Automotive Innovation - Call for Papers: Feature Topic on Intelligent Battery Safety Management

With the enhancement of computing capabilities both at the vehicle and cloud side, the technologies of the multi-physics modelling and deep learning are widespread application in battery research. The battery management systems are developing rapidly towards intelligence, empowering the safe and efficient operation for the new energy vehicles.

However, the research and development of intelligent batteries and intelligent management systems still face challenges, and the level of intelligence in battery systems is still insufficient for large-scale application in the industry chain. Currently, power battery management systems rely on external common sensors to acquire battery operational information, and important internal parameters of the battery can only be indirectly obtained through parameter identification or estimation methods. This approach cannot directly obtain internal battery parameters, which affects the robustness of state estimation and fault diagnosis methods. Therefore, exploring multi-dimensional signal acquisition schemes for implanted intelligent batteries, integrating signals from multiple sources of sensors to build high-precision multi-physics coupling models, and obtaining high-quality internal battery parameters for fault diagnosis and state estimation in intelligent battery management systems is a method to improve the safety and practicality of battery systems. In addition, in the era of cloud computing and big data, it is an urgent problem to utilize large-scale real vehicle operation data to achieve more accurate state estimation and safety management. This not only enables data-driven battery state estimation and fault diagnosis methods to obtain more accurate results, but also makes it possible to achieve intelligent battery management through the coordination of multiple devices such as charging piles and intelligent road networks. This broadens the concept of intelligent batteries and battery management systems, provides more choices in battery management functions, helps to enhance the overall competitiveness of new energy vehicles, and facilitates the promotion of intelligent battery technology to larger-scale power battery modules.

In this Feature Topic, we hope to bring together experts from the intelligent vehicle community to discuss the progress of the latest works on Intelligent Battery Safety Management and to give readers a clear picture of the advances that are to come. Welcome topics include, but are not strictly limited to, the following:

• Lithium-ion Battery Health Prediction and Fault Diagnosis Based on Implantable Sensor. 
• Multi-dimensional Information Fusion for Lithium-ion Battery Safety Management.
• Multi-Physics Field Modelling and State Estimation for Lithium-ion Batteries under Various Operating Conditions.
• Power Battery State Estimation based on Large-scale Real Vehicle Operation Data.

Important Dates Submission Deadline: Sept. 30, 2024

Guest EditorsProfessor Zhenpo Wang, Beijing Institute of Technology, China
Professor Zhenpo Wang, Beijing Institute of Technology, Director of the National Engineering Research Center for Electric Vehicles, China
Professor Remus Teodorescu, Aalborg University, Denmark
Associate Professor Xiaoyu Li, Hebei University of Technology, China
Dr. Abbas Fotouhi, Cranfield University, UK
Dr. Yunhong Che, Aalborg University, DenmarkSubmission GuidelinesPapers submitted to Automotive Innovation must be written in excellent English, contain original work, and not be published or under review elsewhere. All papers will undergo a peer review process in accordance with the journal's reviewing policy.
Please note the following info while submitting:Please submit online via www.springer.com/42154, be sure to select Topical Collection: Intelligent Battery Safety Management. Papers should be submitted in two separate files: 1) Blinded Manuscript (paper title, abstract, keywords, and full text); 2) Title Page (paper title, author affiliation, acknowledgment, and any other information related to the authors' identifications).If any problems, please feel free to contact the journal editorial office via email: jai-editor@sae-china.org (this opens in a new tab).