  State of health (SOH) estimation for lithium-ion battery modules with cells
connected in parallel is a challenging problem, especially with cell-to-cell
variations. Incremental capacity analysis (ICA) and differential voltage
analysis (DVA) are effective at the cell level, but a generalizable method to
extend them to module-level SOH estimation remains missing, when only
module-level measurements are available. This paper proposes a new method and
demonstrates that, with multiple features systematically selected from the
module-level ICA and DVA, the module-level SOH can be estimated with high
accuracy and confidence in the presence of cell-to-cell variations. First, an
information theory-based feature selection algorithm is proposed to find an
optimal set of features for module-level SOH estimation. Second, a relevance
vector regression (RVR)-based module-level SOH estimation model is proposed to
provide both point estimates and three-sigma credible intervals while
maintaining model sparsity. With more selected features incorporated, the
proposed method achieves better estimation accuracy and higher confidence at
the expense of higher model complexity. When applied to a large experimental
dataset, the proposed method and the resulting sparse model lead to
module-level SOH estimates with a 0.5% root-mean-square error and a 1.5%
average three-sigma value. With all the training processes completed offboard,
the proposed method has low computational complexity for onboard
implementations.
