Predicting the chemical and physical processes occurring in Lithium-ion cells with high-fidelity electrochemical models is today a critical requirement to accelerate the design and optimization of battery packs for automotive and aerospace applications. One of the common issues associated with electrochemical models is the complexity of parameter identification, particularly when relying only on experimental data obtained via non-invasive techniques. This paper presents a novel approach to improve the common methods of parameter calibration that consists of matching the predicted terminal voltage to test data via optimization methods. The study is conducted for an NMC-graphite cell, modeled using a reduced order Extended Single Particle Model (ESPM). The proposed approach relies on using a large-scale Particle Swarm Optimization (PSO), modified by including a term that accounts for the parameter sensitivity information, such that the rate of convergence and robustness of the algorithm to obtain a consistent solution in the presence of uncertainties in the initial conditions are significantly improved.

Energy storage systems (ESSs), such as lithium-ion batteries, are being used today in renewable grid systems to provide the capacity, power, and quick response required for operation in grid applications, including peak shaving, frequency regulation, back-up power, and voltage support. Each application imposes a different duty cycle on the ESS. This represents the charge/discharge profile associated with energy generation and demand. Different duty cycle characteristics can have different effects on the performance, life, and duration of ESSs. Within lithium-ion batteries, various chemistries exist that own different features in terms of specific energy, power, and cycle life, that ultimately determine their usability and performance. Therefore, the characterization of duty cycles is a key to determine how to properly design lithium-ion battery systems for grid applications. Given the usage-dependent degradation trajectories, this research task is a critical step to study the unique aging behaviors of grid batteries. Significant energy and cost savings can be achieved by the optimal application of lithium-ion batteries for grid-energy storage, enabling greater utilization of renewable grid systems. In this paper, we propose an approach, based on unsupervised learning and frequency domain techniques, to characterize duty cycles for the grid-specific peak shaving applications. Finally, we propose synthetic duty cycles to mimic grid-battery dynamic behaviors for use in laboratory testing.

Aging models are necessary to accurately predict the state of health (SOH) evolution in lithium-ion battery systems when performing durability studies under realistic operations, specifically considering time-varying storage, cycling, and environmental conditions, while being computationally efficient. This article extends existing physics-based reduced-order capacity fade models that predict degradation resulting from the solid electrolyte interface (SEI) layer growth and loss of active material (LAM) in the graphite anode. Specifically, the physics of the degradation mechanisms and aging campaigns for various cell chemistries are reviewed to improve the model fidelity. In addition, a new calibration procedure is established relying solely on capacity fade data and results are presented including extrapolation/validation for multiple chemistries. Finally, a condition is integrated to predict the onset of lithium plating. This allows the complete cell model to predict the incremental degradation under various operating conditions, including fast charging.

Li-ion batteries are the preferred choice of energy storage in many applications. However, the potential for fire and explosion due to mechanical damage remains a safety concern. Currently, there are no criteria for the extent of the mechanical damage under which the batteries are safe to use. Here, we investigate the effects of bending damage to Li-ion cells on their impedance spectra. After the initial characterization of four Li-ion pouch cells, one of the cells underwent a three-point bending load. We measured the impedance spectra of this cell after each increment of loading. The impedance data of the control group cells were collected at the same intervals as the damaged cell. A distributed equivalent circuit model (dECM) was developed using the data from the electrochemical impedance spectroscopy (EIS) procedure. We observed that several model parameters such as the magnitude of constant phase elements had similar trends in the control cells and the bent cell. However, some model parameters such as resistances in parallel with constant phase elements, and the inductor showed dependency on the extent of the damage. These results suggest the potential for use of such parameters as an indicator of mechanical damage when visual inspection of cells is not possible in a battery pack setup. Future steps include investigation of similar trends for other commercial batteries and chemistries and form factors to verify the applicability of the current findings in a broader context.

Electrified vehicle (EV) batteries that have reached the automotive end of life are providing a low-cost energy storage solution for grid-connected systems, such as DC fast charge stations (DCFCs). There are several challenges associated with the integration of second life batteries (SLBs) in power systems, such as the definition of a systematic approach for the concurrent optimization of performance and lifetime with the aim of minimizing the investment and operating costs. This paper proposes the application of automotive SLBs to DCFC stations where high-power grid connection is not available or feasible. The SLBs are charged using a low-power grid connection and then provide DCFC power to the EVs. An optimal control problem has been formulated to identify the energy management control (EMC) strategy that allows minimizing the replacement rate of the SLBs, while ensuring the EV load request is match.