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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. Fault Detection and Diagnostic Method Based on Evolving Datadriven Model for Radiant Heating and Cooling Systems

   Sujit Dahal ; Liping Wang ; James Braun ( January 2022 , International High Performance Buildings Conference)

   Faults in components (valves, sensors, etc.) of radiant floor heating and cooling systems affect the efficiency, cooling and heating capacity as well as the reliability of the system. While various fault detection and diagnostic (FDD) methods have been developed and tested for building systems, FDD algorithms for radiant heating and cooling systems have previously not been available. This paper presents an evolving learning-based FDD approach for a radiant floor heating and cooling system based on growing Gaussian mixture regression (GGMR). The experimental space was controlled with a building automation system (BAS) in which the operating conditions can be monitored, and control parameters can be overridden to desired values. Trend data for normal operation and faulty operation were collected. A total of six fault types with different severities in a radiant floor system were emulated through overriding control parameters. An FDD model based on the GGMR approach was developed with training data and the performance of the model was tested for "known" faults that were including in the training and new "unknown" faults that were implemented in the fault testing. The prediction accuracy for each known fault was extremely high with the lowest prediction accuracy of 98% for one of themore »faults. The algorithm was successful in detecting the new fault as an unknown state before evolving the model and in diagnosing it as a new fault after evolving the model.« less