  Lithium-ion batteries have found their way into myriad sectors of industry to
drive electrification, decarbonization, and sustainability. A crucial aspect in
ensuring their safe and optimal performance is monitoring their energy state.
In this paper, we present the first study on predicting the remaining energy of
a battery cell undergoing discharge over wide current ranges from low to high
C-rates. The complexity of the challenge arises from the cell's
C-rate-dependent energy availability as well as its intricate electro-thermal
dynamics. To address this, we introduce a new definition of remaining discharge
energy and then undertake a systematic effort in harnessing the power of
machine learning to enable its prediction. Our effort includes two parts in
cascade. First, we develop an accurate dynamic model based on integration of
physics with machine learning to capture a battery's voltage and temperature
behaviors. Second, based on the model, we propose a machine learning approach
to predict the remaining discharge energy under arbitrary C-rates and
pre-specified cut-off limits in voltage and temperature. The results from our
experiments show that the proposed approach offers high prediction accuracy and
amenability to training and computation.
