  A key function of battery management systems (BMS) in e-mobility applications
is estimating the battery state of health (SoH) with high accuracy. This is
typically achieved in commercial BMS using model-based methods. There has been
considerable research in developing data-driven methods for improving the
accuracy of SoH estimation. The data-driven methods are diverse and use
different machine-learning (ML) or artificial intelligence (AI) based
techniques. Complex AI/ML techniques are difficult to implement in low-cost
microcontrollers used in BMS due to the extensive use of non-linear functions
and large matrix operations. This paper proposes a computationally efficient
and data-lightweight SoH estimation technique. Online impedance at four
discrete frequencies is evaluated to derive the features of a linear regression
problem. The proposed solution avoids complex mathematical operations and it is
well-suited for online implementation in a commercial BMS. The accuracy of this
method is validated on two experimental datasets and is shown to have a mean
absolute error (MAE) of less than 2% across diverse training and testing data.
