Along with increasingly frequent use of electric and hybrid electric vehicles, the constraints and demands placed on the them become stricter. The most noticeable challenge considering Hybrid Electric Vehicles (HEVs) is to provide an optimalpower flow between multiple electric sources alongside provided as less as possible aging of energy storage components. To provide efficient battery usage with respect to batteries lifetime, it becomes unavoidable to develop battery lifetime models, which do not only reflect the State-of-Heath (SoH) but also allow battery lifetime prediction. The lifetimeoriented battery models have to be integrated in power management. To be used efficiently and to provide optimal power split ensuring mitigation of battery degradation without sacrificing desired power consumption, accurate modeling of battery degradation is of utmost importance. This implies that gradual battery degradation, which is directly affected by applied loading profiles, has to be monitored and used as additional control input. Moreover, the lifetime model developed in this case has to provide model outputs also in the timeframe of power management. In this contribution, a machine state-based lifetime model for electric battery source is developed. In this particular case, different degradation states as well as machine state transitions are identified in accordance to current operating conditions. Here, the change in charging/ discharging rate (C-rate), overcharging/undercharging of the battery (depth-of-discharge), and the temperature are taken in consideration to define machine model states. The End-of-Lifetime (EoL) is defined as deviation between nominal and current ampere-hour (Ah)-throughput. The proposed machine state-based lifetime model is verified based on existing battery lifetime models using simulation setup. The developed lifetime model in this way serve as a prerequisite forits integration into power management with an aim to provide the trade-off between aforementioned conflicting objectives; fuel consumption and battery degradation.
GRAB-ECO for Minimal Fuel Consumption Estimation of Parallel Hybrid Electric Vehicles
