Thermal-Enhanced Adaptive Interval Estimation in Battery Packs With Heterogeneous Cells

The internal states of lithium-ion batteries need to be carefully monitored during operation to manage energy and safety. In this article, we propose a thermal-enhanced adaptive interval observer for state-of-charge (SOC) and temperature estimation for a battery pack. For a large battery pack with hundreds or thousands of heterogeneous cells, each individual cell characteristic is different from others. Practically, applying estimation algorithms on each and every cell would be mathematically and computationally intractable since battery packs are often characterized by combinations of differential equations (state dynamics) and algebraic constraints (Kirchhoff’s laws). These issues are tackled using an interval observer based on monotone/cooperative system theory, whose novelty lies in considering cell heterogeneity and state-dependent parameters as unknown, but bounded uncertainties. The resulting interval observer maps the bounded uncertainties to a feasible set of SOC and temperature estimation for all cells in the pack at each time instant. This work also addresses the significant conservatism under extreme conditions with large currents via a thermal-enhanced adaptive scheme. The proposed interval estimation is scalable and computationally tractable since it is independent of the number of cells in a pack, as numerically demonstrated in a comparison with respect to a state-of-the-art single-cell state observer. The stability and inclusion of the adaptive interval observer are proven and validated through simulations.