LPV Estimation of SOC Based on Electricity Conversion and Hysteresis Characteristic

Chang, Chengcheng; Zheng, Yanping; Sun, Wei; Ma, Zheshu

doi:10.1061/(asce)ey.1943-7897.0000617

Cited by 13 publications

(6 citation statements)

References 11 publications

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“…In order to find the performance limit of HT-PEMFC and the balance between energy and exergy, this paper optimized the dimensionless power density, efficiency, exergy loss rate and EPC of HT-PEMFC [44][45][46][47][48][49]. The doping level (DL), relative humidity (RH), inlet pressure (p) and film thickness (t mem ) corresponding to the optimal performance of HT-PEMFC are obtained.…”

Section: Optiaml Exergy Performance Coefficientmentioning

confidence: 99%

Exergetic Performance Coefficient Analysis and Optimization of a High-Temperature Proton Exchange Membrane Fuel Cell

et al. 2022

Membranes

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Performance of a high-temperature proton exchange membrane fuel cell (HT-PEMFC) and the influence of different parameters on HT-PEMFC is analyzed in this study. Firstly, mathematical expression for energy efficiency, power density, exergy destruction and exergetic performance coefficient (EPC) are derived. Then, the relationship between the dimensionless power density, exergy destruction rate, exergetic performance coefficient (EPC) and energy efficiency is compared. Furthermore, the effect of flow rate, doping level, inlet pressure and film thickness are considered to evaluate the performance of HT-PEMFC. Results show that EPC not only considers exergetic loss rate to minimize exergetic loss, but also considers the power density of HT-PEMFC to maximize its power density and improve its efficiency, so EPC represents a better performance criterion. In addition, increasing inlet pressure and doping level can improve EPC and energy efficiency, respectively.

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Section: Optiaml Exergy Performance Coefficientmentioning

confidence: 99%

Exergetic Performance Coefficient Analysis and Optimization of a High-Temperature Proton Exchange Membrane Fuel Cell

et al. 2022

Membranes

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“…Exergy is the portion of energy that can be converted into useful work during a fully reversible change in the environment [74,75]. Unlike energy analysis, exergy analysis takes into account the energy limitations, losses and conversion efficiency of the system to evaluate the performance of the system [76,77]. Exergy is mainly divided into mass flow exergy and energy flow exergy.…”

Section: Exergy Efficiencymentioning

confidence: 99%

Thermodynamic Modeling and Performance Analysis of Vehicular High-Temperature Proton Exchange Membrane Fuel Cell System

et al. 2022

Membranes

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Since the high temperature proton exchange membrane fuel cells (HT-PEMFC) stack require a range of auxiliary equipments to maintain operating conditions, it is necessary to consider operation of related components in the design of HT-PEMFC systems. In this paper, a thermodynamic model of a vehicular HT-PEMFC system using phosphoric acid doped polybenzimidazole membrane is developed. The power distribution and exergy loss of each component are derived according to thermodynamic analysis, where the stack and heat exchanger are the two components with the greatest exergy loss. In addition, ecological functions and improvement potentials are proposed to evaluate the system performance better. On this basis, the effects of stack inlet temperature, pressure, and stoichiometric on system performance are analyzed. The results showed that the energy efficiency, exergy efficiency and net output power of the system achieved the maximum when the inlet gases temperature is 406.1 K. The system performance is better when the cathode inlet pressure is relatively low and the anode inlet pressure is relatively high. Moreover, the stoichiometry should be reduced to improve the system output performance on the basis of ensuring sufficient gases reaction in the stack.

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“…It should be noted that the SOC is the basis of the charge and discharge control strategy for a battery management system of electrical equipment, which ensures balanced operation of the battery [7]. Accurate estimation of the SOC can enable the power performance of the battery to achieve its full capacity.…”

Section: Introductionmentioning

confidence: 99%

Estimation of state of charge integrating spatial and temporal characteristics with transfer learning optimization

Zhang

Liu

Chuang³

et al. 2023

Meas. Sci. Technol.

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State of charge (SOC) estimation of lithium-ion battery is of vital significance for the control strategy in the battery management system (BMS). To integrate the spatial and temporal characteristics of the data, and to accomplish the transfer of knowledge, a novel convolutional neural network-bidirectional long short-term memory network based on transfer learning optimization (CNN-BiLSTM-TF) is proposed to estimate SOC. In detail, the spatial and temporal features hidden in the data are learned through CNN and BiLSTM, respectively. Furthermore, the CNN-BiLSTM network is established under one working condition and transferred to other working conditions through transfer learning, from which the SOC can be estimated online. A lithium-ion battery dataset is applied to verify the proposed SOC estimation approach. The results of case study demonstrate that the estimation performance of CNN-BiLSTM-TF is better than some existing approaches.

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LPV Estimation of SOC Based on Electricity Conversion and Hysteresis Characteristic

Cited by 13 publications

References 11 publications

Exergetic Performance Coefficient Analysis and Optimization of a High-Temperature Proton Exchange Membrane Fuel Cell

Exergetic Performance Coefficient Analysis and Optimization of a High-Temperature Proton Exchange Membrane Fuel Cell

Thermodynamic Modeling and Performance Analysis of Vehicular High-Temperature Proton Exchange Membrane Fuel Cell System

Estimation of state of charge integrating spatial and temporal characteristics with transfer learning optimization

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