Battery Management Systems

A Redox Flow Battery (RFB) is one of the promising energy storage systems in power grid. An RFB has many advantages such as a quick response, a large capacity, and a scalability. Due to these advantages, an RFB can operate in mixed time scales. Actually, it has been demonstrated that an RFB can be used for load leveling, compensating sag, and smoothing the output of the renewable sources. An analysis on transient behaviors of an RFB is a key issue for these applications. An RFB is governed by electrical, chemical, and fluid dynamics. The hybrid structure makes the analysis difficult. To analyze transient behaviors of an RFB, the exact model is necessary. In this paper, we focus on a change in a concentration of ions in the electrolyte, and simulate the change with a model which is mainly based on chemical kinetics. The simulation results introduces transient behaviors of an RFB in a response to a load variation. There are found three kinds of typical transient behaviors including oscillations. As results, it is clarified that the complex transient behaviors, due to slow and fast dynamics in the system, arise by the quick response to load.

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“…The model of the EMF is derived based on Nersnst's equation [10]. Nersnst's equation gives the equilibrium potential of the battery.…”

Section: Model Equationmentioning

confidence: 99%

Transient behavior of redox flow battery connected to circuit based on global phase structure

Mannari

Hikihara

2018

NOLTA

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“…The Li-ion battery charging is performed by a constant current (CC) mode followed by a constant voltage (CV) mode, i.e., CC/CV procedure [34]. During the CC mode, the charging current stays constant until the voltage rises to a cut-off voltage.…”

Section: Modeling Of Pevs Batteries Chargingmentioning

confidence: 99%

Modeling and Simulation of DC Microgrids for Electric Vehicle Charging Stations

Locment

Sechilariu

2015

Energies

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This paper focuses on the evaluation of theoretical and numerical aspects related to an original DC microgrid power architecture for efficient charging of plug-in electric vehicles (PEVs). The proposed DC microgrid is based on photovoltaic array (PVA) generation, electrochemical storage, and grid connection; it is assumed that PEVs have a direct access to their DC charger input. As opposed to conventional power architecture designs, the PVA is coupled directly on the DC link without a static converter, which implies no DC voltage stabilization, increasing energy efficiency, and reducing control complexity. Based on a real-time rule-based algorithm, the proposed power management allows self-consumption according to PVA power production and storage constraints, and the public grid is seen only as back-up. The first phase of modeling aims to evaluate the main energy flows within the proposed DC microgrid architecture and to identify the control structure and the power management strategies. For this, an original model is obtained by applying the Energetic Macroscopic Representation formalism, which allows deducing the control design using Maximum Control Structure. The second phase of simulation is based on the numerical characterization of the DC microgrid components and the energy management strategies, which consider the power source requirements, charging times of different PEVs, electrochemical storage ageing, and grid power limitations for injection mode. The simulation results show the validity of the model and the feasibility of the proposed DC microgrid power architecture which presents good performance in terms of total efficiency and simplified control. OPEN ACCESSEnergies 2015, 8 4336

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“…Modeling of batteries has been comprehensively studied in recent years, and some are demonstrated to be accurate and efficient enough to model the behaviors of batteries [19][20][21][22][23]. These models make it possible to apply model predictive control (MPC) to manage the battery charging process.…”

Section: Overview Of Proposed Charging Controllermentioning

confidence: 99%

Model Predictive Control-Based Fast Charging for Vehicular Batteries

Yan

Qian

et al. 2011

Energies

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Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method.

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Battery Management Systems

Cited by 134 publications

References 15 publications

Transient behavior of redox flow battery connected to circuit based on global phase structure

Transient behavior of redox flow battery connected to circuit based on global phase structure

Modeling and Simulation of DC Microgrids for Electric Vehicle Charging Stations

Model Predictive Control-Based Fast Charging for Vehicular Batteries

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