Madeleine Ecker’s research while affiliated with RWTH Aachen University and other places

This work compares a state of the art data‐driven model to predict the state of health (SoH) in lithium ion batteries with a new prediction model based on the mechanistic framework. The mechanistic approach attributes the degradation to individual components such as loss of available capacity on each electrode as well as loss of cyclable lithium. By combining the mechanistic framework with data‐driven models for the component losses based on a design of experiment, we achieve a cycle aging model that can predict capacity degradation as well as degradation‐induced changes to the discharge potential curve. Using this cycle aging model alongside with a semi‐empirical calendar aging model, we present a holistic aging model that we validate on independent validation tests containing time‐variant load profiles. While the purely data‐driven model is better at predicting the SoH, the mechanistic model clearly has it advantages in a deeper understanding that can potentially enhance the current methods of tracking and updating the characteristic open‐circuit voltage curve over lifetime.

Accurately predicting aging of lithium-ion batteries would help to prolong their lifespan, but remains a challenge owing to the complexity and interrelation of different aging mechanisms. As a result, aging prediction often relies on empirical or data-driven approaches, which obtain their performance from analyzing large datasets. However, these datasets are expensive to generate and the models are agnostic of the underlying physics and thus difficult to extrapolate to new conditions. In this article, a physical model is used to predict capacity fade caused by solid-electrolyte interphase (SEI) growth in 62 automotive cells, aged with 28 different protocols. Three protocols parametrize the time, current and temperature dependence of the model, the state of charge dependence results from the anode’s open circuit voltage curve. The model validation with the remaining 25 protocols shows a high predictivity with a root-mean squared error of 1.28%. A case study with the so-validated model shows that the operating window, i.e. maximum and minimum state of charge, has the largest impact on SEI growth, while the influence of the applied current is almost negligible. Thereby the presented model is a promising approach to better understand, quantify and predict aging of lithium-ion batteries.

60 large format automotive grade lithium-ion pouch cells with graphite/NMC chemistry are tested following a design of experiment. Realistic driving profiles resembling a plugin-hybrid electric vehicle are used with variation of five aging factors: temperature, maximum and minimum state of charge, charging power, and the ratio of charge depleting vs. charge sustaining cycling. Capacity fade is cleaned from calendar aging and multivariate stepwise linear regression is used to parameterize an empirical model of cyclic capacity fade. Temperature and the ratio between charge depleting and charge sustaining cycling show the biggest impact on cyclic aging, whereas charging power has little effect in the chosen range of aging conditions. The importance of considering interdependencies between aging factors for modeling is pointed out, major interdependencies are found between the factors temperature and charging power and between minimum and maximum state of charge. Leave-one-out cross validation is used to show the capability of the comparatively simple model approach to predict cyclic aging within the tested range.

Accurately predicting aging of lithium-ion batteries would help to prolong their lifespan, but remains a challenge owing to the complexity and interrelation of different aging mechanisms. As a result, aging prediction often relies on empirical or data-driven approaches, which obtain their performance from analyzing large datasets. However, these datasets are expensive to generate and the models are agnostic of the underlying physics and thus difficult to extrapolate to new conditions. In this article, a physical model is used to predict capacity fade caused by solid-electrolyte interphase (SEI) growth in 62 automotive cells, aged with 28 different protocols. Three protocols parametrize the time, current and temperature dependence of the model, the state of charge dependence results from the anode's open circuit voltage curve. The model validation with the remaining 25 protocols shows a high predictivity with a root-mean squared error of 1.28%. A case study with the so-validated model shows that the operating window, i.e. maximum and minimum state of charge, has the largest impact on SEI growth, while the influence of the applied current is almost negligible. Thereby the presented model is a promising approach to better understand, quantify and predict aging of lithium-ion batteries.

60 large format automotive grade lithium-ion pouch cells with graphite/NMC chemistry are tested following a design of experiment (DoE) approach. Realistic driving profiles resembling a plugin-hybrid electric vehicle are used with variation of five aging factors: temperature, maximum and minimum state of charge, charging power, and the ratio of charge depleting vs. charge sustaining cycling.Capacity fade is cleaned from calendar aging and multivariate stepwise linear regression is used to parameterize an empirical model of cyclic capacity fade.Temperature and the ratio between charge depleting and charge sustaining cycling show the biggest impact on cyclic aging, whereas charging power has little effect in the chosen range of aging conditions.

When charging a lithium-ion cell, the deposition of metallic lithium on the negative electrode surface, known as lithium plating, must be avoided. In this paper, the charging process of a commercial high energy lithium-ion pouch cell is investigated. Three-electrode test cells are assembled using electrode materials from the high energy lithium-ion pouch cell together with lithium metal as reference electrode to acquire the potential at the negative electrode-electrolyte interface. During charging, the cells’ current is controlled in a way that the negative electrode potential is maintained constantly slightly above 0 V vs. Li/Li⁺. The resulting current map depending on temperature and state of charge is used to control the charging process of the pouch cell. Following this new charging procedure, a state of charge of 80% is reached in 15 min at 25°C ambient temperature. Different cycle life tests are performed to examine iteratively an approach to how the charging current has to be reduced over the lifetime in order to avoid accelerated aging. To prove the practicability, the method is tested at the battery pack level.

Physico-chemical models are key for a successful use of lithium-ion batteries, especially under extreme conditions. For correctly simulating of the internal battery states and battery aging a suitable set of material properties is needed. This work presents methods to extract these parameters from commercial cells and demonstrates them analyzing a high-power prismatic cell. In a first step, the electrolyte analysis is described, followed by an examination of the active material. The composition as well as the porous structure are measured using optical emission spectroscopy and Hg-porosimetry. To determine the electrochemical properties of the electrode materials, coin cells with lithium as counter electrode are build. With these test cells, open circuit voltage curves and galvanostatic intermittent titration technique measurements are performed to determine the electrode balancing as well as the diffusion constants of the active material. Electrochemical impedance spectroscopy experiments on the full cell are used to determine the charge transfer. In the second part of this paper, a determination of the thermal parameters as well as a validation for the complete parameterization are described.