The future evolution of technological systems dedicated to improve energy efficiency will strongly depend on effective and reliable Energy Storage Systems, as key components for Smart Grids, microgrids and electric mobility. Besides possible improvements in chemical materials and cells design, the Battery Management System is the most important electronic device that improves the reliability of a battery pack. In fact, a precise State of Charge (SoC) estimation allows the energy flows controller to exploit better the full capacity of each cell. In this paper, we propose an alternative definition for the SoC, explaining the rationales by a mechanical analogy. We introduce a novel cell model, conceived as a series of three electric dipoles, together with a procedure for parameters estimation relying only on voltage measures and a given current profile. The three dipoles represent the quasi-stationary, the dynamics and the istantaneous components of voltage measures. An Extended Kalman Filer (EKF) is adopted as a nonlinear state estimator. Moreover, we propose a multi-cell EKF system based on a round-robin approach to allow the same processing block to keep track of many cells at the same time. Performance tests with a prototype battery pack composed by 18 A123 cells connected in series show encouraging results.
In this paper, a reduced-order, Multi-Scale, Multi-Dimensional (MSMD) model is developed to achieve accurate and fast simulation of the 2D distribution of thermal and electrochemical properties across the surface of a large-format Li-ion battery cell. Since the proposed model aims at supporting long-term simulation, virtual design and optimization studies, minimization of the computational complexity is achieved through analytical Model Order Reduction (MOR) based on a Galerkin projection method. The model is then verified against experimental data and a high-fidelity numerical model at various input current conditions. Results show that the computational complexity of the MSMD model is significantly reduced without sacrificing the accuracy in characterizing the distribution of the electrochemical and thermal properties. Finally, the impact of the applied current and thermal boundary conditions on the distribution of transverse current density is also evaluated. Temperature and temperature gradients are some of the key factors that influence the performance and degradation of Li-ion batteries. Despite in Hybrid Electric Vehicles (HEVs) or Electric Vehicles (EVs) the average pack temperature is regulated by the thermal management systems, thermal gradients may still occur within the pack or across individual cells.1-3 For instance, when cooling plates are in contact with only one side of the cells, the heat generated inside each cell will be dissipated to the cooling plates along a preferential direction. Therefore, thermal gradients in the order of ±5• C are common, and particularly in high-power packs for HEVs or high-performance EVs. 4,5 Thermal gradients on the cell surface are particularly critical in large-format batteries because hot spots may induce localized changes in the physical and chemical properties of the electrodes and electrolyte solution, which in turn can increase the local transverse current density and ultimately cause non-uniform degradation of the electrodes.6,7 Multiscale characterization methods have in fact evidenced that the non-uniform utilization of electrode surface leads to highly uneven degradation. 8,9 Furthermore, cell-to-cell variations in large battery packs (due to packaging constraints) may lead to non-uniform heat transfer in packs and induce further thermal gradients among the various cells. 10,11 This motivates the development of procedures and tools that allow for characterization and prediction of thermal gradients in large-format Lithium ion batteries, and understanding their impact on the performance and durability.Multi-Scale, Multi-Dimensional (MSMD) modeling approaches have been proposed to simulate the distribution of surface temperature and Lithium concentration in large format, prismatic cells. As a first approximation, the rate of heat generation within the cell can be approximated as uniform across the surface.12-16 More recently, this simplification has been abandoned in favor of more rigorous approaches that accurately compute the local heat generation rate ...
In this paper the problem of the minimization of active power losses in a real Smart Grid located in the area of Rome is faced by defining and solving a suited multi-objective optimization problem. It is considered a portion of the ACEA Distribuzione S.p.A. network which presents backflow of active power for 20% of the annual operative time. The network taken into consideration includes about 100 nodes, 25 km of MV lines, three feeders and three distributed energy sources (two biogas generators and one photovoltaic plant). The grid has been accurately modeled and simulated in the phasor domain by Matlab/Simulink, relying on the SimPowerSystems ToolBox, following a Multi-Level Hierarchical and Modular approach. It is faced the problem of finding the optimal network parameters that minimize the total active power losses in the network, without violating operative constraints on voltages and currents. To this aim it is adopted a genetic algorithm, defining a suited fitness function. Tests have been performed by feeding the simulation environment with real data concerning dissipated and generated active and reactive power values. First results are encouraging and show that the proposed optimization technique can be adopted as the core of a hierarchical Smart Grid control system. © Springer-Verlag Berlin Heidelberg 2013
In this paper we face the problem of the joint optimization of both topology and network parameters in order to minimize the total active power losses in a real Smart Grid. It is considered a portion of the Italian electric distribution network managed by the ACEA Distribuzione S.p.A. located in Rome which presents back-flows of active power for 20% of the annual operative time. It includes about 1200 user loads, 70 km of MV lines, 6 feeders, a thyristor voltage regulator (TVR) and 6 distributed energy sources (5 generator sets and 1 photovoltaic plant). Network topology can be changed by 106 breakers. The grid has been accurately modelled and simulated in the phasor domain by Matlab/Simulink, relying on the SimPowerSystems ToolBox, following a Multi-Level Hierarchical and Modular approach. Network optimization is faced by defining and solving a suited multi-objective optimization problem, considering suited constraints on nominal operative ranges on voltages and currents, as well as on generator's capability functions, in order to take into account safety and quality of service issues. To this aim it is adopted a genetic algorithm, defining a suited fitness function. Tests have been performed by feeding the simulation environment with real data concerning dissipated and generated active and reactive power values. First results are very interesting, showing that relying on evolutionary computation it is possible to yield a satisfactory power factor correction, confirming that the proposed optimization technique can be adopted as the core of a hierarchical Smart Grid control system. © 2013 IEEE
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