Battery state estimation is fundamental to battery management systems (BMSs). An accurate model is needed to describe the dynamic behavior of the battery to evaluate the fundamental quantities, such as the state of charge (SOC) or the state of health (SOH). This paper presents an overview of the most commonly used battery models, the equivalent electrical circuits, and data-driven ones, discussing the importance of battery modeling and the various approaches used to model lithium batteries. In particular, it provides a detailed analysis of the electrical circuit models commonly used for lithium batteries, including equivalent circuit and thermal models. Furthermore, a comprehensive overview of data-driven approaches is presented. The advantages and limitations of each type of model are discussed. Finally, the paper concludes with a discussion of current research trends and future directions in the field of battery modeling.

A comprehensive review on the problem of choosing a suitable activation function for the hidden layer of a feed forward neural network has been widely investigated. Since the nonlinear component of a neural network is the main contributor to the network mapping capabilities, the different choices that may lead to enhanced performances, in terms of training, generalization, or computational costs, are analyzed, both in general-purpose and in embedded computing environments. Finally, a strategy to convert a network configuration between different activation functions without altering the network mapping capabilities will be presented.

Measuring solar irradiance allows for direct maximization of the efficiency in photovoltaic power plants. However, devices for solar irradiance sensing, such as pyranometers and pyrheliometers, are expensive and difficult to calibrate and thus seldom utilized in photovoltaic power plants. Indirect methods are instead implemented in order to maximize efficiency. This paper proposes a novel approach for solar irradiance measurement based on neural networks, which may, in turn, be used to maximize efficiency directly. An initial estimate suggests the cost of the sensor proposed herein may be price competitive with other inexpensive solutions available in the market, making the device a good candidate for large deployment in photovoltaic power plants. The proposed sensor is implemented through a photovoltaic cell, a temperature sensor, and a low-cost microcontroller. The use of a microcontroller allows for easy calibration, updates, and enhancement by simply adding code libraries. Furthermore, it can be interfaced via standard communication means with other control devices, integrated into control schemes, and remote-controlled through its embedded web server. The proposed approach is validated through experimental prototyping and compared against a commercial device.