Data-Driven modeling for Li-ion battery using dynamic mode decomposition

Abstract

Lithium-ion (Li-ion) batteries are the workhorse of energy storage systems in electric vehicles (EVs) due to their high energy density and desirable characteristics. To obtain an optimized and safe operation, a battery management system (BMS) should be implemented to provide the main safety features based on the estimation of different battery states, which entails an accurate, fast, and adaptive mathematical model. In this paper, a data-driven linear model using dynamic mode decomposition is proposed. The proposed modeling procedure bridges the gap between abstracted models (e.g., circuit-based models) and empirical models (e.g., data-driven models) of Li-ion batteries. Unlike the abstracted models, the proposed model does not impose any assumptions on what the model should be, and also considers the battery as a black box similar to the empirical techniques, and yet gives an interpretable linear model in state space form. In order to generate this model, only one discharge cycle that contains high dynamic content is required, and no other specialized tests are required. Two models are proposed for the Li-ion battery, namely, a model-based estimator based on Kalman filter for BMS purposes and a piecewise model for offline simulation. The results are verified experimentally via a lab scale prototype.

Keywords

• Lithium-ion battery (Li-ion)
• Dynamic mode decomposition (DMD)
• Battery management system (BMS)
• State estimation