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Real-time optimal control of plug-in hybrid vehicles with trip preview

The global optimal solutions of energy management of hybrid electric vehicles depend on future driving conditions. This paper investigates a potential real-time methodology to integrate long-horizon preview information for plug-in hybrid vehicles. Equivalent Fuel Consumption Minimization Strategy (ECMS) is deployed as an instantaneous real-time minimization strategy with parameters adjusted by estimated future driving conditions and obtained either from Dynamic Programming (DP) or from a global ECMS. The preview information is decomposed into future road terrain stored as in-vehicle 3D maps and future velocity estimated from streaming or historic traffic data. We show that with estimated future information, substantial reduction in fuel use, up to 13% is possible compared with a rule-based control strategy without any preview. Further improvement of fuel consumption on the order of 1% could be achieved with exact future information.