학술논문

Modeling and Estimation of Train Traction Characteristics Under Emergency Traction Considering On-board Energy Storage Devices
Document Type
Conference
Source
2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Data Driven Control and Learning Systems Conference (DDCLS), 2023 IEEE 12th. :350-355 May, 2023
Subject
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Learning systems
Force
Estimation
Propulsion
Traction motors
Lithium batteries
Data models
Train emergency energy storage device
Train traction characteristics
Lithium battery
State of power
Language
ISSN
2767-9861
Abstract
When the electric multiple units (EMUs) encounter a power supply failure, it is urgent to formulate a reasonable emergency traction strategy, and rely on the on-board energy storage device to pull to the nearby station as soon as possible. During emergency propelling, the train's maximum traction force is affected by the maximum power of the on-board energy storage device. Therefore, in this paper, we propose a novel model for describing the traction characteristics of the train based on on-board energy storage devices in the case of emergency traction. First, to depict the power relationship between the train traction motor and the on-board energy storage device, an train emergency traction power model is developed. Then, while online updating of battery equivalent model parameters is realized by fitting experimental data, a binary search algorithm is adopted to the long-time state of power (SOP) estimation of on-board lithium batteries (>120s) under the state of charge (SOC), battery design and voltage constraints. Finally, the train simulation experiments show that the proposed method achieves excellent SOC estimation and the accurate description of the emergency traction characteristics of the train.