http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Tiezhou Wu (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
-
Study on SOC Estimation Based on Circular Optimization for RBF Neural Network
Tiezhou Wu,Xiaomin Wu,Mengmeng Yang,Meng Luo 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.6
This paper proposed a circular particle swarm optimization least squares (CPSOLS) method which is consisted of the regularized least squares (RLS) method and the adaptive particle swarm optimization (APSO) algorithm. The RLS algorithm optimized the parameters of the RBF network, aiming at the phenomenon of RLS trapping in the local minimum, introduced the penalty factor and used the global optimization ability of the particle swarm optimization algorithm to make it out of the local minimum; simplified the structure of the RBF network and improved the generalization ability of the network. The APSO algorithm weakened the precocious converge phenomena of the particle swarm optimization algorithm, adopted the adaptive selection of the nonlinear dynamic inertia weight which is guided by the control factor of the battery external characteristic temperature parameters, optimized the link weight of the RBF network, improved the state of charge (SOC) estimation accuracy and real-time performance of the RBF network. Using the Arbin multifunctional battery test system BT2000 to collect the sample data of the battery external characteristic parameters, and using the sample data to train and optimize the RBF neural network, and estimate the SOC of the batteries. The results showed that the optimized RBF network improved the SOC estimation accuracy and real-time performance.
-
A Novel Active Current Disturbance Method Based on Discrete Wavelet Analysis
Wu Tiezhou,Xiong Jinlong,Wu Xiaomin,Luo Meng 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.12
Islanding detection method of active current disturbances in three-phase photovoltaic-to-grid system makes the common connection point (PCC) voltage appear high frequency components on account of load variation, the surge current occurrence and other factors. This high frequency component cause islands misjudgment. Using Db10 wavelet to detect and analysis PCC’s voltage high-frequency components on real-time based on the active current disturbances principle. And selecting an effective wavelet domain values as the voltage harmonic detection amount islanding detection. Islands simulation test is conducted in the case of the inverter output power and load input power to match, the test results show that this method can quickly detect islanding, and effectively prevent pseudo-island phenomenon of false positives.
-
SOC and SOH Joint Estimation of Lithium-Ion Battery Based on Improved Particle Filter Algorithm
Wu Tiezhou,Liu Sizhe,Wang Zhikun,Huang Yiheng 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.1
In order to improve the estimation accuracy of the state of charge (SOC) of lithium ion batteries and accurately estimate the state of health (SOH), this paper proposes an improved fi refl y algorithm to optimize particle fi lter algorithm to estimate the SOC and SOH of lithium batteries. Aiming at the particle degradation problem of the traditional sequential importance sampling in the standard particle fi lter algorithm, the improved fi refl y algorithm is used to replace the re-sampling of the traditional particle fi lter to suppress the particle depletion during the execution of the standard particle fi lter algorithm; Establishing a second-order RC equivalent circuit model and use the recursive least square method with forgetting factor to identify relevant battery parameters. The ohmic resistance is regarded as a characteristic parameter of the battery state of health (SOH), and the battery SOH is estimated on this basis. IFA-PF algorithms are used for the joint estimation of SOC and SOH. Through simulation verifi cation under DST conditions, the accuracy of using the improved particle fi lter algorithm to estimate battery SOC is within 2%, with an average error of 0.81%, and its SOH estimation accuracy remains at about 2%, with an average error of 1.34%, which proves the superiority of the joint estimation algorithm
-
Yuanzhong Xu,Bohan Hu,Tiezhou Wu,Tingyi Xiao 전력전자학회 2022 JOURNAL OF POWER ELECTRONICS Vol.22 No.2
This paper proposes a joint estimation scheme for the state of charge (SoC) and state of health (SoH) for lithium-ion batteries in electric vehicles. The estimation accuracy is improved from four aspects. First, to overcome the shortcomings of the electrochemical model and equivalent circuit model, the battery model is established by a fractional order (FO) model. Second, a genetic algorithm is used to identify the model parameters, realizing optimal parameter identification. Third, the FO adaptive extended Kalman filter-based SoC estimator is developed, and the innovation accuracy of the algorithm is improved by multi- innovation theory. Fourth, the joint estimation of SoC and SoH is formulated through a multi-timescale framework. The proposed model and method are verified through dynamic operating condition experiments, and the main results are as follows. (1) In the entire SoC range, the accuracy of the FO model is better than that of the integer order (IO) model. (2) The effectiveness of the optimized SoC estimation method is verified, and the estimation error can be controlled within 3%. (3) The effectiveness of the proposed joint estimation method in dynamic conditions is verified, and it shows high accuracy.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
