The Chinese government has announced a trial programme to provide for private purchase of new battery-operated electric vehicles (EVs) and for plug-in hybrids in five cities. We investigate the potential impact of these subsidies and charging facilities on demand for EVs, using data from a survey of potential car buyers in China. Building on the understanding of factors and incentives that would likely encourage households to adopt EVs would help to improve policy interventions. Data collection is based on experimental design and stated choice methods through an Internet survey. Choice alternatives include a conventional gasoline, a plug-in hybrid and a pure electrical vehicle. In addition to subsidy and charging facilities, we also investigate the impact of common vehicle attributes such as purchase price, cruising range, refuelling time and the socio-demographic effect. Applying a multinomial logit (MNL) model, we find that subsidies would significantly encourage households to choose a plug-in hybrid or an EV. Compared to pure EVs, people in China are more willing to accept plug-in hybrids at present. Compared to the price factors, charging facilities are less of a concern when households consider the adoption of an EV. Willingness-to-pay and market share simulation are computed based on the estimated parameters for further analysis.

Most of existing researches focus on POMDP modeling or solution. But in some study fields, before obtaining optimal policy from a POMDP, we need first learning a POMDP model from history data. Assumed that history data including observation sequence and action sequence, the state sequence are unobservable, we derive necessary formulas for using EM Algorithm to estimate the parameters of a POMDP model, including the initial state distribution, stochastic transition matrix and observation probability function.

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