Environmental monitoring is extremely important to ensure a safe and wealthy life of both humans and artifacts. Monitoring requirements are extremely different depending on the environment, leading to ad-hoc implementations that lack flexibility. This paper describes an implementation that can be adapted to many different applications and embeds the flexibility required to be deployed and upgraded without the necessity of arranging complex infrastructures. The solution is based on small autonomous wireless sensor nodes, small wireless receivers connected to the Internet and a cloud architecture which provides data storage and delivery to remote clients. The solution permits supervisors on-site to have an immediate idea of the current situation by using their smart-phones, but also to monitor remote sites through the Internet. All measurements are redundantly stored at different concentration levels to guarantee a safe backtrace and to provide quality assurance also in case of network failure or unavailability. The sensing nodes have small impact, with dimensions which can be of less than 2.5 cm x 1.5 cm when the nodes have to acquire only temperature and relative humidity, and a low cost that enables using them in a set-and-forget way for intervals in excess of one year.
As various types of energy storage (ES) types continue to penetrate grid, electric vehicle, and Naval applications, a need arises in extending traditional analysis to cover the revised performance metrics associated with a hybrid energy storage system (HESS). Each ES device has its own respective power density, energy density, response time, and voltage stability under load. In some critical applications, such as ship power systems (SPS), it is recommended to combine two or more ES types to overcome the impediments of the other. In this paper, three different series-configured HESS are mathematically modeled, evaluated, and tested experimentally. Lead acid and lithium ion batteries as well as supercapacitor equivalent circuit models are defined as components for each mixed HESS configuration. The impulse response to a constant and pulsed load was used to evaluate each ES model. The charging of mixed ES technologies was then accomplished using a special controller to handle the unique charging constraints of each ES module. Moreover, this same controller was used to apply a -rolling charging‖ algorithm to extend the operating time of the HESS. The validity of the derived model and controller were validated experimentally through a hardware setup simulating a multi-pulsed load SPS profile.
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