G. Nobile

Real-Time Model-Based Estimation of SOC and SOH for Energy Storage Systems

To obtain a full exploitation of battery potential in energy storage applications, an accurate modeling of electrochemical batteries is needed. In real terms, an accurate knowledge of state of charge (SOC) and state of health (SOH) of the battery pack is needed to allow a precise design of the control algorithms for energy storage systems (ESSs). Initially, a review of effective methods for SOC and SOH assessment has been performed with the aim to analyze pros and cons of standard methods. Then, as the tradeoff between accuracy and complexity of the model is the major concern, a novel technique for SOC and SOH estimation has been proposed. It is based on the development of a battery circuit model and on a procedure for setting the model parameters. Such a procedure performs a real-time comparison between measured and calculated values of the battery voltage while a PI-based observer is used to provide the SOC and SOH actual values. This ensures a good accuracy in a wide range of operating conditions. Moreover, a simple start-up identification process is required based on battery data-sheet exploitation. Because of the low computational burden of the whole algorithm, it can be easily implemented in low-cost control units. An experimental comparison between SOC and SOH estimation performed by suggested and standard methods is able to confirm the consistency of the proposed approach.

Real-time model-based estimation of SOC and SOH for energy storage systems

Accurate modeling of electrochemical batteries is of major concern in designing the control system of Energy Storage Systems (ESS). In particular, a precise estimation of State of Charge (SOC) and State of Health (SOH) parameters strongly affects the full exploitation of battery energy potential in real applications. In this paper a novel real-time estimation method is presented representing a good tradeoff between model accuracy and algorithm complexity. In the proposed approach, SOC and SOH values are determined by a suitable algorithm that continuously performs a comparison between the ESS voltage value, calculated by an adaptive run-time circuital model, and its real value measured at the ESS terminals. The result of such comparison is used to suitably tune two parameters of the ESS circuital model, the no-load voltage and resistive voltage drop, in order to compensate the inaccuracy of the model response due to parameter variations. Initially, to set the parameter of ESS electrical model, the proposed approach requires to carry out short preliminary tests that can be easily implemented in a low cost control units. Experimental results and comparisons with other estimation methods highlight the consistency of the proposed algorithm.

Energy harvesting in roped elevators

In civil buildings, large part of energy consumption of common service is related to lift apparatus operations. Considering the huge diffusion of roped elevators and their reciprocating operating mode, a critical evaluation of energy streams has been done using an accurate model specially developed. Thus, a retrofit kit has been studied and designed able to store the energy during the generating mode of the electrical machine and to recover it back in motor operation. Finally, a comprehensive evaluation of the saved energy parameterized with the numbers of passengers and lift duty cycle is presented, as well as the kit cost estimation and pay-back time.

Energy management in parallel hybrid electric vehicles exploiting an integrated multi-drives topology

The main purpose of this paper is to provide a comprehensive study of an online energy management strategy devoted to hybrid electric vehicle (HEV) parallel drivetrains adopting an integrated multi-drives topology to interface a multi winding induction machine with the on-board hybrid storage units. This solution is designed to allow multi-directional power flows among the storage units and the drivetrain. Basically, the proposed efficiency optimization method continuously searches for the best compromise between the torque demand, system efficiency and power capability of each storage unit. The considered electric drive configuration allows to effectively and independently handle the power flowing in storage units featuring different sizes and DC voltages, while improving the overall reliability. A good agreement between simulations and experimental results is achieved.

Assessment of energy management strategies for battery assisted solar pumping systems

The paper deals with an optimal technical solution to realize autonomous water pumping systems combining a PV electric energy source with battery chemical storage to supply a centrifugal electrical pump. The goal is to accomplish an optimal trade-off between the rated peak power of the PV generator and the power capability of the battery system in order to let the battery assisted solar pumping system operate at high efficiency while maximizing water storage. A novel method to evaluate the overall amount of delivered water has been developed. Results obtained by simulations confirm the effectiveness of the proposed method.

Comparison of common and combined state of charge estimation methods for VRLA batteries

Electrochemical batteries play a key role in electrical and electronics devices such are laptops, cellphones, electric cars, etc. Battery packs in devices having higher power are still too much expensive for wide applications. To achieve an effective exploitation of battery packs it is important to have a robust and reliable battery management. Accurate State of Charge estimation in battery management is the basis of economical and energy performance assessment of battery pack including lifetime extension. The most popular used State of Charge estimation methods are analyzed and compared in this paper including Kalman filter approach. An interesting option is also a combination of two or more methods to achieve effective estimation with acceptable computational demands. Self-learning algorithms such as Neural Networks, Fuzzy Logic or Support Vector Machine are not included in this comparison since these methods need large amount of training data. The goal of this paper is the selection of accurate and simple SOC method suitable for battery pack used in stand-alone energy system.