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模型与数据双驱动的锂电池状态精准估计 被引量:3
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作者 陈清炀 何映晖 +3 位作者 余官定 刘铭扬 徐翀 李振明 《储能科学与技术》 CAS CSCD 北大核心 2023年第1期209-217,共9页
针对电池荷电状态估计常用的模型驱动法与数据驱动法的缺点,本工作提出了一种模型与数据双驱动的锂电池状态精准估计算法。在建立经典二阶电池模型后,先使用扩展卡尔曼滤波器与无迹卡尔曼滤波器组成的双卡尔曼滤波器进行初步的锂电池系... 针对电池荷电状态估计常用的模型驱动法与数据驱动法的缺点,本工作提出了一种模型与数据双驱动的锂电池状态精准估计算法。在建立经典二阶电池模型后,先使用扩展卡尔曼滤波器与无迹卡尔曼滤波器组成的双卡尔曼滤波器进行初步的锂电池系统状态估测,再将初步的估算结果输入LSTM神经网络实现误差纠正,得到最终估测结果。本工作利用来自NASA PCoE的电池数据集对单驱动算法和双驱动算法分别进行了性能测试,结果表明双驱动法在降低了估算系统对数据依赖性的同时提高了估算精度以及算法鲁棒性,结合了两种单驱动法的优点并弥补了各自的缺点,得到了较为优异的结果。 展开更多
关键词 锂电池 电池荷电状态 电池健康状态 模型驱动法 数据驱动 扩展卡尔曼滤波 无迹卡尔曼滤波 LSTM神经网络
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Enterprise modeling method of calculation-independent model level
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作者 吕瑞峰 王刚 +1 位作者 问晓先 徐晓飞 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2009年第5期608-615,共8页
Aimed at deficiencies in the development and implementation of Enterprise Service Architecture (ESA) software, an ESA software developing mode based on Model Driven Architecture (MDA) is put forward. This mode inc... Aimed at deficiencies in the development and implementation of Enterprise Service Architecture (ESA) software, an ESA software developing mode based on Model Driven Architecture (MDA) is put forward. This mode includes a calculation-independent model ( CIM ), a platform-independent model ( PIM ), a platform-specific model (PSM) and a code level. Based on this mode, the modeling architecture of CIM level is presented. CIM here includes a global model, a process model, an information model and an organization model. The modeling elements of global model, process recta-model, information recta-model and organization meta-model are defined in detail and the relationship between them is described. The reflecting relationship between these models is established as well. 展开更多
关键词 enterprise model Model Driven Architecture computation-independent model Enterprise ServiceArchitecture
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Model-based design method of two-axis four-actuator fast steering mirror system 被引量:2
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作者 黑沫 张连超 +2 位作者 周擎坤 鲁亚飞 范大鹏 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第1期150-158,共9页
This work was focused on the model-based design method of two-axis four-actuator(TAFA) fast steering mirror system(FSM), in order to improve the design efficiency. The structure and operation principle commonality of ... This work was focused on the model-based design method of two-axis four-actuator(TAFA) fast steering mirror system(FSM), in order to improve the design efficiency. The structure and operation principle commonality of normal TAFA FSM were investigated. Based on the structure and the commonality, the conditions of single-axis idea, high-frequency resonance and coupling were modeled gradually. Combining these models, a holonomic system model was established to reflect and predict the performance of TAFA FSM. A model-based design method was proposed based on the holonomic system model. The design flow and design concept of the method were described. In accordance with the method, a TAFA FSM was designed. Simulations and experiments of the FSM were done, and the results of them were compared. The compared results indicate that the holonomic system model can well reflect and predict the performance of TAFA FSM. The bandwidth of TAFA FSM is more than 250 Hz; adjust time is less than 15 ms;overshoot is less than 8%; position accuracy is better than 10 μrad; the FSM prototype can satisfy the requirements. 展开更多
关键词 fast steering mirror system model-based design dynamic modeling
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One neural network approach for the surrogate turbulence model in transonic flows 被引量:2
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作者 Linyang Zhu Xuxiang Sun +1 位作者 Yilang Liu Weiwei Zhang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2022年第3期38-51,I0002,共15页
With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbul... With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective. 展开更多
关键词 Deep neural network Turbulence modeling TRANSONIC High Reynolds number
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