Modeling of New Energy Vehicles' Impact on Urban Ecology Focusing on Behavior

TL;DR

This paper develops a computational model to analyze how driving behaviors of new energy vehicles impact urban ecology, highlighting the environmental effects of poor driving habits.

Contribution

It introduces a novel environmental modeling approach using LSTM with Bayesian optimization to simulate vehicle-environment interactions over the vehicle's lifecycle.

Findings

Poor driving behaviors negatively affect urban ecology.

LSTM with Bayesian optimizer effectively simulates vehicle-environment interactions.

Behavioral patterns can be identified to improve ecological impact.

Abstract

The surging demand for new energy vehicles is driven by the imperative to conserve energy, reduce emissions, and enhance the ecological ambiance. By conducting behavioral analysis and mining usage patterns of new energy vehicles, particular patterns can be identified. For instance, overloading the battery, operating with low battery power, and driving at excessive speeds can all detrimentally affect the battery's performance. To assess the impact of such driving behavior on the urban ecology, an environmental computational modeling method has been proposed to simulate the interaction between new energy vehicles and the environment. To extend the time series data of the vehicle's entire life cycle and the ecological environment within the model sequence data, the LSTM model with Bayesian optimizer is utilized for simulation. The analysis revealed the detrimental effects of poor driving behavior on the environment.