An improved system design method for cell-based energy storage systems: A combination of a constraint satisfaction problem with an extended Ragone plot

Abstract

Over the past three decades, advancements in electrical energy storage technologies have substantially enhanced performance and reduced costs, rendering them viable for diverse applications. Cell-based energy storage systems, such as batteries and supercapacitors, are particularly notable for their adaptability and scalable interconnection capabilities. However, the design process for such systems lacks uniform standards, presenting a gap in research for the conceptual system design. This paper introduces an improved system design method (SDM), addressing critical limitations of existing approaches: (a) Shifting from current-based to power-based requirements; (b) Enabling flexible adaptation of operational design points beyond fixed datasheet specifications; (c) Ensuring harmonization of energy storage design with other system components, notably power electronics. Central to our SDM is the novel “extended Ragone plot” (ERP), which maps cell-specific energy–power relations and allows for flexible adjustment of operational limits. Since it can be determined experimentally, the subsequent design method is model-free and can efficiently be executed for different cell types. In the SDM, this ERP is coupled with a constraint satisfaction problem (CSP) to compute a comprehensive design solution space that accommodates all application-specific requirements and component interrelations. It is implemented in a holistic, interactive tool, facilitating mathematical optimization and enabling visual analysis of energy storage system designs. As a proof-of-concept, two case studies on the design of lithium-ion battery systems for stationary grid supply demonstrate the method’s superiority over conventional approaches. While in the first case, an optimal design can be determined immediately, in the second case, there are no valid design solutions based on the cell’s initial operating point. Utilizing the ERP, a practical design solution is achieved by reducing the maximum cell power by approximately 9%, thereby modifying the cell’s available voltage and current limits. This approach provides a possible solution for scenarios where a system design based on data sheets alone is not feasible. A comparative analysis and graphical synthesis of both case studies validate this hypothesis, illustrating the novel system design method’s ability to precalculate and evaluate diverse design scenarios in advance. The source code and data used in this study are openly accessible, offering the SDM as an interactive tool for analyzing and designing energy storage systems.