Modeling of New Energy Vehicles' Impact on Urban Ecology Focusing on Behavior
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 6 Jun 2024.
Builds on
15 of 22 references.
References · 15
- 2018Experience: Understanding Long-Term Evolving Patterns of Shared Electric Vehicle Networkscited by 8
- 2022A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problemcited by 7
- 2020Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Servicecited by 3
- 2021Overcoming limited battery data challenges: A coupled neural network approachcited by 2
- 2013On the Sustainability of Electrical Vehiclescited by 1
- 2016Pedestrian-Aware Engine Management Strategies for Plug-in Hybrid Electric Vehiclescited by 1
- 2017Multi-Period Coordinated Management of Electric Vehicles in Zonal Power Markets: A Convex Relaxation Approachcited by 1
- 2018Integrating Ultra-Fast Charging Stations within the Power Grids of Smart Cities: A Reviewcited by 1
- 2021Comparing Power Processing System Approaches in Second-Use Battery Energy Buffering for Electric Vehicle Chargingcited by 1
- 2022Investigating the Spatiotemporal Charging Demand and Travel Behavior of Electric Vehicles Using GPS Data: A Machine Learning Approachcited by 1
- 2022Electrical Vehicle Fleet Routing Accounting for Dynamic Battery Degradationcited by 1
- 2023Improving Energy Management of Hybrid Electric Vehicles by Considering Battery Electric-Thermal Modelcited by 1
3 more
- 2023Consumer's Behavior Analysis of Electric Vehicle using Cloud Computing in the State of New Yorkcited by 1
- 2023Exploring Different Time-series-Transformer (TST) Architectures: A Case Study in Battery Life Prediction for Electric Vehicles (EVs)cited by 1
- 2023Routing and charging game in ride-hailing service with electric vehiclescited by 1
Built on by
Not cited yet.