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1. An Algorithmic Safety VEST For Li-ion Batteries During Fast Charging

   Peyman Mohtat, Sravan Pannala ( October 2021 , 2021 Modeling, Estimation and Control Conference)

   Fast charging of lithium-ion batteries is crucial to increase desirability for consumers and hence accelerate the adoption of electric vehicles. A major barrier to shorter charge times is the accelerated aging of the battery at higher charging rates, which can be driven by lithium plating, increased solid electrolyte interphase growth due to elevated temperatures, and particle cracking due to mechanical stress. Lithium plating depends on the overpotential of the negative electrode, and mechanical stress depends on the concentration gradient, both of which cannot be measured directly. Techniques based on physics-based models of the battery and optimal control algorithms have been developed to this end. While these methods show promise in reducing degradation, their optimization algorithms' complexity can limit their implementation. In this paper, we present a method based on the constant current constant voltage (CC-CV) charging scheme, called CC-CVησT (VEST). The new approach is simpler to implement and can be used with any model to impose varying levels of constraints on variables pertinent to degradation, such as plating potential and mechanical stress. We demonstrate the new CC-CVησT charging using an electrochemical model with mechanical and thermal effects included. Furthermore, we discuss how uncertainties can be accounted for by considering safetymore »margins for the plating and stress constraints.« less
2. Battery Internal Short Detection Methodology Using Cell Swelling Measurements

   https://doi.org/10.23919/ACC45564.2020.9147956  

   Cai, Ting ; Pannala, Sravan ; Stefanopoulou, Anna G. ; Siegel, Jason B. ( July 2020 , 2020 American Control Conference (ACC))

   Li-ion battery internal short circuits are a major safety issue for electric vehicles, and can lead to serious consequences such as battery thermal runaway. An internal short can be caused by mechanical abuse, high temperature, overcharging, and lithium plating. The low impedance or hard internal short circuit is the most dangerous kind. The high internal current flow can lead to battery temperature increase, thermal runaway, and even explosion in a few seconds. Algorithms that can quickly detect such serious events with a high confidence level and which are robust to sensor noise are needed to ensure passenger safety. False positives are also undesirable as many thermal runaway mitigation techniques, such as activating pyrotechnic safety switches, would disable the vehicle. Conventional methods of battery internal short detection, including voltage and surface temperature based algorithms, work well for a single cell. However, these methods are difficult to apply in large scale battery packs with many parallel cells. In this study, we propose a new internal short detection method by using cell swelling information during the early stages of a battery heating caused by an internal short circuit. By measuring cell expansion force, higher confidence level detection can be achieved for an internalmore »short circuit in an electric vehicle scale battery pack.« less
3. Low Error Estimation of Half-Cell Thermodynamic Parameters from Whole-Cell Li-Ion Battery Experiments: Physics-Based Model Formulation, Experimental Demonstration, and an Open Software Tool

   https://doi.org/10.1149/1945-7111/ac5a1a  

   Hu, Victor W. ; Schwartz, Daniel T. ( March 2022 , Journal of The Electrochemical Society)

   Low C-rate charge and discharge experiments, plus complementary differential voltage or differential capacity analysis, are among the most common battery characterization methods. Here, we adapt the multi-species, multi-reaction (MSMR) half-cell thermodynamic model to low C-rate cycling of whole-cell Li-ion batteries. MSMR models for the anode and cathode are coupled through whole-cell charge balances and cell-cycling voltage constraint equations, forming the basis for model-based estimation of MSMR half-cell parameters from whole-cell experimental data. Emergent properties of the whole-cell, like slippage of the anode and cathode lithiation windows, are also computed as cells cycle and degrade. A sequential least-square optimization scheme is used for parameter estimation from low-C cycling data of Samsung 18650 NMC∣C cells. Low-error fits of the open-circuit cell voltage (e.g., under 5 mV mean absolute error for charge or discharge curves) and differential voltage curves for fresh and aged cells are achieved. We explore the features (and limitations) of using literature reference values for the MSMR half-cell thermodynamic parameters (reducing our whole-cell formulation to a 1-degree-of-freedom fit) and demonstrate the benefits of expanding the degrees of freedom by letting the MSMR parameters be tailored to the cell under test, within a constrained neighborhood of the half-cell reference values. Bootstrapmore »analysis is performed on each dataset to show the robustness of our fitting to experimental noise and data sampling over the course of 600 cell cycles. The results show which specific MSMR insertion reactions are most responsible for capacity loss in each half-cell and the collective interactions that lead to whole-cell slippage and changes in useable capacity. Open-source software is made available to easily extend this model-based analysis to other labs and battery chemistries.« less
4. Battery health management using physics-informed machine learning: Online degradation modeling and remaining useful life prediction

   https://doi.org/10.1016/j.ymssp.2022.109347  

   Shi, Junchuan ; Rivera, Alexis ; Wu, Dazhong. ( November 2022 , Mechanical systems and signal processing)

   Wang, Dong (Ed.)

   Lithium-ion batteries have been extensively used to power portable electronics, electric vehicles, and unmanned aerial vehicles over the past decade. Aging decreases the capacity of Lithium-ion batteries. Therefore, accurate remaining useful life (RUL) prediction is critical to the reliability, safety, and efficiency of the Lithium-ion battery-powered systems. However, battery aging is a complex electrochemical process affected by internal aging mechanisms and operating conditions (e.g., cycle time, environmental temperature, and loading condition). In this paper, a physics-informed machine learning method is proposed to model the degradation trend and predict the RUL of Lithium-ion batteries while accounting for battery health and operating conditions. The proposed physics-informed long short-term memory (PI-LSTM) model combines a physics-based calendar and cycle aging (CCA) model with an LSTM layer. The CCA model measures the aging effect of Lithium-ion batteries by combining five operating stress factor models. The PI-LSTM uses an LSTM layer to learn the relationship between the degradation trend determined by the CCA model and the online monitoring data of different cycles (i.e., voltage, current, and cell temperature). After the degradation pattern of a battery is estimated by the PI-LSTM model, another LSTM model is then used to predict the future degradation and remaining useful lifemore »(RUL) of the battery by learning the degradation trend estimated by the PI-LSTM model. Monitoring data of eleven Lithium-ion batteries under different operating conditions was used to demonstrate the proposed method. Experimental results have shown that the proposed method can accurately model the degradation behavior as well as predict the RUL of Lithium-ion batteries under different operating conditions.« less
5. Universal Cathode Design Strategies to Engineer Cathode Electrolyte Interfaces for High Performance All-Solid-State Batteries

   Zhang, Yuxuan ; Song, Han Wook ; Lee, Sunghwan ( May 2022 , Materials Research Society)

   Metal-ion batteries (e.g., lithium and sodium ion batteries) are the promising power sources for portable electronics, electric vehicles, and smart grids. Recent metal-ion batteries with organic liquid electrolytes still suffer from safety issues regarding inflammability and insufficient lifetime.1 As the next generation energy storage devices, all-solid-state batteries (ASSBs) have promising potentials for the improved safety, higher energy density, and longer cycle life than conventional Li-ion batteries.2 The nonflammable solid electrolytes (SEs), where only Li ions are mobile, could prevent battery combustion and explosion since the side reactions that cause safety issues as well as degradation of the battery performance are largely suppressed. However, their practical application is hampered by the high resistance arising at the solid–solid electrode–electrolyte interface (including cathode-electrolyte interface and anode-electrolyte interface).3 Several methods have been introduced to optimize the contact capability as well as the electrochemical/chemical stability between the metal anodes (i.e.: Li and Na) and the SEs, which exhibited decent results in decreasing the charge transfer resistance and broadening the range of the stable energy window (i.e., lowing the chemical potential of metal anode below the highest occupied molecular orbital of the SEs).4 Nevertheless, mitigation for the cathode in ASSB is tardily developed because: (1) themore »porous structure of the cathode is hard to be infiltrated by SEs;5 (2) SEs would be oxidized and decomposed by the high valence state elements at the surface of the cathode at high state of charge.5 Herein, we demonstrate a universal cathode design strategy to achieve superior contact capability and high electrochemical/chemical stability with SEs. Stereolithography is adopted as a manufacturing technique to realize a hierarchical three-dimensional (HTD) electrode architecture with micro-size channels, which is expected to provide larger contact areas with SEs. Then, the manufactured cathode is sintered at 700 °C in a reducing atmosphere (e.g.: H2) to accomplish the carbonization of the resin, delivering sufficiently high electronic conductivity for the cathode. To avoid the direct exposure of the cathode active materials to the SEs, oxidative chemical vapor deposition technique (oCVD) is leveraged to build conformal and highly conducting poly(3,4-ethylenedioxythiophene) (PEDOT) on the surface of the HTD cathode.6 To demonstrate our design strategy, both NCM811 and Na3V2(PO4)3 is selected as active materials in the HTD cathode, then each cathode is paired with organic (polyacrylonitrile-based) and inorganic (sulfur-based) SEs assembled into two batteries (total four batteries). SEM and TEM reveal the micro-size HTD structure with built-in channels. Featured by the HTD architecture, the intrinsic kinetic and thermodynamic conditions will be enhanced by larger surface contact areas, more active sites, improved infusion and electrolyte ion accessibility, and larger volume expansion capability. Disclosed by X-ray computed tomography, the interface between cathode and SEs in the four modified samples demonstrates higher homogeneity at the interface between the cathode and SEs than that of all other pristine samples. Atomic force microscopy is employed to measure the potential image of the cross-sectional interface by the peak force tapping mode. The average potential of modified samples is lower than that of pristine samples, which confirms a weakened space charge layer by the enhanced contact capability. In addition, through Electron Energy Loss Spectroscopy coupled with Scanning Transmission Electron Microscopy, the preserved interface between HTD cathode and SE is identified; however, the decomposing of the pristine cathode is clearly observed. In addition, Finite element method simulations validate that the diffusion dynamics of lithium ions is favored by HTD structure. Such a demonstrated universal strategy provides a new guideline to engineer cathode electrolyte interface by reconstructing electrode structures that can be applicable to all solid-state batteries in a wide range of chemical conditions.« less