  An accurate and reliable technique for predicting Remaining Useful Life (RUL)
for battery cells proves helpful in battery-operated IoT devices, especially in
remotely operated sensor nodes. Data-driven methods have proved to be the most
effective methods until now. These IoT devices have low computational
capabilities to save costs, but Data-Driven battery health techniques often
require a comparatively large amount of computational power to predict SOH and
RUL due to most methods being feature-heavy. This issue calls for ways to
predict RUL with the least amount of calculations and memory. This paper
proposes an effective and novel peak extraction method to reduce computation
and memory needs and provide accurate prediction methods using the least number
of features while performing all calculations on-board. The model can
self-sustain, requires minimal external interference, and hence operate
remotely much longer. Experimental results prove the accuracy and reliability
of this method. The Absolute Error (AE), Relative error (RE), and Root Mean
Square Error (RMSE) are calculated to compare effectiveness. The training of
the GPR model takes less than 2 seconds, and the correlation between SOH from
peak extraction and RUL is 0.97.
