Abstract

Recent developments in lithium-ion technology have enabled a revolution in the automotive industry. Fully electric vehicles (EVs) operate under distinctly variable conditions, requiring high-voltage battery packs to meet their torque/power demands. Our goal is to provide a simulation engine which, for a given battery pack size, determines when recharging or battery pack replacement are needed. To that end, we study 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 of EV usage profiles, which in turn are a function of generic user-input, such as mission maps, vehicle mechanical characteristics, driving schedules, and battery pack configuration. State space models benefit from the incorporation of metamodels for the ohmic internal resistance and the Coulomb efficiency of the pack. Both meta-models i) effectively introduce additional phenomenology --such as dependency on the magnitude of discharged current and depth of discharge (DoD)--, and ii) provide a link between SOC/SOH and how each discharge cycle affects the health status of the battery pack as a whole. The approach for the simulation engine presented here is stochastic in nature, meaning that prognostics for the SOC and SOH are generated in a particle filter-based scheme. Thus risk and confidence intervals can be obtained for the end-of-discharge and end-of-life respectively