Modeling and Optimization of Batteries

Modeling and simulation-based methods for the optimized development of battery cells and systems as well as for the formation during battery cell production

Pseudo 2D Modell einer Lithium-Ionen Batteriezelle
© Fraunhofer ISE
Oben: Pseudo 2D Modell einer Lithium-Ionen Batteriezelle. Unten: Arbeitsschritte, um aus einer zylindrischen Zelle Proben und Halbzellen zu entnehmen.
Ersatzschaltbild einer Batterie
© Fraunhofer ISE
Oben: einfaches Ersatzschaltbild einer Batterie. Unten: Spannungsantwort auf einen Strompuls und hochgenaue Annäherung der Ersatzschaltbildparameter.
Battery Modeling - images of the graphite anode
© Fraunhofer ISE
Top: images of the graphite anode on the left in the begin of life state, on the right in the aged state. Bottom: Measurement of the capacity fade and model with knee point correlating with the formation of the electrode decomposition top layer.

We have highly accurate models for battery cells as well as for battery systems and are continuously developing them or parameterizing them for new cell types. Depending on the scientific questions, empirical equivalent circuit models or theoretically derived electrochemical models are used. We take great care to precisely identify the respective model parameters by means of measurement campaigns in our battery laboratory. For this purpose, extensive equipment is available for the identification of the electrical, thermal and long-term behavior. A validation of the accuracy of our models also takes place on the basis of the data generated in the battery laboratory. The resulting simulation models are able to represent the performance and efficiency as well as the temperature and aging behavior of battery cells and entire battery systems.

Based on our models, accompanied by measurements in the battery laboratory, we optimize, for example, the design and operational management of battery storage systems so that, especially in connection with thermal management, a long service life as well as maximum efficiency, reliability and safety are achieved. In addition, the models serve as the basis for determining the battery cell´s states in battery management systems or server-based monitoring systems. This includes, for example, precise algorithms for determining the state of charge SOC, the state of aging SOH, the performance SOP as well as the calculation of the remaining energy SOE still available for the application. Innovative algorithms for aging prediction as well as for the prediction of the application-specific remaining useful lifetime (RUL) serve as a basis for significantly increasing the reliability of the storage system and also serve to optimize operational management.

In addition to the actual usage phase, we also analyze the formation behavior of battery cells during manufacturing. Thus, we can accelerate this bottleneck of cell production by adapted formation protocols and thus contribute significantly to the increase of production quality and speed.

With regard to production, application and transfer to recycling processes, we develop procedures for the selection, evaluation and control of battery cells as well as methods for the rapid characterization and qualification of battery systems in use. Depending on the results, further use, transfer to a secondary life application (e.g. transfer of used vehicle batteries to 2nd life storage) or, in the case of advanced aging, transfer to recycling processes are carried out.

For battery safety, we investigate the rapid heat and pressure development in battery cells and develop propagation-inhibiting solutions based on FEM simulations, which are verified by tests under defined laboratory conditions.

Our R&D Services in this Field of Work include:

  • Measurement and determination of model parameters:
    • Equivalent circuit parameters (open circuit voltage characteristic UOCV, resistive and capacitive elements Rx, Cx) as a function of temperature and state of charge and aging  [Journal Paper]
    • Heat capacity, thermal conductivity and entropy change
    • Calendar and usage dependent aging behavior
    • Electrochemical model parameters (e.g. half-cell potential, diffusion coefficients, etc.)
  • Simulation models (electrical, thermal, mechanical, aging)
  • Development of algorithms for condition and aging determination and lifetime prediction
  • Simulation-based development of battery storage systems and their components
    • Cell selection and module/system configuration taking into account the power-side system interfaces
    • Dimensioning and cell connection of the cooling and heating system
    • Optimization of lifetime and efficiency through adapted operation management and charging procedures (e.g. prevention of Li plating)
  • Research of new modeling methods, model extensions and their parameterization.
  • Research into aging behavior and physiochemical relationships.
  • Lifetime analysis, post mortem analysis (electrode microscope, XPS, etc.).