The battery pack accounts for a large share of an Electric Vehicle cost. In this context, making sure that the battery pack life matches the lifetime of the vehicle is critical. The present work proposes a battery aging management framework which is capable of controlling the battery capacity degradation while guaranteeing acceptable vehicle performance in terms of driving range, recharge time, and drivability. The strategy acts on the maximum battery current, and on the depth of discharge. The formalization of the battery management issue leads to a multi-objective, multi-input optimization problem for which we propose an online solution. The algorithm, given the current battery residual capacity and a prediction of the driver's behavior, iteratively selects the best control variables over a suitable control discretization step. We show that the best aging strategy depends on the driving style. The strategy is thus made adaptive by including a self-learnt, Markov-chain-based driving style model in the optimization routine. Extensive simulations demonstrate the advantages of the proposed strategy against a trivial strategy and an offline benchmark policy over a life of 200,000 km.

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. In this paper, six out of these ten cells are tested for a period of 24 months. The aging campaign is a combination of calendar aging and cycling. The cycling portion is designed to replicate real-world usage patterns based on synthetic duty cycles for residential and commercial grid energy storage systems (ESS). Battery cycling alternates between 20 °C and 35 °C to simulate seasonal temperature variations encountered in grid applications. The calendar aging, on the other hand, occurs at room temperature. Periodic assessments of battery degradation during second-life testing are accomplished via Reference Performance Tests for second-life (RPT S) comprising of a combined capacity and pulse power test, and Electrochemical Impedance Spectroscopy (EIS) at three state-of-charge (SOC) values. The data set captures the combined effects of cycling-induced stress and long-term storage.