteria were used to simulate the failure behaviors on these materials under the biaxial indentations. In order to understand the deformation failure mechanisms of ultrathin and multilayer materials within the prismatic cell, a mesoscale layer element model (LEM) with a separator-cathode-separator-anode structure was constructed. The deformation failure of LEM under spherical punches of different sizes was analyzed in detail, and the results were experimentally verified. Furthermore, the n-layer LEM stacked structure numerical model was constructed to calculate the progressive failure mechanisms of cathodes and anodes under punches. The results of test and simulation show the fracture failure of the cathodes under local indentation will trigger the failure of adjacent layers successively, and the internal short circuits are ultimately caused by separator failure owing to fractures and slips in the electrodes. The results improve the understanding of the failure behavior of the component materials in prismatic lithium-ion batteries, and provide some safety suggestions for the battery structure design in the future.

This paper proposes a novel physics-based equivalent circuit model of the lithium-ion battery for electric vehicle applications that has comprehensive electrochemical significance and an acceptable level of complexity. Initially, the physics-based extended single particle (ESP) model is improved by adding a correction term to mitigate its voltage bias. Then, the equivalent circuit model based on the improved extended single particle (ECMIESP) model is derived. In this model, the surface state of charge (SOC) of solid particles is approximated using a capacity and multi first-order resistance-capacity equivalent circuits with only two lumped parameters. The overpotential of electrolyte diffusion is approximated using a first-order resistance-capacitance equivalent circuit. The electrochemical reaction overpotential is characterized by a nonlinear resistance. The voltage accuracies of ECMIESP and conventional 2RC equivalent circuit model (ECM2RC) are compared across the entire SOC range under various load profiles. The results demonstrate that the ECMIESP model outperforms ECM2RC model, particularly at low SOC or when the electrochemical reaction overpotential exceeds 50 mV. For instance, the ECMIESP model shows an 820.4 mV reduction in voltage error compared to the ECM2RC model at the endpoint during a 2C constant current discharge test. Lastly, the ECMIESP model was used for SOC estimation with extended Kalman filter, resulting in significantly improved accuracy compared to the conventional ECM2RC model. Therefore, the ECMIESP model has great potential for real-time applications in enhancing voltage and SOC estimation precision.

This paper compares the mode based lithium-ion battery state of charge (SOC) estimations using offline and online parameters under varying temperature. An innovative offline identification method based on genetic algorithm (GA) is used for off-line identification of battery model parameters. The common extended Kalman filter (EKF) and the joint extended Kalman filter (JEKF) are implemented as the algorithms to implement SOC estimation with offline and online parameters. The SOC estimations by JEKF using online parameters and by EKF using offline parameters from mismatched temperature are compared. The results are as follows. When battery temperature is inaccurate, the inaccurate temperature can result in inaccurate offline parameters parameters, which will further increase the SOC estimation errors by EKF using offline parameters. In contrast, SOC estimation accuracy by JEKF are still accurate when no temperature information is provided, because the parameters are online updated by JEKF.