Simplified quantitative analysis method and its application in the insitu synthesized copper-based azide chips

Copper-based azide(Cu(N_(3))2 or CuN_(3),CA)chips synthesized by in-situ azide reaction and utilized in miniaturized explosive systems has become a hot research topic in recent years.However,the advantages of in-situ synthesis method,including small size and low dosage,bring about difficulties in quantitative analysis and differences in ignition capabilities of CA chips.The aim of present work is to develop a simplified quantitative analysis method for accurate and safe analysis of components in CA chips to evaluate and investigate the corresponding ignition ability.In this work,Cu(N_(3))2 and CuN_(3)components in CA chips were separated through dissolution and distillation by utilizing the difference in solubility and corresponding content was obtained by measuring N_(3)-concentration through spectrophotometry.The spectrophotometry method was optimized by studying influencing factors and the recovery rate of different separation methods was studied,ensuring the accuracy and reproducibility of test results.The optimized method is linear in range from 1.0-25.0 mg/L,with a correlation coefficient R^(2)=0.9998,which meets the requirements of CA chips with a milligram-level content test.Compared with the existing ICP method,component analysis results of CA chips obtained by spectrophotometry are closer to real component content in samples and have satisfactory accuracy.Moreover,as its application in miniaturized explosive systems,the ignition ability of CA chips with different component contents for direct ink writing CL-20 and the corresponding mechanism was studied.This study provided a basis and idea for the design and performance evaluation of CA chips in miniaturized explosive systems.

A novel integrated microfluidic chip for on-demand electrostatic droplet charging and sorting

On-demand droplet sorting is extensively applied for the efficient manipulation and genome-wide analysis of individual cells.However,state-of-the-art microfluidic chips for droplet sorting still suffer from low sorting speeds,sample loss,and labor-intensive preparation procedures.Here,we demonstrate the development of a novel microfluidic chip that integrates droplet generation,on-demand electrostatic droplet charging,and high-throughput sorting.The charging electrode is a copper wire buried above the nozzle of the microchannel,and the deflecting electrode is the phosphate buffered saline in the microchannel,which greatly simplifies the structure and fabrication process of the chip.Moreover,this chip is capable of high-frequency droplet generation and sorting,with a frequency of 11.757 kHz in the drop state.The chip completes the selective charging process via electrostatic induction during droplet generation.On-demand charged microdroplets can arbitrarilymove to specific exit channels in a three-dimensional(3D)-deflected electric field,which can be controlled according to user requirements,and the flux of droplet deflection is thereby significantly enhanced.Furthermore,a lossless modification strategy is presented to improve the accuracy of droplet deflection or harvest rate from 97.49% to 99.38% by monitoring the frequency of droplet generation in real time and feeding it back to the charging signal.This chip has great potential for quantitative processing and analysis of single cells for elucidating cell-to-cell variations.

Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

To address the impact of wind-power fluctuations on the stability of power systems,we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system.First,we employ a strategy that restricts long-and short-term power output deviations to smoothen wind power fluctuations in real time.Second,we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise(SW-ICEEMDAN)strategy to achieve real-time decomposition of the energy storage power,facilitating internal power distribution within the hybrid energy storage system.Finally,we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components.Through simulation validation,we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power.Compared with traditional empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD),and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)decomposition strategies,the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal.Additionally,the state-of-charge of energy storage components fluctuates within a reasonable range,enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

Straw return increases crop production by improving soil organic carbon sequestration and soil aggregation in a long-term wheat-cotton cropping system

Straw return is a promising strategy for managing soil organic carbon(SOC)and improving yield stability.However,the optimal straw return strategy for sustainable crop production in the wheat(Triticum aestivum L.)-cotton(Gossypium hirsutum L.)cropping system remains uncertain.The objective of this study was to quantify the long-term(10 years)impact of carbon(C)input on SOC sequestration,soil aggregation and crop yields in a wheat-cotton cropping system in the Yangtze River Valley,China.Five treatments were arranged with a single-factor randomized design as follows:no straw return(Control),return of wheat straw only(Wt),return of cotton straw only(Ct),return of 50%wheat and 50%cotton straw(Wh-Ch)and return of 100%wheat and 100%cotton straw(Wt-Ct).In comparison to the Control,the SOC content increased by 8.4 to 20.2%under straw return.A significant linear positive correlation between SOC sequestration and C input(1.42-7.19 Mg ha^(−1)yr^(−1))(P<0.05)was detected.The percentages of aggregates of sizes>2 and 1-2 mm at the 0-20 cm soil depth were also significantly elevated under straw return,with the greatest increase of the aggregate stability in the Wt-Ct treatment(28.1%).The average wheat yields increased by 12.4-36.0%and cotton yields increased by 29.4-73.7%,and significantly linear positive correlations were also detected between C input and the yields of wheat and cotton.The average sustainable yield index(SYI)reached a maximum value of 0.69 when the C input was 7.08 Mg ha^(−1)yr^(−1),which was close to the maximum value(SYI of 0.69,C input of 7.19 Mg ha^(−1)yr^(-1))in the Wt-Ct treatment.Overall,the return of both wheat and cotton straw was the best strategy for improving SOC sequestration,soil aggregation,yields and their sustainability in the wheat-cotton rotation system.

Efficient stochastic parallel gradient descent training for on-chip optical processor

In recent years,space-division multiplexing(SDM)technology,which involves transmitting data information on multiple parallel channels for efficient capacity scaling,has been widely used in fiber and free-space optical communication sys-tems.To enable flexible data management and cope with the mixing between different channels,the integrated reconfig-urable optical processor is used for optical switching and mitigating the channel crosstalk.However,efficient online train-ing becomes intricate and challenging,particularly when dealing with a significant number of channels.Here we use the stochastic parallel gradient descent(SPGD)algorithm to configure the integrated optical processor,which has less com-putation than the traditional gradient descent(GD)algorithm.We design and fabricate a 6×6 on-chip optical processor on silicon platform to implement optical switching and descrambling assisted by the online training with the SPDG algorithm.Moreover,we apply the on-chip processor configured by the SPGD algorithm to optical communications for optical switching and efficiently mitigating the channel crosstalk in SDM systems.In comparison with the traditional GD al-gorithm,it is found that the SPGD algorithm features better performance especially when the scale of matrix is large,which means it has the potential to optimize large-scale optical matrix computation acceleration chips.

Exploring the mechanism of electroacupuncture at different acupoints on acute colitis rats based on JAK2/STAT3/SOCS1 signaling pathway

Objective:To investigate the mechanism of JAK2/STAT3/SOCS1 signaling pathway in electroacupuncture of different acupoints on acute colitis rats.Methods:36 SPF SD rats were randomly divided into 6 groups,with 6 rats in each group.The rat model of acute colitis was prepared by enema with glacial acetic acid solution.After the model was established,electroacupuncture was given to each acupoint group,with density wave,frequency 2Hz-50 Hz,intensity 2 mA,muscle tremor as the degree 20 min/time,1 time/day,for 3 consecutive days.Observe the general condition of rats;the pathological changes of colonic mucosa in rats were observed by HE method.The contents of serum interleukin-4(IL-4)and interleukin-8(IL-8)were detected by ELISA.Western blot and RT-PCR were used to detect the expression of JAK2,STAT3,SOCS1 protein and mRNA in rat colon tissue.Results:In contrast to the normal group,the overall condition of the model group was worse,the colonic mucosa was severely damaged,even necrotic,and the ulcer surface was obvious.The content of IL-4 in serum was obviously reduced,and the content of IL-8 was obviously go up(P<0.01).The protein content of JAK2,STAT3 and the expression of JAK2,STAT3 mRNA in colon tissue of rats were obviously go up,while the protein content of SOCS1 and the expression of SOCS1 mRNA were obviously reduced(P<0.01).In contrast to the model group,the general condition of rats in each acupoint group was significantly improved,the damage and necrosis of colonic mucosa and ulcer surface were obviously alleviated,the content of IL-4 in serum was obviously go up,and the content of IL-8 was significantly decreased(P<0.01).The protein content of JAK2,STAT3 and the expression of JAK2,STAT3 mRNA in colon tissue of rats were obviously reduced,while the protein content of SOCS1 and the expression of SOCS1 mRNA were obviously go up(P<0.05,P<0.01).Comparison of different acupoint groups,the colonic mucosal injury in the Zusanli group was significantly reduced,the content of serum IL-4 was significantly increased,and the content of IL-8 was significantly decreased(P<0.05,P<0.01).The protein content and mRNA expression of JAK2 and STAT3 in colon tissue were significantly down-regulated,while the protein content and mRNA expression of SOCS1 were significantly go up(P<0.05,P<0.01).Conclusion:Electroacupuncture at each acupoint can improve the damage of colonic mucosa and reduce the inflammatory response.The therapeutic effect of Zusanli(ST36)is better than that of Tianshu(ST25),Dachangshu(BL25)and Shangjuxu(ST37).The mechanism may be related to the regulation of JAK2/STAT3/SOCS1 signaling pathway related proteins and inflammatory cytokines IL-4 and IL-8.

Analysis of the Tragedy of Vila Socóin Cubatão (São Paulo, Brazil) Using the Haddon Matrix: A Comprehensive Examination of Socio-Environmental Factors and Risk Mitigation Strategies

The tragedy of Vila Socó epitomizes the socio-environmental repercussions of rapid industrialization in Cubatão. Beginning in the 1940s with the construction of the Anchieta highway, the city experienced an influx of migrants drawn by burgeoning industries, leading to unplanned urban growth and the emergence of vulnerable communities like Vila Socó. This article examines the interconnected factors—such as demographic shifts, inadequate planning, and regulatory oversight—that culminated in the devastating fire of 1984, claiming numerous lives and highlighting systemic failures. Utilizing the Haddon Matrix, this study dissects the Vila Socó incident, emphasizing the roles of human error, infrastructure integrity, and socio-economic disparities in disaster causation. By contextualizing the tragedy within Cubatão’s industrial trajectory, it underscores the urgent need for comprehensive risk assessment and proactive mitigation strategies in rapidly developing regions globally. Beyond its immediate focus, this work offers broader insights into the dynamics of industrial disasters and their socio-economic implications. As pipelines continue to play a vital role in global energy infrastructure, the lessons drawn from Vila Socó’s tragedy resonate deeply, emphasizing the imperative of robust safety protocols and accountable governance to prevent similar catastrophes in the future.

Review on the Usage of Synchronous and Asynchronous FIFOs in Digital Systems Design

First-Input-First-Output (FIFO) buffers are extensively used in contemporary digital processors and System-on-Chips (SoC). There are synchronous FIFOs and asycnrhonous FIFOs. And different sized FIFOs should be implemented in different ways. FIFOs are used not only for the pipeline design within a processor, for the inter-processor communication networks, for example Network-on-Chips (NoCs), but also for the peripherals and the clock domain crossing at the whole SoC level. In this paper, we review the interface, the circuit implementation, and the various usages of FIFOs in various levels of the digital design. We can find that the usage of FIFOs could greatly facilitate the signal storage, signal decoupling, signal transfer, power domain separation and power domain crossing in digital systems. We hope that more attentions are paid to the usages of synchronous and asynchronous FIFOs and more sophististicated usages are discovered by the digital design communities.

引入PID反馈的SHAEKF算法估算电池SOC

电池荷电状态(SOC)的估算精度是电动汽车电池组的重要指标。为提升SOC估算精度,在融合Sage-Husa扩展卡尔曼滤波(SHEKF)算法与自适应扩展卡尔曼滤波(AEKF)算法的基础上,增加比例积分微分(PID)反馈环节,形成改进算法。采用粒子群优化(PSO)算法对二阶RC等效电路模型进行参数辨识;用开源电池数据集对模型和算法进行实验和分析。改进的SHAEKF算法在电池动态应力测试(DST)、北京动态应力测试(BJDST)和美国联邦城市驾驶(FUDS)等工况下的平均估计误差都在1%以内,与单纯的融合算法SHAEKF算法相比,最大误差可减小5%。

穿石通痹汤通过调节GABRB1-SOCS1/JAK1/STAT3信号途径对CIA模型大鼠关节炎症损伤的影响

目的 探讨穿石通痹汤对胶原诱导性关节炎(Collagen II induced arthritis,CIA)模型大鼠关节滑膜免疫炎性损伤的疗效及作用机制。方法 SPF级雄性SD大鼠,体质量(200±10)g,采用大鼠尾根部注射牛CⅡ方法制备CIA模型,将成模大鼠按随机数字表法分为模型组、阳性药组、穿石通痹汤组,每组各6只,另取6只未造模大鼠作为正常组。正常组与模型组隔日予去离子水10 ml/kg灌胃,阳性药组予甲氨蝶呤注射液每周0.5 mg/kg腹腔注射,穿石通痹汤组按生药量11.2 g/kg隔日灌胃给药,连续干预4周后处死大鼠,采集血浆、膝关节滑膜及踝关节标本。以HE染色观察大鼠踝关节病理变化;采用Westernblot检测大鼠膝关节滑膜pJAK1、JAK1、p38、GABRB1的蛋白表达水平;ELISA检测血浆IL-6,STAT3,SOCS1表达水平;并采用UHPLC-Q-Exactive Orbitrap MS分析血浆代谢物成分。结果 与正常组比较,模型组大鼠踝关节滑膜明显增生、严重骨质破坏以及炎性细胞浸润;与模型组比较,穿石通痹汤组大鼠关节骨质破坏、关节病变整体评分明显改善。与正常组比较,模型组GABRB1、SOCS1蛋白表达水平显著降低(P<0.01),p38、pJAK1、IL-6,STAT3蛋白表达水平明显升高(P<0.01,P<0.05);与模型组比较,穿石通痹汤组及阳性药组GABRB1、SOCS1蛋白表达水平均显著上调(P<0.01,P<0.05),并明显抑制p38、pJAK1、IL-6,STAT3的蛋白表达(P<0.01),且穿石通痹汤组对GABRB1的上调作用及pJAK1抑制作用显著优于阳性药组(P<0.01);血浆代谢物分析发现甘油磷脂代谢、花生四烯酸代谢、鞘脂代谢等代谢途径及其中50个代谢产物与本病发生密切相关。结论 穿石通痹汤能有效改善CIA模型大鼠关节滑膜免疫炎性代谢性损伤,其机制可能与调节GABRB1-SOCS1/JAK1/STAT3信号途径,调控脂质代谢途径相关。

基于AR-ECM平均差异模型的串联电池组SOC、容量多尺度联合估计方法

考虑电池单体老化差异所致的电池组不一致性,针对串联电池组荷电状态(state of charge,SOC)、容量估计问题,提出一种基于自回归等效电路模型(autoregression equivalent circuit model,AR-ECM)的平均差异模型(mean-difference model,MDM)。基于此模型,提出串联电池组SOC、容量多尺度联合估计算法。该算法由2个部分组成,一是基于AR-ECM的MDM及差异化模型参数辨识策略:条件辨识策略和定频分组辨识策略;二是基于多时间尺度H无穷滤波(multi-timescale H infinity filter,Mts-HIF)的电池组SOC、容量联合估计算法。通过将所提出MDM中的自回归平均模型(autoregression mean model,AR-MM)与传统MDM中的n阶RC平均模型(nRC mean model,nRC-MM)比较,结果表明所提出的AR-MM在复杂运行工况下具有更优的动态跟随性能。依据最小化信息量准则(akaike information criterion,AIC),AR-MM具有更优的复杂度与精度的权衡。通过与基于多时间尺度扩展卡尔曼滤波(multi-timescale extended Kalman filter,Mts-EKF)联合状态估计算法比较,结果表明所提出的Mts-HIF状态估计算法具有更优的鲁棒性、精度和收敛速度。

基于WMIAEKF的锂离子电池SOC与容量联合估算

精确的荷电状态(SOC)估算对可靠的电池管理系统来说十分关键。基于二阶等效电路模型,提出了一种加权多新息自适应扩展卡尔曼滤波(WMIAEKF)算法,该算法可以解决传统多新息算法中误差累积的问题,从而提高SOC估算精度。实验仿真结果表明,所提算法比传统的自适应扩展卡尔曼(AEKF)以及多新息自适应扩展卡尔曼(MIAEKF)精度要高,最大误差控制在1.15%以内。此外,基于该算法提出了一种改进的多时间尺度双卡尔曼滤波算法,其中,WMIAEKF用于SOC估算,AEKF用于容量估算,两者结合对电池的SOC和容量进行实时的联合估算。所提算法能够对电池SOC以及容量进行较精确的估计,在新欧洲行驶工况(NEDC)下,SOC估算误差始终控制在1.2%,并且在面对错误容量初始值时也能保持较好的鲁棒性。

基于LSTM-Attention的锂电池SoC预测

电池荷电状态(Stateof Charge,SoC)对新能源汽车安全行驶非常重要。文章提出一种基于长短期记忆网络(Long Short-Term Memory,LSTM)和注意力机制(Attention)的锂电池SoC预测方法。该方法将电池放电电流、电压、温度等电池工作历史参数作为输入,通过在LSTM神经网络中训练,并在LSTM神经网络中引入注意力机制,聚焦对当前SoC影响更大的历史参数,实现更为精确的锂电池SoC预测。实验证明,本方法比LSTM神经网络具有更好的预测性能,有较好的应用前景。

System-on-a-Chip (SoC) Based Hardware Acceleration for Video Codec

Nowadays, from home monitoring to large airport security, a lot of digital video surveillance systems have been used. Digital surveillance system usually requires streaming video processing abilities. As an advanced video coding method, H.264 is introduced to reduce the large video data dramatically (usually by 70X or more). However, computational overhead occurs when coding and decoding H.264 video. In this paper, a System-on-a-Chip (SoC) based hardware acceleration solution for video codec is proposed, which can also be used for other software applications. The characteristics of the video codec are analyzed by using the profiling tool. The Hadamard function, which is the bottleneck of H.264, is identified not only by execution time but also another two attributes, such as cycle per loop and loop round. The Co-processor approach is applied to accelerate the Hadamard function by transforming it to hardware. Performance improvement, resource costs and energy consumption are compared and analyzed. Experimental results indicate that 76.5% energy deduction and 8.09X speedup can be reached after balancing these three key factors.

基于ASSA-RBF联合算法的三元锂离子电池SOC估计

准确估计三元锂电池的荷电状态(SOC)是保障电动汽车安全稳定运行的基础。针对传统BP神经网络估计精度不高,而RBF神经网络也容易陷入局部最优的问题,提出一种基于自适应麻雀搜索算法与RBF神经网络联合的三元锂电池SOC估计方法。首先,对标准麻雀搜索算法进行改进,采用精英混沌反向机制初始化麻雀种群,采用柯西-高斯变异策略优化麻雀种群中跟随者位置更新公式;然后,使用改进后的麻雀搜索算法对RBF神经网络的初始权值和宽度参数进行寻优,以提升算法对SOC的估计精度;最后,基于三元锂电池的充放电实验数据进行模型验证。结果表明,动态应力测试工况下,所提联合算法模型SOC估计均方根误差为0.694%,平均百分比误差为3.15%,能很好的应用于三元锂电池SOC估计。

基于AFEKF的锂离子电池SOC估算方法

针对利用扩展卡尔曼滤波算法估算锂电池荷电状态时,由于历史数据影响易产生累积误差的问题,提出了一种基于自适应渐消扩展卡尔曼的SOC估算方法。选用Thevenin等效模型并用递推最小二乘法进行电池参数辨识,通过将自适应渐消因子引入EKF算法中,抑制历史数据对当前状态估算的影响,完成锂电池SOC估算。结果表明:AFEKF算法在递推20次时可有效收敛,具有较好鲁棒性,估算SOC的平均误差为1.03%,误差均方根为1.21%,平均运行时间为1.476 s,可以较好地模拟电池的动静态特性。

不同温度下的基于BPNN-AUKF的新型自动水下航行器SOC估计器

本研究提出了一种基于反向传播神经网络(BPNN)和自适应无迹卡尔曼滤波器(AUKF)的SOC(state of charge)估计方法。首先针对电池SOC与端电压之间在不同温度下的关系,研究设计了一系列温度补偿策略,以提高在低温、低SOC条件下的估计精度。其次,利用反向传播神经网络(BPNN)建立了一个耦合了温度补偿策略的电池模型。这个模型能更好地适应低温和低SOC条件下的电池状态变化,提高了SOC估计的准确性。最后,基于BPNN电池模型建立了BPNN-AUKF的SOC估计框架,通过利用测量值与测量预测值之间的信息和残差序列,对系统过程和测量噪声协方差进行估计修正。通过实验验证,发现该方法在低温环境下具有明显优势,相比传统方法能够更准确地估计电池的SOC,且具备较好的泛化能力。这种基于BPNN-AUKF方法的SOC估计器不仅适用于自主无人潜水器(AUV),而且对于其他在复杂环境中工作的车辆也具有广泛的应用价值。

CRISPR/Cas9技术高效制备山羊SOCS2基因编辑胚胎

旨在设计、筛选高效编辑山羊胚胎生长负调控因子SOCS2基因的sgRNA,为通过SOCS2基因编辑创制快长型山羊奠定技术基础。本研究利用在线网站对山羊SOCS2基因SH2结构域保守序列设计2条sgRNA,构建表达载体,体外转录获得sgRNA及Cas9 mRNA;体外检测sgRNA+Cas9蛋白切割靶DNA的效率;在此基础上将屠宰场山羊卵巢分离培养的442个孤雌胚胎进行分组,2个试验组显微注射sgRNA和Cas9 mRNA混合物,对照组注射超纯水,以单个囊胚全基因组DNA扩增产物为模板,PCR扩增靶区域并测序鉴定,发生编辑的样本进行T-A克隆测序检测编辑形式;根据在线网站预测,每条sgRNA选择5个错配数最少的潜在脱靶位点进行脱靶检测。结果表明,在SOCS2基因83和96号氨基酸密码子附近区域获得2条sgRNA并成功构建表达载体,体外转录获得高质量sgRNA和Cas 9 mRNA;两条sgRNA均可引导Cas9蛋白在体外完全切割靶DNA;SOCS2-sg-83对胚胎的编辑效率为94.1%,160个T-A克隆中有152个在靶位点出现插入、缺失或替换,概率为95.0%,其中81.3%发生了移码突变,9.4%的克隆缺失83号氨基酸密码子,累计90.6%的克隆实现了SOCS2基因功能性敲除;SOCS2-sg-96对胚胎的编辑效率为50.0%,80个T-A克隆中有70个在靶位点出现插入、缺失或替换,概率为87.5%,移码突变概率为75.0%,5.0%的克隆缺失了96号氨基酸密码子,累计80.0%的克隆实现了SOCS2基因功能性敲除。另外,在所有已编辑胚胎中均未发现脱靶。综上,SOCS2-sg-83可实现山羊胚胎SOCS2基因的高效、精准编辑和敲除,为高效制备SOCS2基因敲除山羊,培育快长型肉用山羊奠定了技术基础。

基于MIAEKF的多温度下锂电池SOC估计

随着锂电池在电动车辆中的广泛应用,准确估计电池荷电状态(SOC)对于电池的安全性和使用性能至关重要。然而,变化温度环境条件下,传统Kalman估计方法降低电池SOC估计准确性。为解决上述问题,本研究提出了基于多新息自适应扩展卡尔曼滤波(MIAEKF)的SOC估计方法。首先,基于多组不同温度下测试实验获取的电池数据,利用带遗忘因子的最小二乘法(FFRLS)进行参数辨识,获得了多温度下不同SOC阶段的电池参数。其次,利用函数拟合的方法建立了以SOC、温度为自变量,电池参数为因变量的函数模型,用于描述电池参数的动态行为。最后,在此基础上引入了MIAEKF算法,该算法结合了自适应和多新息的思想,通过滑动窗口自适应调整过程噪声与测量噪声协方差,并将窗口的新息均值作为窗口最新时刻后验估计的新息来增加误差新息。选择合适的窗口长度与新息均值系数能够有效提升SOC估计精度。实验数据验证的结果表明,基于MIAEKF的SOC估计方法在相同条件下相较于传统的扩展卡尔曼滤波(EKF)、自适应扩展卡尔曼滤波(AEKF)表现出更高的估计精度和稳定性。在多温度下,通过引入电池参数函数模型,MIAEKF能够自适应多个温度下的SOC估计,并且估计误差均在±1%以内。

基于GA-ELM的锂电池SOC估计及主动均衡

锂电池的状态估计和主动均衡是提高电池性能和延长使用寿命的关键技术,针对参数模型的荷电状态(State of charge,SOC)估计方法忽略电动汽车实际工况而导致的估计偏差较大的问题,提出一种基于遗传算法的极限学习机(GA-ELM)神经网络算法来估计电池的荷电状态SOC,通过遗传算法优化了ELM的参数,提高估计精度和泛化能力,并在UDDS工况数据下进行训练与测试。同时采用双向Buck-Boost均衡拓扑结构,该拓扑结构能够快速实现电池间的能量传递,同时又降低了传递路径的复杂性。通过遗传算法的极限学习机估计出的SOC作为均衡变量,利用Matlab/Simulink仿真平台进行试验。结果表明,提出的GA-ELM神经网络平均误差为0.15%,而传统的ELM神经网络平均误差为0.56%,因此提出的神经网络能够更精确地估计SOC;同时电池组之间能够快速完成能量均衡,证明了所提方案的可行性。