An enhanced bilayer long short-term memory method for energy consumption estimation of electric buses with real-time passenger load
Authors:
- Xiaonian ShanXSXiaonian Shan
- Qi LiQLQi Li
- Changxin WanCWChangxin Wan
- Ming OuyangMOMing Ouyang
- Peng HaoPHPeng Hao
- Guoyuan WuGWGuoyuan Wu
- Matthew BarthMBMatthew Barth
Abstract
No abstract availableFunding Information
- Natural Science Foundation of Jiangsu Province (BK20242054)
- Tongji University (K202302)
- Ministry of Education
- National Natural Science Foundation of China (52002113)
This publication has 21 references indexed in Scilit:
- Resource configuration and daily operational scheduling for urban electric bus route under the hybrid power supply strategyEnergy, 2025
- Evaluating different artificial neural network forecasting approaches for optimizing district heating network operationEnergy, 2024
- A novel battery SOC estimation method based on random search optimized LSTM neural networkEnergy, 2024
- SOH estimation of lithium-ion batteries based on multi-feature deep fusion and XGBoostEnergy, 2024
- Modeling risk characterization networks for chemical processes based on multi-variate dataEnergy, 2024
- State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteriesEnergy, 2023
- Consumption estimation method for battery-electric buses using general line characteristics and temperatureEnergy, 2022
- Trip energy consumption estimation for electric busesCommunications in Transportation Research, 2022
- State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural networkEnergy, 2021
- Power-based electric vehicle energy consumption model: Model development and validationApplied Energy, 2016