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.

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.

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.

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.

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.

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).

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.

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.

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

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.