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一阶分析法 2

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2R-1C模型；嵌入式系统；参数估计；非迭代方法；二次型 1

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LED，颜色漂移，光通量衰减，流明衰减 1

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A brief review on key technologies in the battery management system of electric vehicles

Kailong LIU, Kang LI, Qiao PENG, Cheng ZHANG

《机械工程前沿（英文）》 2019年第14卷第1期页码 47-64 doi: 10.1007/s11465-018-0516-8

摘要： Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

关键词： battery management system battery modelling battery state estimation battery charging

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An operating state estimation model for integrated energy systems based on distributed solution

Dengji ZHOU, Shixi MA, Dawen HUANG, Huisheng ZHANG, Shilie WENG

《能源前沿（英文）》 2020年第14卷第4期页码 801-816 doi: 10.1007/s11708-020-0687-y

摘要： In view of the disadvantages of the traditional energy supply systems, such as separate planning, separate design, independent operating mode, and the increasingly prominent nonlinear coupling between various sub-systems, the production, transmission, storage and consumption of multiple energy sources are coordinated and optimized by the integrated energy system, which improves energy and infrastructure utilization, promotes renewable energy consumption, and ensures reliability of energy supply. In this paper, the mathematical model of the electricity-gas interconnected integrated energy system and its state estimation method are studied. First, considering the nonlinearity between measurement equations and state variables, a performance simulation model is proposed. Then, the state consistency equations and constraints of the coupling nodes for multiple energy sub-systems are established, and constraints are relaxed into the objective function to decouple the integrated energy system. Finally, a distributed state estimation framework is formed by combining the synchronous alternating direction multiplier method to achieve an efficient estimation of the state of the integrated energy system. A simulation model of an electricity-gas interconnected integrated energy system verifies the efficiency and accuracy of the state estimation method proposed in this paper. The results show that the average relative errors of voltage amplitude and node pressure estimated by the proposed distributed state estimation method are only 0.0132% and 0.0864%, much lower than the estimation error by using the Lagrangian relaxation method. Besides, compared with the centralized estimation method, the proposed distributed method saves 5.42 s of computation time. The proposed method is more accurate and efficient in energy allocation and utilization.

关键词： integrated energy system state estimation electricity-gas coupling energy system nonlinear coupling distributed solution

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一类基于改进Weibull分布模型的电力电缆寿命评估方法

张铁岩,王承民,孙秋野,张化光

《中国工程科学》 2008年第10卷第10期页码 42-46

摘要：

对电缆的剩余寿命进行评估是电缆寿命管理的关键环节，以往电缆寿命评估的方法中有Arrhenius方法和Weibull分布模型。对这两种方法分别进行分析，特别是确定Arrhenius模型中激活能的计算，以及应用Weibull分布模型对电缆寿命进行评估。最后以实际电缆的状态监测数据，对简化的Arrhenius模型进行仿真，说明所提出的方法是有效的。

关键词：电缆寿命评估 Arrhenius模型 Weibull分布

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ionbattery

《能源前沿（英文）》 doi: 10.1007/s11708-023-0891-7

摘要： As the intersection of disciplines deepens, the field of battery modeling is increasingly employing various artificial intelligence (AI) approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation. This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning (ML), one of the many branches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neural network (NN) methods in ML for lithium-ion battery SOH simulation and prediction. NN is one of the important branches of ML, in which the application of NNs such as backpropagation NN, convolutional NN, and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention. Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency, low energy consumption, high robustness, and scalable models. In the future, NN can make a greater contribution to lithium-ion battery management by, first, utilizing more field data to play a more practical role in health feature screening and model building, and second, by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent. The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science, reliability, stability, and robustness of lithium-ion battery management.

关键词： machine learning lithium-ion battery state of health neural network artificial intelligence

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Intelligent algorithm for optimal meter placement and bus voltage estimation in ring main distribution

L. RAMESH, N. CHAKRABORTY, S. P. CHOWDHURY

《能源前沿（英文）》 2012年第6卷第1期页码 47-56 doi: 10.1007/s11708-011-0159-5

摘要： The advancement in power distribution system poses a great challenge to power engineering researchers on how to best monitor and estimate the state of the distribution network. This paper is executed in two stage processes. The first stage is to identify the optimal location for installation of monitoring instrument with minimal investment cost. The second stage is to estimate the bus voltage magnitude, where real time measurement is conducted and measured through identified meter location which is more essential for decision making in distribution supervisory control and data acquisition system (DSCADA). The hybrid intelligent technique is applied to execute the above two algorithms. The algorithms are tested with institute of electrical and electronics engineers (IEEE) and Tamil Nadu electricity board (TNEB) benchmark systems. The simulated results proves that the swarm tuned artificial neural network (ANN) estimator is best suited for accurate estimation of voltage with different noise levels.

关键词： artificial intelligence power distribution control state estimation

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Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information

Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD

《机械工程前沿（英文）》 2015年第10卷第2期页码 154-167 doi: 10.1007/s11465-015-0336-z

摘要：

The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge (SOC) trajectory in plug-in hybrid electric vehicles (PHEVs). The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categorizing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm (AIA). The objective function of the optimizer is derived from a computationally efficient artificial neural network (ANN) which is trained by a database obtained from a high-fidelity model of the vehicle built in the Autonomie software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the baseline vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.

关键词： trip information preview intelligent transportation state-of-charge trajectory builder immune systems artificial neural network

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智能电网状态估计方法最新进展综述 Review Article

Gang WANG, Georgios B. GIANNAKIS, Jie CHEN, Jian SUN

《信息与电子工程前沿（英文）》 2019年第20卷第1期页码 4-17 doi: 10.1631/FITEE.1800590

摘要：随着大量不确定可再生能源注入、大规模工业和个体用户市场参与、恶意智能仪表数据篡改等，精确、快速、鲁棒的状态估计方法对未来智能电网系统变得尤为重要。然而，目前电力系统采用的数据采集与监视控制系统只能获取系统状态（即系统所有节点的电压相量）的非线性测量数据。最新智能电网状态估计研究正着力于解决非线性测量数据带给可扩展性状态估计方法建模和计算方面的挑战。为使读者更好理解该领域最新进展，本文综述了基于非线性最小二乘和最小绝对误差的智能电网状态估计方法。为更好比较不同状态估计方法性能，首先描述了智能电网状态估计问题的克拉美罗下界。针对网络攻击问题，引入新的电力系统测量数据攻击模型，并介绍相应鲁棒状态估计方法。最后，分析配电网系统状态估计最新研究进展和挑战。仿真实验验证了该状态估计方法和理论的有效性和优点。

关键词：状态估计；克拉美罗下界；可行解追逐；半正定松弛；近线性算法；复合优化；网络攻击；坏数据检测

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基于多模型融合驱动的锂离子动力电池荷电状态和容量联合估计研究 Article

熊瑞, 王榘, Weixiang Shen, 田金鹏, 穆浩

《工程（英文）》 2021年第7卷第10期页码 1471-1484 doi: 10.1016/j.eng.2020.10.022

摘要：

锂离子动力电池（LIB）已成为各种电动载运工具的首选电源系统，包括电动汽车、电动轮船、电动火车和电动飞机。在全气候全寿命周期运行的电动载运工具中，锂离子电池的能量管理需要实时准确估计电池的荷电状态（SOC）和容量。本文提出了一种多阶段模型融合算法可协同估计SOC和容量。首先，基于正态分布假设，利用模型在不同老化状态下的残差均值和方差计算权重，建立参数稳定的融合模型。其次，将具有预测性的微分增益引入比例-积分观测器（PIO）以提高收敛速度。再次，将多阶段融合模型与比例-积分-微分观测器（PIDO）结合，建立了一种融合算法，可实现复杂应用环境下SOC和容量的协同估计。然后，讨论了融合算法的收敛性和抗噪性能。最后，搭建硬件在环平台，验证了融合算法的性能。不同老化状态和温度下的验证结果表明，融合算法可以实现SOC和容量的高精度协同估计，误差分别在2%和3.3%以内。

关键词：荷电状态容量估计模型融合比例-积分-微分观测器硬件在环

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A survey of photon mapping state-of-the-art research and future challenges

Chun-meng KANG,Lu WAN,Yan-ning XU,Xiang-xu MENG

《信息与电子工程前沿（英文）》 2016年第17卷第3期页码 185-199 doi: 10.1631/FITEE.1500251

摘要： Global illumination is the core part of photo-realistic rendering. The photon mapping algorithm is an effective method for computing global illumination with its obvious advantage of caustic and color bleeding rendering. It is an active research field that has been developed over the past two decades. The deficiency of precise details and efficient rendering are still the main challenges of photon mapping. This report reviews recent work and classifies it into a set of categories including radiance estimation, photon relaxation, photon tracing, progressive photon mapping, and parallel methods. The goals of our report are giving readers an overall introduction to photon mapping and motivating further research to address the limitations of existing methods.

关键词： Global illumination Photon mapping Radiance estimation Photon relaxation Progressive photon mapping

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智能电网状态估计中用于提高数据完整性的超分辨率感知技术 Article

梁高琪, 刘国龙, 赵俊华, 刘艳丽, 顾津锦, 孙广中, 董朝阳

《工程（英文）》 2020年第6卷第7期页码 789-800 doi: 10.1016/j.eng.2020.06.006

摘要：然后提出了一种新颖的基于机器学习的SRP方法，即超分辨率状态估计网络（super resolution perception net for state estimation, SRPNSE）来提高状态估计的数据完整性

关键词：状态估计低频数据高频数据超分辨率感知数据完整性

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A framework for stochastic estimation of electric vehicle charging behavior for risk assessment of distribution

Salman HABIB, Muhammad Mansoor KHAN, Farukh ABBAS, Muhammad NUMAN, Yaqoob ALI, Houjun TANG, Xuhui YAN

《能源前沿（英文）》 2020年第14卷第2期页码 298-317 doi: 10.1007/s11708-019-0648-5

摘要： Power systems are being transformed to enhance the sustainability. This paper contributes to the knowledge regarding the operational process of future power networks by developing a realistic and stochastic charging model of electric vehicles (EVs).Large-scale integration of EVs into residential distribution networks (RDNs) is an evolving issue of paramount significance for utility operators. Unbalanced voltages prevent effective and reliable operation of RDNs. Diversified EV loads require a stochastic approach to predict EVs charging demand, consequently, a probabilistic model is developed to account several realistic aspects comprising charging time, battery capacity, driving mileage, state-of-charge, traveling frequency, charging power, and time-of-use mechanism under peak and off-peak charging strategies. An attempt is made to examine risks associated with RDNs by applying a stochastic model of EVs charging pattern. The output of EV stochastic model obtained from Monte-Carlo simulations is utilized to evaluate the power quality parameters of RDNs. The equipment capability of RDNs must be evaluated to determine the potential overloads. Performance specifications of RDNs including voltage unbalance factor, voltage behavior, domestic transformer limits and feeder losses are assessed in context to EV charging scenarios with various charging power levels at different penetration levels. Moreover, the impact assessment of EVs on RDNs is found to majorly rely on the type and location of a power network.

关键词： electric vehicles (EVs) residential distribution networks (RDNs) voltage unbalance factor (VUF) state-of charge (SOC) time-of-use (TOU)

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电池安全——从锂离子电池到固态电池

禹习谦, 陈汝颂, 甘露雨, 李泓, 陈立泉

《工程（英文）》 2023年第21卷第2期页码 9-14 doi: 10.1016/j.eng.2022.06.022

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一种针对测试误差、参数误差和负荷突变故障分析的多功能动态状态估计器 Article

Mehdi AHMADI JIRDEHI,Reza HEMMATI,Vahid ABBASI,Hedayat SABOORI

《信息与电子工程前沿（英文）》 2016年第17卷第11期页码 1218-1227 doi: 10.1631/FITEE.1500301

摘要：本文提出了一种基于卡尔曼滤波理论的动态状态估计新算法，可有效探测、识别并校正电力系统中的测试误差和支路参数误差。同时，该算法亦可成功探测和识别电力系统中的负荷突变。该方法在每段取样时间内，采用三种归一化向量对误差进行处理，包括归一化测量残差，归一化拉格朗日乘子，以及归一化新息向量。在IEEE14节点测试系统上对所提出的算法进行了可行性和效能验证，并通过对数值结果的呈示和讨论，说明了该方法的精确度。

关键词：动态状态估计器；卡尔曼滤波；测试误差；支路参数误差；负荷突变

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Mapping the trends and prospects of battery cathode materials based on patent landscape

《能源前沿（英文）》页码 822-832 doi: 10.1007/s11708-023-0900-x

摘要： Advancing portable electronics and electric vehicles is heavily dependent on the cutting-edge lithium-ion (Li-ion) battery technology, which is closely linked to the properties of cathode materials. Identifying trends and prospects of cathode materials based on patent analysis is considered a kernel to optimize and refine battery related markets. In this paper, a patent analysis is performed on 6 popular cathode materials by comprehensively considering performance comparison, development trend, annual installed capacity, technology life cycle, and distribution among regions and patent assignees. In the technology life cycle, the cathode materials majorly used in electric vehicle have entered maturity stage, while the lithium cobalt oxide (LCO) cathode that is widely used in portable electronics is still in the growth stage. In global patent distributions, China holds more than 50% of total patents. In the top 10 patent assignees of 6 cathode materials, 2 institutes are from China with the rest being Japan (6) and Republic of Korea (2), indicating that the technology of cathode materials in China is relatively scattered while cathode research is highly concentrated in Japan and Republic of Korea. Moreover, the patent distribution has to consider practical issues as well as the impacts of core patents. For example, the high cost discourages the intention of applying international patents. This paper is expected to stimulate battery research, understand technical layout of various countries, and probably forecast innovative technology breakthroughs.

关键词： patent analysis cathode batteries technology life cycle

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Powertrain control of a solar photovoltaic-battery powered hybrid electric vehicle

P. PADMAGIRISAN, V. SANKARANARAYANAN

《能源前沿（英文）》 2019年第13卷第2期页码 296-306 doi: 10.1007/s11708-018-0605-8

摘要： This paper proposes a powertrain controller for a solar photovoltaic battery powered hybrid electric vehicle (HEV). The main objective of the proposed controller is to ensure better battery management, load regulation, and maximum power extraction whenever possible from the photovoltaic panels. The powertrain controller consists of two levels of controllers named lower level controllers and a high-level control algorithm. The lower level controllers are designed to perform individual tasks such as maximum power point tracking, battery charging, and load regulation. The perturb and observe based maximum power point tracking algorithm is used for extracting maximum power from solar photovoltaic panels while the battery charging controller is designed using a PI controller. A high-level control algorithm is then designed to switch between the lower level controllers based on different operating conditions such as high state of charge, low state of charge, maximum battery current, and heavy load by respecting the constraints formulated. The developed algorithm is evaluated using theoretical simulation and experimental studies. The simulation and experimental results are presented to validate the proposed technique.

关键词： battery management system hybrid electric vehicles (HEVs) maximum power point tracking (MPPT) solar photovoltaic