• Energy Research
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A Y-type air-cooled structure has been proposed to improve the heat dissipation efficiency and temperature uniformity of battery thermal management systems (BTMSs) by reducing the flow path of air. By combining computational fluid dynamics (CFD) methods, the influence of the depths of the distribution and convergence plenums on the airflow velocity through battery cells was analyzed to improve heat dissipation efficiency. Adjusting the width of the first and ninth cooling channels can change the air velocity of these two channels, thereby improving the temperature uniformity of the BTMS. Further discussion was conducted regarding the influences of inlet and outlet depths. When the inlet width and outlet width were 20 mm, the maximum temperature and maximum temperature difference of the Y-type BTMS were 39.84 °C and 0.066 °C at a discharge rate of 2.5 °C, respectively; these temperatures were 1.537 °C (3.68%) and 0.059 °C (47.2%) lower than those of the T-type model. Meanwhile, the energy consumption of the sample also decreased by 13.1%. The results indicate that the heat dissipation performance of the proposed Y-type BTMS was improved, achieving excellent temperature uniformity, and the energy consumption was also reduced.

This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles.

The subject of this study was Si-C composites for lithium-ion battery (LIB) anodes obtained by carbonization of nanodispersed silicon with carbon monofluoride. To determine the possibility of increasing the degree of graphitization of nanodispersed carbon forming shells around the silicon particles at lower temperatures, nickel in the form of an alcoholic solution of Ni(NO3)2 was introduced as a catalyst into the pellets of the resulting composite. The XRD, Raman scattering and EDS methods were used to investigate changes both in the phase and elemental composition of materials resulting from the annealing of the Ni-containing Si-C composite over the temperature range of 500–1100 °C. It was found for the first time that nickel silicides that emerged during the annealing became catalysts and, at the same time, intermediate products, of cubic silicon carbide (β-SiC) synthesis, which reduced its temperature formation from ~1100 °C to ~800 °C. The same compounds had a catalytic effect on the carbon atom association, leading to an increase in the degree of its graphitization. The influence of changing the composition of the investigated material on the electrochemical characteristics of the obtained negative LIB electrodes was traced.

Lithium-ion batteries (LIBs) suffer from charging difficulties, capacity decay, and severe ageing in a low-temperature environment. In this work, we suggest a theoretical study and strategy for improving the low-temperature resistance of LiMn2O4(LMO) pouch cells, by introducing a photothermal conversion layer composed of copper and single-walled carbon nanotubes. A three-dimensional electrochemical–thermal coupling model for a lithium manganate battery is established, in which the photothermal conversion layer is attached on the surface of the cathode collector, and the effect of lug design is also discussed. The changes in the battery temperature field, and improvements in electrochemical performance before and after light preheating, are analyzed. The results show that, when the photothermal conversion film is applied, the LMO pouch cell’s temperature rises 2.7 °C/min in a −5 °C environment, and the surface-temperature averaging is improved. The concentration of lithium embedded in the anode is significantly increased, and the charging speed is enhanced by 20%. The batteries with a single-sided lug design exhibit better performance compared with those with a two-sided lug design. Validation of the presented model is performed, by comparing it with the experimental Panasonic UF653445ST commercial battery datasheet. This work provides theoretical guidance on improving the low-temperature performance of pouch cells, based on the photothermal conversion method.

A two-layer coordinated control strategy is proposed to solve the power allocation problem faced by electric–hydrogen hybrid energy storage systems (HESSs) when compensating for the fluctuating power of the DC microgrid. The upper-layer control strategy is the system-level control. Considering the energy storage margin of each energy storage system, fuzzy logic control (FLC) is used to make the initial power allocation between the battery energy storage system (BESS) and the multistack fuel cell system (MFCS). The lower-layer control strategy is the device-level control. Considering the individual differences and energy-storage margin differences of the single-stack fuel cell (FC) in an MFCS, FLC is used to make the initial power allocation of the FC. To improve the hydrogen-to-electricity conversion efficiency of the MFCS, a strategy for optimization by perturbation (OP) is used to adjust the power allocation of the FC. The output difference of the MFCS before and after the adjustment is compensated for by the BESS. The simulation and experiment results show that the mentioned coordinated control strategy can enable the HESS to achieve adaptive power allocation based on the energy storage margin and obtain an improvement in the hydrogen-to-electricity conversion efficiency of the MFCS.

The drying process of electrodes might seem to be a simple operation, but it has profound effects on the microstructure. Some unexpected changes can happen depending on the drying conditions. In prior work, we developed the multiphase-smoothed-particle (MPSP) model, which predicted a relative increase in the carbon additive and binder adjacent to the current collector during drying. This motivated us to undertake the present experimental investigation of the relationship between the drying rate and microstructure and transport properties for a typical anode and cathode. Specifically, the drying rate was controlled by means of temperature for both an NMC532 cathode and graphite anode. The material distribution was analyzed using a combination of cross-section SEM images and the energy-dispersive X-ray spectroscopy elemental maps. The binder concentration gradients were developed in both the in- and through-plane directions. The through-plane gradient is evident at a temperature higher than 150 °C, whereas the in-plane variations resulted at all drying temperatures. The measurements identified an optimum temperature (80 °C) that results in high electronic conductivity and low ionic resistivity due to a more uniform binder distribution. Trends in transport properties are not significantly altered by calendering, which highlights the importance of the drying rate itself on the assembled cell properties.

The intrinsic high safety of rechargeable aqueous batteries makes them particularly advantageous in the field of large-scale energy storage. Among them, rechargeable Zn–Mn batteries with high energy density, low cost, high discharge voltage, and nontoxicity have been considered as one of the most promising aqueous battery systems. However, exiting research on manganese-based cathode materials mainly focuses on diverse manganese oxides analogs, while reports on other promising manganese-based analogs with high performance are still limited. Herein, we report a MnCO3 cathode material, which can be manufactured on a large scale by a facile coprecipitation method. Interestingly, the MnCO3 can spontaneously be converted into MnO2 material during the charging process. The Zn–MnCO3 battery delivers a highly specific capacity (280 mAh g−1) even at the high current density of 50 mA g−1. It is also noteworthy that the battery with a high loading mass (7.2 mg cm−2) exhibits good reversibility of charge–discharge for 2000 cycles, showing a competitive cycling stability in aqueous systems.

This paper presents a novel sorting algorithm for modular multilevel inverters (MMCs) with integrated batteries, designed to ensure the uninterrupted operation of electric vehicles (EVs) under post-fault conditions. The proposed system structure consists of an MMC with four full-bridge modules per phase, where one module acts as a spinning reserve during normal operation. The algorithm addresses a single switch fault per phase by operating the faulted module in half-bridge mode, ensuring all batteries remain operational and maintaining full power output and battery capacity without any noticeable changes for the vehicle operator. Unlike conventional fault-tolerant strategies that often reduce power output or disable affected modules, the proposed algorithm isolates the faulty switch while preserving system output. This approach avoids derating and eliminates the need for immediate maintenance, enabling the EV to continue operating under fault conditions. Simulation and experimental results validate the effectiveness of the algorithm under a single switch fault scenario, demonstrating its ability to maintain autonomy and consistent power delivery. This work demonstrates a fault-tolerant MMC principle, offering a robust and scalable solution for enhancing reliability and user satisfaction in EV power systems.