A new multi-modal framework utilizing open-source data is proposed for evaluating the state of health of electric vehicle batteries.
A multi-modal deep learning framework for evaluating the state of health (SOH) of electric vehicle batteries using historical operational data from 300 diverse electric vehicles to accurately estimate battery degradation.
Researchers from various institutions including Chang’an Automobile Co., Ltd. and multiple universities.
The study analyzed data collected over three years, with the framework details published recently on January 30, 2025.
Data was collected from electric vehicles monitored across various geographical locations by the participating organizations.
The accurate evaluation of battery health is pivotal for the efficiency and reliability of electric vehicles, especially as battery degradation affects range and performance.
The analysis uses new deep learning techniques and publicly available vehicle data to improve the accuracy of SOH estimations compared to existing methods.
Researchers also made the dataset publicly available to encourage future studies in the field.
"The proposed paradigm exhibits considerable potential for numerous applications in state estimation and diagnostics in multi-sensor systems."
"High-quality, detailed, and informative data are essentials for battery SOH estimation and lifetime forecasting."
"The method promises not just remarkable reductions in time, economic outlay, and energy consumption but also advancements in intelligent BMS and cloud-based monitoring platforms for improved EV use."
The article will begin with the significance of accurate battery health evaluation, highlighting the challenges posed by traditional methods and introducing the new framework as a potential solution.
This section will explain the importance of battery state of health (SOH), the traditional methods used, and the role of data collection and analysis.
The article will detail how the multi-modal framework works, based on historical vehicle data and the innovations implemented.
This section will present the study's results and discuss the impact of these findings on the future of electric vehicles and battery management systems.
The article will summarize the key points, stressing the promise of the new framework and the importance of publicly available data for future research advancements.