Tortuosity Determination of Battery Electrodes and Separators by Impedance Spectroscopy

Abstract

Lithium ion battery performance at high charge/discharge rates is largely determined by the ionic resistivity of an electrode and separator which are filled with electrolyte. Key to understand and to model ohmic losses in porous battery components is porosity as well as tortuosity. In the first part, we use impedance spectroscopy measurements in a new experimental setup to obtain the tortuosities and MacMullin numbers of some commonly used separators, demonstrating experimental errors of <8%. In the second part, we present impedance measurements of electrodes in symmetric cells using a blocking electrode configuration, which is obtained by using a non-intercalating electrolyte. The effective ionic resistivity of the electrode can be fit with a transmission-line model, allowing us to quantify the porosity dependent MacMullin numbers and tortuosities of electrodes with different active materials and different conductive carbon content. Best agreement between the transmission-line model and the impedance data is found when constant-phase elements rather than simple capacitors are used. Motivation.-Advanced battery models are a valuable tool for evaluating the performance, safety, and life-time of lithium ion batteries, since they can provide insight into the kinetics and the transport characteristics of batteries, which are not or only partially accessible by experiments. To obtain quantitative and meaningful numerical results, the choice of appropriate physical models and boundary conditions with the corresponding, accurately determined, kinetic and transport parameters are key issues. For numerical simulations of battery systems, the ion-transport model for concentrated electrolyte solutions introduced by Newman et al.1 is frequently used. Since the microscopic geometry of actually used porous electrodes and separators are largely unknown, a homogenization approach is applied for the macroscopic description of porous media. In this case, the influence of the microstructure on the macroscopic behavior is modeled by additional geometric parameters such as the porosity ε and the tortuosity τ. The porosity ε is a well-defined property of a porous medium, which can be determined easily. In contrast, the effective tortuosity of separators and particularly of electrodes are more difficult to quantify, and, to further complicate the matter, many different definitions for the tortuosity τ are used in the literature. Thus, the different tortuosity definitions will be presented prior to reviewing the literature concerned with determining the tortuosity or the effective ionic conductivity of porous battery separators and electrodes.

Influence of the Binder on Lithium Ion Battery Electrode Tortuosity and Performance

“…3 Although the slurry mixing and electrode preparation procedure was not optimized, we find clear trends, i.e., higher tortuosities for higher amounts of binder and, more importantly, a wide range of tortuosity values for graphite electrodes of similar porosity and loading (from 2.7 ± 0.1 to 10.2 ± 0.3) for different types of polymeric binder. By addition of high surface area conductive carbon, the tortuosity of the electrodes with a binder layer thickness above 6 nm decreases.…”

“…3,14 In the first part of our analysis we will demonstrate the effect of binder and conductive carbon additives on electrode tortuosity and thereafter give an overview of the range of experimentally obtained tortuosities for two water and three NMP (n-methyl-2-pyrrolidone) based binder systems. Subsequently, charging rate performance tests in three-electrode cells for electrodes with largely different tortuosities are presented, illustrating the clear correlation between the tortuosity of the anode electrode and its rate capability.…”

Tortuosity in electrochemical devices: a review of calculation approaches

“…[35][36][37][38][39][40][41][42][43][44] Moreover, it has been implemented as a standard addition to predicting microstructures in electrochemistry models, such as in the COMSOL Multiphysics modelling software (COMSOL, Inc.). 23 However, predictions given by the Bruggeman correlation are not always consistent with experimental results. 23 Figure 2 compare several derived Bruggeman exponents and scaling parameters for different porous materials for battery applications.…”

“…23 However, predictions given by the Bruggeman correlation are not always consistent with experimental results. 23 Figure 2 compare several derived Bruggeman exponents and scaling parameters for different porous materials for battery applications. These were each extracted as a function of several experimental measurement points and used to extrapolate the presented curves as function of porosity.…”

Towards Low Resistance Nonaqueous Redox Flow Batteries

“…58 The disagreement in ε/τ indicates that separator wetting plays a significant role in determining R mem . We also present the experimentally determined ratio of electrolyte-to-separator conductivity (κ/κ eff , MacMullin number), 59 which is ≈2× higher for Celgard as compared to Daramic, and offers another descriptor of the effect of electrolyte conductivity on separator conductivity. The MacMullin numbers for Celgard are in good agreement with a prior report.…”