Li-Ion Battery Charging with a Buck-Boost Power Converter for a Solar Powered Battery Management System

Shiau, Jaw-Kuen; Ma, Chien-Wei

doi:10.3390/en6031669

Cited by 32 publications

(15 citation statements)

References 32 publications

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“…Therefore, a battery management system (BMS) plays a vital role in improving battery performance and optimizing battery operation in a safe and reliable manner. A BMS is composed of hardware and software in order to protect A BMS may use a physics-based battery model based on electrochemical principles [21] typically describing with a set of partial differential equations (PDE). A physics-based battery PDE model can account for the diffusion, intercalation, and electrical dynamics of a battery.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery

et al. 2016

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A fractional derivative system identification approach for modeling battery dynamics is presented in this paper, where fractional derivatives are applied to approximate non-linear dynamic behavior of a battery system. The least squares-based state-variable filter (LSSVF) method commonly used in the identification of continuous-time models is extended to allow the estimation of fractional derivative coefficents and parameters of the battery models by monitoring a charge/discharge demand signal and a power storage/delivery signal. In particular, the model is combined by individual fractional differential models (FDMs), where the parameters can be estimated by a least-squares algorithm. Based on experimental data, it is illustrated how the fractional derivative model can be utilized to predict the dynamics of the energy storage and delivery of a lithium iron phosphate battery (LiFePO 4 ) in real-time. The results indicate that a FDM can accurately capture the dynamics of the energy storage and delivery of the battery over a large operating range of the battery. It is also shown that the fractional derivative model exhibits improvements on prediction performance compared to standard integer derivative model, which in beneficial for a battery management system. Keywords: fractional differential model (FDM); energy storage and delivery; system identification; battery management system (BMS); least squares-based state-variable filter (LSSVF) method

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Section: Introductionmentioning

confidence: 99%

Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery

et al. 2016

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“…Therefore, the total conduction losses can be described as: 4 2 cTotal cQ cTR cIND cD Figure 3 shows the switching frequency effect on the total conduction losses at output current Io = 10 A according to Equation (25). As shown in Figure 3, higher switching frequency results in lower conduction losses.…”

Section: Total Conduction Lossesmentioning

confidence: 99%

“…The PSFB converter provides ZVS for all primary switches, therefore the switching losses can be reduced significantly, and high efficiency and low Electro-Magnetic Interference (EMI) can also be achieved [1,2]. This converter has been widely employed in high power density applications, however, the converter operates under a wide range of load variations in battery charger applications [3,4]. The PSFB converter loses its ZVS capability under light-load conditions, then switching losses are increased significantly, and the efficiency becomes much lower [5].…”

Section: Introductionmentioning

confidence: 99%

High Efficiency Variable-Frequency Full-Bridge Converter with a Load Adaptive Control Method Based on the Loss Model

Zhao

Liu

et al. 2015

Energies

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Abstract:In this paper, a load adaptive control method to improve the efficiency and dynamic performance of the Phase-Shifted Full-Bridge (PSFB) converter which works under a wide range of load conditions is presented. The proposed control method can be used as a battery charger since this application demands a wide range of load conditions. The composition of the PSFB converter's losses and the loss analysis model are both discussed. According to this model, the optimum switching frequency which results in minimum power loss is adopted to improve the efficiency. The relationship between switching frequency and power loss is formulated over a wide load range. Indicated by this kind of relationship, the proposed controller adjusts the switching frequency at different load currents. Moreover, an adaptive gain adjustment controller is applied to replace the traditional controller, with the aim to improve the dynamic performance which is influenced by the changes of the switching frequency and load current. In addition, the experimental results show that the maximum improvement of efficiency is up to 20%. These results confirm the effectiveness of the proposed load adaptive control method.

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“…To maximize the use of available solar power drawn from the solar panel and to widen the applications of solar energy, several studies have investigated the design and applications of buck-boost converters [3][4][5][6][7]. Few studies have developed buck-boost converters for portable applications [4,5], whereas the study in [6] proposed a buck-boost-cascaded converter for high-power applications such as fuel-cell electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…Few studies have developed buck-boost converters for portable applications [4,5], whereas the study in [6] proposed a buck-boost-cascaded converter for high-power applications such as fuel-cell electric vehicles. Furthermore, an extensive analysis and design of Li-ion battery charging with the use of a four-switch type synchronous buck-boost power converter was presented in [7]. In the current research, we conducted a comparative study for MPPT evaluation by using different buck-boost converter topologies through circuit simulation, including Zeta, a single-ended primary-inductor converter (SEPIC), and four-switch type synchronous buck-boost converters.…”

Section: Introductionmentioning

confidence: 99%

Circuit Simulation for Solar Power Maximum Power Point Tracking with Different Buck-Boost Converter Topologies

et al. 2014

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Abstract:The power converter is one of the essential elements for effective use of renewable power sources. This paper focuses on the development of a circuit simulation model for maximum power point tracking (MPPT) evaluation of solar power that involves using different buck-boost power converter topologies; including SEPIC, Zeta, and four-switch type buck-boost DC/DC converters. The circuit simulation model mainly includes three subsystems: a PV model; a buck-boost converter-based MPPT system; and a fuzzy logic MPPT controller. Dynamic analyses of the current-fed buck-boost converter systems are conducted and results are presented in the paper. The maximum power point tracking function is achieved through appropriate control of the power switches of the power converter. A fuzzy logic controller is developed to perform the MPPT function for obtaining maximum power from the PV panel. The MATLAB-based Simulink piecewise linear electric circuit simulation tool is used to verify the complete circuit simulation model.

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Li-Ion Battery Charging with a Buck-Boost Power Converter for a Solar Powered Battery Management System

Cited by 32 publications

References 32 publications

Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery

Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery

High Efficiency Variable-Frequency Full-Bridge Converter with a Load Adaptive Control Method Based on the Loss Model

Circuit Simulation for Solar Power Maximum Power Point Tracking with Different Buck-Boost Converter Topologies

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