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Electric vehicles (EVs) are spreading fast as they promise to provide betterperformance and comfort, but above all, to help face climate change. Despitetheir success, their cost is still a challenge. Lithium-ion batteries are oneof the most expensive EV components, and have become the standard for energystorage in various applications. Precisely estimating the remaining useful life(RUL) of battery packs can encourage their reuse and thus help to reduce thecost of EVs and improve sustainability. A correct RUL estimation can be used toquantify the residual market value of the battery pack. The customer can thendecide to sell the battery when it still has a value, i.e., before it exceedsthe end of life of the target application, so it can still be reused in asecond domain without compromising safety and reliability. This paper proposesand compares two deep learning approaches to estimate the RUL of Li-ionbatteries: LSTM and autoencoders vs. CNN and autoencoders. The autoencoders areused to extract useful features, while the subsequent network is then used toestimate 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 deployedapplication. Such measures include (1) avoiding using non-measurable variablesas input, (2) employing appropriate datasets with wide variability anddifferent conditions, and (3) predicting the remaining ampere-hours instead ofthe number of cycles. The results show that the proposed methods can generalizeon datasets consisting of numerous batteries with high variance.

To Charge or to Sell? EV Pack Useful Life Estimation via LSTMs, CNNs, and Autoencoders