Model Based Battery Management System (BMS) for Electric Transportation and Renewable Microgrids

Lithium ion batteries are a promising technology to reduce the dependence on fossil fuels for transportation and for the creation of resilient and reliable renewable microgrids. However, battery manufacturers overdesign lithium ion batteries used in electric vehicle to ensure safety and life due to the uncertainty of the internal states of the cell. Proactive battery management systems (BMS) and advanced sensing technologies offer an opportunity to significantly reduce the cost and weight of transportation batteries, and circumvent problems arising due to capacity fade and safety concerns. This talk will describe how multiscale electrochemical engineering models, mathematical model reformulation and the use of robust algorithms can alleviate some of these problems to help electrify the transportation industry by improving the range of variables that are predictable and controllable in a battery in real-timewithin an electric vehicle. The use of battery models in a BMS will be analyzed. The interplay between the fundamental depth in modeling, choice of numerical algorithms, and application driven problem formulation will be presented. The validity of implementation in a microcontroller environment for model predictive control (MPC) will be addressed and demonstrated. In addition, preliminary results on aggressive sizing and control strategies for batteries in renewable microgrids will be presented.

Acknowledgements: I would like to acknowledge my current and past students and postdoctoral associates for the results presented.