Model Predictive Control for Multi-Port Modular Multilevel Converters in Electric Vehicles Enabling HESDs
IEEE Transactions on Energy Conversion
In this paper, the authors propose a model predictive control (MPC) algorithm for multi-port modular multilevel converters (MP-MMCs). MP-MMCs are used to enable the use of hybrid energy storage devices (HESDs) in a scalable energy management system (EMS) for electric vehicle (EV) applications. HESDs refer to the use of multiple types of energy storage cells in an EV drivetrain system. In this paper, battery cells are sized for the EV energy density, while ultra-capacitor cells are used for high acceleration periods. This system reduces the EV drivetrain's weight and size due to eliminating high-power inverters and their filtering components. Using MPC, this system can achieve the following control objectives: 1) extend the battery cells lifetime and driving range by shielding them from high power pulses, 2) balance the state of charge levels of every storage cell, 3) increase the system efficiency through optimizing the supplied motor voltage and reducing the switching losses. Moreover, the proposed solution provides means for onboard high-power charging of EV storage cells. Finally, validation results are provided in the paper using a developed hardware prototype, co-simulations, and hardware in the loop system to verify the system's effectiveness.
Battery management system, electric vehicle drivetrain, electric vehicles, hybrid energy storage devices, model predictive control, multilevel modular converters, ultracapacitors
Mohamed O. Badawy, Mohit Sharma, Carlos Hernandez, Ali Elrayyah, Samuel Guerra, and Joshua Coe. "Model Predictive Control for Multi-Port Modular Multilevel Converters in Electric Vehicles Enabling HESDs" IEEE Transactions on Energy Conversion (2022): 10-23. https://doi.org/10.1109/TEC.2021.3089668