Parameter Estimation and Model Discrimination for a Lithium-Ion Cell

Two different models were used to obtain transport and kinetic parameters using nonlinear regression from experimental charge/discharge curves of a lithium-ion cell measured at 35°C under four rates, C/5, C/2, 1C, and 2C, where the C rate is 1.656A . The Levenberg-Marquardt method was used to estimate parameters in the models such as the diffusion of lithium ions in the positive electrode. A confidence interval for each parameter was also presented. The parameter values lie within their confidence intervals. The use of statistical weights to correct for the scatter in experimental data as well as to treat one set of data in preference to other is illustrated. An F-test was performed to discriminate between the goodness of fit obtained from the two models

Simulation of the Oxygen Reduction Reaction at an RDE in 0.5 M H\u3csub\u3e2\u3c/sub\u3eSO\u3csub\u3e4\u3c/sub\u3e Including an Adsorption Mechanism

Oxygen reduction on the surface of a rotating disk electrode (RDE) in 0.5 M H2SO4 is simulated by including mass transfer, adsorption, and charge transfer. A generalized model for the adsorption and reaction of several species is introduced. The oxygen reduction reaction is simulated as a limiting case where oxygen is the only species adsorbed, and oxygen reduction is the only reaction that takes place on the surface of the electrode. The model is based on the Nernst–Planck equations for mass transfer and the Butler–Volmer equation for electrochemical kinetics. The simulated polarization curves capture the change in the Tafel slopes, which are observed experimentally but cannot be explained by the normal four-electron-transfer mechanism. The adsorption model is compared with the four-electron-transfer model by fitting experimental data to both models using a nonlinear parameter estimation technique. The effects of changes in some important kinetic parameters are demonstrated

Simulation of Polarization Curves for Oxygen Reduction Reaction in 0.5 M H\u3csub\u3e2\u3c/sub\u3eSO\u3csub\u3e4\u3c/sub\u3e at a Rotating Ring Disk Electrode

A cylindrical two-dimensional model based on the Nernst–Planck equations, the Navier–Stokes equation, and the continuity equation is used to simulate the oxygen reduction reaction in 0.5MH2SO4 at a rotating ring disk electrode. Concentration distributions and a potential profile are obtained as a function of the axial and radial distances from the center of the electrode surface. Polarization curves are simulated to interpret experimental results by studying various reaction mechanisms, i.e., the four-electron-transfer reduction of oxygen, the two-electron-transfer reduction of oxygen, a combination of the above two reactions, mechanisms with reduction of peroxide to water, and/or the heterogeneous chemical decomposition of peroxide. Special attention is devoted to the effect of peroxide

Parameter Estimation and Life Modeling of Lithium-Ion Cells

Lithium-ion pouch cells were cycled at five different temperatures (5, 15, 25, 35, and 45°C ), and rate capability studies were performed after every hundred cycles. The data were used with a simple physics-based model to estimate parameters that capture the capacity fade in the cell, with cycling. The weight of active material within each electrode was estimated as a function of time, using rate capability data at the C/33 rate. The C-rate for these cells is 1.656 A. The capacity fade due to the loss of active material and that due to the loss of cyclable lithium were quantified. It was found that while the loss of cyclable lithium is the limiting cause for the capacity decay of the cell during the first 200 cycles, the loss of active carbon, which is the anode material, becomes limiting for these cells. The loss of active material leads to a drastic decrease in cell capacity at higher temperatures

Theoretical Analysis of Stresses in a Lithium Ion Cell

A mathematical model to simulate the generation of mechanical stress during the discharge process in a dual porous insertion electrode cell sandwich comprised of lithium cobalt oxide and carbon is presented. The model attributes stress buildup within intercalation electrodes to two different aspects: changes in the lattice volume due to intercalation and phase transformation during the charge/discharge process. The model is used to predict the influence of cell design parameters such as thickness, porosity, and particle size of the electrodes on the magnitude of stress generation. The model developed in this study can be used to understand the mechanical degradation in a porous electrode during an intercalation/deintercalation process, and the use of this model results in an improved design for battery electrodes that are mechanically durable over an extended period of operation

Quantifying Cell-to-Cell Variations in Lithium Ion Batteries

Lithium ion batteries have conventionally been manufactured in small capacities but large volumes for consumer electronics applications. More recently, the industry has seen a surge in the individual cell capacities, as well as the number of cells used to build modules and packs. Reducing cell-to-cell and lot-to-lot variations has been identified as one of the major means to reduce the rejection rate when building the packs as well as to improve pack durability. The tight quality control measures have been passed on from the pack manufactures to the companies building the individual cells and in turn to the components. This paper identifies a quantitative procedure utilizing impedance spectroscopy, a commonly used tool, to determine the effects of material variability on the cell performance, to compare the relative importance of uncertainties in the component properties, and to suggest a rational procedure to set quality control specifications for the various components of a cell, that will reduce cell-to-cell variability, while preventing undue requirements on uniformity that often result in excessive cost of manufacturing but have a limited impact on the cells' performance

Online Estimation of the State of Charge of a Lithium Ion Cell

A procedure to predict the state of charge of a lithium ion cell using experimental data (cell potential vs time), as it becomes available, is presented. The procedure is based on the physics of the system and provides a realistic estimate of the state of charge of the cell as a function of time, for a given set of properties of the electrodes. An electrochemical cell model is used to obtain an Extended Kalman filter (EKF) for estimating the state of charge (SOC) of a lithium ion cell in which the negative electrode is the limiting electrode. The method could also be used for a cell in which the positive electrode is limiting

A Framework to Analyze the Requirements of a Multiport Megawatt-Level Charging Station for Heavy-Duty Electric Vehicles

Widespread adoption of heavy-duty (HD) electric vehicles (EVs) will soon necessitate the use of megawatt (MW)-scale charging stations to charge high-capacity HD EV battery packs. Such a station design needs to anticipate possible station traffic, average and peak power demand, and charging/wait time targets to improve throughput and maximize revenue-generating operations. High-power direct current charging is an attractive candidate for MW-scale charging stations at the time of this study, but there are no precedents for such a station design for HD vehicles. We present a modeling and data analysis framework to elucidate the dependencies of a MW-scale station operation on vehicle traffic data and station design parameters and how that impacts vehicle electrification. This framework integrates an agent-based charging station model with vehicle schedules obtained through real-world vehicle telemetry data analysis to explore the station design and operation space. A case study applies this framework to a Class 8 vehicle telemetry dataset and uses Monte Carlo simulations to explore various design considerations for MW-scale charging stations and EV battery technologies. The results show a direct correlation between optimal charging station placement and major traffic corridors such as cities with ports, e.g., Los Angeles and Oakland. Corresponding parametric sweeps reveal that while good quality of service can be achieved with a mix of 1.2-megawatt and 100-kilowatt chargers, the resultant fast charging time of 35–40 min will need higher charging power to reach parity with refueling times