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Solar Power Manager 5V

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SKU TPX00046 Barcode 6959420913473 Show more
Original price €0
Original price €8,95 - Original price €8,95
Original price
Current price €8,95
€8,95 - €8,95
Current price €8,95
VAT included

A complete small power and high-efficiency solar power management module!

Overview

FRobot Solar Power Manager series are designed for IoT projects and renewable energy projects, providing safe and high-efficiency embedded solar power management modules for makers and application engineers.

Solar Power Manager 5V is a small power and high-efficiency solar power management module designed for 5V solar panel. It features as MPPT (Maximum Power Point Tracking) function, maximizing the efficiency of the solar panel. The module can provide up to 900mA charging current to 3.7V Li battery with USB charger or solar panel.

The ON/OFF controllable DC-DC converters with 5V 1A output satisfies the needs of various solar power projects and low-power applications.
The module also employs various protection functions for battery, solar panel and output, which greatly improves the stability and safety of your solar projects.


Get Inspired

PROJECT HUB
Smelling Fresh, Feeling Fresh!
Smelling Fresh, Feeling Fresh!
Project Tutorial by robotasticdc

Use an Arduino Nicla Sense ME to see if you need to freshen up after a workout

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Detecting HVAC failures early with an Arduino Nicla Sense ME and edge ML
Detecting HVAC failures early with an Arduino Nicla Sense ME and edge ML
April 4, 2024

Having constant, reliable access to a working HVAC system is vital for our way of living, as they provide a steady supply of fresh, conditioned air. In an effort to decrease downtime and maintenance costs from failures, Yunior González and Danelis Guillan have developed a prototype device that aims to leverage edge machine learning to predict issues before they occur. The duo went with a Nicla Sense ME due to its onboard accelerometer, and after collecting many readings from each of the three axes at a 10Hz sampling rate, they imported the data into Edge Impulse to create the model. This time, rather than using a classifier, they utilized a K-means clustering algorithm — which is great at detecting anomalous readings, such as a motor spinning erratically, compared to a steady baseline. Once the Nicla Sense ME had detected an anomaly, it needed a way to send this data somewhere else and generate an alert. González and Guillan's setup accomplishes the goal by connecting a Microchip AVR-IoT Cellular Mini board to the Sense ME along with a screen, and upon receiving a digital signal from the Sense ME, the AVR-IoT Cellular Mini logs a failure in an Azure Cosmos DB instance where it can be viewed later on a web app. To read more about this preventative maintenance project, you can read the pair’s write-up here on Hackster.io.

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