Increasing longevity remains one of the open challenges for Lithium-ion (Li-ion) battery technology. We envision a health-conscious advanced battery management system, which implements monitoring and control algorithms that increase battery lifetime while maintaining performance. For such algorithms, real-time battery capacity estimates are crucial. In this paper, we present an online capacity estimation scheme for Li-ion batteries. The key novelty lies in: 1) leveraging thermal dynamics to estimate battery capacity and 2) developing a hierarchical estimation algorithm with provable convergence properties. The algorithm consists of two stages working in cascade. The first stage estimates battery core temperature and heat generation based on a two-state thermal model, and the second stage receives the core temperature and heat generation estimation to estimate state-of-charge and capacity. Results from numerical simulations and experimental data illustrate the performance of the proposed capacity estimation scheme.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering