More Like this

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. 

   more » « less
2. 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 life (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. 

   more » « less
3. Electric Vehicle Battery Simulation: How Electrode Porosity and Thickness Impact Cost and Performance

   https://doi.org/10.1115/DETC2021-71511  

   Zhao, Yixin; Behdad, Sara (August 2021, the ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, IDETC/CIE 2021)

   Lithium-ion batteries almost exclusively power today’s electric vehicles (EVs). Cutting battery costs is crucial to the promotion of EVs. This paper aims to develop potential solutions to lower the cost and improve battery performance by investigating its design variables: positive electrode porosity and thickness. The open-access lithium-ion battery design and cost model (BatPac) from the Argonne National Laboratory of the United States Department of Energy, has been used for the analyses. Six pouch battery systems with different positive materials are compared in this study (LMO, LFP, NMC 532/LMO, NMC 622, NMC 811, and NCA). Despite their higher positive active material price, nickel-rich batteries (NMC 622, NMC 811, and NCA) present a cheaper total pack cost per kilowatt-hour than other batteries. The higher thickness and lower porosity can reduce the battery cost, enhance the specific energy, lower the battery mass but increase the performance instability. The reliability of the results in this study is proven by comparing estimated and actual commercial EV battery parameters. In addition to the positive electrode thickness and porosity, six other factors that affect the battery's cost and performance have been discussed. They include energy storage, negative electrode porosity, separator thickness and porosity, and negative and positive current collector thickness. 

   more » « less
4. Chemical sensing by interfacial voltage

   https://doi.org/10.1016/j.xcrp.2022.101119  

   Wang, Yecheng; Zhang, Shuwen; Bai, Yang; Jia, Kun; Suo, Zhigang (November 2022, Cell Reports Physical Science)

   "Developing methods for chemical sensing is of importance in broad ap­ plications, including food safety, healthcare, and ecology. The work herein describes an approach to chemical sensing by interfacial voltage. A test electrode is coated with a dielectric and a receptor. When the test electrode contacts an electrolyte, the receptor adsorbs an analyte from the electrolyte. The adsorption generates an interfacial voltage, a measurement of which reports the concentration of the an­ alyte. This design de-integrates two aspects of sensing: adsorption and detection. Consequently, the test electrode can be made of any elec­ tronic conductor. This flexibility enables sensors to be fabricated without microelectronic facilities. Several species of ions and organic molecules are detected, and a wearable chemical sensor worn on a fingertip is demonstrated. Needle-shaped electrodes are developed to test soft biological tissues. Chemical sensing by interfacial voltage holds promise for the development of ubiquitous sensing technology." 

   more » « less
5. Nonlinear Observer and Simplified Equivalent Circuit Model-based EKF-SOC Estimator of a Rechargeable LiFePo4 cell

   Park, Jinhyeong; Kim, Gunwoo; Na, Woonki; Lim, Cheolwoo; Kim, Jonghoon (May 2019, IEEE ICPE 2019 conference in Busan, Korea)

   The lithium iron phosphate (LFP) battery has more nonlinear characteristic than other battery type. For this reason, when we use electrical equivalent circuit model and the extended Kalman filter (EKF) for estimating the SOC, the estimation performance can be decreased in the nonlinear region. This paper proposes an advance estimation method of state of charge (SOC) for lithium iron phosphate (LFP) batteries. To improve the model accuracy, this paper utilizes the nonlinear observer for identifying the internal parameters of batteries. Furthermore, to reduce the nonlinear effect of the LFP batteries, this paper recast the Kalman process. Therefore, through the proposed method, the performance of SOC estimation can be more accurate and the computational burden is decreased when we apply the embedded system. 

   more » « less