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

arXiv: https://arxiv.org/abs/2406.06602 · HTML: https://mlsanity.com/paper/2406.06602/

## 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 (most cited first, 15 of 15)

- [Experience: Understanding Long-Term Evolving Patterns of Shared Electric Vehicle Networks](https://mlsanity.com/paper/1812.07499/) (2018)
- [A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem](https://mlsanity.com/paper/2206.03185/) (2022)
- [Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service](https://mlsanity.com/paper/2004.05146/) (2020)
- [Overcoming limited battery data challenges: A coupled neural network approach](https://mlsanity.com/paper/2111.15348/) (2021)
- [On the Sustainability of Electrical Vehicles](https://mlsanity.com/paper/1311.6015/) (2013)
- [Pedestrian-Aware Engine Management Strategies for Plug-in Hybrid Electric Vehicles](https://mlsanity.com/paper/1612.07568/) (2016)
- [Multi-Period Coordinated Management of Electric Vehicles in Zonal Power Markets: A Convex Relaxation Approach](https://mlsanity.com/paper/1710.01359/) (2017)
- [Integrating Ultra-Fast Charging Stations within the Power Grids of Smart Cities: A Review](https://mlsanity.com/paper/1801.09174/) (2018)
- [Comparing Power Processing System Approaches in Second-Use Battery Energy Buffering for Electric Vehicle Charging](https://mlsanity.com/paper/2104.14976/) (2021)
- [Investigating the Spatiotemporal Charging Demand and Travel Behavior of Electric Vehicles Using GPS Data: A Machine Learning Approach](https://mlsanity.com/paper/2203.00135/) (2022)
- [Electrical Vehicle Fleet Routing Accounting for Dynamic Battery Degradation](https://mlsanity.com/paper/2203.04642/) (2022)
- [Improving Energy Management of Hybrid Electric Vehicles by Considering Battery Electric-Thermal Model](https://mlsanity.com/paper/2302.13157/) (2023)
- [Consumer's Behavior Analysis of Electric Vehicle using Cloud Computing in the State of New York](https://mlsanity.com/paper/2306.01888/) (2023)
- [Exploring Different Time-series-Transformer (TST) Architectures: A Case Study in Battery Life Prediction for Electric Vehicles (EVs)](https://mlsanity.com/paper/2308.03260/) (2023)
- [Routing and charging game in ride-hailing service with electric vehicles](https://mlsanity.com/paper/2309.05120/) (2023)

## 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.