SOLUTION SHOWCASE
Next Generation Lithium–Sulfur Batteries: From Mission Requirements to Pack Level Integration – Gamma Technologies
Dimecres 09, 13:00h - 13:15h
| Discovery Stage
Open Access
2026-09-09 13:00
2026-09-09 13:15
Europe/Madrid
Next Generation Lithium–Sulfur Batteries: From Mission Requirements to Pack Level Integration – Gamma Technologies
Lithium–sulfur (Li–S) batteries represent a promising next generation technology, offering high gravimetric energy density and strong potential for weight critical applications. However, their successful adoption depends on translating cell level performance into efficient system level design and application driven sizing. This work addresses the integration and dimensioning of Li–S technology at the pack level based on realistic mission requirements.
In contrast to conventional lithium-ion systems, Li–S batteries exhibit distinct performance characteristics that strongly influence system design. Their lower volumetric energy density and highly nonlinear efficiency behavior must be explicitly considered during pack integration. As a result, conventional rule of thumb sizing approaches, such as fixed energy to power ratios or predefined oversizing factors, are no longer applicable. Instead, battery sizing is derived directly from the mission specific power demand profile, from which the required energy is determined and translated into a tailored battery configuration. This ensures that the system is optimally dimensioned for the intended operational requirements.
A key element of this work is the implementation of a calibrated Li–S model within the GT-AutoLionSulfur simulation environment, enabling fast and reliable prediction of battery behavior under realistic load conditions. This facilitates rapid evaluation of design variants and supports efficient pack level sizing early in the development process.
Application-oriented case studies, particularly in aviation, demonstrate how mission-driven sizing combined with fast simulation enables improved system performance. The proposed methodology provides a practical pathway for deploying Li–S batteries in real world applications, accelerating early-stage development, and supporting informed engineering decisions.
Discovery Stage
Lithium–sulfur (Li–S) batteries represent a promising next generation technology, offering high gravimetric energy density and strong potential for weight critical applications. However, their successful adoption depends on translating cell level performance into efficient system level design and application driven sizing. This work addresses the integration and dimensioning of Li–S technology at the pack level based on realistic mission requirements.
In contrast to conventional lithium-ion systems, Li–S batteries exhibit distinct performance characteristics that strongly influence system design. Their lower volumetric energy density and highly nonlinear efficiency behavior must be explicitly considered during pack integration. As a result, conventional rule of thumb sizing approaches, such as fixed energy to power ratios or predefined oversizing factors, are no longer applicable. Instead, battery sizing is derived directly from the mission specific power demand profile, from which the required energy is determined and translated into a tailored battery configuration. This ensures that the system is optimally dimensioned for the intended operational requirements.
A key element of this work is the implementation of a calibrated Li–S model within the GT-AutoLionSulfur simulation environment, enabling fast and reliable prediction of battery behavior under realistic load conditions. This facilitates rapid evaluation of design variants and supports efficient pack level sizing early in the development process.
Application-oriented case studies, particularly in aviation, demonstrate how mission-driven sizing combined with fast simulation enables improved system performance. The proposed methodology provides a practical pathway for deploying Li–S batteries in real world applications, accelerating early-stage development, and supporting informed engineering decisions.