Controls and Battery Management Systems

UT researchers are leaders in model-based Battery Management Systems (BMS) for improved battery lifetime and performance and in the control, estimation and optimization of electric and hybrid dynamical systems. In collaboration with experimental researchers, they are also optimizing battery designs and developing next generation battery materials based on multiscale and multiphysics models.

Research Areas

• Novel, fast codes and models for BMS
• Model-based battery material design and battery design optimization
• Models for next-generation materials including Li-S and Li-metal batteries
• Control of energy systems that include batteries using reduced order modeling – optimal control, switching control, non-minimum phase systems and control, real-time monitoring and control
• Hybrid power system development
• Control, modeling, estimation, optimization and diagnosis of dynamical systems, especially for hybrid and electrified vehicles

Project Examples

• Fastest reported code for a battery model, including patented, robust solvers for a range of applications. See M.A.P.L.E. Lab for Free Codes and Solvers
• Model-based charging profiles – led to 2X improvement in lifetime of 18 Ah LiBs
• Dynamic performance control of vanadium redox flow cell battery
• Control frameworks for hybrid EVs and locomotives and integrated wind turbines/energy storage systems
• Medium-duty eTruck: Pilot electrified fleets in urban and regional applications

Facilities

• M.A.P.L.E. Lab – Battery cyclers (up to 60A), module testers, thermal chambers, EIS and ultra-fast programmable load capabilities (<10µs rise time)
• Mobility Systems Lab - Full-instrumented autonomous hybrid electric vehicle, electric medium-duty trucks, in-wheel motor electric vehicles, four-wheel-steering-drive scaled electric vehicles

Faculty in This Area

Dongmei (Maggie) Chen
Venkat Subramanian
Junmin Wang