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Forecasting the consumption evolution of battery electric vehicles under dynamic market conditions: The case study of Jiangsu Province, China

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Analysis predicts battery electric vehicle market share in Jiangsu Province, highlighting machine learning advantages.

Forecasted battery electric vehicle sales reveal significant market growth under dynamic conditions, indicating robust market potential and infrastructure needs.
The artificial neural network model effectively incorporates covariates to predict changes in BEV sales, demonstrating over a 20% improvement in accuracy.
Utilizing time-series models, the study evaluates the impact of variables like fuel-to-electricity ratio and charging infrastructure on BEV market trends.
These findings suggest that machine learning methods can greatly enhance market forecasting accuracy, helping stakeholders optimize sales strategies and infrastructure deployment.

Qiao et al. (2025) studied this question.

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