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

Abstract

The internal states of Lithium-ion batteries need to be carefully monitored during operation to manage energy and safety. In this paper, 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 are 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 as well as 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. The present work 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. Stability and inclusion of the adaptive interval observer are proven and validated through simulations.

Cell-Level State of Charge Estimation for Battery Packs Under Minimal Sensing

“…Consequently it is noted that although fewer measurement signals weaken cell-level observability, the analysis in this paper provides considerable incentives to significantly reduce the number of sensors in a battery pack. Future work will explore the effects of temperature [59] on cell-level state estimation with reduced sensing.…”

Modelling and Estimation in Lithium-Ion Batteries: A Literature Review

“…A straightforward method to implement this idea is to forward simulate the system (34) for a set of different initial conditions and gradually eliminate all the initial conditions that do not agree with the measured signal (y). This method can be implemented by means of a stochastic framework [93][94][95][96][97][98] (see [99] for more technical details about the approach) or a deterministic framework [100][101][102] (see [103,104] for more technical details about the approach). Moreover, under some observability assumptions, it can be proven that this method eventually retrieves the true initial condition.…”

State of Temperature Estimation of Li-Ion Batteries Using 3rd Order Smooth Variable Structure Filter

“…This filter is designed for nonlinear plants and can operate without full measurements. For states without associated measurements, Luenberger's transformation is used to decrease the estimation error [18].…”

“…This study used a more accurate heat generation model by considering the effects of changes in entropy and overpotential on heat generation. An interval VOLUME XX, 2017 observer is used on battery packs to estimate the SOC and the temperature of the cells, and the difference between the proposed observer and Extended Kalman Filter (EKF)in terms of computational costs shows its superiority [18]. Moreover, EKF is used on a novel simplified thermoelectric model, which includes a simplified thermal model interrelated to the electric model, to estimate the core temperature [19].…”