The focus of this project is to enhance the accuracy of state-of-charge (SOC) and state-of-health (SOH) algorithms within the battery management systems of light electric vehicles. Surveys have consistently identified poor charging infrastructure, high costs, and range anxiety as the main deterrents preventing consumers from switching to electric vehicles. Our goal is to address range anxiety and increase electric vehicle adoption by improving the accuracy of algorithms in battery management systems. Our solution estimates the SOC and SOH of a battery via an iterative process using an extended Karman filter to monitor the battery's temperature and current values and comparing it against measured values until the convergence is achieved.