State estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore, this paper introduces, on the one hand, a new lithium-ion battery dataset with dynamic validation data over degradation and, on the other hand, a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman-filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The paper describes the dataset, the models, the parameterisation, the implementation of the state estimations, and their validation using parts of the dataset, resulting in SOC and SOH estimations over the entire battery life. The results show that the dataset can be used to extract parameters, design models based on it, and validate it with dynamically degraded battery cells. The work provides an approach and dataset for better performance evaluations, applicability, and reliability investigations.

The market of accumulator based technologies is changing very fast, especially the electro mobility market due to different decisions made to reduce the impact on climate change. In most markets battery packs are used, which means accumulators are connected to form a battery system consisting of, from a few accumulators up to thousands. Research of battery technologies often focuses on single cells, however considering the applications it is important to know the behaviour of different cells in a system. This paper deals with the inhomogeneities which influence the electrical behaviour of the system in simulation and measurement with a special focus on parallel connected cells.

As a superior solution to the developing demand for energy storage, lithium-ion batteries play an important role in our daily lives. To ensure their safe and efficient usage, battery management systems (BMSs) are often integrated into the battery systems. Among other critical functionalities, BMSs provide information about the key states of the batteries under usage, including state of charge (SOC) and state of health (SOH). This paper proposes a data-driven approach for the joint online estimation of SOC and SOH utilizing multi-task learning (MTL) approaches, particularly highlighting cross-stitch units and cross-stitch networks. The proposed model is able to achieve an accurate estimation of SOC and SOH in online applications with optimized information sharing and multi-scale implementation. Comprehensive results on training and testing of the model are presented. Possible improvements for future work are also discussed in the paper.

The paper presents two approaches to generating load cycles for electrical energy storage systems. A load cycle is described as the operation of an energy storage system. The cycles can include different metrics depending on the storage application. Load cycle analysis using the rainflow counting method is employed to understand and validate the metrics of the load cycles generated. Current load cycle generation can involve clustering methods, random microtrip methods, and machine learning techniques. The study includes a random microtrip method that utilises the Random Pulse Method (RPM) and enhances it to develop an improved version called the Gradient Random Pulse Method (gradRPM), which includes the control of stress factors such as the gradient of the state of charge (SOC). This method is relatively simple but, in many cases, it fulfills its purpose. Another more sophisticated method to control stress factors has been proposed, namely the Information Maximising-Recurrent Conditional Generative Adversarial Network (Info-RCGAN). It uses a deep learning algorithm to follow a machine learning-based, data-driven load cycle generation approach. Both approaches use the measurement dataset of a BMW i3 over multiple years to generate new synthetic load cycles. After generating the load cycles using both approaches, they are applied in a laboratory environment to evaluate the stress factors and validate how similar the synthetic data are to a real measurement. The results provide insights into generating simulation or testing data for electrical energy storage applications.

The state estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore this paper introduces a, on one hand, a new lithium-ion battery data set with dynamic validation data over degradation and on the other hand a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The paper describes the dataset, the models, the parameterisation, the implementation of the state estimations, and their validation using parts of the dataset resulting in a SOC and SOH estimation over battery life. The results show that the dataset can be used to extract parameters, design models based on it and validate with dynamically degraded battery cells.

The paper introduces an approach to extract information from measurements and generate new load cycles for electrical energy storage systems. Load cycle analysis is performed using rainflow counting, which helps evaluate data and identify stress factors. Load cycle generation can involve clustering methods, random micro-trip methods, and machine learning techniques. The study utilises the Random Pulse Method (RPM) and presents an improved version called the Gradient Random Pulse Method (gradRPM) that allows control over stress factors such as the gradient of the State of Charge (SOC). Another method called Information Maximizing-Recurrent Conditional Generative Adversarial Network (Info-RCGAN) has been developed, and it utilises a deep learning algorithm for data-driven load profile generation with control over stress factors. The results demonstrate the effectiveness of the gradRPM and Info-RCGAN methods in generating load profiles based on the given parameters. The findings provide valuable insights into designing simulation data or testing data for electrical energy storage applications, aiding in improving and understanding system behaviour and requirements.