The optimisation of energy generation in a photovoltaic (PV) system is necessary to let the PV cells operate at the maximum power point (MPP) corresponding to the maximum efficiency. Since the MPP varies, based on the irradiation and cell temperature, appropriate algorithms must be utilised to track the MPP. This is known as maximum power point tracking (MPPT). Different MPPT algorithms, each with its own specific performance, have been proposed in the literature. A so-called perturb and observe (P&O) method is considered here. This method is widely diffused because of its low-cost and ease of implementation. When atmospheric conditions are constant or change slowly, the P&O method oscillates close to MPP. However, when these change rapidly, this method fails to track MPP and gives rise to a waste of part of the available energy. An adaptive P&O method is proposed in this study that has faster dynamics and improved stability compared to the traditional P&O. The MPPT algorithm was set up and validated by means of numerical simulations and experimental tests, confirming the effectiveness of the method

Energy storage has become a key issue for achieving goals connected with increasing the efficiency of both producers and users. In particular, supercapacitors currently seem to be interesting devices for many applications because they can supply high power for a significant amount of time and can be recharged more quickly than electrochemical batteries. In different applications, a combination of the two devices, batteries and supercapacitors, could be used to develop a high-efficiency storage system with a high dynamic performance. Because of the diffusion of supercapacitors, a good model is needed to represent their behavior in different applications. Some models have been proposed that characterize supercapacitors working at either low or high frequencies. In this paper, the authors present a full-frequency-range model that can be used to represent all of the phenomena that involve supercapacitors. Moreover, to realize a simple and useful tool, the authors present a simple procedure to identify the parameters of the model that can be used to characterize a supercapacitor before use

This paper evaluates the performance of modular multilevel converters with integrated battery cells when used as traction drives for battery electric vehicles. In this topology, individual battery cells are connected to the dc link of the converter submodules, allowing the highest flexibility for the discharge and recharge. The traditional battery management system of battery electric vehicles is replaced by the control of the converter, which individually balances all the cells. The performance of the converter as a traction drive is assessed in terms of torque-speed characteristic and power loss for the full frequency range, including field weakening. Conduction and switching losses for the modular multilevel converter are calculated using a simplified model, based on the datasheet of power devices. The performance of the modular multilevel converter is then compared with a traditional two-level converter. The loss model of the modular multilevel converter is finally validated by experimental tests on a small-scale prototype of traction drive

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Nowadays, battery storage systems are very important in both stationary and mobile applications. In particular, lithium ion batteries are a good and promising solution because of their high power and energy densities. The modeling of these devices is very crucial to correctly predict their state of charge (SoC) and state of health (SoH). The literature shows that numerous battery models and parameters estimation techniques have been developed and proposed. Moreover, surveys on their electric, thermal, and aging modeling are also reported. This paper presents a more complete overview of the different proposed battery models and estimation techniques. In particular, a method for classifying the proposed models based on their approaches is proposed. For this classification, the models are divided in three categories: mathematical models, physical models, and circuit models.