Recent developments in lithium-ion technology have enableda revolution in the automotive industry. Fully electric vehicles(EVs) operate under distinctly variable conditions, requiringhigh-voltage battery packs to meet their torque/powerdemands. Our goal is to provide a simulation engine which,for a given battery pack size, determines when rechargingor battery pack replacement are needed. To that end, westudy both the State-of-Charge (SOC) and the State-of-Health(SOH) indicators, using discrete state space models for both.Predictions are based on a probabilistic characterization ofEV usage profiles, which in turn are a function of genericuser-input, such as mission maps, vehicle mechanical characteristics,driving schedules, and battery pack configuration.State space models benefit from the incorporation of metamodelsfor the ohmic internal resistance and the Coulombefficiency of the pack. Both meta-models i) effectively introduceadditional phenomenology-such as dependency onthe magnitude of discharged current and depth of discharge(DoD)-, and ii) provide a link between SOC/SOH and howeach discharge cycle affects the health status of the batterypack as a whole. The approach for the simulation engine presentedhere is stochastic in nature, meaning that prognosticsfor the SOC and SOH are generated in a particle filter-basedscheme. Thus risk and confidence intervals can be obtainedfor the end-of-discharge and end-of-life respectively.