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Photo-supercapacitors (PSCs) have great potential as devices that enable both the conversion and storage of solar energy. However, it remains challenging to design new dual-acting electrodes providing high energy density and capacitance with self-powering capacity. Herein, we successfully prepared dual electrodes using ZnO-nanoflake and Ti3C2Tx-based MXene paper. By utilization of the MXene to the PSC device, capacitive contribution to the total capacitance increased ∼4 times compared to the bare ZnO-nanoflake-based PSC due to the surface redox reactions and electrically double layer formation. Maximum energy and power density of 57.3 μWhcm−2and 11.1 mWcm−2, respectively were obtained for ZnO/MXene//MXene-PSC@UV irradiation and at a 2.5 V. Areal capacitance of this device calculated from CV was as high as 151.5 mFcm−2. Moreover, the ZnO/MXene//MXene-PSC device was highly stable over 10,000 GCD cycles with 100 % CE and 98 % Cp-retention under daylight.

The effective and efficient preparation of highly electrocatalytic porous carbon electrodes is essential to enhance the competitiveness of vanadium redox flow battery (VRFB). In this work, electrochemically exfoliated graphene oxides (Exf–GOs) are prepared from graphite foil at large scale by a continuous Exf–GOs production system equipped with multi-electrodes and subsequent separation process. The Exf–GOs are deposited on the activated carbon paper (A−CP) substrate by a direct and simple infiltration–deposition approach. Collective electro-kinetics analysis applying cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) revealed that the resulting Exf–GO/A−CP electrodes exhibit two orders magnitude greater intrinsic electrocatalytic activities than the A−CP electrode for the VO2+/VO2+redox reactions. A trade-off between the Exf–GO deposition amount and the open pore volume through the Exf–GO/A−CP electrodes exist that has critical impacts on the overall electro-kinetic performance of these composite electrodes. Compared to the A−CP electrode, the Exf–GO/A−CP electrode provided the VRFB with more than two-fold enhancements in the energy storage capacity, and at least (10–15) % higher the voltage and energy efficiencies. The results collectively demonstrate the high catalytic activities of the Exf–GO/A−CP composite electrodes for VRFBs prepared in the effective, highly scalable, and process-efficient approach.

The expanding market of new energy vehicles has raised an urgent demand for battery safety. As a crucial component of pouch batteries, the performance of aluminum-plastic film directly impacts the overall safety of the battery. This paper conducts a macro-level study on the mechanical performance of aluminum-plastic film and presents a comprehensive modeling method for simulating the film's behavior. Since aluminum-plastic film is a thin membrane material, conventional methods for measuring material fracture parameters are not applicable. To address this issue, the paper designs a series of experiments using simulation techniques and obtains calibrated parameters for the ductile fracture criterion of the aluminum-plastic film. Consequently, a simulation model is developed to accurately represent the fracture characteristics. The simulation results effectively reflect the actual mechanical properties and fracture behavior of the aluminum-plastic film.

Large, reliable, and economically viable battery energy storage systems (BESSs) play a crucial role in electrifying the maritime industry. In this paper, we draw from the experiences of over 750 recent commercial marine BESS installations to bridge the gap between research findings and industrial needs in four key areas: (i) Decision-making for installations: We introduce a go/no-go-decision matrix for assessing the feasibility of installations in a maritime context. (ii) Safe and cost-effective installations: This study evaluates the risks and expenses associated with these BESS installations, including retrofitting a 500 kWh BESS (total costs: 1.3 million euros; 2600 euros per kWh), installing a 4.5 MWh BESS (5 million euros; 1100 euros per kWh), and an unsuccessful attempt to retrofit an 800 kWh BESS. (iii) Operation analysis: We delve into the operational outcomes of BESSs deployed on 47 offshore supply vessels (OSVs) (ranging from 452 to 1424 kWh) and a large 4.5 MWh BESS on a newly constructed cruise ship. The application of the equivalent full cycle (EFC) method reveals that the operational EFCs were notably lower than the designed EFCs. The proposed two new evaluation criteria assess the annual fuel saving resulting from BESS installed per kWh and per EFC. Over a two-year period, the 4.5 MWh BESS demonstrated fuel saving of 1–2 % as compared to the 5 % target. Addressing converter losses during low-power BESS operation modes necessitates further investigation. (iv) Further development: This study advocates for research aimed at enhancing safety measures, exploring onshore/offshore power supply and charging, optimizing multi-objective operations, and progressing towards zero emissions. The insights gathered in this paper can serve as a valuable resource for ship support ship owners and operators seeking to kick-off faster or to install more BESSs on their vessels and optimize their operational effectiveness.

The lead-acid battery as a direct current emergency power supply to the substation is subjected to long-term floating charge ageing, which is a special working condition. However, there is limited research on estimating the battery life for floating charge ageing. Compared to other parameters, Electrochemical Impedance Spectroscopy (EIS) is closely related to changes in the battery. This paper investigates a simple and effective method based on EIS to estimate the state of health (SOH) of lead-acid batteries used in substation backup power supply. In this paper, a high-temperature floating charge ageing experiment was carried out to simulate the ageing process of backup power supply in a substation and shorten the experimental time. The Pearson correlation coefficient of impedance from 0.01 Hz to 1000 Hz was calculated with the battery's SOH, and the impedances at three frequencies (19.860 Hz, 9.931 Hz, and 3.946 Hz) with high correlation coefficients were selected as indicators of battery SOH. The proposed health indicators and method were validated by batteries retired from the substations, and the results showed that the impedances selected could be used as indicators of lead-acid batteries, with an average estimation error of 2.723 %.

In the swiftly evolving micro-grid landscape, this paper explores the pivotal role of residential consumers and their active engagement. The primary focus is on revolutionizing customer management through the integration of smart homes, with an emphasis on facilitating demand response and bolstering operational efficiency. Also, the innovative Power Generation, Electricity Storages, and Energy Consumption in Smart Homes (PG–ES–ECSH) approach is introduced, meticulously designed for next-generation technologies, and delve into an smart home load control system. This system, employing Extreme Machine Learning (EML) and an imprecise reasoning framework, aims to enhance computational efficiency in managing diverse electrical energy sources and small-scale power generation appliances. The decision-making process of the framework incorporates real-time monitoring of environmental data, power generation, and battery storage. Our research significantly contributes to the evolution of smart homes and micro-grids by embracing technologies crucial for the future, such as machine learning and the Internet of Things (IoT). In this paper, the application of IoT and EML are used to enhance the connection between users in the microgrid and the learning rate of the proposed system, respectively. Emphasizing the burgeoning significance of energy management in smart homes, our study underscores the profound impact of energy resources on overall quality of life. With the increasing prevalence of smart homes, there is a growing demand for efficient energy utilization and management. Noteworthy is the PG–ES–ECSH method's impressive accuracy ratio of 96 % and a precision rate of 92 %. These findings highlight the imperative need for cost-effective energy management solutions, particularly in the face of escalating electricity consumption and the integration of novel power resources.

Accurate battery remaining useful life (RUL) prediction plays an important role in ensuring reliable operation of electric vehicles. In this paper, a hybrid model based on Bayesian optimization of deep convolutional neural network and long short-term memory neural network (BO-DCNN-LSTM) is proposed for battery RUL prediction. Feature extraction of raw charging characteristic curves is performed by the multilayer CNN and preliminary capacity prediction is performed by the multilayer LSTM. The model performance is explored with different training, validation and testing strategies and different prediction starting points. Validation using NASA battery aging data shows that the mean absolute error (MAE) and root mean square error (RMSE) of the RUL prediction are 0.0139 Ah and 0.0195 Ah, respectively, when the prediction starting point is the 50th cycle. In addition, this paper visualizes the process of how the Bayesian optimization (BO) algorithm searches for the global optimal combinations in the high-dimensional hyperparameter space and discusses the impact of these hyperparameters on the prediction, filling the gap in this part of the research.

This paper presents the Non-Circulating Power Flow (NCPF) enabled Dual Active Bridge (DAB) converter controller strategy. The proposed DAB controller eliminates the non-circulating power flow for all the four conventional modulation algorithms, i.e., Single Phase Shift (SPS), Extended Phase Shift (EPS), Dual Phase Shift (DPS) and Triple Phase Shift (TPS) modulation schemes. This leads to lesser current stress and power loss across the DAB converter switches due to reduced peak inverse current. The proposed controller strategy finds various applications, such as in a Solid-State Transformer (SST) enabled microgrid, or for Electric Vehicle (EV) Energy Storage Unit (ESU: battery, supercapacitor) integrations, etc. The proposed NCPF operation reduces battery (and supercapacitor) current stress by upto 71.8 %, and improves operative environment, thus fostering its health and lifecycle. In NCPF mode, if the rating of installed switches (current and voltage ratings) remains unchanged, its power transfer capability is marginally reduced by mere 10.0 %. In addition to this, the operating range (i.e., controlling range of phase shift (Φ) and modulation index (m)) remains unaffected. This paper summarizes the DAB NCPF operation in restricted power flow mode as a function of Φ and m through 3-D and 4-D graphs. Finally, working of NCPF DAB is tested via both simulation and hardware platforms. Comparative results are shown for conventional and proposed modulation schemes on hardware platform, to showcase the elimination of circulating current in both forward and reverse DAB operating modes. It is inferred that proposed controller is worthy of real-world implementations.

In order to address the issue of controller stability reduction caused by the fixed damping injection method, this paper provides a variable damping injection for the passive control strategy optimization method in the superconducting magnetic energy storage (SMES) system based on the capacitor-inductor-inductor-capacitor (CLLC) topology structure. First, the paper analyzes the CLLC-SMES system's charging and discharging principle and gives a port-controlled Hamiltonian-with-dissipation (PCHD) model for the passive controller. Then, the range of damping values is clarified, and the attention is paid to understanding how changes in damping parameters affect system stability on both the alternating current (AC) and direct current (DC) sides. After that, using the self-learning methodology of the back propagation (BP) neural network, a variety of damping parameters and optimization goals are added, and the BP neural network controllers on the AC and DC side are created to provide the variable damping injection for the passive system. Finally, the active and reactive power, the AC side and DC side voltage, and the superconducting magnet discharge current are computed and implemented in a simulation model of the CLLC-SMES passive control system based on the BP neural network. According to the simulation results, the system with the variable damping injection reaches the steady state earlier and has less overshoot than the system with the fixed damping injection. Additionally, the optimization method of the passive control strategy with the variable damping injection based on the BP neural network is capable of improving the power quality and system stability.

With the rapid development of the social economy, people pay more attention to food safety, and the construction of cold stores in China has also developed rapidly. At the same time, the energy problem is increasingly serious at present, the “dual carbon” goal has made energy conservation and emission reduction become the focus of attention. This paper systematically reviews the low-carbon technology applied in cold store from two perspectives: refrigeration technology and cold storage technology. This paper introduces the development process of conventional mechanical refrigeration in cold stores and the current clean energy refrigeration technology at the forefront of cold store development. It summarizes the future development trend of conventional cold store refrigeration and the advantages and disadvantages of clean energy refrigeration. Then, combined with the dual carbon background, the advantages of cold storage technology are presented from the perspective of energy saving, cost reduction, and temperature stability improvement. It focuses on the phase change cold storage technology commonly used in cold stores and summarizes the advantages of phase change cold store at the present stage and the configuration principle of phase change materials in cold stores. Finally, the future development of cold store is prospected.