Description
SPECIFICATIONS
Brand Name: Aideepen
Certification: CE
DIY Supplies: Electrical
Data Record: No
Electronic: No
High-concerned chemical: None
Material: Plastic
Model Number: 22.5W IP5356 Fast Charging Module
Origin: Mainland China
Output Voltage: 5V 9V 12V
Size: 4.3 * 3.5 * 1.25 cm
Style: 22.5W IP5356 Fast Charging Module
Type: 22.5W IP5356 Fast Charging Module
Features
1. Supports PD/QC/VOOC multi-protocol fast charging, compatible with mainstream device charging needs
2. Maximum output power up to 22.5W, stable voltage and current for efficient rapid charging
3. Built-in multi-protection mechanisms, comprehensive safety safeguards against overvoltage, over-discharge, and overcharge
4. High conversion efficiency circuit design minimizes power loss for stable, efficient charging
5. Multiple ports enable plug-and-play operation, supporting simultaneous charging of multiple devices
Parameter
Product Name: 22.5W Fast Charging Module
Fast Charging Chip: IP5356
Charging Parameters: 5V2A/5V3A/9V2.2A/12V1.6A
Output Parameters: 5V2A/5V3A/9V2.2A/12V1.6A
A Ports: 1 Type-C port + 2 USB ports + 1 Micro port Configuration: 2 Type-C fast charging ports (one supports both charging and output) + 2 USB fast charging output ports
User Guide
1. Regarding activating phone fast charging: Phone fast charging protocols are categorized as public or proprietary. This motherboard employs an intelligent fast-charging chip compatible with mainstream public fast-charging protocols, supporting rapid charging for various smartphone brands. Before ordering, please confirm your phone supports public protocols to avoid fast-charging failure!
2. Regarding heat generation during charging: When the power bank operates, current flows through the motherboard and chips, causing energy conversion losses. These losses dissipate as heat. During fast charging, higher power output results in more noticeable heat—a normal physical phenomenon. 3. Battery capacity display may appear inaccurate immediately after connection. The battery meter features self-learning capacity recognition. Accuracy will normalize after several charge/discharge cycles.
Package include
Module*1






























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