New Energy Automobile Electrolytic Power Rectifier Aging Test Equipment with 9kW Heat Capacity and 485 Communication

The 100A New Energy Automobile Electrolytic Power Rectifier Aging Test Equipment is designed for comprehensive testing and burn-in of power rectifiers in automotive applications, featuring advanced monitoring capabilities and robust safety features.

• Incoming line: three-phase 380V / 100A
• Power distribution: AC output adopts three-phase five-wire system, single-phase voltage supplies power to the product, with three-phase equalization in layers (a b c a b c)
• Control: power output of each layer can be controlled by monitoring
• Includes 19-inch LCD display and keyboard, LCD human interactive interface

• Overall layout: 9 layers in product area with 8 slots per layer
• Connection method: hard connection with fixed external interface including power supply output, load input, 485 signal, and address signal
• Air duct design: bottom air inlet with one or two top fans
• Heat capacity design: 9kW
• Safety regulation design: meets safety standards with fire prevention and emergency protection functions

• PC: supports multiple 485 serial communications, Windows 7 system, 500GB hard drive
• Includes 22-inch monitor, mouse, keyboard, wireless barcode gun (computer host shell grounded)
• Monitoring objects:
  • Voltage and current information of all slots
  • Set value and status value information of all load modules
  • Device/PLC temperature setting value, switching value setting value, status value information
  • Fan control board speed information

• Operation interface: computer + terminal software with integrated SMU interface
• Communication: 485 network port between host computer and monitoring system
• Cabinet ambient temperature monitoring and control
• SMU monitors status of each burn-in module
• Total burn-in time setting: 0-9999H (adjustable via computer terminal software)
• Supports output voltage judgment range setting for layered slots (enables simultaneous burn-in of multiple similar models)