Abstract
The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.
Highlights
In China, vehicle emissions have been increased rapidly recent years
Given that the singular spectrum analysis (SSA) and vector autoregressive model (VAR) models are obtained and able to be used for forecasting, the forecast accuracy level can be evaluated mathematically with Mean absolute percentage error (MAPE) and Normalized root-mean-square error (RMSE)
Both models are applied to forecast the sales of battery EVs (BEVs) and plug-in hybrid electric vehicle (PHEV) in China with 60-month observations from January 2011 to December 2015
Summary
In 2014, China’s crude oil import dependency reached 59.6%, the future economic development would be restricted by the inadequate energy supply of traditional fossil fuel [1]. As a statistical standard in China, EV sales are recorded in two categories, namely, battery EVs (BEVs) and plug-in EVs (PHEVs). BEVs derive all power from battery packs while PHEVs derive some power from electricity and some from gasoline. Both usages lead to positive environmental effects and the improvement of urban health [3], for example, plug-in hybrid electric vehicle (PHEV) technology offers a possible approach to reducing and dependency on oil as a transportation fuel [4]. Forecasts of market demand are divided into BEV and PHEV sales amounts, and the overall performance can be drawn by combining the results
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