This study introduced and evaluated a new Carbon Nanotube (CNT) sheet-based method for battery temperature management, aimed at enhancing the performance of Li-ion batteries in subzero environments. This method addressed critical challenges such as startup failures, capacity loss, and the poor performance of the Li-ion battery in extreme cold conditions, particularly for industrial applications like forklifts operating at temperatures as low as −30 °C. Without CNT heating, the battery performance dropped significantly in low-temperature environments. At −20 °C, the battery delivered only 63.4% of its capacity, with minimal self-heating. At −30 °C, it failed almost entirely, shutting down after just 45 s. In contrast, CNT heating greatly enhanced performance. The CNT sheet quickly warmed the battery to 0 °C—within 97 s at −20 °C and 141 s at −30 °C—allowing it to recover up to 90% of its capacity. These improvements resulted in enhanced capacity and energy output compared to batteries without CNT heating, which suffered from severe performance losses, including a negligible capacity and energy output under −30 °C. It can be concluded that the CNT sheet-based approach provides superior thermal conductivity, rapid heating, and exceptional energy conversion efficiency, enabling extended battery life and enhanced operational reliability in subzero environments. Its scalability and affordability position it as a transformative innovation for industrial applications reliant on efficient battery performance in extreme cold environments.

As the concern of climate change and environmental issues escalates, the demand for renewable and sustainable energy increases. The energy generation through wind turbines, Solar panel, and other methods of energy generation requires a reliable energy storage system to efficiently store the surplus energy for usage where energy generation would otherwise be unavailable. This paper introduces the development of a 3kWh Battery-super-capacitor Hybrid Energy Storage system, the outcome, and potential improvements for future development. It is built using lithium-iron-phosphate (LFP) batteries with prismatic lithium-ion capacitors (LIC). Additionally, the built-in battery management system (BMS) allows data visualization and provides the necessary protection for the system.

This paper introduces a phase change material (PCM) based heat sink (PCMHS) to reduce the idle and operating temperature of a direct current (DC)/DC converter in an underwater battery power system. The developed macro-encapsulated PCM enclosure features a dampening assembly that maximizes thermal contact and eliminates void space catered for volumetric expansion of the PCM during melting. The operating temperature of the DC/DC converter at steady-state (both idle and full-load cycle condition) is estimated via a validated thermal model. A percentage improvement of at least 27% can be achieved by PCMHS at different load cycle conditions tested. The PCM's volume expansion of approximately 6.4% corresponds to the measured cover movement of around 3 mm. The relationship between the state of expansion (or melt state) to solid-state of the PCM is also established.

Application of battery power systems increases in the marine and offshore industry. Most applications target to reduce the energy consumptions when the battery power system is wholly or partially used. The hardware and software of the battery power system design for underwater application are described. The testing of the battery power system is successfully performed in a water tank at low temperature of 4 degrees C that mimics subsea operating condition where most remotely-operated vehicle (ROV) operates. The average variation of state-of-charge for the 12-cell is approximately 5 percent after active cell balancing. The battery management system is capable of estimating the state-of-charge and using the data to perform the active cell balancing on each cell with different imbalanced state-of-charge values of at least 30 percent.

This paper introduces a pseudo three-dimensional electrochemical-thermal coupled battery model for a cylindrical Lithium Iron Phosphate battery. The model comprises a pseudo two-dimensional electrochemical cell model coupled with three-dimensional lumped thermal model. The cell is disassembled to obtain the physical dimensions of the cell components. The thermal characteristics of the cell are studied during the discharge process over a range of temperatures and discharge rates. The validity of the numerical model is demonstrated experimentally via a 26,650 cylindrical Lithium Iron Phosphate/graphite battery cylindrical cell. Instead of infrared thermal images, series of regression models are utilized to quantify the thermal behavior at various depth of discharge under various discharge rates. The results demonstrated that the battery cell performs differently at a lower ambient temperature and lower discharge rate where the exothermic reactions are milder.

A robust macro-encapsulation design of a low-temperature phase change material based thermal energy storage unit is presented. The developed macro-encapsulation container eliminates the need for the 20% air void space catered for the volumetric expansion of the phase change material during melting in conventional designs. A study was conducted to validate the effectiveness of the design on thermal conductivity with volumetric expansion on a 2-dimensional axis symmetrical model. The results from the numerical and experimental approaches came to a good agreement that heat transfer has improved. The thermal robustness of the proposed design was successfully demonstrated after multiple thermal cycles on the design.

Lithium-ion battery (LIB) power systems have been commonly used for energy storage in electric vehicles. However, it is quite challenging to implement a robust real-time fault diagnosis and protection scheme to ensure battery safety and performance. This paper presents a resilient framework for real-time fault diagnosis and protection in a battery-power system. Based on the proposed system structure, the self-initialization scheme for state-of-charge (SOC) estimation and the fault-diagnosis scheme were tested and implemented in an actual 12-cell series battery-pack prototype. The experimental results validated that the proposed system can estimate the SOC, diagnose the fault and provide necessary protection and self-recovery actions under the load profile for an electric vehicle.

This paper presents an integrated state-of-charge (SOC) estimation model and active cell balancing of a 12-cell lithium iron phosphate (LiFePO4) battery power system. The strong tracking cubature extended Kalman filter (STCEKF) gave an accurate SOC prediction compared to other Kalman-based filter algorithms. The proposed groupwise balancing of the multiple SOC exhibited a higher balancing speed and lower balancing loss than other cell balancing designs. The experimental results demonstrated the robustness and performance of the battery when subjected to current load profile of an electric vehicle under varying ambient temperature.

Lithium Iron Phosphate (LiFePO 4 ) batteries have obtained extensive interests for the high energy density, little contamination, and ready availability. To enhance the compatibility of the batteries in electrical systems, the accurate estimation of the state of charge (SOC) is remarkably significant. Conventionally, Kalman filter algorithm and its derivations can be utilized for SOC estimation. To obtain SOC values more precisely and faster, this paper proposes a modified strong tracking cubature Kalman filter (MSTCKF). The estimation algorithm robustness is strengthened with the adoption of eigenvalue decomposition, which ensures the positive definiteness and symmetry of the priori error covariance matrix. Corresponding simulations are provided to compare the SOC estimation accuracy of MSTCKF, strong tracking cubature Kalman filter (STCKF), cubature Kalman filter (CKF) and extended Kalman filter (EKF). By setting different initial SOC errors, the results show that MSTCKF averagely performs more accurate than STCKF, CKF, and EKF by 19.82%, 26.74%, and 46.63% respectively.

A computational efficient battery pack model with thermal consideration is essential for simulation prototyping before real-time embedded implementation. The proposed model provides a coupled equivalent circuit and convective thermal model to determine the state-of-charge (SOC) and temperature of the LiFePO4 battery working in a real environment. A cell balancing strategy applied to the proposed temperature-dependent battery model balanced the SOC of each cell to increase the lifespan of the battery. The simulation outputs are validated by a set of independent experimental data at a different temperature to ensure the model validity and reliability. The results show a root mean square (RMS) error of 1.5609 × 10−5 for the terminal voltage and the comparison between the simulation and experiment at various temperatures (from 5 °C to 45 °C) shows a maximum RMS error of 7.2078 × 10−5.

A study is conducted to examine the effects of void spaces of air in phase change material based thermal energy storage (PCM-TES) system. A thermal simulation and analysis on a 2d axis symmetrical model is performed to simulate the effect of phase change, buoyancy driven convection and heat transfer. Two models are compared, one with a 20% air space and the other without. Both models are consistent in PCM volume. Results obtained from the simulation are validated by experimental results and showed good agreement. The model with the void space demonstrated characteristics which resembles a sensible heat storage system rather than the constant temperature characteristics of a latent heat storage system. The significant volume taken up by the void space could be reduced and replaced with more phase change material (PCM); which will increase the heat storage capacity of the system. Hence, understanding the effects of void spaces will be paramount in the development of future PCM-TES system designs.

Unmanned underwater vehicles (UUV) are widely used for survey and mining of natural resources in the region of underwater and seabed in the last two decades. In underwater, vision and depth perception will be poor, thus navigating and maneuvering a remotely operated vehicles (ROVs) will not be an easy task. The umbilical cable might get entangled. In the event that the ROV loses power from its umbilical cord or an autonomous underwater vehicle (AUV) power failure, an emergency power system will be needed to power the critical equipment on the ROV or AUV to facilitate its recovery and to limit the loss of data. In this paper, a large format 2 KWh lithium iron phosphate (LiFePO4) battery stack power system is proposed for the emergency power system of the UUV. The LiFePO4 stacks are chosen due to their high energy density, modularity and ready availability. The proposed LiFePO4 battery system includes the design and development of a smart battery management system (BMS) with high efficiency active cell balancing technology and intelligent self-learning battery state of charge (SOC) estimation for the LiFePO4 battery. The proposed BMS will lead to better utilization of battery's potential capacity and maximize the cycle life of the battery. The battery system has a pressure-resistance enclosure to eliminate extra battery pressure chamber and associated risks, therefore increase the reliability of the power system amidst high pressures down to 3km of deep-water. (C) 2017 The Authors. Published by Elsevier Ltd.

设计了基于差分运放和光耦开关的硬件采样模块,以及基于LabVIEW的算法程序、数据采集及界面控制的软件处理模块的燃料电池单电池电压特性巡检系统.该系统精度高、抗干扰性强、测试电压路数多,而且能实现数据的实时采集、显示和保存,并对电压异常情况进行警示.本系统已成功应用于百瓦级阴极开放式燃料电池堆的测试,实验证明具有良好的测试精度与稳定性.

In order to improve life cycle and safety of underwater energy storage equipment,the research provides a smart battery management system (SBMS) for power lithium iron phosphate (LiFePO4) stack,and focuses on the cells equalization circuit.Two optimized schemes have been proposed for the Buck-Boost circuit based on inductors in order to improve performance of the equalizing time and the efficiency.The fuzzy current controller is used in system.A constant current charge-discharge experimental study is done on the underwater LiFePO4 stack and comparisons among the performances of three equalizing schemes are carried out.The optimized balancing scheme is implemented on a small electric vehicle,which verifies that the battery stack usage capacity has been improved.

常规LiFePO4动力电池组SOC(state of charge)估计方法难以同时满足复杂工况下SOC预测的可靠性与初值不敏感性,为解决该问题,提出一种针对电池组工况特性下的扩展卡尔曼滤波算法.该算法基于电池组工况放电特性,提取其特征参数并进行模式分类,根据在电池充放电时不同参数与区间,对卡尔曼滤波模型进行动态参数补偿,加快SOC向真值的收敛速度,并减少SOC估计误差,实现算法对SOC估计初值的不敏感性.最后使用美国机车工况测试UDDS标准模型,对实际采集的电池模型进行仿真实验,其结果验证了所提出的算法可行性和有效性.

An improved method of balancing circuit is presented considering the problem of balancing speed slow and high losses in Buck-Boost circuit for serially connected battery stack. Different balancing schemes are designed based on Li Fe PO4 battery stack in laboratory. The improved scheme control is integrated, focused on the effect on the improvement scheme to balancing speed and loss based on the battery practical model.The simulation and experiment results show that the optimized balancing control system decrease the balancing time and reduce the loss.

Safety is important in a lithium-ion battery power system. It is necessary to adopt an effective fault diagnosis method to keep the battery power system in the good working status. In this paper, Genetic Algorithm (GA) is integrated to build a single hidden layer Back-Propagation Neural Network (BPNN) for fault diagnosis. In the process of training the neural network, GA is used to initialize and optimize the connection weights and thresholds of the neural network. Several faults are detected by the proposed GA optimized fault diagnosis scheme. Simulation results show that the proposed fault diagnosis scheme provides satisfactory results.

Lithium Iron phosphate (LiFePO4) battery has obtained extensive attention of researchers for its high energy density, little contamination and ready availability. In this paper, different numbers of RC branches in the equivalent Thevenin circuit model are explored by comparing accuracy of curve fitting with in-house experimental data. Besides, battery system with 6 cells of second order equivalent circuit is modeled using Matlab/Simscape. A multirate strong tracking extended filter (MRSTEKF) is proposed by introducing the multirate control strategy and lifting technology into strong tracking extended Kalman filter (STEKF) to improve tracking stability and estimation precision of state of charge (SOC). Root mean square error (RMSE) is exploited to evaluate the performance of the algorithms of extended Kalman filter (EKF), STEKF and MRSTEKF. Simulation results demonstrate that the proposed MRSTEKF is faster than EKF and STEKF by 55.34% and 49.51%, and is more precise by 52.66% and 33.88%.

Load step response,single cell voltage distribution and surface temperature distribution tests were conducted on a home-made air breathe proton exchange membrane fuel cell(PEMFC).The experimental results show:regular exhaust at anode can maintain a stable operation of air breath PEMFC for a long time;under heavy load,the single cell voltage uniformity is low,which results in low voltage at both ends and high voltage at middle of the stack;there is a similar behavior between the stack temperature distribution and single cell voltage distribution.This research had certain guidance and reference value on improving the efficiency of the air-breath PEMFC.