From Battery Materials to Energy Management
Computational models bridge next-generation energy materials to battery management systems
Clean Energy Institute Graduate Fellow Manan Pathak collects data from battery materials to help determine the best operating point.
The secret to long battery life and efficient energy storage will rely as much on predictive and efficient computational and mathematical models as new chemistries and mechanics. When math meets chemical and materials engineering, we could be a step closer to electrifying our transportation system and creating resilient and reliable microgrids powered with renewable energy.
Fundamentally, there are two ways to significantly improve batteries. One is by developing new materials or chemistries; the second is developing next- generation aggressive battery management systems that maximize the efficiency. The University of Washington Clean Energy Institute (CEI) is working on both. The bridge that brings the two together lies in the laboratory of Dr. Venkat Subramanian, who draws on CEI’s deep knowledge of battery materials and devices to develop software that can continuously monitor and control the inner-workings of a battery. The result is robust algorithms and models for battery management systems.
“High energy and power batteries are critical for the next generation of clean energy grid and transportation technologies. They store energy from renewables like solar and wind then make it available later when needed. They also can dispatch energy in a blink, so they enable the grid and transportation to operate at maximum efficiency.”
Subramanian, the Washington Research Foundation Innovation Associate Professor of Chemical Engineering and Clean Energy at UW, and his research group have developed a new software model that can be applied to predict the performance of battery systems.
Batteries are complex systems. Differences in chemistry, mechanics, and thermal behavior make it challenging to develop a battery model that replicates what happens inside an operating battery. And, while modern chemical engineering practice, control and hardware engineering involves a great deal of linear problem solving, applying these techniques to battery systems with multiple variables, or non-linear equations, is still in its infancy without theoretical proofs in same cases.
However, Subramanian’s Modeling, Analysis, and Process Control Lab for Electrochemical Systems (or M.A.P.L.E. lab) is developing a mathematical approach that shows promise in determining the best operating point for a battery, or other energy storage device. The model considers different operating conditions such as temperature, current charge time and cycles and finds the optimal mix of each.
“Since our approach is model based, it can be adapted to newer chemistries and materials as they arrive from different researchers in the world,” Subramanian said.
Members of the M.A.P.L.E. lab include (l-r) Chintan Pathak, Yanbo Qi, Suryanarayana Kolluri, Prof. Venkat Subramanian, Manan Pathak and Seongbeom Lee.
The research group's recent paper in Computers and Chemical Engineering demonstrates how battery models can be solved 100% of the time without failure. The combined initialization and simulation avoids iterations and guarantees success in the simulation of battery models. The work was also recently featured in Green Car Congress and presented at the Electrochemical Society conference by CEI Graduate Fellow Manan Pathak. The method has received significant attention from battery and software manufacturers, including representatives from international companies.
The M.A.P.L.E. model will lead to batteries that are safer, have higher storage capacity, recharge faster and last longer, according to Subramanian. "Our group is taking the chemical engineering models from papers and pen and moving it to hardware integration and power electronics for transportation batteries and grid applications."