聚焦行为的新能源汽车对城市生态影响建模
Modeling of New Energy Vehicles' Impact on Urban Ecology Focusing on Behavior
AI总结：
本文提出环境计算建模方法，利用带贝叶斯优化器的LSTM扩展车辆全生命周期与生态环境时序数据，评估不良驾驶行为对城市生态的负面影响。
摘要：
新能源汽车需求的激增，源于节约能源、减少排放以及改善生态氛围的迫切需要。通过对新能源汽车开展行为分析并挖掘其使用模式，可以识别出特定模式。例如，电池过载、在低电量状态下运行以及超速行驶，都会对电池性能产生不利影响。为评估此类驾驶行为对城市生态的影响，本文提出了一种环境计算建模方法，用于模拟新能源汽车与环境之间的相互作用。为在模型序列数据中扩展车辆全生命周期和生态环境的时间序列数据，采用带 Bayesian optimizer（贝叶斯优化器）的 LSTM 模型进行仿真。分析揭示了不良驾驶行为对环境的有害影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
