Abstract
The major challenge for the electric cars to become a feasible alternative for fossil fueled vehicle is to overcome the range anxiety. To meet out the churning demand of high power and range, the energy capacity of battery is increasing whilst maintaining the architecture. Due to high energy density and delicate modules, batteries are prone to functional and structural failure. The major source for the battery failure modes are the excitations from the random road load vibrations. Since, the battery has a direct relationship to the safety & performance of the entire vehicle, it is vital to study the structural integrity of the battery. The intent of the paper is to develop a CAE methodology to predict the life of the battery for the actual road load condition. The transient load signal from the test measurements is converted to power spectral density data (PSD). Frequency response analysis is performed accounting modal damping to find the stresses. These stress values are mapped with PSD data to determine the RMS stresses acting on the component. Subsequently, fatigue analysis is performed using these RMS stresses along with appropriate material S-N curve, to accurately predict the life of the battery. The conventional approach to calculate the life of the component, is to perform normal modal analysis to find the modal stress. Then, a modal transient analysis is done using the time series load, obtained from the test measurements to compute the actual displacement. A modal superposition method is used to superimpose these stresses and modal displacement along with the material S-N curve to perform fatigue analysis. This paper put forth an alternative approach, by using the PSD data to calculate the life, simplifying the process and also yielding an accurate result (since damping is considered). A comparison of the result with Transient and PSD method to the actual test results are discussed. The results are categorized into two sections, one dealing with the transient fatigue analysis and other with the fatigue analysis using PSD data. The critical failure region of the battery is identified from the results. A ninety percent correlation is observed between the simulation result and the actual test values, validating the methodology.