A general battery model for PV system simulation

A battery model is proposed as a tool to simulate and optimize photovoltaic (PV) / storage systems. the normalized form of the equations with respect to the battery capacity allows us to generalize its use for any type and size of lead-acid batteries. The validity of this model to represent the battery voltage evolution during charge, overcharge and discharge processes and to predict the performance of solar systems under different operational conditions is analysed. Moreover, the battery efficiency losses are presented as a function of the upper regulation thresholds of the charge controllers and the size of the array and storage systems in a domestic application in the climate of Madrid.     

An analytical model for predicting the remaining battery capacity of lithium-ion batteries

Predicting the residual energy of the battery source that powers a portable electronic device is imperative in designing and applying an effective dynamic power management policy for the device. This paper starts up by showing that a 30% error in predicting the battery capacity of a lithium-ion battery can result in up to 20% performance degradation for a dynamic voltage and frequency scaling algorithm. Next, this paper presents a closed form analytical expression for predicting the remaining capacity of a lithium-ion battery. The proposed high-level model, which relies on online current and voltage measurements, correctly accounts for the temperature and cycle aging effects. The accuracy of the high-level model is validated by comparing it with DUALFOIL simulation results, demonstrating a maximum of 5% error between simulated and predicted data.     

Intelligent control battery equalization for series connected lithium-ion battery strings

An intelligent battery equalization scheme based on fuzzy logic control is presented to adaptively control the equalizing process of series-connected lithium-ion batteries. The proposed battery equalization scheme is a bidirectional dc-dc converter with energy transferring capacitor that can be used to design the bidirectional nondissipative equalizer for a battery balancing system. Furthermore, it can be designed as a ripple-free converter for improving the input current distortion of the battery charge supply power system. A fuzzy-logic-controlled strategy is constructed with a set of membership functions to prescribe the cells equalizing behavior within a safe equalizing region for rapid cell voltage balancing. The simulation and experimental results show the advantage of the predicted equalizing performance of the lithium-ion battery stacks. The proposed fuzzy logic control battery equalization controller can abridge the equalization time about 32%. The proposed method maintains safe operation during the charge/discharge state in each lithium-ion cell of the battery strings.     

大容量锂离子电池储能系统散热研究

基于锂离子动力电池生热模型仿真与实验的研究,对局部强化射流冷却散热系统开展了储能系统电池模块不同充放电倍率下的流热数值仿真,实现了电池模块储能系统在不同工况下热行为仿真。研究结果表明:局部强化射流冷却散热系统能够有效降低电池温度,但是随着充放电倍率的增大,电池模块温度升高,各单体电池之间的温差增大,不利于单体之间的协调性和电池模块性能的发挥,进而影响其安全性和可靠性。     

动力锂离子电池部分荷电和深度放电状态下的脉冲充电技术

针对动力锂离子电池的使用特点和其自身的充放电特性,以提高其充电效率,延长循环寿命为目的,设计制造了间歇-正负脉冲智能充电器,实现了正、负脉冲交替充电。模拟部分荷电和深度放电的实际使用特点,进行不同的充电模式循环对比实验,同时对失效单体电池进行了容量恢复实验。实验中对电池进行正负级电位测量分析,验证对其结构的恢复作用,并对修复结果稳定性进行验证。实验结果表明,间歇-正负脉冲充电提高了正负极的活性,抑制了浓差极化的形成,且修复结果稳定,从而有效提高了电池的循环寿命。     

Dynamic electric arc model for electronic circuit simulation

The authors present a physics based electric arc model which can simulate both static and dynamic characteristics of an arc load controlled by an electronic circuit. The proposed model was developed from modifications of the classical Cassie and Mayr equations. It has been test implemented in the Saber circuit simulator. There has been a considerable degree of correspondence between the simulated and experimental results     

Dynamic model of communicating hydrocephalus for surgery simulation

We propose a dynamic model of cerebrospinal fluid and intracranial pressure regulation. In this model, we investigate the coupling of biological parameters with a 3-D model, to improve the behavior of the brain in surgical simulators. The model was assessed by comparing the simulated ventricular enlargement with a patient case study of communicating hydrocephalus. In our model, cerebro-spinal fluid production-resorption system is coupled with a 3-D representation of the brain parenchyma. We introduce a new bi-phasic model of the brain (brain tissue and extracellular fluid) allowing for fluid exchange between the brain extracellular space and the venous system. The time evolution of ventricular pressure has been recorded on a symptomatic patient after closing the ventricular shunt. A finite element model has been built based on a computed tomography scan of this patient, and quantitative comparisons between experimental measures and simulated data are proposed.     

Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery

Electrical power system (EPS) is one of the most critical sub-systems of the spacecraft. Lithium-ion battery is the vital component is the EPS. Remaining useful life (RUL) prediction is an effective mean to evaluate the battery reliability. Autoregressive model (AR) and particle filter (PF) are two traditional approaches in battery prognosis. However, the parameters in a trained AR model cannot be updated which will cause the under-fitting in the long term prediction and further decrease the RUL prediction accuracy. On the other hand, the measurement function in the PF algorithm cannot be obtained in the long term prediction process. To address these two challenges, a hybrid method of IND-AR model and PF algorithm are proposed in this work. Compared with basic AR model, a nonlinear degradation factor and an iterative parameter updating method are utilized to improve the long term prediction performance. The capacity prediction results are applied as the measurement function for the PF algorithm. The nonlinear degradation factor can make the linear AR model suitable for nonlinear degradation estimation. And once the capacity is predicted, the state-space model in the PF is activated to obtain an optimized result. Optimized capacity prediction result of each cycle is utilized to re-train the regression model and update the parameters. The predictor keeps working iteratively until the capacity hit the failure threshold to calculate the RUL value. The uncertainty involved in the RUL prediction result is presented by PF algorithm as well. Experiments are conducted based on commercial lithium-ion batteries and real-applied satellite lithium-ion batteries. The results have high accuracy in capacity fade prediction and RUL prediction of the proposed method. The real applied lithium-ion battery can meet the requirement of spacecraft. All the experiments results show great potential of the proposed framework.     

碳纳米管在锂离子电池正极中的应用研究

以碳纳米管及Supper-p/KS-6作为正极导电剂分别制作了锂离子电池。利用SEM研究了电池电极的显微结构,并对电池的电化学性能进行了综合评价。结果表明,与Supper-p/KS-6导电剂相比,碳纳米管导电剂有效降低了电池内阻,显著提高了电池在大倍率情况下的性能,并改善了电池的循环性能:内阻从120 m降到了100 m,10.0C(C为放电倍率)及15.0C时的放电容量分别为0.5C时的90.4%和80.7%,500次循环后的容量保持率达89.0%。     

锂动力电池寿命预测研究进展

综述了国内外对于锂动力电池储存寿命和循环寿命预测模型的研究进展。阐述了以容量衰减、电阻增加与衰退为基础建立的寿命预测模型的研究成果。统一储存寿命和循环寿命预测,并综合考虑我国动力电池使用条件和外部环境情况,这对锂动力电池的应用具有现实意义。     

红外焦平面阵列成像动态仿真系统

红外热像仪的研制离不开各种图像处理算法的设计、仿真和优化;结合虚拟仪器技术和可编程多模式驱动技术,开发了红外焦平面阵列成像动态仿真系统软硬件平台;利用这一平台,通过图形化软件编程, 可进行多模式红外焦平面阵列的各种图像处理算法的动态仿真,实时验证其成像效果,及时进行修改和优化。工程应用实践表明：系统对盲元检测与补偿、非均匀性校正和直方图修正等算法的验证与优化取得了满意的效果;该系统为红外热像仪设计仿真、算法的适用性和实时性验证以及算法的优化提供了灵活实用的工具。     

Heuristic Kalman optimized particle filter for remaining useful life prediction of lithium-ion battery

Accurate prediction of the remaining useful life of a faulty component is important to the prognosis and health management of any engineering system. In recent times, the particle filter algorithm and several variants of it have been used as an effective method for this purpose. However, particle filter suffers from sample degeneracy and impoverishment. In this study, we introduce the Heuristic Kalman algorithm, a metaheuristic optimization approach, in combination with particle filtering to tackle sample degeneracy and impoverishment. Our proposed method is compared with the particle swarm optimized particle filtering technique, another popular metaheuristic approach for improvement of particle filtering. The prediction accuracy and precision of our proposed method is validated using several Lithium ion battery data sets from NASA® Ames research center.     

An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction

Lithium-ion rechargeable batteries are widely used as power sources for mobile phones, laptops and electric cars, and gradually extended to military communication, navigation, aviation, aerospace and other fields. Accurate remaining useful life (RUL) prediction of lithium-ion battery plays an important role in avoiding serious security and economic consequences caused by failure to supply required power levels. Thus, the RUL prediction for lithium-ion battery has become a critical task in engineering practices. With its superiority in handling nonlinear and non-Gaussian system behaviors, the particle filtering (PF) technique is widely used in the remaining life prediction. However, the choice of importance function and the degradation of diversity in sampling particles limit the estimation accuracy. This paper presents an improved PF algorithm, that is, the unscented particle filter (UPF) based on linear optimizing combination resampling (U-LOCR-PF) to improve the prediction accuracy. In one aspect, the unscented Kalman filter (UKF) is used to generate a proposal distribution as an importance function for particle filtering. In the other aspect, the linear optimizing combination resampling (LOCR) algorithm is used to overcome the particle diversity deficiency. It should be noted that the step coefficient K can affect the performance of LOCR algorithm, and the fuzzy inference system is applied to determine the value of step coefficient K. According to the analysis results, it can be seen that the proposed prognostic method shows higher accuracy in the RUL prediction of lithium-ion battery, compared with the existing PF-based and UPF-based prognostic methods.     

Prognostics of Li(NiMnCo)O2-based lithium-ion batteries using a novel battery degradation model

Some lithium-ion battery materials show two-phase degradation behavior with evident inflection points, such as lithium nickel manganese cobalt oxide (Li(NiMnCo)O₂ or NMC) cells. A model-based Bayesian approach is proposed in this paper to predict remaining useful life (RUL) for these types of batteries. First, a two-term logarithmic model is developed to capture the degradation trends of NMC batteries. By fitting the battery degradation data, it is experimentally demonstrated that the developed model is superior to existing empirical battery degradation models. A particle filtering–based prognostic method is then incorporated into the model to estimate the batteries' possible degradation trajectories. Correspondingly, the RUL values of NMC batteries are expressed in terms of probability density function. The effectiveness of the developed method is verified with our collected experimental data. The results indicate that the proposed prognostic method can achieve higher predictive accuracy than the existing two-term exponential model.     

基于MAX1758的智能锂离子电池充电器设计

MAX1758是Maxim公司生产的锂离子电池充电器,可实现智能充电,自动监测调节电流、电压、温度等参数,为锂离子电池充电器提供了一种新的安全、高效的设计方案.给出了硬件电路和软件流程.     

Dynamic simulation model of indoor wideband directional channels

Culled from existing channel measurements, a dynamic indoor wideband directional-channel simulator is constructed by incorporating the spatial-temporal properties as well as the time-varying nature of propagation environments. A two-state semi-Markov model is used to account for the births and deaths of scattering clusters, which represents an improvement over the conventional Markov model where the duration in each state is forced to follow an exponential distribution. The spatial-temporal variations of clusters within their lifespan are modeled based on the real-life measurement results. The ranges of angle of arrival (AOA), angle of departure (AOD), and time of arrival (TOA) for each cluster are partitioned into finite number of intervals using the deterministic simulation methods. Subsequently, the cluster properties at different intervals can be derived from the empirical findings in previous channel-measurement campaigns. The efficacy of the proposed simulator is demonstrated by applying it to characterize a time-varying multiple-input multiple-output (MIMO) channel.     

激光切割锂电池负极极片复合材料的数值模拟

为了探究负极极片复合材料的切割性质, 采用有限元模型, 对激光切割锂离子电池负极极片复合材料温度场进行数值模拟计算。由温度场分布取得了负极切缝宽度与切缝深度的尺寸大小, 从中研究激光功率、切割速率和光斑半径对负极表层材料切缝宽度与切缝深度的影响。结果表明, 负极表层切缝宽度随激光功率和光斑半径的增加而增大, 随切割速率增大而减小; 切缝深度随激光功率增加而增大, 随切割速率和光斑半径增大而减小。切割至中间铜箔后, 切缝深度变化速率趋缓, 负极材料的复合结构对切缝深度存在明显影响; 在功率为170W、光斑半径为47μm、切割速率变化至600mm/s左右时, 效果最为明显, 切割深度在该参量下达到60μm, 且突破这一阈值后增长速率得到明显提升直至极片完全切断。这一结果可为激光应用于锂离子电池极片复合材料切割提供参考。     

面密度对锂离子电池快充性能的影响研究

面密度是影响锂离子电池快充性能的主要因素之一。采用扫描电子显微镜、X射线衍射仪及粒度分析表征正负极材料形貌和结构,研究不同面密度镍钴锰酸锂/石墨锂离子电池的内阻、倍率性能、循环寿命和安全性能。结果表明,电池的内阻随着面密度的增加而增大,正极面密度从190 g/m~2提高到340 g/m~2,电池均呈现出良好的5C快速充放电性能和安全性能。正极面密度不超过280 g/m~2时,电池依然保持优异的10C快速充放电性能,容量达到1C倍率的93.4%以上。正极面密度为250 g/m~2的电池展示出最佳的5C倍率充放电寿命,2000次充放电后的容量保持率高达98.1%。而正极面密度为340 g/m~2的电池5C倍率充放电寿命衰减较快。