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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)

   null (Ed.)

   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 safety margins for the plating and stress constraints. 

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2. State of Health Estimation of Electric Vehicle Batteries Using Transformer-based Neural Network

   Zhao, Yixin ; Behdad, Sara ( August 2023 , Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2023)

   Electric vehicles (EVs) are considered an environmentally friendly option to conventional vehicles. As the most critical module in EVs, batteries are complex electrochemical components with nonlinear behavior. On-board battery system performance is also affected by complicated operating environments. Real-time EV battery in-service status prediction is tricky but vital to enable fault diagnosis and aid in the prevention of dangerous occurrences. Data-driven models with advantages in time series analysis can be used to capture the degradation pattern from data about certain performance indicators and predict the battery states. The Transformer model is capable of capturing long-range dependencies efficiently using a multi-head attention block mechanism. This paper presents the implementation of a standard Transformer and an encoder-only Transformer neural network to predict EV battery state of health (SOH). Based on the analysis of the lithium-ion battery from NASA Prognostics Center of Excellence website's publicly accessible dataset, 28 features related to the charge and discharge measurement data are extracted. The features are screened using Pearson correlation coefficients. The results show that the filtered features can effectively improve the accuracy of the model as well as the computational efficiency. The proposed standard Transformer shows good performance in SOH prediction. 

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3. 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 internal short circuit in an electric vehicle scale battery pack. 

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

   Dahal, Sujit ; Wang, Liping ; Braun, James ( 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 the 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. 

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5. 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. Bootstrap 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. 

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