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Available capacity of lithium-ion batteries is directly linked to the mileage of the electric vehicle. The cell imbalance is recognized as a significant concern hindering the full utilization of pack capacity. Following the emerging concept of battery reconfiguration, this article proposes a dual-scale hierarchical equalization scheme enabled by a novel four-switch reconfigurable topology. In particular, a four-switch reconfigurable topology is proposed, for the first time, which enjoys the benefits of flexible reconfigurability, moderate complexity, and high fault tolerance. Relying on the new topology, a hierarchical equalization strategy is proposed incorporating the intramodule time-sharing intervention and inter-module splitting recombination. This endeavor contributes to achieving all-cell flexibility, which further promises the all-cell equalization and maximum capacity utilization. Hardware-in-the-loop results validate that the proposed reconfigurable topology-enabled hierarchical equalization strategy can improve the pack capacity utilizing rate by 11.3%. [ABSTRACT FROM AUTHOR]

Accurate monitoring of the internal statuses is highly valuable for the management of the lithium-ion battery (LIB). This article proposes a thermal-model-based method for multistate joint observation, enabled by a novel smart battery design with an embedded and distributed temperature sensor. In particular, a novel smart battery is designed by implanting the distributed fiber optical sensor internally and externally. This promises a real-time distributed measurement of LIB internal and surface temperature with a high space resolution. Following this endeavor, a low-order joint observer is proposed to coestimate the thermal parameters, heat generation rate, state of charge, and maximum capacity. Experimental results disclose that the smart battery has space-resolved self-monitoring capability with high reproducibility. With the new sensing data, the heat generation rate, state of charge, and maximum capacity of LIB can be observed precisely in real time. The proposed method validates to outperform the commonly-used electrical-model-based method regarding the accuracy and the robustness to battery aging. [ABSTRACT FROM AUTHOR]

In this article, a computationally efficient state estimation method for lithium-ion (Li-ion) batteries is proposed based on a degradation-conscious high-fidelity electrochemical–thermal model for advanced battery management systems. The computational burden caused by the high-dimensional nonlinear nature of the battery model is effectively eased by adopting an ensemble-based state estimator using the singular evolutive interpolated Kalman filter (SEIKF). Unlike the existing schemes, it shows that the proposed algorithm intrinsically ensures mass conservation without imposing additional constraints, leading to a battery state estimator simple to tune and fast to converge. The model uncertainty caused by battery degradation and the measurement errors are properly addressed by the proposed scheme as it adaptively adjusts the error covariance matrices of the SEIKF. The performance of the proposed adaptive ensemble-based Li-ion battery state estimator is examined by comparing it with some well-established nonlinear estimation techniques that have been used previously for battery electrochemical state estimation, and the results show that excellent performance can be provided in terms of accuracy, computational speed, and robustness. [ABSTRACT FROM AUTHOR]

State of health (SOH) is critical to the management of lithium-ion batteries (LIBs) due to its deep insight into health diagnostic and protection. However, the lack of complete charging data is common in practice, which poses a challenge for the charging-based SOH estimators. This article proposes a multistage SOH estimation method with a broad scope of applications, including the unfavorable but practical scenarios of heavily partial charging. In particular, different sets of health indicators (HIs), covering both the morphological incremental capacity features and the voltage entropy information, are extracted from the partial constant-current charging data with different initial charging voltages to characterize the aging status. Following this endeavor, artificial neural network based HI fusion is proposed to estimate the SOH of LIB precisely in real time. The proposed method is evaluated with long-term aging experiments performed on different types of LIBs. Results validate several superior merits of the proposed method, including high estimation accuracy, high tolerance to partial charging, strong robustness to cell inconsistency, and wide generality to different battery types. [ABSTRACT FROM AUTHOR]

Fast charging is an enabling technique for the large-scale penetration of electric vehicles. This article proposes a knowledge-based, multiphysics-constrained fast charging strategy for lithium-ion battery (LIB), with a consciousness of the thermal safety and degradation. A universal algorithmic framework combining model-based state observer and a deep reinforcement learning (DRL)-based optimizer is proposed, for the first time, to provide a LIB fast charging solution. Within the DRL framework, a multiobjective optimization problem is formulated by penalizing the over-temperature and degradation. An improved environmental perceptive deep deterministic policy gradient (DDPG) algorithm with priority experience replay is exploited to tradeoff smartly the charging rapidity and the compliance of physical constraints. The proposed DDPG-DRL strategy is compared experimentally with the rule-based strategies and the state-of-the-art model predictive controller to validate its superiority in terms of charging rapidity, enforcement of LIB thermal safety and life extension, as well as the computational tractability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Electrochemical impedance spectroscopy (EIS) is a promising technique for the diagnostic of lithium-ion battery (LIB). However, its onboard application is still challenging considering the limitation of in-vehicle environment. Motivated by this, this paper proposes an equivalent sampling-enabled module-level battery impedance measurement method, which shows a strong fidelity for lithium plating diagnostic. A module-level perturbation topology is designed allowing for the generation of high-precision perturbation current with reasonable space occupation. A simplified equivalent sampling method is exploited for the first time with the proposed topology, enabling the EIS sampling with low hardware cost and full compatibility with the commercially-ready battery monitoring chips. With the proposed prototype, an EIS feature-based nondestructive method is further proposed for the in-situ lithium plating diagnostic of LIB. Experimental results suggest that the proposed prototype can promise accurate EIS measurement within a broad frequency range from 0.1 Hz to 1160 Hz. Moreover, the capacity calibration and post-mortem tests validate the accuracy of the proposed EIS feature-based lithium plating diagnostic method. • A module-level in-situ EIS measurement structure is proposed. • Equivalent sampling technique is utilized to reduce the requirements of sampling. • A lithium plating diagnostic method is developed based on proposed EIS method. • A prototype device is manufactured for validation of the concept. [ABSTRACT FROM AUTHOR]

• A precheck and fault diagnosis method is proposed for PEMFC engine in cold climates. • The proposed method identifies the faulty components accurately to avoid damage. • The conducted investigations reveal the freeze-out phenomenon other than FC stack. Cold-start is a challenging issue for proton exchange membrane (PEM) fuel cell engine due to the high-probability of freeze-out. Apart from the clog inside the channel, the freeze among components is equally critical especially in the system-level engine. Motivated by this, a precheck and fault diagnosis method is proposed, for the first time, to ensure the functional reliability of PEM fuel cell engine in cold climate. Based on a 40-kW stack, the proposed method includes three stages of precheck, preheat and self start-up, covering eight susceptible components within both air and hydrogen subsystems. Through the speed-and pressure-related detections, the alarm signals alert when not passing the threshold judgements. Experimental results validate the identifiability among multiple component-level faults and the fidelity of diagnostic in application. Moreover, during several months of investigations, the significance of the proposed method is further emphasized by the destructive and underlying damage without precheck. With the help of the proposed method, a successful start-up from −20 °C takes advantage of 48% chemical energy heating the fuel cell engine and maintaining steady operation for more than 5 mins. According to the Sankey map, 3.75% parasitic power indicates the functional reliability of tested engine. The conducted investigations reveal the freeze-out phenomenon other than fuel cell stack, giving a deeper insight into precheck and fault diagnosis issue with respect to fault type, influential component and improvement measurement. [ABSTRACT FROM AUTHOR]

The accurate estimation of the state-of-health (SOH) is vital to the life management of lithium-ion batteries (LIBs). In this article, we propose 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 is extracted and evaluated, while eventually several cautiously selected ones are used to estimate the SOH of LIBs 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. [ABSTRACT FROM AUTHOR]

The installation of current sensors on lithium-ion batteries (LIBs) can be challenging due to practical constraints in specific applications like portable electronics and smart batteries. Motivated by this, our letter proposes a method for online load current and state-of-charge (SOC) coestimation, which mitigates the need of installing the current sensor for LIB management. The essence is to transform the state observation into a constrained optimization problem, which is solved numerically in a moving horizon framework to allow the online coestimation of SOC and input current. Experimental results suggest that the proposed method can coestimate the load current and SOC of LIB precisely even if the current sensor is absent. The encouraging results are insightful for reducing the structural complexity and cost of future LIB utilization. [ABSTRACT FROM AUTHOR]

Accurate estimation of state of charge (SOC) is critical to the safe and efficient utilization of a battery system. Model-based SOC observers have been widely used due to their high accuracy and robustness, but they rely on a well-parameterized battery model. This article scrutinizes the effect of measurement noises on model parameter identification and SOC estimation. A novel parameterization method combining instrumental variable (IV) estimation and bilinear principle is proposed to compensate for the noise-induced biases of model identification. Specifically, the IV estimator is used to reformulate an overdetermined system so as to allow coestimating the model parameters and noise variances. The coestimation problem is then decoupled into two linear subproblems which are solved efficiently by a two-stage least squares algorithm in a recursive manner. The parameterization method is further combined with a Luenberger observer to estimate the SOC in real time. Simulations and experiments are performed to validate the proposed method. Results reveal that the proposed method is superior to existing method in terms of the immunity to noise corruption. [ABSTRACT FROM AUTHOR]

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance. [ABSTRACT FROM AUTHOR]

Accurate estimation of power capacity is critical to ensure battery safety margins and optimize energy utilization. Power capacity estimators based on online identified equivalent circuit model have been widely investigated due to the high accuracy and affordable computing cost. However, the impact of noise corruption which is common in practice on such estimators has never been investigated. This paper scrutinizes the effect of noises on model identification, state of charge (SOC) and power capacity estimation. An online model identification method based on adaptive forgetting recursive total least squares (AF-RTLS) is proposed to compensate the noise effect and attenuate the identification bias of model parameters. A Luenberger observer is further used in combination with the AF-RTLS to estimate the SOC in real time. Leveraging the estimated model parameters and SOC, a multiconstraint analytical method is proposed to online estimate the power capacity. Simulation and experimental results verify that the proposed method is superior in terms of estimation accuracy and the robustness to noise corruption. [ABSTRACT FROM AUTHOR]

Abstract Model-based observers appeal to both research and industry utilization due to the high accuracy and robustness. To further improve the robustness to dynamic work conditions and battery ageing, the online model identification is integrated to the state estimation, giving rise to the co-estimation methods. This paper systematically compares three types of co-estimation methods for the online state of charge of lithium-ion battery. This first method is dual extended Kalman filter which uses two parallel filters for co-estimation. The second method is a typical data-model fusion method which uses recursive least squares for model identification and extended Kalman filter for state estimation. Meanwhile, a noise compensating method based on recursive total least squares and Rayleigh quotient minimization is exploited for online model identification, which is further designed in conjunction with the extended Kalman filter to estimate the state of charge. Simulation and experimental studies are carried out to compare the performances of three methods in terms of the accuracy, convergence property, and noise immunity. The computing cost and tuning effort are further discussed to give insights to the application prospective of different methods. Highlights • Three co-estimation methods are compared for lithium-ion battery application. • A noise compensating method is exploited for co-estimation purpose. • Noise effects on different methods are analyzed with simulations and experiments. • The computing cost and tuning effort are discussed. [ABSTRACT FROM AUTHOR]

Abstract Accurate monitoring of state of charge (SOC) and capacity loss is critical for the management of vanadium redox flow battery (VRB) system. This paper proposes a novel autoregressive exogenous model for the vanadium redox flow battery, based on which the model-based monitoring of state of charge and capacity loss is investigated. The offline parameterization based on genetic algorithm and the online parameterization based on recursive least squares are investigated for the proposed model to compare the model accuracy and robustness. Leveraging the parameterized model, an H-infinity observer is exploited to estimate the battery state of charge and capacity in real time. Experimental results suggest that the proposed autoregressive exogenous model can accurately simulate the dynamic behavior of vanadium redox flow battery. Compared with the offline model based method, the observer based on online adaptive model is superior in terms of the accuracy of modeling, state of charge estimation and capacity loss monitoring. The proposed method is also verified with high robustness to the uncertain algorithmic initialization, electrolyte imbalance, and the change of system design and work conditions. Highlights • Autoregressive exogenous modeling with online adaption is proposed for VRB. • A method for online SOC and capacity loss monitoring is proposed for VRB. • H-infinity observer is used for state co-estimation. • The proposed method is verified with lab-scale experiments. [ABSTRACT FROM AUTHOR]

Thermoelectric generator is a solid-state energy converter which can convert waste heat directly into electricity. During the past decades, thermoelectric materials have been widely investigated whereas the integrated design of thermoelectric generators have been less studied. This paper proposes and implements a framework for the design of thermoelectric generators, consisting of thermoelectric modules and heat exchangers, based on the Taguchi method. As compared with previous researches which optimize the thermoelectric module alone and assume fixed temperature or fixed heat fluxes for the thermoelectric modules, this work proposes a methodology to optimize the thermoelectric module and the heat exchanger together. Five design parameters (namely the height, the fill-ratio, the ratio of cross-sectional area of n-type material over p-type material of thermoelectric module, the length and the material of the heat exchanger) were analyzed for two different applications, waste heat recovery from marine and automotive engines. In order to perform the analysis, a L 27 (3 5 ) orthogonal array was employed to assess all of the design parameters returning the maximum output power. By analysis of variance, it is found that the thermoelectric module height is the most important design parameter contributing for the 69.6% and 30.25% in automotive and marine application, respectively. And the optimal design parameter set are also determined in both applications. [ABSTRACT FROM AUTHOR]

The accurate aging evaluation is one of the important factors to find the optimal operation mode of the regional wind-hydrogen integrated energy system (RWHIES). In this article, a wind turbine (WT) power prediction method is proposed based on wind physical information and topography characteristics (WPITC). By making full use of the modified numerical weather data, the wind power can be predicted by combining wind conditions and terrain information. Under the guidance of the WT power prediction model, the aging state is evaluated and a RWHIES model is proposed, which allows striving to maximizing the production capacity with the minimal cost. An improved multi-objective dragonfly algorithm (IMODA) is proposed to address the optimization problem. Finally, three different cases are conducted to analyze the relationship between cost and capacity, which indicates that the proposed approach can improve the accuracy and speed of the cost and capacity optimization for RWHIES. [Display omitted] • The proposed WPITC-Based WT power prediction model for aging evaluation. • Aging evaluation horizons of WHIES is no longer limited to short term power. • An IMODA is used to solve the optimization problem of cost and capacity. [ABSTRACT FROM AUTHOR]

The long-term operation of the vanadium redox flow battery is accompanied by ion diffusion across the separator and side reactions, which can lead to electrolyte imbalance and capacity loss. The accurate online monitoring of capacity loss is therefore valuable for the reliable and efficient operation of vanadium redox flow battery system. In this paper, a model-based online monitoring method is proposed to detect capacity loss in the vanadium redox flow battery in real time. A first-order equivalent circuit model is built to capture the dynamics of the vanadium redox flow battery. The model parameters are online identified from the onboard measureable signals with the recursive least squares, in seeking to keep a high modeling accuracy and robustness under a wide range of working scenarios. Based on the online adapted model, an observer is designed with the extended Kalman Filter to keep tracking both the capacity and state of charge of the battery in real time. Experiments are conducted on a lab-scale battery system. Results suggest that the online adapted model is able to simulate the battery behavior with high accuracy. The capacity loss as well as the state of charge can be estimated accurately in a real-time manner. [ABSTRACT FROM AUTHOR]

The state-of-charge (SOC) observer with online model adaption generally has high accuracy and robustness. However, the unexpected sensing of noises is shown to cause the biased identification of model parameters. To address this problem, a novel technique which integrates a recursive total least squares (RTLS) with an SOC observer is proposed to enhance the online model identification and SOC estimate. An efficient method is exploited to solve the Rayleigh quotient minimization which lays the basis of the RTLS. The number of multiplies, divides, and square roots is elaborated to show the low computational complexity of the developed RTLS. Simulation and experimental results show that the proposed RTLS-based observer attenuates the model identification bias caused by noise corruption effectively, and, thereby, provides a more reliable estimation of SOC. The proposed method is further compared with several available methods to highlight its superiority in terms of accuracy and the robustness to noise corruption. [ABSTRACT FROM PUBLISHER]

Abstract: To explore the global mechanism of Ermiaowan on hyperuricemia regulation, the holistic function of Ermiaowan for hyperuricemia in rats was firstly assessed by the urinary metabonomics method which was based on ultra‐high performance liquid chromatography with electrospray ionization quadrupole time‐of‐flight mass spectrometry. The urinary targeted metabonomics approach combined with the serum biochemical analysis and histological assay was conducted to verify the research result. As a result, the significant differences in metabolic profiles were observed from Ermiaowan‐treated group, model group, and healthy control group by using multivariate statistical approaches. Twenty therapeutic related metabolites were identified in response to the therapeutic effects of Ermiaowan, which were mainly associated with purine metabolism, pyrimidine metabolism, tryptophan metabolism, tricarboxylic acid cycle, and tyrosine metabolism. In addition, the urinary targeted metabonomics study showed that Ermiaowan can better restore the disturbed pathways than Phellodendri cortex and Atractylodis rhizome. The biochemical assay and histopathological assay confirmed that Ermiaowan could significantly reduce uric acid and fibrosis areas of kidney. These results provided new insights into the mechanism of Ermiaowan on hyperuricemia. [ABSTRACT FROM AUTHOR]

The ageing of lithium-ion battery (LIB) is critical from the application perspective. In this paper, an electrochemical based electrical (ECBE) model is developed to link the model parameters to specific ageing mechanisms. Based on the model parameterization, we report on time resolved degradation processes of LIB during the cycle ageing. The sequence of internal components degradation mechanisms in graphite/lithium cobalt oxide (LCO) coin cells subjected to up to 1000 galvanostatic charge-discharge cycles are revealed. It is found the LIB degradation proceeds according to a series of inter-connected processes, including: i) Solid electrolyte interphase (SEI) formation on the graphite electrode surfaces; ii) LCO phase transformation from active hexagonal to less active spinel phases; iii) Joule heating due to the increased cell resistance resulting in cracking and re-formation of SEI together with surface spallation at the LCO electrode, as well as degradation of the separator; iv) Active Li losses owing to metal plating on the electrodes during overcharge and overdischarge; v) Exfoliation of the graphite anode and the degradation of LCO cathode; and vi) Intergranular contact disconnection within electrodes. These processes contribute to the overall decay in the storage capacity and the LIB terminal voltage differently as shown experimentally. [ABSTRACT FROM AUTHOR]

Reliable online estimation of state of charge (SOC) and capacity is critically important for the battery management system (BMS). This paper presents a multi-timescale method for dual estimation of SOC and capacity with an online identified battery model. The model parameter estimator and the dual estimator are fully decoupled and executed with different timescales to improve the model accuracy and stability. Specifically, the model parameters are online adapted with the vector-type recursive least squares (VRLS) to address the different variation rates of them. Based on the online adapted battery model, the Kalman filter (KF)-based SOC estimator and RLS-based capacity estimator are formulated and integrated in the form of dual estimation. Experimental results suggest that the proposed method estimates the model parameters, SOC, and capacity in real time with fast convergence and high accuracy. Experiments on both lithium-ion battery and vanadium redox flow battery (VRB) verify the generality of the proposed method on multiple battery chemistries. The proposed method is also compared with other existing methods on the computational cost to reveal its superiority for practical application. [ABSTRACT FROM AUTHOR]

• Estimated battery SOH using CNN model based on the raw data. • A high tolerance to the partial charging condition. • Introduced transfer learning method to reduce the offline training data. • Satisfied generality to different types of batteries. State of health (SOH) estimation is essential to the health diagnostic of lithium-ion battery. The data-driven approach with charging feature extraction is promising for online SOH estimation and has been widely explored over years. However, their deployment can be barriered by the lack of complete charging data in real-world applications. Motivated by this, this paper proposes an artificial intelligence-based SOH estimator using the transient phase between constant current (CC) and constant voltage (CV) charging, which is easily obtained in real-world charging scenarios. Specifically, a convolutional neural network (CNN) model is proposed to explain the relationship between the charging data and the SOH. Following this endeavor, the transfer learning is exploited for model mitigation and SOH estimation on different battery types, relying on much reduced amount of data for efficient CNN model re-training. The validation experiments are conducted based on the aging data obtained on LiNiCoAlO 2 (NCA) and LiCoO 2 (LCO) cells. Results suggest that the proposed method realizes accurate SOH estimation requiring only a short segment from the CC-CV transient phase, so that can meet a broad range of real-world charging scenarios. Moreover, the efficient model transfer promises expected performance with different battery types. The short version of the paper was presented at ICAE2021, Nov 29 - Dec 5, 2021. This paper is a substantial extension of the short version of the conference paper. [ABSTRACT FROM AUTHOR]

Laportea bulbifera (Sieb. et Zucc.) Wedd., a plant with a long history of medicinal use, possesses uncertainly defined medicament portions while its antioxidant capacity remains largely unexplored. To gain a better understanding of its medicinal value, this study focused on investigating the Laportea bulbifera aboveground part (LBAP) and the Laportea bulbifera root (LBR). Through an assessment of the bioactive compound content, a significant finding emerged: the LBR exhibited notably higher levels of these bioactive phytochemicals compared to the LBAP. This observation was further reinforced by the antioxidant assays, which demonstrated the superiority of the LBR's antioxidant capacity. The experimental results unequivocally indicate that the root is the optimal medicament portion for Laportea bulbifera. Furthermore, it was discovered that the presence of alcohol in the extraction solvent significantly enhanced the extraction of active ingredients, with the methanol extract of LBR performing the best among the extracts tested. Consequently, this extract was selected for further research. Leveraging cutting-edge UHPLC-ESI-Q-TOF-MS technology, the methanol extract of LBR was meticulously analyzed, revealing the presence of 41 compounds, primarily belonging to the phenolics and fatty acids. Remarkably, stability experiments demonstrated that the phenolics in the methanol extract maintained their stability across various pH values and during in vitro simulations of the human digestive system, albeit showing gradual degradation under high temperatures. Furthermore, the oxidative stability tests conducted on oils revealed the potential of the methanol extract as a stabilizer for olive oil and sunflower oil. Moreover, oral acute toxicity studies confirmed the low toxicity of the methanol extract, further supporting its safe use for medicinal purposes. Of particular note, histopathological examination and biochemical analysis affirmed the remarkable protective effects of the methanol extract against d-galactosamine-induced liver damage. These findings underscore the therapeutic potential of the methanol extract from the LBR in the treatment of diseases associated with oxidative imbalance. [ABSTRACT FROM AUTHOR]

An accurate battery model is the prerequisite for reliable state estimate of vanadium redox battery (VRB). As the battery model parameters are time varying with operating condition variation and battery aging, the common methods where model parameters are empirical or prescribed offline lacks accuracy and robustness. To address this issue, this paper proposes to use an online adaptive battery model to reproduce the VRB dynamics accurately. The model parameters are online identified with both the recursive least squares (RLS) and the extended Kalman filter (EKF). Performance comparison shows that the RLS is superior with respect to the modeling accuracy, convergence property, and computational complexity. Based on the online identified battery model, an adaptive peak power estimator which incorporates the constraints of voltage limit, SOC limit and design limit of current is proposed to fully exploit the potential of the VRB. Experiments are conducted on a lab-scale VRB system and the proposed peak power estimator is verified with a specifically designed “two-step verification” method. It is shown that different constraints dominate the allowable peak power at different stages of cycling. The influence of prediction time horizon selection on the peak power is also analyzed. [ABSTRACT FROM AUTHOR]

Reliable state estimate depends largely on an accurate battery model. However, the parameters of battery model are time varying with operating condition variation and battery aging. The existing co-estimation methods address the model uncertainty by integrating the online model identification with state estimate and have shown improved accuracy. However, the cross interference may arise from the integrated framework to compromise numerical stability and accuracy. Thus this paper proposes the decoupling of model identification and state estimate to eliminate the possibility of cross interference. The model parameters are online adapted with the recursive least squares (RLS) method, based on which a novel joint estimator based on extended Kalman Filter (EKF) is formulated to estimate the state of charge (SOC) and capacity concurrently. The proposed joint estimator effectively compresses the filter order which leads to substantial improvement in the computational efficiency and numerical stability. Lab scale experiment on vanadium redox flow battery shows that the proposed method is highly authentic with good robustness to varying operating conditions and battery aging. The proposed method is further compared with some existing methods and shown to be superior in terms of accuracy, convergence speed, and computational cost. [ABSTRACT FROM AUTHOR]

State of charge (SOC) estimators with online identified battery model have proven to have high accuracy and better robustness due to the timely adaption of time varying model parameters. In this paper, we show that the common methods for model identification are intrinsically biased if both the current and voltage sensors are corrupted with noises. The uncertainties in battery model further degrade the accuracy and robustness of SOC estimate. To address this problem, this paper proposes a novel technique which integrates the Frisch scheme based bias compensating recursive least squares (FBCRLS) with a SOC observer for enhanced model identification and SOC estimate. The proposed method online estimates the noise statistics and compensates the noise effect so that the model parameters can be extracted without bias. The SOC is further estimated in real time with the online updated and unbiased battery model. Simulation and experimental studies show that the proposed FBCRLS based observer effectively attenuates the bias on model identification caused by noise contamination and as a consequence provides more reliable estimate on SOC. The proposed method is also compared with other existing methods to highlight its superiority in terms of accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

A key function of battery management system (BMS) is to provide accurate information of the state of charge (SOC) in real time, and this depends directly on the precise model parameterization. In this paper, a novel multi-timescale estimator is proposed to estimate the model parameters and SOC for vanadium redox flow battery (VRB) in real time. The model parameters and OCV are decoupled and estimated independently, effectively avoiding the possibility of cross interference between them. The analysis of model sensitivity, stability, and precision suggests the necessity of adopting different timescales for each estimator independently. Experiments are conducted to assess the performance of the proposed method. Results reveal that the model parameters are online adapted accurately thus the periodical calibration on them can be avoided. The online estimated terminal voltage and SOC are both benchmarked with the reference values. The proposed multi-timescale estimator has the merits of fast convergence, high precision, and good robustness against the initialization uncertainty, aging states, flow rates, and also battery chemistries. [ABSTRACT FROM AUTHOR]

The present study focuses on the dynamic electro-thermal modeling for the all-vanadium redox flow battery (VRB) with forced cooling strategies. The Foster network is adopted to dynamically model the heat dissipation of VRB with heat exchangers. The parameters of Foster network are extracted by fitting the step response of it to the results of linearized CFD model. Then a complete electro-thermal model is proposed by coupling the heat generation model, Foster network and electrical model. Results show that the established model has nearly the same accuracy with the nonlinear CFD model in electrolyte temperature prediction but drastically improves the computational efficiency. The modeled terminal voltage is also benchmarked with the experimental data under different current densities. The electrolyte temperature is found to be significantly influenced by the flow rate of coolant. As compared, although the electrolyte flow rate has unremarkable impact on electrolyte temperature, its effect on system pressure drop and battery efficiency is significant. Increasing the electrolyte flow rate improves the coulombic efficiency, voltage efficiency and energy efficiency simultaneously but at the expense of higher pump power demanded. An optimal flow rate exists for each operating condition to maximize the system efficiency. [ABSTRACT FROM AUTHOR]

Abstract: The present study focuses on dynamic thermal-hydraulic modeling for the all-vanadium flow battery and investigations on the impact of stack flow patterns on battery performance. The inhomogeneity of flow rate distribution and reversible entropic heat are included in the thermal-hydraulic model. The electrolyte temperature in tanks is modeled with the finite element modeling (FEM) technique considering the possible non-uniform distribution of electrolyte temperature. Results show that the established model predicts electrolyte temperature accurately under various ambient temperatures and current densities. Significant temperature gradients exist in the battery system at extremely low flow rates, while the electrolyte temperature tends to be the same in different components under relatively high flow rates. Three stack flow patterns including flow without distribution channels and two cases of flow with distribution channels are compared to investigate their effects on battery performance. It is found that the flow rates are not uniformly distributed in cells especially when the stack is not well designed, while adding distribution channels alleviates the inhomogeneous phenomenon. By comparing the three flow patterns, it is found that the serpentine–parallel pattern is preferable and effectively controls the uniformity of flow rates, pressure drop and electrolyte temperature all at expected levels. [Copyright &y& Elsevier]

• Hierarchy Ⅰ method is a least squares-based moving horizon estimator. • Hierarchy Ⅱ method is a total least squares-based moving horizon estimator. • Hierarchy III method is an input-free moving horizon estimator. • Hierarchy Ⅱ method is highly robust to the corruption of sensing noises. • Hierarchy III method realizes accurate estimation without needing current sensor. Accurate current measurement is indispensable for the management of lithium-ion battery (LIB), especially for the state-of-charge (SOC) estimation. However, accurate current sensing is challenging in electric vehicles (EVs) due to the electromagnetic interference. Moreover, the currents across the parallel branches of battery pack are even unmeasurable due to the absence of current sensor. Motivated by this, this paper proposes a hierarchical soft measurement framework for the load current and SOC addressing different degrees of current sensor uncertainty. Rooted from a common least squares (LS)-based state optimization problem, a total least square (TLS)-based modification is proposed and solved to compensate for the measurement disturbances, and in accordance to estimate the SOC more accurately. One step further, an input-free optimization method is proposed to co-estimate the SOC and load current without using the current measurements. Simulation and experimental results suggest that the proposed hierarchical framework can realize high-fidelity co-estimation of the SOC and load current, especially in the adverse scenarios of both strong noise corruption and current sensor malfunction/missing. The encouraging results open new paradigms for both the high-robustness current-free SOC estimation and the hardware-free soft current measurement of LIB. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on NO x emission prediction and operating parameters optimization for coal-fired boilers. Support Vector Regression (SVR) model based on CGA (Conventional Genetic Algorithm) was proposed to model the relationship between the operating parameters and the concentration of NO x emission. Then CGA and two modified algorithms, the Quantum Genetic Algorithm (QGA) and SAGA (Simulated Annealing Genetic Algorithm), were employed to optimize the operating parameters of the coal-fired boiler to reduce NO x emission. The results showed that the proposed SVR model was more accurate than the widely used Artificial Neural Network (ANN) model when employed to predict the concentration of NO x emission. The mean relative error and correlation coefficient calculated by the proposed SVR model were 2.08% and 0.95, respectively. Among the three optimization algorithms implemented in this paper, the SAGA showed superiority to the other two algorithms considering the quality of solution within a given computing time. The SVR plus SAGA method was preferable to predict the concentration of NO x emission and further to optimize the operating parameters to achieve low NO x emission for coal-fired boilers. [Copyright &y& Elsevier]

Solid oxide fuel cell (SOFC) becomes increasingly popular in dc microgrid applications. Controlling SOFC is challenging because the dynamics of SOFC are difficult to maintain under complex internal reactions and changing operating conditions. To solve these problems, this article proposes a novel adaptive model predictive control (AMPC) algorithm, which adopts a parameter estimator to update the system parameters online. The robustness of the proposed AMPC is investigated under different microgrid scenarios, including the overload, underload, short-circuit, and significant dc bus voltage drop situations. The proposed AMPC algorithm produces superior SOFC control performance over the conventional model predictive control (MPC), Wiener MPC, and PI and fuzzy PI controllers. Furthermore, it significantly reduces the system model dependence that is shared by nearly all the model-based SOFC control methods. The convergence of parameter estimation in the proposed AMPC is rigorously proved. The effectiveness of the proposed algorithm is validated through hardware-in-the-loop experiments under various operating conditions and system parameter variations. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) has seen widespread applications in a variety of fields like the renewable penetration, electrified transportation, and portable electronics. A reliable battery management system (BMS) is critical to fulfill the expectations on the reliability, efficiency and longevity of LIB systems. Recent research progresses have witnessed the emerging technique of smart battery and the associated management system, which can potentially overcome the deficiencies met by traditional BMSs. Motivated by this, this paper reviews the research progresses on the smart cell and smart battery system from multiple aspects, including the system design, sensing techniques, and the potential innovation of system integration. The transition from conventional LIB system towards higher smartness and the incurred advantages/challenges are overviewed. Special focuses are given to the existing and emerging cell-level, multi-dimensional (electro-mechanical-thermal) sensing techniques that may play critical roles in the management of smart cells. The mitigation from external to internal embedded sensing and its potential benefits/impacts to the performance of battery system, as well as the general requirement for sensor design and integration, are discussed systematically. Considering the design innovation and data transmission burden, the potential change of system-level smart battery integration is further discussed as an open outlook. • The development of smart battery system is overviewed. • Advanced sensing techniques for LIB multi-dimensional measurement are reviewed. • LIB management methods using multi-dimensional measurements are reviewed. • The integration of smart battery system and associated challenges are discussed. [ABSTRACT FROM AUTHOR]

With the advent of sustainable and clean energy, lithium-ion batteries have been widely utilised in cleaner productions such as energy storage systems and electrical vehicles, but the management of their electrode production chain has a direct and crucial impact on the battery performance and production efficiency. To achieve a cleaner production chain of battery electrode involving strongly-coupled intermediate parameters and control parameters, a reliable approach to quantify the feature importance and select the key feature variables for predicting battery intermediate products is urgently required. In this paper, a Gaussian process regression-based machine learning framework, which incorporates powerful automatic relevance determination kernels, is proposed for directly quantifying the importance of four intermediate production feature variables and analysing their influences on the prediction of battery electrode mass load. Specifically, these features include three intermediate parameters from the mixing step and a control parameter from the coating step. After deriving four different automatic relevance determination kernels, the importance of these four feature variables based on a regression modelling is comprehensively analysed. Comparative results demonstrate that the proposed automatic relevance determination kernel-based Gaussian process regression models could not only quantify the importance weights for reliable feature selections but also help to achieve satisfactory electrode mass load prediction. Due to the data-driven nature, the proposed framework can be conveniently extended to improve the analysis and control of battery electrode production, further benefitting the manufactured battery yield, efficiencies and performance to achieve cleaner battery production. Image 1 • A data-driven framework is designed for mass load prediction in battery production. • Importance of four production feature variables of interest is directly quantified. • Performance of four ARD kernels is comparatively studied via GPR model. • Effects of these sensitive features on the mass load predictions are analysed. • Designed framework can benefit the optimisation for cleaner battery production. [ABSTRACT FROM AUTHOR]

The accurate diagnostic of internal short circuit (ISC) is critical to the safety of lithium-ion battery (LIB), considering its consequence to disastrous thermal runaway. Motivated by this, this article proposes a novel ISC diagnostic method with a high robustness to measurement disturbances and the capacity fading. Particularly, a multistate-fusion ISC diagnostic method leveraging polarization dynamics instead of the conventional charge depletion is proposed within a model-switching framework. This is well-proven to eliminate the vulnerability of diagnostic to battery aging. Within this framework, the recursive total least squares method with variant forgetting is exploited, for the first time, to mitigate the adverse effect of measurement disturbances, which contributes to an unbiased estimation of the ISC resistance. The proposed method is validated both theoretically and experimentally for high diagnostic accuracy as well as the strong robustness to battery degradation and disturbance. [ABSTRACT FROM AUTHOR]

• Signal-disturbance interface in model parameterization process is analyzed. • A separable nonlinear least squares problem is formulated. • A noise-tolerant method is proposed for unbiased model parameterization. • Efficient and recursive version of parameterization method is exploited. • The proposed method is easily extended for use in model-based management system. A well-parameterized battery model is prerequisite of the model-based estimation and control of lithium-ion battery (LIB). However, the unexpected yet inevitable noises may markedly discount the identification of model parameters in real applications. This paper focuses on the noise-immune and unbiased model parameter identification for LIB. The signal-disturbance interface in LIB model identification is firstly analyzed by reformulating an overdetermined nonlinear system, on the premise of a cautiously-designed instrumental vector estimator. The multi-variable identification is then solved in the framework of a separable nonlinear least squares (SNLS) problem via a novel two-step method combining least squares (LS) and variable projection algorithm (VPA), to co-estimate the noise variances and unbiased model parameters. A numerical solver is further exploited for the proposed LSVPA, giving rise to a recursive and computational efficient algorithmic architecture which is favorable for online applications. The proposed method is validated with both simulations and experiments in terms of the noise tolerance and the parameterization accuracy. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC) is a highly promising renewable energy conversion technology. However, durability issues have hindered their large-scale commercialization process. Performance degradation prediction is an essential component of PEMFC prognostics and health management and is critical for extending the service life of fuel cell. Given that, this paper proposes a novel data-driven prediction model that fuses multi-head self-attention (MHSA) mechanism and bi-directional long short-term memory (BiLSTM). This model can effectively capture different types of dependencies from large-scale high-dimensional data and achieve global information modeling. Specifically, the preprocessed historical voltage data and PEMFC system operating parameters are fed into the proposed prediction model. Where BiLSTM understands the contextual information and temporal dependencies in sequence data by calculating the hidden states in both forward and backward directions. MHSA captures the complex relationships and extracts key information in the input sequence by simultaneously learning multiple sets of attention weights between different locations. Finally, the proposed model is validated based on the health monitoring data under stationary and quasi-dynamic conditions. The validation results indicate that the proposed model can ensure absolute errors of less than 0.6 × 10−3 V for at least 71.9% of the prediction results under stationary and quasi-dynamic conditions (less than 1.2 × 10−3 V for at least 97.6% of prediction results). • A prediction model fusing BiLSTM network and MHSA mechanism is proposed. • MHSA mechanism is used to further extract the key information form the sequence. • Considered the impact of different dimensions of health monitoring data on PEMFC. • Quantified the potential of the MHSA mechanism in improving predictive accuracy. • The proposed model achieves accurate predictions under different current conditions. [ABSTRACT FROM AUTHOR]

Medium-frequency (MF) transformer has gained much popularity in power conversion systems. Temperature control is a paramount concern, as the unexpected high temperature declines the safety and life expectancy of transformer. The scrutiny of losses and thermal-fluid behavior are thereby critical for the design of MF transformers. This paper proposes a coupled, semi-numerical model for electromagnetic and thermal-fluid analysis of MF oil natural air natural (ONAN) transformer. An analytical model that is based on spatial distribution of flux density and AC factor is exploited to calculate the system losses, while the thermal-hydraulic behavior is modelled numerically leveraging the computational fluid dynamics (CFD) method. A close-loop iterative framework is formulated by coupling the analytical model-based electromagnetic analysis and CFD-based thermal-fluid analysis to address the temperature dependence. Experiments are performed on two transformer prototypes with different conductor types and physical geometries for validation purpose. Results suggest that the proposed model can accurately model the AC effects, losses, and the temperature rises at different system components. The proposed model is computationally more efficient than the full numerical method but it reserves accurate thermal-hydraulic characterization, thus it is promising for engineering utilization. [ABSTRACT FROM AUTHOR]

The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of an LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation’s alertness and numerical stability so as to achieve an accurate online adaption of model parameters. Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of an LIB. Simulation and experimental studies are performed to verify the feasibility of the proposed data–model fusion method. The proposed method is shown to effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high stability, and high accuracy. [ABSTRACT FROM AUTHOR]

Technical challenges facing determination of battery available power arise from its complicated nonlinear dynamics, input and output constraints, and inaccessible internal states. Available solutions often resorted to open-loop prediction with simplified battery models or linear control algorithms. To resolve these challenges simultaneously, this paper formulates an economic nonlinear model predictive control to forecast a battery's state-of-power. This algorithm is built upon a high-fidelity model that captures nonlinear coupled electrical and thermal dynamics of a lithium-ion battery. Constraints imposed on current, voltage, temperature, and state-of-charge are then taken into account in a systematic fashion. Illustrative results from several different tests over a wide range of conditions demonstrate that the proposed approach is capable of accurately predicting the power capability with the error less than 0.2% while protecting the battery from undesirable reactions. Furthermore, the effects of temperature constraints, prediction horizon, and model accuracy are quantitatively examined. The proposed power prediction algorithm is general and then can be equally applicable to different lithium-ion batteries and cell chemistries where proper mathematical models exist. [ABSTRACT FROM AUTHOR]

Fast charging strategies have gained an increasing interest toward the convenience of battery applications but may unduly degrade or damage the batteries. To harness these competing objectives, including safety, lifetime, and charging time, this paper proposes a health-aware fast charging strategy synthesized from electrochemical system modeling and advanced control theory. The battery charging problem is formulated in a linear time-varying model predictive control algorithm. In this algorithm, a control-oriented electrochemical–thermal model is developed to predict the system dynamics. Constraints are explicitly imposed on physically meaningful state variables to protect the battery from hazardous operations. A moving horizon estimation algorithm is employed to monitor battery internal state information. Illustrative results demonstrate that the proposed charging strategy is able to largely reduce the charging time from its benchmarks while ensuring the satisfaction of health-related constraints. [ABSTRACT FROM PUBLISHER]

Lithium-ion battery charging management has become an enabling technology towards a paradigm shift of electrified mobility. Fast charging is desired for convenience improvements but may excessively degrade battery's health or even cause safety issues. This paper proposes a novel algorithm to manage battery charging operations using a model-based control approach. Based on a fully coupled electrothermal model, the fast charging strategy is formulated as a linear-time-varying model predictive control problem, for the first time. Constraints are explicitly imposed to protect the battery from overcharging and overheating. To enable the state-feedback control, unmeasurable battery internal states including state-of-charge and core temperature are estimated via a nonlinear observer using noisy measurements of current, voltage, and surface temperature. Illustrative results demonstrate that the proposed approach is able to optimally balance time and temperature increase. In addition, it is shown from simulations that the model predictive control based charging algorithm appears promising for real-time implementation. [ABSTRACT FROM AUTHOR]

Electrical vehicles have the capability to lessen the severe threats of energy crisis and environment pollution. The Lithium ion battery as a promising solution for the energy storage in vehicular applications is briefly introduced in this paper. The adverse effects of improper temperature, including performance degradation, potential thermal runaway, temperature non-uniformity and low temperature performance are described afterwards. The thermal model, electrochemical model, equivalent circuit model and electrochemical/electrical-thermal coupling methods are also elaborated for the accurate battery modeling. More importantly, this review detailedly summarizes the progress on battery thermal management systems (BTMSs) including the air, liquid, boiling, heat pipe and solid-liquid phase change based strategies during recent years. Influence factors and development focus of different BTMSs are stated elaborately. Passive cooling systems utilizing the latent heat during the phase change process are more attractive options compared to the conventional single phase forced air and liquid cooling methods. However, there still exist some challenges to be addressed before commercializing. In addition, different methods could be combined to meet the requirements of various applications. The improvement of these existing BTMSs is supposed to be paid more attention to enhance Li-the performance and safety of Li-ion batteries. [ABSTRACT FROM AUTHOR]

Battery-related faults have become the most intractable problem hindering the further prosperity of fields like electric vehicle and grid energy storage. This paper is devoted to constructing a novel diagnostic framework for the faults in series battery packs, resorting to signal imaging and convolutional neural network (CNN) techniques. First, the voltage synchronicity between adjacent cells in a pack is quantified using the recursive correlation coefficient which can percept system anomalies sensitively. Then, reliant on the Gramian Angular Field (GAF) and Markov Transition Field (MTF) transformations, the correlation coefficient series is converted into pseudo images, the textures of which are full of informative details regarding system state. Finally, CNN models are employed to analyze the images for fault symptoms, thereby detecting fault occurrence, inferring fault type and evaluating fault grade. To obtain realistic dataset, different types and severities of faults are physically triggered on a li-ion battery pack. Experimental verification results indicate that the proposed framework can give accurate and reliable judgements on fault specifics, with the accuracy rates of fault type isolating and severity grading as 99.63% and 63.6% on GAF images, and as 99.75% and 58.7% on MTF images, respectively. • An improved correlation coefficient utilized to percept anomalies in series battery packs. • Signal imaging techniques employed to emphasize cross-temporal characteristics as image textures. • A CNN-based diagnostic framework proposed to infer fault type and evaluate fault grade. • Realistic dataset of different types and severities of faults to verify the framework. [ABSTRACT FROM AUTHOR]

• An enhanced version of soft actor critic algorithm is proposed to develop a smart fast charging strategy for the LIB. • The proposed strategy is performed within a cyber-physical system-based framework. • The proposed framework improves the convenience of strategy development in practical applications. • The proposed strategy is highly adaptive to the change of charging environment. • The stability of proposed DRL algorithm with respect to pattern of reward are discussed. Fast charging of lithium-ion battery (LIB) is critical for the further popularity of electric vehicles. However, the partial pursuit of high-power charging can violate the physical limits of LIB, and further cause unexpected side effects or even catastrophic safety issues. Motivated by this, this paper proposes a multi-state-constrained fast charging strategy for LIB, enabled by a novel deep reinforcement learning (DRL) technique. In particular, the SAC-Lagrange DRL is developed, for the first time, to train the fast-charging strategy assisted by an electro-thermal model for unmeasurable state perceiving. The proposed strategy is further performed within a cyber-physical system-based management framework, where the complicated training is carried out in the cloud, while the trained low-complexity policy is executed in the onboard controller to mitigate the risk of high computing burden. Hardware-in-Loop tests and practical charging experiments are carried out for validation. Results reveal that the proposed strategy can boost the charging speed remarkably while smartly avoid violating the electrical and thermal safety limits of LIB. Compared with the advanced DDPG-based and SAC-based strategies, the proposed one has improved the optimality and stability, and thus enjoys more guaranteed convenience and versatility in practical charging applications. [ABSTRACT FROM AUTHOR]

The National Grid in the U.K. proposed the firm frequency response (FFR) documents on recent developments regarding tendering options for active balancing mechanism units, specifically for batteries. However, frequent bipolar converting and instantaneous high power demand challenge battery lifetime and operation cost in the FFR. Combining a battery with a supercapacitor (SC) has several advantages, but the system cost may rise. Targeting the FFR service, this article presents a new variable voltage control within a semiactive battery SC hybrid scheme. In the proposed hybrid energy storage system, the dc bus voltage is controllable to improve the SC use ratio while reducing the converter's cost. A power management strategy integrating fuzzy logic with dynamic filtering method is proposed benefiting in broadening the filtering flexibility while reducing battery degradation. In addition, both the long-term simulations and scaled-down experiments are implemented to verify the proposed topology with the control strategy and power management by quantitative comparisons of battery degradations and SC use ratios. [ABSTRACT FROM AUTHOR]

• The water and thermal balance of the air-cooled fuel cell is researched. • The flooding degree of the kW-grade fuel cell is investigated. • The efficiency of the stack can be improved by 3-–5%. Air-cooled proton exchange membrane fuel cells (AC-PEMFC) have wide applications due to their simple structure, and high efficiency. However, AC-PEMFC also needs effective methods to further improve the dynamic response and output power in the kW-grade power. Thus, this paper analyzed the dynamic characteristics and stability of the designed kW-grade AC-PEMFC at the beginning. Then, the effect of purging and active short-circuit activation strategy (ASC) are studied to solve the flooding and Pt oxidation problems, respectively. The results are as follows: (1) The AC-PEMFC capability of water and thermal self-regulation is limited in high power. Thus, active water management strategies based on flooding degree need to be researched; (2) To improve performance consistency and stability of the system, the ASC has high effectiveness on the performance improvement and dynamic performance consistency due to Pt catalyst oxidation prevention, note that the operating efficiency of the stack can be effectively improved 3-5% by using ASC. These results provide a novel reference for portable power source design and unmanned aerial vehicle design to improve the performance of kW-grade AC-PEMFC. [ABSTRACT FROM AUTHOR]

Abstract: State of charge (SOC) estimation is a key issue for battery management since an accurate estimation method can ensure safe operation and prevent the over-charge/discharge of a battery. Traditionally, open circuit voltage (OCV) method is utilized to estimate the stack SOC and one open flow cell is needed in each battery stack [1,2]. In this paper, an alternative method, extended Kalman filter (EKF) method, is proposed for SOC estimation for VRBs. By measuring the stack terminal voltages and applied currents, SOC can be predicted with a state estimator instead of an additional open circuit flow cell. To implement EKF estimator, an electrical model is required for battery analysis. A thermal-dependent electrical circuit model is proposed to describe the charge/discharge characteristics of the VRB. Two scenarios are tested for the robustness of the EKF. For the lab testing scenarios, the filtered stack voltage tracks the experimental data despite the model errors. For the online operation, the simulated temperature rise is observed and the maximum SOC error is within 5.5%. It is concluded that EKF method is capable of accurately predicting SOC using stack terminal voltages and applied currents in the absence of an open flow cell for OCV measurement. [Copyright &y& Elsevier]

It is crucial to accurately calculate the cost function of the energy management strategy (EMS) of the hybrid powertrain to improve the hydrogen economy of the system. This paper proposes an EMS for fuel cell hybrid electric vehicles (FCHEV) based on improved dynamic programming (DP) and air supply optimization to improve economy and reliability. Taking the maximum net power output of the FC system as the target, the optimal oxygen excess ratio (OER) and cathode pressure of the FC system under different current densities are solved by using PSO. A velocity prediction method based on Bi-LSTM is developed to predict short-term velocity changes in real time. The DP algorithm is introduced and the EMS of the DP algorithm based on short-term velocity prediction is developed for real-time hybrid powertrain optimization and management. Based on the results of energy allocation and optimal gas supply conditions of FCs, the cost function of EMS is modified to reallocate the power of the FC system and battery. The results demonstrate that the proposed method achieves the lowest hydrogen consumption compared to the other two algorithms. Remarkably, it reduces the fuel cost by up to 8.85 % compared to the commonly used online DP algorithm. • An EMS for FCHEV is developed based on improved DP and air supply optimization. • Optimize the best working conditions of the air supply system under different current densities. • An EMS was designed based on DP optimization under short-term condition prediction. • The cost function of the real-time EMS is modified in real-time. [ABSTRACT FROM AUTHOR]