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We present a one-dimensional, radial, coupled degradation-electrochemical-thermal (DET) model of a large format cylindrical lithium ion cell. The model consists of reduced order equations that describe the electrochemical phenomena, including that associated with degradation, coupled with an approximate model of thermal behavior. The reduced order electrochemical model, which is approximated from the pseudo-two-dimensional (P2D) electrochemical model using a Padé approximation method, computes the variation of electrochemical variables and heat generation terms. Simultaneously, a coupled thermal model computes the temperature distribution in the radial direction of the cell. The results from DET model compare favorably to those obtained from solving the 1D radial coupled degradation-electrochemical-thermal partial differential equations in COMSOL Multiphysics, however the DET model returns these results in significantly reduced computational times. Importantly, the model capability in providing insightful information of cell degradation and temperature in a computationally efficient manner paves the way for the health-conscious, real-time optimal control of large format cylindrical cells.

We present a reduced order model for a lithium ion battery in which Padé approximants are used to simplify complex, transcendental, transfer functions associated with the linearized, pseudo 2-dimensional (P2D) electrochemical model of the battery. The resulting transfer functions take the form of simple, rational polynomial functions, which can be used to compute any variable at any location within a one-dimensional representation of the battery domain. Corrections for nonlinear behavior are also incorporated into the reduced model. The results obtained using the reduced model compare favorably to those from the full (nonlinear) P2D model and the computational time required to produce these results is significantly reduced. Importantly, the form of the reduced model means that variables can be evaluated at specific discrete locations within the cell domain, without the need to compute all values of the variable at all discrete locations, as is the case with the spatial discretization methods most commonly used to implement the P2D model. We show that this results in further significant time savings and enhances the suitability of the model for variety of applications.