| 1. |
- Bian, Xiaolei, et al.
(författare)
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A model for state-of-health estimation of lithium ion batteries based on charging profiles
- 2019
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Ingår i: Energy. - : Elsevier Ltd. - 0360-5442 .- 1873-6785. ; 177, s. 57-65
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Tidskriftsartikel (refereegranskat)abstract
- Using an equivalent circuit model to characterize the constant-current part of a charging/discharging profile, a model is developed to estimate the state-of-health of lithium ion batteries. The model is an incremental capacity analysis-based model, which applies a capacity model to define the dependence of the state of charge on the open circuit voltage as the battery ages. It can be learning-free, with the parameters subject to certain constraints, and is able to give efficient and reliable estimates of the state-of-health for various lithium ion batteries at any aging status. When applied to a fresh LiFePO 4 cell, the state-of-health estimated by this model (learning-unrequired or learning-required)shows a close correspondence to the available measured data, with an absolute difference of 0.31% or 0.12% at most, even for significant temperature fluctuation. In addition, NASA battery datasets are employed to demonstrate the versatility and applicability of the model to different chemistries and cell designs.
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| 2. |
- Bian, Xiaolei, et al.
(författare)
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A Novel Model-based Voltage Construction Method for Robust State-of-health Estimation of Lithium-ion Batteries
- 2021
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Ingår i: IEEE Transactions on Industrial Electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0046 .- 1557-9948. ; 68:12, s. 12173-12184
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Tidskriftsartikel (refereegranskat)abstract
- Accurate estimation of the state-of-health (SOH) is vital to the life management of lithium-ion batteries (LIBs). This paper proposes a fusion-type SOH estimation method by combining the model-based feature extraction and data-based state estimate. Particularly, a novel model-based voltage construction method is proposed to eliminate the unfavorable numerical condition and reshape the disturbance-free incremental capacity (IC) curves. Leveraging the modified IC curves, a set of informative features-of-interest are extracted and evaluated, while eventually several cautiously-selected ones are used to estimate the SOH of LIB accurately. Furthermore, the impact of model order on the estimation performance is scrutinized, to give insights into the parameterization in practical applications. Long-term cycling tests on different types of LIB cells are used for evaluation. The proposed method is validated with a good robustness to the cell inconsistency, temperature uncertainty, noise corruption, and a satisfied generality to different battery chemistries.
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| 3. |
- Bian, Xiaolei, et al.
(författare)
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A two-step parameter optimization method for low-order model-based state of charge estimation
- 2020
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Ingår i: IEEE Transactions on Transportation Electrification. - : Institute of Electrical and Electronics Engineers Inc.. - 2332-7782. ; , s. 1-1
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Tidskriftsartikel (refereegranskat)abstract
- The state of charge (SOC) estimation is an enabling technique for the efficient management and control of lithium-ion batteries (LIBs). This paper proposes a novel method for online SOC estimation which manifest itself with both high accuracy and low complexity. Particularly, the particle swarm optimization (PSO) algorithm is exploited to optimize the model parameters to ensure a high modeling accuracy. Following this endeavor, the PSO algorithm is used to tune the error covariances of extended Kalman filter (EKF) leveraging the early-stage segmental data of LIB utilization. Within this PSO-based tuning framework, the searching boundary is derived by scrutinizing the error transition property of system. Experiments are performed to validate the proposed two-step PSO-optimized SOC estimation method. Results show that even by using a simple first-order model, the proposed method can give rise to a high SOC accuracy which is comparative to those using complex high-order models. The proposed method is validated to excavate fully the potential of model-based estimators so that the computationally expensive model upgrade can be avoided. IEEE
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| 4. |
- Bian, Xiaolei, et al.
(författare)
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An open circuit voltage-based model for state-of-health estimation of lithium-ion batteries : Model development and validation
- 2020
-
Ingår i: Journal of Power Sources. - : Elsevier. - 0378-7753 .- 1873-2755. ; 448
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Tidskriftsartikel (refereegranskat)abstract
- An open circuit voltage-based model for state-of-health estimation of lithium-ion batteries is proposed and validated in this work. It describes the open circuit voltage as a function of the state-of-charge by a polynomial of high degree, with a lumped thermal model to account for the effect of temperature. When applied for practical use, the model requires a prior learning from the initial charging or discharging data for the sake of parameter identification, using e.g. a nonlinear least squares method, but it is undemanding to implement. The study shows that the model is able to estimate the state-of-health of a LiFePO4 cell cycled under conditions where the temperature has fluctuated significantly with a relative error less than 0.45% at most. A short part of a constant current profile is enough for state-of-health estimation, and the effect of size and location of voltage window on the model's accuracy is also studied. In particular, the reason of accuracy change with different voltage windows is explained by incremental capacity analysis. Additionally, the versatility and flexibility of the model to different chemistries and cell designs are demonstrated.
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| 5. |
- Bian, Xiaolei, 1990-
(författare)
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State Estimation of Lithium-ion Batteries
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To guarantee the safety operation, the key states of lithium-ion battery, e.g., the state of charge and the state of health, must be estimated and monitored accurately. This thesis is mainly to develop models and algorithms to accurately and robustly estimate the key battery states, based on the available measurements i.e., current and voltage. All the work is based on four published papers and can be divided into three parts.The first part of this work presents a two-step parameter optimization method for online state of charge estimation of lithium-ion battery. The particle swarm optimization is exploited for model parametrization and extended Kalman filter tuning. Within this particle swarm optimization-based framework, the searching boundary is derived by scrutinizing the error transition property of the test system, which can narrow the searching region and increase the computational efficiency. In general, the proposed method can well exploit the potential of model-based estimators, leading to a robust model compatibility and optimized performance.In the second part of this thesis, two novel models are developed to estimate the state of health of lithium-ion battery. The first one is an open circuit voltage-based model, which describes the open circuit voltage as a function of the state of charge by a polynomial, with a lumped thermal model to account for the effect of temperature. It requires a prior learning from the initial constant-current profile. The second model is an incremental capacity analysis-based model, which defines the dependence of the state of charge on the open circuit voltage using a capacity model. It can be learning-free, with the parameters subject to certain constraints. Both models use an equivalent circuit model to characterize the constant-current profiles and a nonlinear least squares method to identify the involving parameters. These two models are validated by aging experiments, and the results show that both can give accurate state-of-health estimation.The third part of the thesis introduces a fusion-type state-of-health estimator by combining the model-based profile reconstruction and the incremental capacity analysis-based state estimation. The above-mentioned open circuit voltage-based model is employed here to mitigate the noise-induced unfavorable numerical conditions and to modify the incremental capacity curves. Leveraging the modified incremental capacity curves, a set of feature-of-interests are extracted and evaluated, and several cautiously selected ones are used to estimate the state of health of lithium-ion battery. Long-term cycling tests on different lithium-ion batteries are used for validation. This fusion-type method has comparable accuracy and better robustness, compared with the model-based methods. Moreover, the proposed estimator has a good generality to different batteries and also promises an excellent robustness against cell inconsistency, noise corruption, temperature variety, and profile partialness.
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| 6. |
- Bian, Xiaolei, et al.
(författare)
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State-of-Health Estimation of Lithium-Ion Batteries by Fusing an Open Circuit Voltage Model and Incremental Capacity Analysis
- 2022
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Ingår i: IEEE transactions on power electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-8993 .- 1941-0107. ; 37:2, s. 2226-2236
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Tidskriftsartikel (refereegranskat)abstract
- The state of health (SOH) is a vital parameter enabling the reliability and life diagnostic of lithium-ion batteries. A novel fusion-based SOH estimator is proposed in this study, which combines an open circuit voltage (OCV) model and the incremental capacity analysis. Specifically, a novel OCV model is developed to extract the OCV curve and the associated features-of-interest (FOIs) from the measured terminal voltage during constant-current charge. With the determined OCV model, the disturbance-free incremental capacity (IC) curves can be derived, which enables the extraction of a set of IC morphological FOIs. The extracted model FOI and IC morphological FOIs are further fused for SOH estimation through an artificial neural network. Long-term degradation data obtained from different battery chemistries are used for validation. Results suggest that the proposed fusion-based method manifests itself with high estimation accuracy and high robustness.
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| 7. |
- Deng, Pengyi, et al.
(författare)
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Optimal online energy management strategy of a fuel cell hybrid bus via reinforcement learning
- 2024
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Ingår i: Energy Conversion and Management. - 0196-8904. ; 300
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Tidskriftsartikel (refereegranskat)abstract
- An energy management strategy (EMS) based on reinforcement learning is proposed in this study to enhance the fuel economy and durability of a fuel cell hybrid bus (FCHB). Firstly, a comprehensive powertrain system model for the FCHB is established, mainly including the FCHB's power balance, fuel cell system (FCS) efficiency, and aging models. Secondly, the state–action space, state transition probability matrix (TPM), and multi-objective reward function of Q-learning algorithm are designed to improve the fuel economy and the durability of power sources. The FCHB's demand power and battery state of charge (SOC) serve as the state variables and the FCS output power is used as the action variable. Using the demonstration FCHB data, a state TPM is created to represent the overall operation. Finally, an EMS employing Q-learning is formulated to optimize the fuel economy of FCHB, maintain battery SOC, suppress FCS power fluctuations, and enhance FCS lifetime. The proposed EMS is tested and verified through hardware-in-the-loop (HIL) tests. The simulation results demonstrate the effectiveness of the proposed strategy. Compared to a rule-based EMS, the Q-learning-based EMS can improve the energy economy by 7.8%. Furthermore, it is only a 3.7% difference to the best energy economy under dynamic optimization, while effectively reducing the decline and enhancing the durability of the FCS.
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| 8. |
- Deng, Zhongwei, et al.
(författare)
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Battery health evaluation using a short random segment of constant current charging
- 2022
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Ingår i: iScience. - : Elsevier BV. - 2589-0042. ; 25:5
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Tidskriftsartikel (refereegranskat)abstract
- Accurately evaluating the health status of lithium-ion batteries (LIBs) is significant to enhance the safety, efficiency, and economy of LIBs deployment. However, the complex degradation processes inside the battery make it a thorny challenge. Data-driven methods are widely used to resolve the problem without exploring the complex aging mechanisms; however, random and incomplete charging-discharging processes in actual applications make the existing methods fail to work. Here, we develop three data-driven methods to estimate battery state of health (SOH) using a short random charging segment (RCS). Four types of commercial LIBs (75 cells), cycled under different temperatures and discharging rates, are employed to validate the methods. Trained on a nominal cycling condition, our models can achieve high-precision SOH estimation under other different conditions. We prove that an RCS with a 10mV voltage window can obtain an average error of less than 5%, and the error plunges as the voltage window increases.
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| 9. |
- Deng, Zhongwei, et al.
(författare)
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Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data
- 2022
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Ingår i: IEEE transactions on power electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-8993 .- 1941-0107. ; 37:5, s. 5021-5031
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Tidskriftsartikel (refereegranskat)abstract
- The rapid development of battery technology has promoted the deployment of electric vehicles (EVs). To ensure the healthy and sustainable development of EVs, it is urgent to solve the problems of battery safety monitoring, residual value assessment, and predictive maintenance, which heavily depends on the accurate state-of-health (SOH) estimation of batteries. However, many published methods are unsuitable for actual vehicle conditions. To this end, a data-driven method based on the random partial charging process and sparse Gaussian process regression (GPR) is proposed in this article. First, the random capacity increment sequences (oQ) at different voltage segments are extracted from the partial charging process. The average value and standard deviation of oQ are used as features to indicate battery health. Second, correlation analysis is conducted for three types of batteries, and high correlations between the features and battery SOH are verified at different temperatures and discharging current rates. Third, by using the proposed features as inputs, sparse GPR models are constructed to estimate the SOH. Compared with other data-driven methods, the sparse GPR has the highest estimation accuracy, and its average maximum absolute errors are only 2.88%, 2.52%, and 1.51% for three different types of batteries, respectively.
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| 10. |
- Guo, Wenchao, et al.
(författare)
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Early diagnosis of battery faults through an unsupervised health scoring method for real-world applications
- 2024
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Ingår i: IEEE Transactions on Transportation Electrification. - 2332-7782. ; 10:2, s. 2521-2532
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Tidskriftsartikel (refereegranskat)abstract
- Battery fault diagnosis is critical to ensure the safe and reliable operation of electric vehicles or energy storage systems. Early diagnosis of battery faults can enable timely maintenance and reduce potential accidents. However, the lead time for detection is still relatively insufficient, and the identification of target vehicle with unidentified fault type has generally been neglected. To fill the gap, an unsupervised health scoring method for early diagnosis of battery faults is proposed in this paper. First, considering the properties of field data, new features and four types of feature sets related to battery health and fault status are derived for each cell. Then, a novel strategy is proposed to transform a typical classification problem into a quantitative scoring problem by performing multiple clustering. To produce ample clustering results, three cluster algorithms based on different principles are used and the features are randomly divided into feature subsets. By coupling temperature information, early determination of thermal runaway faults can be achieved. Finally, the real-world cloud data of three typical accidents are employed for verification, the results indicate that the proposed approach can innovatively achieve the detection of the abnormal cells at the level of days in advance, demonstrating excellent performance.
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| 11. |
- Guo, Wenchao, et al.
(författare)
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Rapid online health estimation for lithium-ion batteries based on partial constant-voltage charging segment
- 2023
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Ingår i: Energy. - 0360-5442. ; 281
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Tidskriftsartikel (refereegranskat)abstract
- Battery health evaluation is vital for ensuring the security and reliability of lithium-ion batteries. However, the currently proposed methods generally require high-quality input data for feature extraction in online applications. To overcome this obstacle, this paper proposes a rapid online health estimation method only based on partial constant-voltage (CV) charging segment. Firstly, through primary analysis of battery test data, the evolution of CV charging current is confirmed to be correlated with battery capacity. Subsequently, the current evolution constant of CV charging phase is mathematically formulated and quantitatively characterized using a novel health indicator (HI). Besides, charging time and charging capacity are also extracted as HIs to comprehensively capture the CV charging behavior and enhance the robustness of data-driven models. Considering the user's charging habits, an optimized CV segment is determined, enabling a significant reduction in data size and coverage. Finally, three data-driven methods are employed to construct health estimation models by using the extracted HIs, and the best performance is achieved by Gaussian process regression with MAE and RMSE lower than 0.8% and 1%, respectively. Remarkably, the proposed method demonstrates superiority in dealing with sparse sampling, and satisfactory results with 2.9% error under the sparsity of 10 s are obtained.
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| 12. |
- He, J., et al.
(författare)
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Comparative study of curve determination methods for incremental capacity analysis and state of health estimation of lithium-ion battery
- 2020
-
Ingår i: Journal of Energy Storage. - : Elsevier. - 2352-152X .- 2352-1538. ; 29
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Tidskriftsartikel (refereegranskat)abstract
- Incremental capacity analysis (ICA) is a favorable candidate for state of health (SOH) estimation of lithium-ion battery (LIB). Although abundant works have been carried out on the ICA-based methods, a comprehensive comparison of them to clarify the application boundary is still lacking. Moreover, more efficient method for extracting more informative features of interest (FOIs) for SOH estimation is less explored. Motivated by this, this paper performs a comparative study over the filtering-based and the voltage-capacity (VC) model-based ICA methods with respect to the IC fitting accuracy, robustness to aging and the computing cost. In this framework, a set of novel FOIs different from traditional ones are captured along with the parameterization of VC models. Comparative results reveal the optimality of revised Lorentzian VC model with three peaks (RL-VC-3) for both LiFePO4 (LFP) and LiNi1/3Co1/3Mn1/3O2 (NCM) battery. The mean relative errors of capacity modeling are 0.34% and 0.15%, respectively. The newly captured FOIs have been further validated with high linearities with the reference capacity, offering deep insights into more straightforward SOH estimation for LIB. Illustrative case studies suggest that particular FOIs can offer accurate SOH estimation with absolute error of 0.079% and 0.661% respectively for the LFP and NCM battery.
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| 13. |
- He, Jiangtao, et al.
(författare)
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State-of-Health Estimation of Lithium-Ion Batteries Using Incremental Capacity Analysis Based on Voltage-Capacity Model
- 2020
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Ingår i: IEEE Transactions on Transportation Electrification. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7782. ; 6:2, s. 417-426
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Tidskriftsartikel (refereegranskat)abstract
- State of health (SOH) is critical to evaluate the life expectancy of lithium-ion battery (LIB), thus should be estimated accurately in practical applications. This article proposes a computationally efficient model-based method for SOH estimation of LIB. A revised Lorentzian function-based voltage-capacity (VC) (RL-VC) model is exploited to accurately capture the voltage plateaus of LIB which reflect the material-level phase transition phenomenon. A full set of new features of interest (FOIs) is extracted by simply fitting the RL-VC model leveraging data collected from the constant-current charging process. Correlation analysis is then performed for the captured FOIs, based on which linear models are calibrated to estimate the battery SOH. The proposed method is validated with experimental data from different battery chemistries. The results show that the extracted FOIs have high linearities with the battery capacity, suggesting a good potential for SOH estimation and better feasibility over traditionally used methods. The proposed method shows a high accuracy for battery SOH estimation and an expected robust performance against the initial aging status and practical cycling condition.
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| 14. |
- Hu, Jian, et al.
(författare)
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Residual Statistics-Based Current Sensor Fault Diagnosis for Smart Battery Management
- 2022
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Ingår i: IEEE Journal of Emerging and Selected Topics in Power Electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-6777 .- 2168-6785. ; 10:2, s. 2435-2444
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Tidskriftsartikel (refereegranskat)abstract
- Current sensor fault diagnostic is critical to the safety of lithium-ion batteries (LIBs) to prevent over-charging and over-discharging. Motivated by this, this article proposes a novel residual statistics-based diagnostic method to detect two typical types of sensor faults, leveraging only the 50 current-voltage samples at the startup phase of the LIB system. In particular, the load current is estimated by using particle swarm optimization (PSO)-based model matching with measurable initial system states. The estimation residuals are analyzed statistically with Monte-Carlo simulation, from which an empirical residual threshold is generated and used for accurate current sensor fault diagnostic. The residual evaluation process is well proved with high robustness to the measurement noises and modeling uncertainties. The proposed method is validated experimentally to be effective in current sensor fault diagnosis with low miss alarm rate (MAR) and false alarm rate (FAR).
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| 15. |
- Zhao, Ruikai, et al.
(författare)
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Comparative study on energy efficiency of moving-bed adsorption for carbon dioxide capture by two evaluation methods
- 2021
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Ingår i: Sustainable Energy Technologies and Assessments. - : ELSEVIER. - 2213-1388 .- 2213-1396. ; 44
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Tidskriftsartikel (refereegranskat)abstract
- Because of the fast heat transfer and the low pressure drop, the technology of moving-bed adsorption for carbon dioxide capture is gathering the momentum in the last decade. The primary aim of this paper is to investigate the influence of various parameters on the energy-efficiency performance of moving-bed adsorption for CO2 capture. The relevant parameters involve desorption temperature, desorption pressure, CO2 capture rate and CO(2 )mole fraction of flue gas. The energy efficiency assessment of moving bed is performed and compared in the light of the minimum separation work and the second-law efficiency. Moreover, two evaluation approaches, which are the thermodynamic carbon pump model and regeneration separation model, are employed and compared as well. Results indicate that the values of minimum separation work for CO(2 )capture by moving bed, which are calculated by regeneration separation model, are about 15% higher than those of thermodynamic carbon pump model under the same conditions. Furthermore, the second-law efficiencies of both models are approximately 10% under the given conditions. It is also found that the regeneration separation model is closer to real status owing to the additional consideration of the adsorption isotherm equilibrium data.
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| 16. |
- Zou, Zhi, 1989-, et al.
(författare)
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A single-cell system of flow electrode capacitive mixing (F-CapMix) with a cross chamber for continuous energy production
- 2022
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Ingår i: Sustainable Energy & Fuels. - : Royal Society of Chemistry (RSC). - 2398-4902. ; 7:2, s. 398-408
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Tidskriftsartikel (refereegranskat)abstract
- The generation of electricity from salinity difference energy between seawater and freshwater through a Capacitive Mixing (CapMix) system with solid electrodes was limited by intermittent energy production. In this study, a single-cell CapMix system using flow-electrode (F-CapMix) with a cross-chamber configuration was examined to produce electricity continuously from simulated seawater and freshwater. The effects of the flow-electrode electrolyte concentration, activated carbon loading, amounts of carbon black, and connected external resistance on the system performance were investigated. The results suggest that the system performance can be enhanced by increasing the activated carbon loading and carbon black amounts. Furthermore, to achieve the maximum power density of the system, the external resistance should be matched to the internal resistance. The maximum power density of the presented single-cell F-CapMix system was 74.3 mW m−2, which was comparable to those of previous CapMix and F-CapMix systems. In addition, this study also reveals that using only carbon black as the flow electrode is capable of producing electricity continuously for long-term operation. In summary, these results indicate the potential of F-CapMix and provide developing directions for further optimization.
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| 17. |
- Zou, Zhi, et al.
(författare)
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Applicability of different double‐layer models for the performance assessment of the capacitive energy extraction based on double layer expansion (Cdle) technique
- 2021
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Ingår i: Energies. - : MDPI AG. - 1996-1073. ; 14:18, s. 5828-
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Tidskriftsartikel (refereegranskat)abstract
- Capacitive energy extraction based on double layer expansion (CDLE) is a renewable method of harvesting energy from the salinity difference between seawater and freshwater. It is based on the change in properties of the electric double layer (EDL) formed at the electrode surface when the concentration of the solution is changed. Many theoretical models have been developed to describe the structural and thermodynamic properties of the EDL at equilibrium, e.g., the Gouy– Chapman–Stern (GCS), Modified Poisson–Boltzmann–Stern (MPBS), modified Donnan (mD) and improved modified Donnan (i‐mD) models. To evaluate the applicability of these models, especially the rationality and the physical interpretation of the parameters that were used in these models, a series of single‐pass and full‐cycle experiments were performed. The experimental results were compared with the numerical simulations of different EDL models. The analysis suggested that, with optimized parameters, all the EDL models we examined can well explain the equilibrium charge–voltage relation of the single‐pass experiment. The GCS and MPBS models involve, how-ever, the use of physically unreasonable parameter values. By comparison, the i‐mD model is the most recommended one because of its accuracy in the results and the meaning of the parameters. Nonetheless, the i‐mD model alone failed to simulate the energy production of the full‐cycle CDLE experiments. Future research regarding the i‐mD model is required to understand the process of the CDLE technique better.
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| 18. |
- Zou, Zhi, 1989-, et al.
(författare)
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Comparative study on the performance of a two-cell system of Flow Electrode Capacitive Mixing (F-CapMix) for continuous energy production
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Capacitive mixing (CapMix), one of the most recent techniques that extract salinity gradient energy from the salt difference between seawater and freshwater, has attracted more attention in recent years. However, one big challenge that remains to solve in the traditional CapMix is to produce energy in a continuous way instead of an intermittent way. Here we achieve effective continuous energy production by using a two-cell flow electrode CapMix (F-CapMix) system. The performance of the F-CapMix is investigated under different experimental parameters that influence the power production of the system. Results show that the two-cell F-CapMix system is capable of continuously producing energy for long-term operation. Generally, the power density is dependent on the activated carbon loading, amounts of carbon black, feedwater flow rate, flow electrode flow rate, and the connected external resistance. Increasing the carbon loading and carbon black amounts is beneficial for improving power production. A slower flow rate of flow electrode and feedwater could improve the system’s performance. The external resistance should be matched to the internal resistance of the cell to achieve maximum power production. These results indicate the potential of F-CapMix and provide directions for its further optimizatio
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| 19. |
- Zou, Zhi, 1989-, et al.
(författare)
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Comparative study on the performance of a two-cell system of Flow Electrode Capacitive Mixing (F-CapMix) for continuous energy production
- 2023
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Ingår i: Journal of Energy Storage. - : Elsevier BV. - 2352-152X .- 2352-1538. ; 73, s. 109031-
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, Capacitive Mixing (CapMix) has garnered growing interest as a novel method for harnessing energy from the salinity gradient between seawater and freshwater. However, the challenge of extracting energy in a continuous way remains to be solved in traditional CapMix system. In this study, we demonstrate the feasibility of achieving continuous energy extraction through the use of a two-cell flow electrode Capacitive Mixing (F-CapMix) system. The performance of the F-CapMix system is evaluated under various experimental conditions including the activated carbon loading, carbon black additives, velocity of the flow electrode and feed water and external resistance in the circuit. The results suggest that the power density of the system can be significantly increased by approximately 800 % or 400 % with an increase in the carbon loading or the addition of carbon black additives, respectively. Meanwhile, reducing the flow rate of the flow electrode and feedwater from 20 mL/s to 5 mL/s was found to improve the system's performance. In addition, it is crucial that the external resistance is matched to the internal resistance of the cell for achieving a maximum power density. These results highlight the potential of F-CapMix and provide guidance for its further optimization.
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| 20. |
- Zou, Zhi, et al.
(författare)
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Comparative study on the performance of capacitive mixing under different operational modes
- 2022
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Ingår i: Energy Reports. - : Elsevier BV. - 2352-4847. ; 8, s. 7325-7335
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Tidskriftsartikel (refereegranskat)abstract
- Capacitive mixing (CapMix) is a renewable method of extracting energy from the salinity difference between seawater and freshwater. In this study, we systematically investigate the system behavior and performance of the CapMix system under four operational modes namely, capacitive energy extraction based on double layer expansion (CDLE), capacitive energy extraction based on the Donnan potential (CDP), and CDP with additional charging of constant voltage (CDP-CV) and constant current (CDPCC). The results indicate that the application of additional charging in the CDP technique can break the limits of the Donnan potential and significantly improve the system's performance. Accordingly, in terms of energy production and average power density, CDP-CC and CDP-CV are the two superior operational modes, followed by CDP and CDLE. In addition, our results reveal that CDP-CC is determined by the accumulated charge and applied current. CDLE is dependent on the applied voltage, while CDPCV is not sensitive to the applied voltage. Increasing the external load can considerably increase the energy production of both CDLE and CDP. In summary, the findings in this study provide practical information for the optimization and application of CapMix technologies.
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