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RISS 인기검색어
- 검색키워드
- 저자 : Zi-hao Hu (검색결과 3 건)
- 무료
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- 유료
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A Novel Battery State of Health Estimation Method Based on Outlier Detection Algorithm
Chang-hao Piao,Zi-hao Hu,Ling Su,Jian-fei Zhao 대한전기학회 2016 Journal of Electrical Engineering & Technology Vol.11 No.6
A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.
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A Novel Battery State of Health Estimation Method Based on Outlier Detection Algorithm
Piao, Chang-hao,Hu, Zi-hao,Su, Ling,Zhao, Jian-fei The Korean Institute of Electrical Engineers 2016 Journal of Electrical Engineering & Technology Vol.11 No.6
A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.
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Li Zheng,Zhang Zi-Hao,Wang Jin-Song,Wang Kang-Tao,Guo Xiao-Qiang,Fan Dao-Hu,Sun He-Xu 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.5
Aiming at the problem that the load disturbance generated by the permanent magnet synchronous linear motor (PMSLM) control system will affect its control performance and the load thrust of the PMSLM cannot be observed well, this design proposes two different load disturbance observers, namely the traditional Luenberger observer and novel internal model control (IMC) observer. The IMC observer observes the load disturbance in the form of proportional and integral, which can effectively improve the identification convergence speed and the compensation control effect. While observing the load disturbance, the two observers in this design can also use the generated thrust current compensation as the feedforward compensation of the system, to achieve disturbance suppression and effectively improve the control performance and robustness of the system. The PMSLM control system based on two observers is built in the simulation platform and the superiority of the designed control system is verified. Through the comparison and verification of the two observers and the no-observer system, it can be concluded that the control strategy can accurately track the load disturbance in the PMSLM, simplify the overall structure of the system, and effectively enhance the anti-disturbance capability and dynamic response capability of the PMSLM.
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