In modern Battery Management Systems (BMSs), it is significant to obtain an accurate battery model to estimate the states of the battery such as State of Charge (SoC), State of Health (SoH), State of Power (SoP), State of Safety (SoS) etc. Traditional lithium-ion batteries (LIBs) have some drawbacks in terms of safety and energy density. To overcome these drawbacks, all-solid-state batteries (ASSBs) are being developed as an alternative solution for conventional lithium-ion batteries. The focus of this study is on all-solid-state batteries and their modeling based on the equivalent circuit model. On the other hand, the modeling of a cell needs an immense amount of data and long test duration time. Instead of cell characterization test data, the all-solid-state cell is modeled by using DC internal resistance (DC-IR) information. During this study, two different equivalent circuit models containing series-connected RC pairs with and without ohmic resistance are investigated. In addition, the equivalent circuit model parameters are derived via Genetic Algorithm. Moreover, measured and simulated resistance values are compared with Mean Absolute Error (MAE) criteria for two different equivalent circuit models. Finally, the plausibility of the obtained models are analyzed and compared with experimental Hybrid Pulse Power Characterization (HPPC) test results.

The design of a state of health (SoH) observer for lithium-ion cells based on the identification of continuous time impedance models is presented in this paper. For purely data-driven battery models, the use of continuous-time system identification methods enables a better interpretation of the model parameters, which is a key issue for estimating the SoH. Moreover, non-uniformly sampled data can be directly used for model construction. The performance of the proposed concepts is validated by means of real measurement data from accelerated ageing tests.

A battery model identification approach, based on non-uniformly sampled data, aiming to reflect the nonlinear dynamic behavior of a lithium-ion cell is presented in this work. To accurately predict the voltage response, the underlying model should reproduce the fast and slow dynamics of the battery cell. Therefore direct identification from non-uniformly sampled measurement data based on continuous-time model identification is applied. To take into account the nonlinear behavior of the battery, local linear model partitioning for the state of charge is performed. The resulting dynamic battery model is able to accurately predict the system response. With a parameter conversion to physically interpretable parameters, based on an equivalent circuit model, the parameter variance among similar cells and the temperature dependency of the model identification are investigated as well as the parameter characteristics over time. All results are based on non-uniformly sampled input output measurement data of three identical lithium-ion power cells.

This paper introduces a method, which precisely estimates deviations in the State-of-Charge and resistance values of the cells in a battery pack. The aim of the algorithm is to provide the required information a) for an early detection of cell failures and b) to decide, which cells must be charged or discharged by the charge equalization circuit. The problem is formulated as an inverse problem because the cell resistance values can not be estimated during operation modes of zero or constant battery current and a regularization term ensures that the estimation problem is stable. The paper will present test results from a battery pack, where the algorithm has been implemented in a Battery Control Unit.