Impedance-based Performance Analysis of Micropatterned Polymer Electrolyte Membrane Fuel Cells

Micropatterns applied to proton exchange membranes can improve the performance of polymer electrolyte fuel cells; however, the mechanism underlying this improvement is yet to be clarified. In this study, a patterned membrane electrode assembly (MEA) was compared with a flat one using electrochemical impedance spectroscopy and distribution of relaxation time analysis. The micropattern positively affects the oxygen reduction reaction by increasing the reaction area. However, simultaneously, the pattern negatively affects the gas diffusion because it lengthens the average oxygen transport path through the catalyst layer. In addition, the patterned MEA is more vulnerable to flooding, but performs better than the flat MEA in low-humidity conditions. Therefore, the composition, geometry, and operating conditions of the micropatterned MEA should be comprehensively optimized to achieve optimal performance.

A hybrid method for lithium-ion batteries state-of-charge estimation based on gated recurrent unit neural network and an adaptive unscented Kalman filter

Among the battery state of charge estimation methods, the Kalman-based filter algorithms are sensitive to the battery model while the neural network-based algorithms are decided by hyperparameters. In this paper, a hybrid approach composed of a gated recurrent unit neural network and an adaptive unscented Kalman filter method is proposed. A gated recurrent unit neural network is first used to acquire the nonlinear relationship between the battery state of charge and battery measurement signals, and then an adaptive unscented Kalman filter is utilized to filter out the output noise of the neural network to further improve estimation accuracy. The hybrid method avoids the establishment of accurate battery models and the search for optimal hyperparameters. The data of dynamical street test and US06 test are used as training dataset and validation dataset, respectively, while the data collected from the tests under federal urban driving schedules and Beijing driving cycle conditions are taken as testing dataset. As compared with some hybrid methods proposed in other literature, the hybrid method has the best estimation accuracy and generalization for various driving cycles at different ambient temperatures. The root mean square error and the mean absolute error all are less than 1.5%, and the maximum absolute error are less than 2%. In addition, it also exhibits powerful robustness against the abnormal values of the battery signals and can converge to the true value in just 5 seconds.

A novel heat generation acquisition method of cylindrical battery based on core and surface temperature measurements

Heat generation measurements of the lithium-ion battery are crucial for the design of the battery thermal management system. Most previous work uses the accelerating rate calorimeter (ARC) to test heat generation of batteries. However, utilizing ARC can only obtain heat generation of the battery operating under the adiabatic condition, deviating from common operation scenarios with heat dissipation. Besides, using ARC is difficult to measure heat generation of the high-rate operating battery because the battery temperature easily exceeds the maximum safety limit. To address these problems, we propose a novel method to obtain heat generation of cylindrical battery based on core and surface temperature measurements and select the 21700 cylindrical battery as the research object. Based on the method, total heat generation at 1C discharge rate under the natural convection air cooling condition in the environmental chamber is about 3.2 kJ, and the average heat generation rate is about 0.9 W. While these two results measured by ARC are about 2.2 kJ and 0.6 W. This gap also reflects that different battery temperature histories have significant impacts on heat generation. In addition, using our approach, total heat generation at 2C discharge rate measured in the environmental chamber is about 5.0 kJ, with the average heat generation rate being about 2.8 W. Heat generation results obtained by our method are approximate to the actual battery operation and have advantages in future applications.

Lithium-ion power battery grouping: A multisource data fusion based clustering approach and distributed deployment

Consistence of lithium-ion power battery significantly affects the life and safety of battery modules and packs. To improve the consistence, battery grouping is employed, assembling batteries with similar electrochemical characteristics to make up modules and packs. Therefore, grouping process boils down to unsupervised clustering problem. Current used grouping approaches include two aspects, static characteristics based and dynamic based. However, there are three problems. First, the common problem is underutilization of multi-source data. Second, for the static characteristics based, there is grouping failure over time. Third, for the dynamic characteristics based, there is high computational complexity. To solve these problems, we propose a distributed multisource data fusion based battery grouping approach. The proposed approach designs an effective network structure for multisource data fusion, and a self supervised scheme for feature extraction from both static and dynamic multisource data. We apply our approach on real battery modules and test state of health (SOH) after charging-discharging cycles. Experimental results indicate that the proposed scheme can increase SOH of modules by 3.89%, and reduce the inconsistence by 68.4%. Meanwhile, with the distributed deployment the time cost is reduced by 87.9% than the centralized scheme.

Facile synthesis and characterization of the Mn-MOF electrode material for flexible supercapacitors

Metal-organic frameworks (MOFs) due to their porosity and well-defined structures are considered to be very promising electrode materials for the construction of high-performance supercapacitor. In this paper, manganese-based metal organic frameworks (Mn-MOF) were prepared on the surface of carbon cloth (CC) by hydrothermal method. The morphology and structure of the electrode material were characterized by SEM, XRD, FT-IR, and XPS. Its electrochemical studies show that the Mn-MOF electrode materials exhibit low charge transfer resistance, the excellent specific capacitance of 433.5 mF·cm−2 in 1.0 M Na2SO4 aqueous solution at the current density of 0.8 mA·cm−2. It is noteworthy that the flexible electrode has excellent cycle stability and 105% capacitance retention even after 5000 cycles at a current density of 5 mA·cm−2. The high electrochemical performance of Mn-MOF/CC flexible electrode materials can be attributed to its three-dimensional porous structure.

Short-term prediction of remaining life for lithium-ion battery based on adaptive hybrid model with Long Short-Term Memory neural network and optimized particle filter

As an important energy storage device, lithium-ion batteries have vast applications in daily production and life. Therefore, the remaining useful life prediction of such batteries is of great significance, which can maintain the efficacy and reliability of the system powered by lithium-ion batteries. For predicting remaining useful life of lithium-ion batteries accurately, an adaptive hybrid battery model and an improved particle filter are developed. Firstly, the adaptive hybrid model is constructed, which is a combination of empirical model and long-short term memory neural network model such that it could characterize battery capacity degradation trend more effectively. In addition, the adaptive adjustment of the parameters for hybrid model is realized via optimization technique. Then, the beetle antennae search based particle filter is applied to update the battery states offline constructed by the proposed adaptive hybrid model, which can improve the estimation accuracy. Finally, remaining useful life short-term prediction is realized online based on long short-term memory neural network rolling prediction combined historical capacity with online measurements and latest offline states and model parameters. The battery data set published by NASA is used to verify the effectiveness of proposed strategy. The experimental results indicate that the proposed adaptive hybrid model can well represent the battery degradation characteristics, and have a higher accuracy compared with other models. The short-term remaining useful life prediction results have good performance with the errors of 1 cycle, 3 cycles, and 1 cycle, above results indicate proposed scheme has a good performance on short-term remaining useful life prediction.

A novel trigger mechanism for a dual-filter to improve the state-of-charge estimation of lithium-ion batteries

State-of-charge (SOC) estimation is essential in the energy management of electric vehicles. In the context of SOC estimation, a dual-filter based on the equivalent circuit model represents an important research direction. The trigger for parameter filter in a dual filter has a significant influence on the algorithm, despite which it has been studied scarcely. The present paper, therefore, discusses the types and characteristics of triggers reported in the literature and proposes a novel trigger mechanism for improving the accuracy and robustness of SOC estimation. The proposed mechanism is based on an open-loop model, which determines whether to trigger the parameter filter based on the model voltage error. In the present work, particle filter (PF) is used as the state filter and Kalman filter (KF) as the parameter filter. This dual filter is used as a carrier to compare the proposed trigger with three other triggers and single filter algorithms, including PF and unscented Kalman filter (UKF). According to the results, under different dynamic cycles, initial SOC values, and temperatures, the root-mean-square error of the SOC estimated using the proposed algorithm is at least 34.07% lower than the value estimated using other approaches. In terms of computation time, the value is 4.67%. Therefore, the superiority of the proposed mechanism is demonstrated.

Special Section on Mass and Charge Transport in Fuel Cells and Metal-ion Batteries

Given the increasing energy demand and carbon dioxide emission, countries all over the world are vigorously developing sustainable and clean energy. Fuel cells and metal-ion batteries that directly convert chemical energy into electric energy have been receiving ever-increasing attention for energy conversion and storage in several applications such as portable, mobile, and stationary applications. Nowadays, not understanding mass and charge transport in fuel cells and metal-ion batteries, which results in low performance and durability, are still challenges for their large-scale commercialization. For example, the insufficient interaction of catalyst/ionomer/reactant as a result of fuel cells lacking the ion-conducting, reactant-delivering, or proton-conducting pathways leads to the deactivated triple-phase boundary. Meanwhile, the metal-ions transport in the interface of solid active materials and electrolyte, and the charge transport including ions transport in the electrolyte, and electron transport in the solid phase, are not well known in advanced metal-ion batteries. An ideal electrode architecture that boosts the performance and durability of cells and batteries needs the electrode design to meet all the requirements of electrochemical kinetics and mass and charge transport characteristics.

Thermodynamic and dynamic analysis off a wind-powered off-grid industrial building integrated with solid oxide fuel cell and electrolyzer for energy management and storage

In this study, an islanded microgrid system is proposed that integrates identical stacks of solid oxide fuel cell and electrolyzer to achieve a thermally self-sustained energy storage system. Thermal management of the SOEC is achieved by use of heat from the SOFC with a heat exchanger network and control strategies. While the SOFC meets the building electricity demand and heat from its electrochemical reactions is transferred to the SOEC for endothermic heat and standby demands. Each component is physically modelled in Simulink and ultimately integrated at the system level for dynamic analyses. The current work simulates a system comprised of a wind farm in Palm Springs, CA coupled with the SOEC (for H2 generation), and an industrial building powered by the SOFC. Results from two-weeks of operation using measured building and wind data showed that despite fluctuating power profiles, average temperature and local temperature gradients of both the SOEC and SOFC were within desired tolerances. However, for severe conditions of wind power deficit, H2 had to be supplied from previous windy days' storage or imported.

Graphene Oxide-Induced Carbon Nanoporous Framework towards Advanced Composite Anode Material for Lithium-Ion Batteries

Porous structures in anode materials are of importance to accommodate volume dilation of active matters. In the present case, a carbon nanoporous framework is hydrothermally synthesized from glucose in the presence of graphene oxide (GO), together with in situ active Fe3O4 nanoparticles within it. The composite anode material has outstanding electrochemical performance, including high specific capacity, excellent cyclic stability and superior rate capability. The specific capacity stays at 830.8 mAhg−1 after 200 cycles at 1 A/g, equivalent to a high capacity retention of 88.7%. The findings provide valuable clues to tailor morphology of hydrothermally carbonized glucose for advanced composite anode materials of lithium-ion batteries.