Lithium-ion batteries have revolutionized our everyday lives by laying the foundation for a wireless, interconnected and fossil-fuel-free society. Additionally, the demand for Li-ion batteries has seen a dramatic increase, as the automotive industry shifts up a gear in its transition to electric vehicles. To optimize the power and energy that can be delivered by a battery, it is necessary to predict the behavior of the cell under different loading conditions. However, electrochemical cells are complicated energy storage systems with nonlinear voltage dynamics. There is a need for accurate dynamic modeling of the battery system to predict behavior over time when discharging. The study conducted in this work develops an intuitive model for electrochemical cells based on a mechanical analogy. The mechanical analogy is based on a three degree of freedom spring-mass-damper system which is decomposed into modal coordinates that represent the overall discharge as well as the mass transport and the double layer effect of the electrochemical cell. The dynamic system is used to estimate the cells terminal voltage, open-circuit voltage and the mass transfer and boundary layer effects. The modal parameters are determined by minimizing the error between the experimental and simulated time responses. Also, these estimated parameters are coupled with a thermal model to predict the temperature profiles of the lithium-ion batteries. To capture the dynamic voltage and temperature responses, hybrid pulse power characterization (HPPC) tests are conducted with added thermocouples to measure temperature. The coupled model estimated the voltage and temperature responses at various discharge rates within 2.15% and 0.40% standard deviation of the error. Additionally, to validate the functionality of the developed dynamic battery model in a real system, a battery pack is constructed and integrated with a brushless DC motor (BLDC) and a load. Moreover, because of the unique pole ori (open full item for complete abstract)