Second-life lithium-ion battery aging dataset based on grid storage cycling.

This paper describes an experimental dataset of used lithium-ion battery cells cycled on grid storage synthetic duty cycles to study their feasibility for second-life applications. Data were collected at the Stanford Energy Control Laboratory at Stanford University, CA, USA. The ten INR21700-M50T battery cells with graphite/silicon anode and Nickel-Manganese-Cobalt (NMC) cathode had been previously tested over a period of 23 months according to the Urban Dynamometer Driving Schedule (UDDS) discharge driving profile.

Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries.

Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data.

Evaluating the feasibility of batteries for second-life applications using machine learning.

This article presents a combination of machine learning techniques to enable prompt evaluation of retired electric vehicle batteries as to either retain those batteries for a second-life application and extend their operation beyond the original and first intent or send them to recycling facilities. The proposed algorithm generates features from available battery current and voltage measurements with simple statistics, selects and ranks the features using correlation analysis, and employs Gaussian process regression enhanced with bagging. This approach is validated over publicly available aging datasets of more than 200 with slow and fast charging cells, with different cathode chemistries, and for diverse operating conditions.

Lithium-ion battery aging dataset based on electric vehicle real-driving profiles.

This paper describes the experimental dataset of lithium-ion battery cells subjected to a typical electric vehicle discharge profile and periodically characterized through diagnostic tests. Data were collected at the Stanford Energy Control Laboratory, at Stanford University. The INR21700-M50T battery cells with graphite/silicon anode and Nickel-Manganese-Cobalt cathode were tested over a period of 23 months according to the Urban Dynamometer Driving Schedule (UDDS) discharge driving profile and the Constant Current (CC)-Constant Voltage (CV) charging protocol designed at different charging rates - ranging from C/4 to 3C.

Pushing the Eenvelope in Battery Estimation Algorithms.

Accurate estimation of lithium-ion battery health will (a) improve the performance and lifespan of battery packs in electric vehicles, spurring higher adoption rates, (b) determine the actual extent of battery degradation during usage, enabling a health-conscious control, and (c) assess the available battery life upon retiring of the vehicle to re-purpose the batteries for "second-use" applications. In this paper, the real-time validation of an advanced battery health estimation algorithm is demonstrated via electrochemistry, control theory, and battery-in-the-loop (BIL) experiments. The algorithm is an adaptive interconnected sliding mode observer, based on a battery electrochemical model, which simultaneously estimates the critical variables such as the state of charge (SOC) and state of health (SOH).