Ampace JP30 Drones Weight v power trade off in pack design - how to pick the right balance.
Discover the Ampace JP30 cell for drones, optimised for weight and power balance, ensuring efficient UAV battery pack design and performance.
Value Propositions
Cylindrical 18650 form factor for compact design.
Nominal capacity of 11.1 Wh and 3.0 Ah for reliable energy supply.
Top-quartile volumetric energy density of 629 Wh/l for lightweight applications.
Maximum continuous discharge of 56.0 A for high power demands.
Gravimetric power density of 4144 W/kg, ideal for dynamic UAV operations.
About the Cell
The Ampace JP30 cell features a cylindrical 18650 form factor, providing a nominal capacity of 11.1 Wh and 3.0 Ah. With a volumetric energy density of 629 Wh/l, it ranks in the top-quartile compared to the database median of 542 Wh/l, making it suitable for applications where space and weight are critical. Its gravimetric energy density of 222 Wh/kg is also competitive, offering a balance of weight and energy output. The cell supports a maximum continuous discharge of 56.0 A, which is significantly above the median of 30 A, ensuring it can handle high power demands typical in drone applications. Additionally, the volumetric power density of 11739 W/l is among the highest in the database, facilitating rapid energy delivery during flight. This combination of features makes the JP30 an excellent choice for UAV battery pack design, particularly where performance and efficiency are paramount.
Application Challenges
In the context of drones, the challenge of balancing weight and power in battery pack design is crucial. Drones require lightweight components to maximise flight time and payload capacity, while also needing sufficient power to perform various tasks. The Ampace JP30 cell addresses this challenge effectively with its high energy density and robust discharge capabilities. For UAVs, optimising battery performance is essential to extend flight times and improve mission endurance. The JP30's specifications allow for efficient energy use, which is vital for applications such as industrial inspections, where every minute of airtime counts. Furthermore, the ability to prevent overheating and ensure safe operation under high discharge conditions is critical for maintaining reliability in demanding environments.
Why this Cell
The Ampace JP30 cell is specifically designed to meet the rigorous demands of drone applications, particularly in achieving an optimal weight-to-power ratio. With a maximum continuous discharge rate of 56.0 A, it is well-suited for high energy demands, outperforming the median of 30 A in the database. This capability allows for effective UAV battery optimisation, ensuring that drones can operate efficiently under varying loads. The cell's volumetric energy density of 629 Wh/l positions it in the top-quartile compared to the median of 542 Wh/l, making it an ideal choice for lightweight drone battery packs. Additionally, the high gravimetric power density of 4144 W/kg supports dynamic flight profiles, enhancing overall drone performance. These attributes make the JP30 a compelling option for custom UAV battery packs, enabling engineers to design systems that maximise endurance and reliability.
How Model-Based Design Helps
Simulation and model-based design play a crucial role in selecting the right battery cell for drone applications. By modelling load profiles, thermal behaviour, and voltage response, engineers can accurately predict how the Ampace JP30 cell will perform under various conditions. This approach allows for the assessment of energy consumption and thermal management, ensuring that the cell can deliver the required thrust and energy throughout the flight envelope. For instance, simulating the thermal rise during high discharge scenarios helps identify potential overheating issues, enabling the selection of cells that maintain performance without compromising safety. Furthermore, using cell-specific data in simulations facilitates accurate predictions of state-of-charge (SoC), which is essential for mission planning and reliability. Ultimately, this method reduces the need for costly trial-and-error testing, streamlining the design process and ensuring that the best cells are chosen for UAV applications.
