Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method

Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region. This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression. General health indicators are extracted from the partial discharge process. The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction. The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic. Besides, only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction. Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack, even with only 50 cycles for model fine-tuning, which can save about 90% time for the aging experiment. Thus, it largely reduces the time and labor for battery pack investigation. The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell, which can reveal the weakest cell for maintenance in advance.

Design of an Automated Assembly Station for Process Development of All-Solid-State Battery Cell Assembly

Today, lithium-ion batteries are a promising technology in the evolution of electro mobility, but still have potential for improvement in terms of performance, safety and cost. In order to exploit this potential, one promising approach is the replacement of liquid electrolyte with solid-state electrolyte and the use of lithium metal electrode as an anode instead of graphite based anodes. Solid-state electrolytes and the lithium metal anode have favorable electrochemical properties and therefore enable significantly increased energy densities with inherent safety. However, these materials are both, mechanically and chemically sensitive. Therefore, material-adapted processes are essential to ensure quality-assured manufacturing of all-solid-state lithium-ion battery cells. This paper presents the development of a scaled and flexible automated assembly station adapted to the challenging properties of the new all-solid-state battery materials. In the station various handling and gripping techniques are evaluated and qualified for assembly of all-solid-state battery cells. To qualify the techniques, image processing is set up as a quality measurement technology. The paper also discusses the challenges of enclosing the entire assembly station in inert gas atmosphere to avoid side reactions and contamination of the chemically reactive materials.

A Data-Driven Framework to Predict Lithium-Ion Battery Cell Imbalance for Real-Time Battery Management Systems

Models that can predict battery cells’ thermal and electrical behaviors are necessary for real-time battery management systems to regulate the imbalance within battery cells. This work introduces a Gaussian Process Regression (GPR)-based data-driven framework that succeeds the Multi-Scale Multi-Dimensional (MSMD) modeling structure. The framework can make highly accurate predictions at the same level as full-order full-distribution simulations based on MSMD. A pseudo-2D model is used to generate training data and is combined with a process that shifts computation burdens from real-time battery management systems to lab data preparation. The testing results highlight the reliability of the GPR-based data-driven framework in terms of accuracy and stability under various operational conditions.

Methodology for Defining the Interaction between Product Characteristics and Specific Product Complexity—Evaluated on Electrodes for Lithium-Ion Batteries

This study is about a method for evaluating specific product complexity. In this context, an efficient and scalable method for the development of a specific complexity assessment of highly complex products is presented. Furthermore, existing evaluation methods are analysed according to effort and benefit, thus showing the research gap and the need for the method to be developed. The procedure for the development of an indicator for the specific evaluation of product complexity is presented in five steps and an exemplary complexity indicator for lithium ion battery cells is developed. This index is then applied, and the complexity of commercial battery cells from the application is evaluated. Based on these evaluations, final potentials of the method are shown and a recommendation for a reduction in product complexity is provided. The developed method for complexity assessment is scalable in its effort and offers implementation into existing complexity management. The method allows quick adaptation or extension and, thus, well-founded decision making. By standardizing the evaluation and taking objectively measurable complexity characteristic values as a basis, a holistic and objective evaluation tool is shown, which can thus become a decisive success factor for manufacturers of complex products.