Education within Center for Research on Smart Battery and AI to X

This intensive four-day course offers a comprehensive and advanced exploration of energy storage systems, emphasizing their critical role in modern power grids. As power systems increasingly integrate renewable energy sources and demand for reliable, efficient, and sustainable energy grows, energy storage systems are becoming indispensable. This course is meticulously designed to provide participants with a thorough understanding of the key components and technologies that drive energy storage systems, focusing on their integration into power grids. 

Lithium-ion batteries have a wide range of applications. The improvement in their energy and power densities have greatly enhanced the growth of e-mobility. Higher penetration of electric vehicles can happen faster with safer, more reliable and intelligent ali-ion energy storage systems.

This two-day course develops the concept of ‘Smart Batteries (SB)’ for Li-ion cells. Smart battery integrates power electronics and intelligent control to the cells. This is done by using a half-bridge circuit connected across the cell terminals. The course introduces the operation of the SB with the integrated half bridge circuit.

The course gives a detailed overview of the state-of-the-art battery management systems, chargers/charging methods. This discussion evolves into the advantages of the SB in making smart BMS and energy efficient charging methods and lifetime improvement. The design of the SB, optimal device selection, PCB design for different geometries of the cells (prismatic, pouch and cylindrical ) will be discussed. The SB also has intelligent control and the course introduces the communication architecture and controller selection for the SB management systems. Simulation exercises in Simulink/Plecs/LTSpice will be used as tools to understand and appreciate the SB concept and hardware architecture. 

Lithium-ion batteries have a wide range of applications, and their safe and reliable operation is essential. However, due to the complex electrochemical reaction of the battery, the battery performance parameters show strong nonlinearity with aging. Therefore, as the main technologies in BMS, battery state estimation and lifetime prediction remain challenges. Artificial Intelligence (AI) technologies possess immense potential in inferring battery state and can extract aging information (i.e., health indicators) from measurements and relate them to battery performance parameters, avoiding a complex battery modeling process. Therefore, this course aims to introduce the application of AI in Smart Battery state estimation.

This two-day course introduces AI methods for estimating/predicting batteries’ state of charge (SOC), state of health (SOH), state of temperature (SOT), and remaining useful life (RUL). Key aspects include laboratory data preparation, data preprocessing, AI model training and selection.

In addition to the classic algorithms of AI, e.g., support vector regression, Gaussian process regression, neural networks, transfer learning, and multitask learning, feature extraction and selection methods will be included in the discussion.

In terms of training, two modes will be introduced (depending on the accuracy, robustness, and computation complexity of the selected AI algorithm), i.e., with feature extraction and without feature extraction. According to multiple case studies, the strengths and drawbacks of different AI algorithms will be compared.

Exemplifications of some of the discussed topics will be made through exercises in Python and MATLAB.

Lithium-ion batteries have a wide range of applications. The improvement in their energy and power densities have greatly enhanced the growth of e-mobility. Higher penetration of electric vehicles can happen faster with safer, more reliable and intelligent ali-ion energy storage systems.

This two-day course develops the concept of ‘Smart Batteries (SB)’ for Li-ion cells. Smart battery integrates power electronics and intelligent control to the cells. This is done by using a half-bridge circuit connected across the cell terminals. The course introduces the operation of the SB with the integrated half bridge circuit.

The course gives a detailed overview of the state-of-the-art battery management systems, chargers/ charging methods. This discussion evolves into the advantages of the SB in making smart BMS and energy efficient charging methods and lifetime improvement. The design of the SB, optimal device selection, PCB design for different geometries of the cells (prismatic, pouch and cylindrical ) will be discussed. The SB also has intelligent control and the course introduces the communication architecture and controller selection for the SB management systems.

Simulation exercises in Simulink/Plecs/LTSpice will be used as tools to understand and appreciate the SB concept and hardware architecture.

Find the detailed course program on PhD Moodle here.

Lithium-ion batteries have a wide range of applications, and their safe and reliable operation is essential. However, due to the complex electrochemical reaction of the battery, the battery performance parameters show strong nonlinearity with aging. Therefore, as the main technologies in BMS, battery state estimation and lifetime prediction remain challenges. Artificial Intelligence (AI) technologies possess immense potential in inferring battery state, and can extract aging information (i.e., health indicators) from measurements and relate them to battery performance parameters, avoiding a complex battery modeling process. Therefore, this course aims to introduce the application of AI in Smart Battery state estimation.

This two-day course introduces AI methods for estimating/predicting batteries’ state of charge (SOC), state of health (SOH), state of temperature (SOT), and remaining useful life (RUL). Key aspects include laboratory data preparation, data preprocessing, AI model training and selection.

In addition to the classic algorithms of AI, e.g., support vector regression, Gaussian process regression, neural networks, transfer learning, and multitask learning, the feature extraction and selection methods will be included in the discussion.

In terms of training, two modes will be introduced (depending on the accuracy, robustness, and computation complexity of the selected AI algorithm), i.e., with feature extraction and without feature extraction. According to multiple case studies, the strength and drawbacks of different AI algorithms will be compared.

Exemplifications of some of the discussed topics will be made through exercises in Python and MATLAB.

Find the detailed course program on PhD Moodle here.