AESA uses electric vehicles as the carrier to promote the development of energy storage science, and promote the integrated development of vehicles, electricity, power, control, materials, systems, artificial intelligence and other disciplines.The implementation of the strengthen knowledge foundation plan aims to:

1. Exploring the theory and application basic technology of electric vehicles to promote the integration and systematic development of the knowledge system;

2. Consolidate the common basic knowledge of the basic theory and engineering application of the research group for graduate students, and improve the ability of graduate students to integrate and transcend;

3. Inspire the enthusiasm of team advantage knowledge sharing, cultivate graduate students’ academic communication ability and pursuit the ultimate craftsmanship;

4. Accumulate valuable learning materials for undergraduates/postgraduates engaged in subject research.

Since the strong base learning program was launched in June 2020, 18 lectures (Chinese language) have been conducted on battery aging mechanism, thermal characteristics, state estimation, safety management, fault diagnosis and energy storage collaborative management.

L01. EIS and application for Equivalent Circuit model and Fractional models

L02. Application of Optimization in the Control Field of Multi-power System

L03. Principles of H Filter and application in batteries control

L04. Principles of Kalman Filter Algorithm and Its Application in Battery State

L05. Neural Networks, Deep learning and its application in the field of battery

L06. Battery thermal modeling theory and application cases

L07. Time series prediction and its application in power battery management

L08. Preliminary design of hardware schematic diagram and application

L09. battery electrochemical model

L10. Battery electrochemical-thermal coupling modeling theory

L11. Battery system modeling state estimation and inconsistency evaluation

L12. Battery model in Application in Fault Diagnosis

L13. Driving platform construction and Software and hardware application

L14. External short circuit test, mechanism and modeling of power battery

L15. Mechanism analysis and diagnosis method of power battery aging

L16. Probabilistic basis of filtering algorithm and its application in batteeries

L17. Experimental design of pulse compound heating of power battery

L18. Thermal runaway mechanism and modeling of battery

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Links: Beijing Institute of Technology    Applied Energy    MIT-Ju Li Group    Chinese J. ME    Sch. Mech Engin