Publications

2007

Sikha, Godfrey, and Ralph E. White. 2007. “Analytical Expression for the Impedance Response of an Insertion Electrode Cell”. Journal of The Electrochemical Society 154 (1): A43. https://doi.org/10.1149/1.2372695.
An analytical expression for the impedance response of an insertion cathode/separator/foil anode cell sandwich is presented. The analytical expression includes the impedance contributions from interfacial kinetics, double-layer adsorption, and solution-phase and solid-phase diffusion processes. The validity of the analytical solution is ascertained by comparison with the numerical solution obtained for a /polypropylene/lithium metal cell. The flexibility of the analytical solution is utilized to analyze various limiting conditions. An expression to estimate solid-phase diffusion coefficient of insertion species in a porous electrode influenced by the solution-phase diffusion process is also derived.
Zhang, Qi, and Ralph E. White. 2007. “Moving Boundary Model for the Discharge of a LiCoO[sub 2] Electrode”. Journal of The Electrochemical Society 154 (6): A587. https://doi.org/10.1149/1.2728733.
A moving boundary model in a spherical LiCoO2 particle is presented to account for the diffusion controlled phase transition in LiCoO2 solid particles, and this model is incorporated into a porous electrode model for the LiCoO2 electrode. The simulation results agree well with the experimental data of a LiCoO2 electrode. A study of the flux distribution in the porous electrode shows that the phase transition phenomenon in the LiCoO2 particles has a significant effect on the flux distribution by changing the solid phase diffusion resistance in the particles.

2006

Santhanagopalan, Shriram, and Ralph E. White. (2024) 2006. “Online Estimation of the State of Charge of a Lithium Ion Cell”. Journal of Power Sources 161 (2): 1346-55. https://doi.org/10.1016/j.jpowsour.2006.04.146.
A procedure to predict the state of charge of a lithium ion cell using experimental data (cell potential versus 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. © 2006 Elsevier B.V. All rights reserved.
Zhang, Qi, Qingzhi Guo, and Ralph E. White. 2006. “A New Kinetic Equation for Intercalation Electrodes”. Journal of The Electrochemical Society 153 (2): A301. https://doi.org/10.1149/1.2142287.
The kinetic equation used to predict the current being passed in an intercalation electrode depends on the overpotential and the composition of the intercalating species. The overpotential depends on the difference between the potential in the solid phase and in the solution phase, both of which depend on the composition of the intercalation species in the solid and solution phases, respectively. Furthermore, the overpotential depends on the local open circuit potential OCP of the intercalation electrode. Consequently, the kinetic equation and the OCP equation are coupled and must be written accordingly; that is, they must be written in a consistent way. In this work, a new kinetic equation, which is derived along with a thermodynamic OCP equation from the same reaction rate expression, is presented for intercalation electrodes. The thermodynamic equation is used to fit the OCP data of carbon MCMB-2528 and LiCoO2 electrodes. Comparison of the predictions from the new kinetic equation and the traditional Butler-Volmer equation used in the literature indicates that the new kinetic equation better describes the kinetic current–potential relationship for intercalation electrodes
Wang, Qingsong, Jinhua Sun, and Chunhua Chen. 2006. “Thermal Stability of LiPF6/EC + DMC + EMC Electrolyte for Lithium Ion Batteries”. Rare Metals 25 (6): 94-99. https://doi.org/10.1016/S1001-0521(07)60052-7.
The thermal stability of lithium-ion battery electrolyte could substantially affect the safety of lithium-ion battery. In order to disclose the thermal stability of 1.0 mol·L-1 LiPF6/ethylene carbonate (EC) + dimethyl carbonate (DMC) + ethylmethyl carbonate (EMC) electrolyte, a micro calorimeter C80 micro calorimeter was used in this paper. The electrolyte samples were heated in argon atmosphere, and the heat flow and pressure performances were detected. It is found that LiPF6 influences the thermal behavior remarkably, with more heat generation and lower onset temperature. LiPF6/EC shows an exothermic peak at 212°C, with a heat of reaction -355.4 J·g-1. DMC based LiPF6 solution shows two endothermic peak temperatures at 68.5 and 187°C, in argon filled vessel at elevated temperature. EMC based LiPF6 solution shows two endothermic peak temperatures at 191 and 258°C. in argon filled vessel. 1.0 mol·L-1 LiPF6/EC + DMC + EMC electrolyte shows an endothermic and exothermic process one after the other at elevated temperature. By comparing with the thermal behavior of single solvent based IiPF6 solution, it can be speculated that LiPF6 may react with EC, DMC and EMC separately in 1.0 mol·L-1 LiPF6/EC + DMC + EMC electrolyte, but the exothermic peak is lower than that of 1.0 mol·L-1 LiPF6/EC solution. Furthermore, The 1.0 mol·L-1 LiPF6/EC + DMC + EMC electrolyte decomposition reaction order was calculated based on the pressure data, its value is n = 1.83, and the pressure rate constants kp = 6.49 × 10-2 kPa·-0.83·min-1.
Renganathan, Sindhuja, Qingzhi Guo, Vijay A. Sethuraman, John W. Weidner, and Ralph E. White. (2024) 2006. “Polymer Electrolyte Membrane Resistance Model”. Journal of Power Sources 160 (1): 386-97. https://doi.org/10.1016/j.jpowsour.2006.01.098.
A model and an analytical solution for the model are presented for the resistance of the polymer electrolyte membrane of a H2/O2 fuel cell. The solution includes the effect of the humidity of the inlet gases and the gas pressure at the anode and the cathode on the membrane resistance. The accuracy of the solution is verified by comparison with experimental data. The experiments were carried out with a Nafion 112 membrane in a homemade fuel cell test station. The membrane resistances predicted by the model agree well with those obtained during the experiments. © 2006.
Santhanagopalan, Shriram, Qingzhi Guo, Premanand Ramadass, and Ralph E. White. 2006. “Review of Models for Predicting the Cycling Performance of Lithium Ion Batteries”. Journal of Power Sources 156 (2): 620-28. https://doi.org/10.1016/j.jpowsour.2005.05.070.
A rigorous pseudo two-dimensional model to simulate the cycling performance of a lithium ion cell is compared with two simplified models. The advantage of using simplified models is illustrated and their limitations are discussed. It is shown that for 1C or less discharge rates a simple ordinary differential equation (ODE) model can be used to predict accurately the potential as a function of time. For rates higher than 1C, simplifications to the rigorous model are suggested that reduce the solution time for the model. © 2005 Elsevier B.V. All rights reserved.
Kumaresan, Karthikeyan, Qingzhi Guo, Premanand Ramadass, and Ralph E. White. (2024) 2006. “Cycle Life Performance of Lithium-Ion Pouch Cells”. Journal of Power Sources 158 (1): 679-88. https://doi.org/10.1016/j.jpowsour.2005.08.058.
Cycle life studies have been done on lithium-ion pouch cell with LiCoO2 as cathode and meso-carbon micro-beads (MCMB) as anode at five different temperatures. By using the rate capability tests done at the same temperature as that of cycling and the half cell studies done on the fresh and cycled individual electrodes, the stoichiometric windows of individual electrodes at the beginning and at the end of cycling have been estimated. By analyzing these estimates along with the X-ray diffraction studies and half cell studies on cycled cathodes, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and half cell studies on cycled anodes, possible causes of increased capacity fade with increasing temperature were found to be the anode active material loss, probably due to a solvent/salt reduction reaction on the anode. Lithium deposition on the anode also has been identified as a possible side reaction during later stages of cycling at 35 and 45 °C. © 2005 Elsevier B.V. All rights reserved.
Ning, Gang, Ralph E. White, and Branko N. Popov. (2024) 2006. “A Generalized Cycle Life Model of Rechargeable Li-Ion Batteries”. Electrochimica Acta 51 (10): 2012-22. https://doi.org/10.1016/j.electacta.2005.06.033.
A generalized first principles based charge-discharge model to simulate the cycle life behavior of rechargeable Li-ion batteries has been developed. The model is based on loss of the active lithium ions due to the electrochemical parasitic reaction and rise of the anode film resistance. The effect of parameters such as depth of discharge (DOD), end of charge voltage (EOCV) and overvoltage of the parasitic reaction on the cycle life behavior has been quantitatively analyzed. The experimental results obtained at charge rate of 1 C, discharge rate of 0.5 C, EOCV of 4.0 V and DOD of 0.4 were used to validate the cycle life model. Good agreement between the simulations and the experiments has been achieved up to 1968 cycles. Simulation of a battery subjected to multiple cycling regimes has also been demonstrated. © 2005 Elsevier Ltd. All rights reserved.

2005

Gomadam, Parthasarathy M., Ralph E. White, and John W. Weidner. 2005. “Modeling Heat Conduction in Spiral Geometries”. Proceedings - Electrochemical Society PV 2002-30 (10): 146-59. https://doi.org/10.1149/1.1605743.
A two-dimensional energy balance (the 2D model) is reduced to a one-dimensional energy balance (the 1D-Radial-Spiral model) by a coordinate transformation approach. The 1D-Radial-Spiral model, even though one-dimensional, captures both radial and spiral heat conductions over a wide range of design parameters. By comparing the temperature predictions of the 1D-Radial-Sp1D-Radial-Spiraliral model and the 2D model, parameter ranges are identified where spiral conduction is important and where the 1D-Radial-Spiral model holds. The 1D-Radial-Spiral model provides a 60-fold savings in computation time over the two-dimensional model. When coupled to electrochemistry, the 2D model takes approximately 20 hours to simulate a 2C discharge of a Li-ion battery, while the 1D-Radial-Spiral model takes about 20 min.