Wenda Fang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Wenda Fang has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Guangwu Dai
CATARC Automotive Test Center (Changzhou) Company Limited, Changzhou 213000, China
1 shared publication - Ping Wang
Institute for Energy Research, Jiangsu University, Zhenjiang 212013, China
1 shared publication - Siheng Yang
School of Energy Engineering, Zhejiang University, Hangzhou 310013, China
1 shared publication - Tao Yan
CATARC Automotive Test Center (Changzhou) Company Limited, Changzhou 213000, China
1 shared publication - Weijia Qian
Institute for Energy Research, Jiangsu University, Zhenjiang 212013, China
1 shared publication - Yongjun Tian
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710000, China
1 shared publication
Latest Publications
- Open Access
Data-Driven Prediction of Li-Ion Battery Thermal Behavior: Advances and Applications in Thermal Management
Lithium-ion batteries (LIBs) are critical for various applications, and effective thermal management is important for their safety, performance, and lifespan. Traditional physics-based modeling of battery thermal behavior is computationally complex and requires detailed parameters. Using data-driven modeling to predict thermal characteristics of batteries offers a promising alternative. This review comprehensively examines the utilization of data-driven methods in predicting LIB thermal behavior and designing battery thermal management systems. It explores commonly used data-driven techniques and focuses on their applications in predicting heat generation, temperature distribution, and cooling performance. Specific data-driven models for battery thermal prediction are presented, with a comparative analysis of their strengths and weaknesses. The review concludes that data-driven models can effectively predict battery thermal behavior, offering computational efficiency compared to physics-based simulations. Future research directions include hybrid data-driven/physical modeling, ensemble modeling, and incorporating explainable artificial intelligence techniques to enhance model interpretability. These advancements will lead to more accurate and interpretable models, contributing to the safe and efficient applications of LIB systems.
Open AccessData-Driven Prediction of Li-Ion Battery Thermal Behavior: Advances and Applications in Thermal Management
Lithium-ion batteries (LIBs) are critical for various applications, and effective thermal management is important for their safety, performance, and lifespan. Traditional physics-based modeling of battery thermal behavior is computationally complex and requires detailed parameters. Using data-driven modeling to predict thermal characteristics of batteries offers a promising alternative. This review comprehensively examines the utilization of data-driven methods in predicting LIB thermal behavior and designing battery thermal management systems. It explores commonly used data-driven techniques and focuses on their applications in predicting heat generation, temperature distribution, and cooling performance. Specific data-driven models for battery thermal prediction are presented, with a comparative analysis of their strengths and weaknesses. The review concludes that data-driven models can effectively predict battery thermal behavior, offering computational efficiency compared to physics-based simulations. Future research directions include hybrid data-driven/physical modeling, ensemble modeling, and incorporating explainable artificial intelligence techniques to enhance model interpretability. These advancements will lead to more accurate and interpretable models, contributing to the safe and efficient applications of LIB systems.