  Electric vehicles (EVs) are spreading fast as they promise to provide better
performance and comfort, but above all, to help face climate change. Despite
their success, their cost is still a challenge. Lithium-ion batteries are one
of the most expensive EV components, and have become the standard for energy
storage in various applications. Precisely estimating the remaining useful life
(RUL) of battery packs can encourage their reuse and thus help to reduce the
cost of EVs and improve sustainability. A correct RUL estimation can be used to
quantify the residual market value of the battery pack. The customer can then
decide to sell the battery when it still has a value, i.e., before it exceeds
the end of life of the target application, so it can still be reused in a
second domain without compromising safety and reliability. This paper proposes
and compares two deep learning approaches to estimate the RUL of Li-ion
batteries: LSTM and autoencoders vs. CNN and autoencoders. The autoencoders are
used to extract useful features, while the subsequent network is then used to
estimate the RUL. Compared to what has been proposed so far in the literature,
we employ measures to ensure the method's applicability in the actual deployed
application. Such measures include (1) avoiding using non-measurable variables
as input, (2) employing appropriate datasets with wide variability and
different conditions, and (3) predicting the remaining ampere-hours instead of
the number of cycles. The results show that the proposed methods can generalize
on datasets consisting of numerous batteries with high variance.
