Key Functionalities in Battery Management Systems for Transportation Electrification
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D2: Electrochem: Batteries, Fuel Cells, Capacitors".
Deadline for manuscript submissions: 25 February 2025 | Viewed by 175
Special Issue Editors
2. Chair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, 52074 Aachen, Germany
Interests: battery management; battery modelling; machine learning; battery degradation diagnosis and prognosis
Interests: parameters identification; state estimation; health and safety management
Special Issues, Collections and Topics in MDPI journals
Interests: energy management; electrified vehicle; energy storage system; optimal control
Interests: battery modeling and monitoring; health prognostics and maintenance; energy storage
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Driven by concerns about global warming and policy directives, the development of carbon-neutral economics through electric mobility is gaining increasing popularity. Lithium-ion batteries are among the most promising options for transportation electrification due to their high energy density, long cycle life, and low self-discharge rate. However, the application of lithium-ion batteries in electric vehicles still faces many challenges. Battery degradation and safety, unmeasurable state of charge, slow charging rate, and difficulty in fault diagnosis and prognosis all pose great challenges for transportation electrification. Battery management systems are essential to ensure the high efficiency, reliability, and safety of lithium-ion batteries in operation. The key functionalities of battery management systems include state estimation, degradation diagnosis and prognosis, fault diagnosis and prediction, and safe and efficient charge/discharge control.
Advanced key functionalities in battery management systems can potentially improve not only the usage of batteries but also the manufacturing based on positive feedback in field operation. With this in mind, this Special Issue aims to develop advanced key functionalities in battery management systems for electric vehicles. Original and high-quality research, reviews, and perspectives are invited for publication. Potential topics include, but are not limited to, the following:
- Battery modelling with physics-based modelling and artificial intelligence;
- State estimation in electrode, cell, module, and pack level;
- Battery degradation diagnosis, prognosis, and optimization;
- Battery thermal monitoring, management design, and control strategy;
- Safety mechanisms, early warning, diagnosis, and prediction;
- Advanced experiment and characterization;
- Emerging sensor technologies for battery management;
- Series/parallel analysis and cell balancing for battery pack;
- Advanced energy management for electric vehicle.
Dr. Qiao Wang
Dr. Zhongwei Deng
Dr. Yue Wu
Dr. Yunhong Che
Guest Editors
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Keywords
- electric vehicles
- battery management systems
- key functionalities
- physics-based modelling
- artificial intelligence
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