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.

通过行为分析和挖掘新能源汽车的使用模式，可以识别出特定模式。对于行为指标如过载电池、低电量操作和高速驾驶对电池性能的不良影响，提出了一种环境计算建模方法来评估这些行为对城市生态的影响。通过 LSTM 模型和贝叶斯优化器对时间序列数据进行模拟扩展，揭示了不良驾驶行为对环境的负面效应。