Koopman-Based Model Predictive Control for Simultaneous State of Charge and Temperature Balancing of Lithium-Ion Cells
Abstract
Cell-to-cell electrical and thermal variations produce nonuniform state of charge (SoC) and temperature distributions in lithium-ion battery packs. This paper proposes a Koopman-operator-based framework for simultaneous SoC and temperature balancing of series-connected cells. Cell-specific Koopman predictors identified using extended dynamic mode decomposition are assembled into neighboring-cell error dynamics that retain heterogeneity and thermal-disturbance effects. A Tikhonov-regularized feedforward controller provides numerically robust compensation without excessive current amplification, while constrained Koopman-based model predictive control (KMPC) regulates the residual errors through a convex quadratic program. Three-cell simulations validate the controller under two consecutive Urban Dynamometer Driving Schedule cycles. KMPC achieves balancing performance comparable to nonlinear model predictive control (NMPC). Relative to the uncontrolled case, KMPC reduces the RMSEs of the neighboring-cell SoC and core temperature differences by 66-81% and 57-76%, respectively, while reducing the computation time per optimization from 0.0259s for NMPC to 0.0028s for KMPC.