• Energy Research

Over the past decade, the growing demand for sustainable energy has led to significant interest in photovoltaic (PV) power generation. Due to its intermittent nature, accurate PV power forecasting is essential for the efficient management and monitoring of PV systems. In this context, the accuracy of meteorological data is critical. However, it is not always possible to obtain such data on a local basis, and often some information, such as irradiance, is obtained from models whose characteristics are not known in detail. To overcome this limitation, this study evaluates five synthetic features that combine clear-sky global horizontal irradiance and cloudiness data to estimate total irradiance in the absence of direct measurements. A light gradient boosting model is used to evaluate the predictive performance of a model using these synthetic features compared to a model based on conventional meteorological inputs, including irradiance. The results, evaluated over a reference week, show that the feature labeled χ5 slightly improves model accuracy (passing from an RMSE of 84.013 to 87.232 W/kWp and R2 from 0.888 to 0.875). These results show that synthetic features can achieve comparable results and in some cases even improve prediction performance.

This paper proposes a low-complexity online state of charge estimation method for LiFePO4 battery in electrical vehicles. The proposed method is able to achieve accurate state of charge with less computational efforts in comparison with the nonlinear Kalman filters, and also can provide state of health information for battery management system. According to the error analysis of equivalent circuit model with two resistance and capacitance, two proportional-integral filters are designed to compensate the errors from inaccurate state of charge and current measurements, respectively. An error dividing process is proposed to tune the contribution of each filter to the finial estimation results, which enhances the validation and accuracy of the proposed method. Recursive least squares filter can provide the state of health information and updates the parameters of battery model online to eliminate the errors caused by parameters uncertainty. The proposed method is compared with extend Kalman filter in regards to accuracy and execution time. The execution time of the proposed method is measured on Zynq board platform to validate its suitability for online implementation. In this paper, the proposed method is able to obtain less than 1% error for state of charge estimation.

Lithium-Ion batteries are playing an essential role in electric vehicles and renewable sources development. In order to reduce the charging time, high power chargers are necessary. However, lithium-ion chemistry limits the maximum current and charging speed. The diffusion rate of lithium ions into the electrodes determines the rate of charging. The slow lithium diffusion, especially experienced after high current rates, inevitably results in concentration polarization. The increase of the concentration polarization, in addition to the growth of the charging time, may lead to a faster battery deterioration. To deal with this obstacle, the Pulse Charging (PC) protocol has been proposed. There is no common opinion about the benefits given by the PC to the battery charging process in comparison with the conventional constant-current, constant-voltage (CCCV) protocol. Nevertheless, the purpose of this work is to provide an overview of possible methods that can be used to generate current pulses, without focusing on its advantages. Different techniques with the corresponding control algorithms have been implemented and analyzed through simulations in MATLAB/Simulink environment.

This paper presents a thorough prediction of DC-link voltage switching ripples in the three-phase four-leg inverters operating in balanced and unbalanced working conditions. The unbalanced modes examined here employ the highest degree of AC current imbalance while still preserving three-phase operation. This behavior can be found in many grid-connected or standalone grid-forming three-phase converters that supply “heavy” single-phase loads, comprising a recent trend in smart-grid, smart electric vehicle (EV)-charging applications. In this sense, for instance, the smart EV chargers might be employed in conditions when different power is drawn/injected from/to the grid, providing power conditioning services to the latter. The analysis of three-phase four-leg inverters is then extended to single-phase operations typical of home-charging or vehicle-to-home (V2H) applications. Their performances in terms of DC-link voltage switching ripple are demonstrated. Two of the most common carrier-based PWM modulation techniques are employed to drive the three-phase inverter—namely, sinusoidal PWM and centered PWM (carrier-based analogy of the space vector modulation). The derived mathematical expressions of peak-to-peak and RMS values of DC-link voltage switching ripple for balanced and unbalanced conditions are handy for designing the associated DC-link capacitor and estimating the overall efficiency of the converter. Extensive numerical simulations and experimental tests have been performed to validate the presented analytical developments.

Electric car-sharing (ECS) is an increasingly popular service in many European cities. The management of an ECS fleet is more complex than its thermal engine counterpart due to the longer ”refueling“ time and the limited autonomy of the vehicles. To ensure adequate autonomy, the ECS provider needs high-capacity charging hubs located in urban areas where available peak power is often limited by the system power rating. Lastly, electric vehicle (EV) charging is typically entrusted to operators who retrieve discharged EVs in the city and connect them to the charging hub. The timing of the whole charging process may strongly differ among the vehicles due to their different states of charge on arrival at the hub. This makes it difficult to plan the charging events and leads to non-optimal exploitation of charging points. This paper provides a smart charging (SC) method that aims to support the ECS operators’ activity by optimizing the charging points’ utilization. The proposed SC promotes charging duration management by differently allocating powers among vehicles as a function of their state of charge and the desired end-of-charge time. The proposed method has been evaluated by considering a real case study. The results showed the ability to decrease charging points downtime by 71.5% on average with better exploitation of the available contracted power and an increase of 18.8% in the average number of EVs processed per day.

In this paper, the concept of smart battery pack is introduced. The smart battery pack is based on wireless feedback from individual battery cells and is capable to be applied to electric vehicle applications. The proposed solution increases the usable capacity and prolongs the life cycle of the batteries by directly integrating the battery management system in the battery pack. The battery cells are connected through half-bridge chopper circuits, which allow either the insertion or the bypass of a single cell depending on the current states of charge. This consequently leads to the balancing of the whole pack during both the typical charging and discharging time of an electric vehicle and enables the fault-tolerant operation of the pack. A wireless feedback for implementing the balancing method is proposed. This solution reduces the need for cabling and simplifies the assembling of the battery pack, making also possible a direct off-board diagnosis. The paper validates the proposed smart battery pack and the wireless feedback through simulations and experimental results by adopting a battery cell emulator.

In the context of smart cities, direct current overhead contact lines, usually adopted to power urban transportation systems such as trolleybuses, tramways, metros, and railways, can serve as a backbone to connect different modern emerging technologies. Among these, in-motion charging (IMC) trolleybuses with on-board batteries are expected to be very impactful on the DC network’s power flow and may require specific voltage and current control. These factors motivate the development of a simulation tool able to emulate these devices’ absorption and their effect on the supply infrastructure. The main innovative value of the work is to improve a simulation model of a trolleybus grid through a data-driven approach by using measurements of voltage and current output from a traction substation. The measurements are essential for understanding the behavior of vehicle weight variation throughout the day. Thanks to this information, a characterization of the current draw by conventional trolleybuses and IMC trolleybuses is then provided for each trolleybus route in a specific power section of the Bologna trolleybus system. By integrating the variation in vehicle weight within the model, a simulation of a possible daily operation of a trolleybus feeding section has been performed, obtaining a 7% error between the daily energy calculated from the simulation and that obtained through measurements. This analysis demonstrates the feasibility of the adopted simulation tool, which can also be used to evaluate additional hypothetical trolleybus operation scenarios. One of these possible scenarios considers IMC vehicles, and it is also evaluated in this paper.