My Account Log in

1 option

An Integrated Data-Driven and Physics-Based Approach for Dynamic Operation Simulation of Electric Vehicles South China University of Technology

SAE Technical Papers (1906-current) Available online

View online
Format:
Book
Conference/Event
Author/Creator:
Jing, Hao, author.
Contributor:
HU, Jianyao
Ou, Shiqi(Shawn)
Ouyang, Jianheng
Conference Name:
WCX SAE World Congress Experience (2025-04-08 : Detroit, Michigan, United States)
Language:
English
Physical Description:
1 online resource cm
Place of Publication:
Warrendale, PA SAE International 2025
Summary:
To address the challenges of complex operational simulation for Electric Vehicles (EVs) caused by spatial-temporal variations and driver behavior heterogeneity, this study introduces a dynamic operation simulation model that integrates both data-driven and physics-based principles, referred to as the Electric Vehicle-Dynamic Operation Simulation (EV-DOS) model. The physics-based component encompasses critical aspects such as the powertrain energy transfer module, heat transfer module, charge/discharge module, and battery state estimation module. The data-driven component derives key features and labels from second-by-second real-world vehicle driving status data and incorporates a Long Short-Term Memory (LSTM) network to develop a State-of-Health (SOH) prediction model for the EV power pack. This model framework combines the interpretability of physical modeling with the rapid simulation capabilities of data-driven techniques under dynamic operating conditions. Finally, this study validates the hybrid model using one year of real-world driving data, and the simulation results showed that, under various spatial-temporal conditions and different driver behaviors, the monthly average energy consumption estimation error remains consistently low, with the majority of cases falling below 1.0 kWh/100 km, while the SOH prediction error remains below 0.8%. These results demonstrate the model's reliability for energy consumption and battery health estimation, providing robust support for EV performance analysis and energy management
Notes:
Vendor supplied data
Publisher Number:
2025-01-8604
Access Restriction:
Restricted for use by site license

The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.

My Account

Shelf Request an item Bookmarks Fines and fees Settings

Guides

Using the Library Catalog Using Articles+ Library Account