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

TL;DR

This paper compares deep learning methods, including LSTM, CNN, and autoencoders, for estimating the remaining useful life of EV battery packs, emphasizing real-world applicability and generalization across diverse datasets.

Contribution

It introduces a practical approach for RUL estimation using measurable variables and predicts ampere-hours, enhancing real-world deployment of battery health assessment methods.

Findings

Methods generalize well across diverse battery datasets

Autoencoders effectively extract features for RUL prediction

Predictions based on ampere-hours improve accuracy

Abstract

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.