Multiphysics Field Co-simulation Research Based on Electric Drive System for a Specific Type of Car

Electric drive systems for new energy cars are complex systems that should have multivariate,strong coupling,and non-linear characteristics and should also involve the multiphysics field.The singular simulation software used at present in the modeling of electric drive systems cannot simulate the influences of all the physics fields on the operating system.The co-simulation model used in this paper was based on a specific type of car.The motor control algorithm model was built in MATLAB/Simulink,the electromagnetic finite element model of the motor was built in ANSYS EM-Maxwell,and the motor controller hardware circuit was built in ANSYS EM-Simplorer.To make real-time connections among these software platforms,a multi-software co-simulation platform was built,and the co-simulation platform’s simulation results were input into STAR CCM+software to enable finite element modeling of the motor and running of thermal analysis.When compared with the electric drive system model built using single Simulink software,the simulation results from this co-simulation platform were more realistic and were shown to be closer to reality when the dynamic characteristics of the electric drive system’s power semiconductor switching devices and the motor’s electromagnetic characteristics were considered.Finally,by benchmarking the multiphysics field co-simulation platform simulation results using dyno bench test results,the validity of the co-simulation platform was verified and the development of the multiphysics field co-simulation of the basic electric drive system was complete.

Implementation for a cloud battery management system based on the CHAIN framework

An intelligent battery management system is a crucial enabler for energy storage systems with high power output,increased safety and long lifetimes.With recent developments in cloud computing and the proliferation of big data,machine learning approaches have begun to deliver invaluable insights,which drives adaptive control of battery management systems(BMS)with improved performance.In this paper,a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed,with the composition and function of each link described.Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network(CHAIN)framework to provide multi-scale insights,more advanced and efficient algorithms can be used to realize the state-of-X es-timation,thermal management,cell balancing,fault diagnosis and other functions of traditional BMS system.The battery intelligent monitoring and management platform can visually present battery performance,store working-data to help in-depth understanding of the microscopic evolutionary law,and provide support for the development of control strategies.Currently,the cloud-based BMS requires more effects on the multi-scale inte-grated modeling methods and remote upgrading capability of the controller,these two aspects are very important for the precise management and online upgrade of the system.The utility of this approach is highlighted not only for automotive applications,but for any battery energy storage system,providing a holistic framework for future intelligent and connected battery management.