Reliance RS50 Drones Mission feasibility assessment - asses what missions or use cases are possible or not using a go/no-go decision using simulation.
Explore the Reliance RS50 cell for drones, designed for mission feasibility assessments, ensuring optimal performance and reliability in critical scenarios.
Value Propositions
Cylindrical 21700 form factor for efficient design.
Nominal capacity of 18.0 Wh and 5.0 Ah for reliable energy supply.
Top-quartile volumetric power density of 10,008 W/l for high-performance applications.
Gravimetric energy density of 269 Wh/kg, ensuring lightweight solutions.
Maximum continuous discharge of 70 A, ideal for demanding drone operations.
About the Cell
The Reliance RS50 cell features a cylindrical 21700 form factor, providing a nominal capacity of 18.0 Wh and 5.0 Ah. With a volumetric energy density of 715 Wh/l, it stands out in the market, being among the highest in the database. The gravimetric energy density of 269 Wh/kg ensures that the cell remains lightweight, which is crucial for drone applications. Additionally, the volumetric power density of 10,008 W/l places it in the top-quartile compared to the median of 2,029 W/l, making it suitable for high-performance UAVs. The maximum continuous discharge rate of 70 A allows for robust performance under demanding conditions, while the maximum continuous charge rate of 15 A supports quick turnaround times for drone missions. This combination of features makes the RS50 an excellent choice for various UAV applications, particularly in scenarios requiring high energy and power output.
Application Challenges
In the realm of drones, mission feasibility assessment is critical. The ability to determine what missions or use cases are possible hinges on reliable battery performance. The RS50 cell's nominal capacity of 18.0 Wh is essential for ensuring that drones can complete their tasks without mid-air failures. In scenarios where drones operate in extreme environments, such as cold weather or high altitudes, the energy density of the battery becomes a key factor in mission success. The RS50's high volumetric energy density of 715 Wh/l allows for longer flight times, which is vital for applications like surveillance or search and rescue. Furthermore, the high discharge rate of 70 A ensures that drones can handle sudden power demands without overheating, addressing the challenge of battery thermal management. Accurate predictions of state of charge (SoC) are also crucial, as they inform operators about the remaining energy and help prevent unexpected shutdowns during critical missions.
Why this Cell
The Reliance RS50 cell is particularly well-suited for drones due to its impressive specifications. With a maximum continuous discharge rate of 70 A, it is positioned in the top-quartile compared to the median of 30 A in the database, making it ideal for high-demand applications. The cell's gravimetric energy density of 269 Wh/kg ensures that it remains lightweight, which is essential for improving UAV mission endurance. This lightweight design, combined with a volumetric energy density of 715 Wh/l, allows for longer flight times, addressing the pain point of extending drone flight time. The RS50's performance metrics make it a strong candidate for custom UAV battery packs, as it can be tailored to meet specific mission requirements while ensuring safety and reliability.
How Model-Based Design Helps
Simulation and model-based design play a crucial role in optimising the performance of the Reliance RS50 cell in drone applications. By modelling load profiles, engineers can predict how the cell will behave under various conditions, including thermal rise and voltage sag. This predictive capability is essential for ensuring that the RS50 can deliver the required thrust and energy throughout the entire flight envelope. For instance, simulations can help identify the optimal charge and discharge rates, ensuring that the cell operates within safe limits while maximising efficiency. Additionally, by using cell-specific data, engineers can accurately assess the usable energy available for missions, enabling informed go/no-go decisions. This approach not only enhances the reliability of drone operations but also reduces the need for costly trial-and-error testing, ultimately leading to more efficient and effective UAV designs.
