Lithium Battery Prismatic 3.2V 100AH Rechargeable Lifepo4 Battery Cell For EV/Golf Cart/Solar Systems

Items	Parameter
Battery Type	rechargeable lithium battery
Nominal Capacity	102Ah at 0.3C
Minimum capacity	100Ah at 0.3C
Nominal Voltage	3.2V
Internal Impedance	≤1.0mΩ(by AC 1KHz)
End of charge voltage	3.65V at CC mode
End of discharge voltage	2.2V
Stanard charge current	50A at 0.5C
Max continuous discharge current	200A(under 2C)
Max pulse discharge current	500A(5C under 5S)
Max pulse charge current	100A (pulse current with 2C under 10S)
Cycle life	≥2500times(100%DOD)/4000times(80%DOD)
Weight	2.65kg
Cell size	36*130*290mm
Operating temperature	charge:0-50℃, discharge:-20-50℃ storag:-10-45℃

A novel system developed by MIT researchers automatically “learns” how to schedule data-processing operations across thousands of servers — a task traditionally reserved for imprecise, human-designed algorithms. Doing so could help today’s power-hungry data centers run far more efficiently.

Data centers can contain tens of thousands of servers, which constantly run data-processing tasks from developers and users. Cluster scheduling algorithms allocate the incoming tasks across the servers, in real-time, to efficiently utilize all available computing resources and get jobs done fast.

Traditionally, however, humans fine-tune those scheduling algorithms, based on some basic guidelines (“policies”) and various tradeoffs. They may, for instance, code the algorithm to get certain jobs done quickly or split resource equally between jobs. But workloads — meaning groups of combined tasks — come in all sizes. Therefore, it’s virtually impossible for humans to optimize their scheduling algorithms for specific workloads and, as a result, they often fall short of their true efficiency potential.