  The wide usage of Lithium-ion batteries (LIBs) requires a deep understanding
about battery health. Estimation of battery state-of-health (SOH) is a crucial
but yet still challenging task. Pure model-based methods may suffer from
inaccuracy and instability of parameter estimations in lifelong aging. While
pure data-driven methods rely heavily on quality and quantity of training set,
causing lack of generality when extrapolating into unseen cases. In this paper,
we propose an aging-aware equivalent circuit model (ECM), which combines
model-based and data-driven techniques for SOH estimation. Gaussian process
(GP) regression is incorporated in ECM to modelling parameters dependency on
operating condition and aging time. The state space formulation of GP is used
to enable a computationally efficient co-estimation framework of both
parameters and states. Samples from two Open datasets are used to validate
model performance, which gives accurate estimation for capacity and impedance.
The learnt impedance function can be lined to the shape change of open circuit
voltage (OCV) versus SOC curve and thus providing a way to further estimate
changes of differential voltage (dV/dQ) curves.
