Generic Model Control for Lithium-Ion Batteries - II

Thursday, 1 June 2017: 09:40
Grand Salon C - Section 15 (Hilton New Orleans Riverside)
M. Pathak (University of Washington, Seattle), S. Kolluri (University of Washington), and V. R. Subramanian (Pacific Northwest National Laboratory, University of Washington, Seattle)
Battery Management Systems (BMS) are critical to safe and efficient operation of lithium-ion batteries and accurate prediction of the internal states [1]. Smarter BMS that can estimate and implement optimal charging profiles in real-time are important for advancement of the Li-ion battery technology. Estimating optimal profiles using physics-based models is computationally expensive because of the non-linear and stiff nature of the model equations, involving the need for constrained non-linear optimization [2-12]. In this work, we present an alternative approach to control batteries applying the concept of differential geometry. This approach is known as Generic Model Control (GMC), or Reference System Synthesis [13,14]. This work enables robust stabilization and control of battery models to set-point as an alternative approach, eliminating the need to perform optimization of nonlinear models.

This work builds on our previous work of implementation of GMC for differential variables occurring in a model consisting of differential algebraic equations (DAEs), and extends it to set point based control for algebraic variables. Conventional GMC is applicable for cases in which the output is typically independent of control [13]. We also extend it for cases for which the measured variable can be an explicit function of the manipulated variable.

As compared to the generic model control approaches implemented by previous researchers, we have implemented the same concept using direct DAE numerical solvers [15]. The results are presented for single input single objective, and for constrained problems. The proposed approach is compared with standard control approaches and is found to be equally robust if not better.


The work presented herein was funded in part by the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000275 and the Clean Energy Institute at the University of Washington, Seattle.


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