Comparison of the Short-Term Forecasting Accuracy on Battery Electric Vehicle between Modified Bass and Lotka-Volterra Model: A Case Study of China

Shunxi Li,Guofang Zhang,Hang Chen

doi:10.1155/2017/7801837

Shunxi Li, Guofang Zhang + Show 1 more

Open Access

https://doi.org/10.1155/2017/7801837

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Abstract

The potential demand of battery electric vehicle (BEV) is the base of the decision-making to the government policy formulation, enterprise manufacture capacity expansion, and charging infrastructure construction. How to predict the future amount of BEV accurately is very important to the development of BEV both in practice and in theory. The present paper tries to compare the short-term accuracy of a proposed modified Bass model and Lotka-Volterra (LV) model, by taking China’s BEV development as the case study. Using the statistics data of China’s BEV amount of 21 months from Jan 2015 to Sep 2016, we compare the simulation accuracy based on the value of mean absolute percentage error (MAPE) and discuss the forecasting capacity of the two models according to China’s government expectation. According to the MAPE value, the two models have good prediction accuracy, but the Bass model is more accurate than LV model. Bass model has only one dimension and focuses on the diffusion trend, while LV model has two dimensions and mainly describes the relationship and competing process between the two populations. In future research, the forecasting advantages of Bass model and LV model should be combined to get more accurate predicting effect.

Highlights

To resolve the problem of environmental pollution and energy shortage, the development of new energy vehicles has been paid much attention
How to predict the future amount of battery electric vehicle (BEV) accurately is very important to the development of BEV both in practice and in theory
In view of the limited historical data, in the estimation of the parameters of the Bass model of the electric vehicle in China, the parameters of BEV in China were determined by comparing the parameters of other consumer products

Summary

Introduction

To resolve the problem of environmental pollution and energy shortage, the development of new energy vehicles has been paid much attention. Gu et al (2010) made a forecast on the vehicle ownership based on the provincial data of China [2]. Ma et al (2009) used the AHP method and logit regression model to forecast the market share of new energy vehicles in China [3]. Lotka-Volterra model has been used to predict the ownership of CVs and BEVs. Lotka-Volterra model is a classical method to simulate natural ecosystems, especially when it is used for population ecosystem. Lotka-Volterra model is a classical method to simulate natural ecosystems, especially when it is used for population ecosystem This model and its mathematical expressions are widely applied for describing different populations competing for environment resources and the relations among them. We try to explore the forecasting accuracy between LV models initially based on the benchmark of Bass model

Methodology

Data and Result

Conclusion