Increasing demand from the automotive sector for hybrid-electric and electric vehicles has pushed manufacturers to the limits of contemporary automotive battery technology. Automotive applications demand large amounts of energy and power and must operate safely, reliably, and durably at these scales. Without detailed information about the internal states of the battery, safety, reliability, and durability can still be achieved by an over-design of the battery pack, with a major drawback of increased costs.
This talk is concerned with the challenging task of providing the battery management system (BMS) with the appropriate algorithms for an optimal utilization of the battery. Equivalent circuit models, utilized in typical BMS of portable electronics, have only limited prediction capabilities here due to the aggressive utilization strategies encountered in automotive applications. Furthermore, these models in general lack any internal state information about the battery. In contrast, at the core of an advanced BMS lays a physics-based electrochemical model, which accurately predicts the performance of the battery over the entire operating regime. After a short primer on lithium-ion battery modeling, this talk will cover selected topics in BMS, such as estimation of internal states and parameters of the battery, as well as the design of optimal charging strategies.
Reinhardt Klein is a graduate student research engineer at the Research and Technology Center of the Robert Bosch LLC in Palo Alto, California. He studied engineering cybernetics at the University of Stuttgart, where he received the diploma degree in 2009. He is currently pursuing the Ph.D. degree at the Otto von Guericke University Magdeburg. His main research interests include modeling, estimation, and control of energy conversion systems with a particular focus on electrochemical systems.