Pde estimation techniques for advanced battery management systems - part i: Soc estimation

Abstract — A critical enabling technology for electrified vehi-cles and renewable energy resources is battery energy storage. Advanced battery systems represent a promising technology for these applications, however their dynamics are governed by rel-atively complex electrochemical phenomena whose parameters degrade over time and vary across material design. Moreover, limited sensing and actuation exists to monitor and control the internal state of these systems. As such, battery management systems require advanced identification, estimation, and control algorithms. In this paper we examine state-of-health (SOH) estimation, framed as a parameter identification problem for parabolic PDEs and nonlinearly parameterized output func-tions. Specifically, we utilize the swapping identification method for unknown parameters in the diffusion partial differential equation (PDE). A nonlinear least squares method is applied to the output function to identify its unknown parameters. These identification algorithms are synthesized from the single particle model (SPM). In a companion paper we examine a new battery state-of-charge (SOC) estimation algorithm based upon the backstepping method for PDEs. I