Physics-based electrochemical models of lithium-ion cells require knowledge of electrode open-circuit potential (OCP) as a function of stoichiometry. To determine the OCP relationships for a cell built from unknown active materials, we might run low-rate constant-current laboratory tests on half cells built from harvested electrodes to collect related discharge and charge data. However, processing data from these tests must overcome three problems: the “data-quality problem,” the “missing-data problem,” and the “inaccessible-lithium problem.” This paper introduces a simple histogram-based method to overcome the data-quality problem and compares five different approaches to overcome the missing-data and inaccessible-lithium problems. These methods rely in part on a physics-based thermodynamic model for multiple-species multiple-reaction (MSMR) systems, which is flexible enough to accommodate different cell chemistries and simple enough to be utilized in real-time battery management systems. The five methods are validated in simulations and are then applied to physical half-cell data to produce OCP estimates for graphite and NMC electrodes from a commercial cell.

This paper introduces a novel application of model predictive control (MPC) to cell-level charging of a lithium-ion battery utilizing an equivalent circuit model of battery dynamics. The approach employs a modified form of the MPC algorithm that caters for direct feed-though signals in order to model near-instantaneous battery ohmic resistance. The implementation utilizes a 2 nd-order equivalent circuit discrete-time state-space model based on actual cell parameters; the control methodology is used to compute a fast charging profile that respects input, output, and state constraints. Results show that MPC is well-suited to the dynamics of the battery control problem and further suggest significant performance improvements might be achieved by extending the result to electrochemical models.

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This paper addresses the problem of estimating the present value of electrochemical internal variables in a lithium-ion cell in real time, using readily available measurements of cell voltage, current, and temperature. The variables that can be estimated include any desired set of reaction flux and solid and electrolyte potentials and concentrations at any set of one-dimensional spatial locations, in addition to more standard quantities such as state of charge. The method uses an extended Kalman filter along with a one-dimensional physics-based reduced-order model of cell dynamics. Simulations show excellent and robust predictions having dependable error bounds for most internal variables.