Bosisa 12V/20A 24V/10A Smart Battery Charger Lifepo4 Lead-Acid Agm/Gel Trickle Charger

Features:

Microprocessor Control (CPU): This battery charger is equipped with a microprocessor control that ensures efficient and safe charging. The CPU manages the charging process and adjusts the charging parameters accordingly.

7-STAGE AUTOMATIC CHARGING: This charger follows a 7-stage automatic charging process, which ensures optimal charging performance. It protects the battery from overcharging and can be left connected indefinitely without causing any harm to the battery.

Wide Compatibility: This charger is suitable for various battery types, including Calcium, Gel AGM, LiFePo4, Wet, and -acid batteries. It can also help restore drained and sulphated batteries, making it versatile and practical.

Multiple Protections: Built-in safeguards such as polarity protection, output short protection, non-battery link protection, over-voltage protection, and over-temperature protection ensure safe and reliable charging. The charger also features a cooling fan to prevent overheating during the charging process.

Efficient and Reliable: With an input voltage range of 100-240V AC and a high efficiency of approximately 85%, this charger delivers power effectively and reduces energy waste. It supports both 12V and 24V output, making it suitable for various applications.

Specifications:

Switchmode technology: Yes

Polarity protection: Yes

Output short protection: Yes

Non battery link protection: Yes

Over voltage protection: Yes

Over temperature protection: Yes

Cooling: Fan

Input voltage: 100-240V AC, 50-60Hz

Rated output: 12V 20A , 24V 10A 14.6V 29.2v LiFeP04

Minimum start voltage: 8.0V

Battery range: 2-400Ah

Thermal protect: 65'C+/-5'C

Efficiency: App. 85%.

Package Content:

Charger 1

Instruction manual 1

Note:

1. The real color of the item may be slightly different from the pictures shown on website caused by many factors such as brightness of your monitor and light brightness.
2. Please allow slight manual measurement deviation for the data.