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The plug-in hybrid electric vehicles (PHEV), utilizing more battery power, has become a next-generation HEV with great promise of higher fuel economy. A nearly global optimization based charge-depletion control strategy is developed for PHEV power management by using the Intelligent Transportation Systems (ITS). Trip model is obtained via GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling. The Gipps car-following model [42] is used for the local road trip modeling along with the synchronization of the traffic signal sequences. The gas-kinetic model [53] was used for the freeway trip modeling. These trip modeling schemes are validated with simulation.

For electric vehicle (EV) operations, a major concern for the customers is whether the available charge in the on-board battery pack could sustain a specific trip or not. Within foreseeable future, the charging infrastructure will still remain relatively short. The time for battery charging is also significant longer compared to that for filling gasoline tank. It is thus of practical benefit for EV operation to predict the battery energy demand for a specific trip a prior. In this paper, we present a trip-specific scheme for estimating the battery SOC change based on the trip information from GIS and ITS traffic data. In particular, this study incorporates the stochastic nature of traffic data. For the EV battery pack, the probabilistic change of battery SOC can thus be estimated throughout the spatial domain by combining the stochastic driving cycle, vehicle propulsion dynamics and battery dynamics.

Ever increasing acceptance of electric vehicles relies on better operation and control of large battery packs. The individual cells in the large battery packs cannot have identical characteristics and may degrade differently due to its manufacturing variability and other factors. It is beneficial to evaluate the performance gain by replacing certain battery modules/cells during actual driving. We have a two-fold objective for this research. First, we are developing an on-line battery module degradation diagnostic scheme using the intrinsic signals of a battery pack equalization circuit. Therefore, a battery “health map” can be constructed and updated in real time. Secondly, based on the derived battery health map, the performance of the battery pack will be evaluated for customer specified trip so as to evaluate the “worthiness of replacing” certain modules/cells.