Traditional thermal power units are continuously replaced by renewable energies,of which fluctuations and intermittence impose pressure on the frequency stability of the power system.Electrolytic aluminum load(EAL)acc...Traditional thermal power units are continuously replaced by renewable energies,of which fluctuations and intermittence impose pressure on the frequency stability of the power system.Electrolytic aluminum load(EAL)accounts for large amount of the local electric loads in some areas.The participation of EAL in local frequency control has huge application prospects.However,the controller design of EAL is difficult due to the measurement noise of the system frequency and the nonlinear dynamics of the EAL’s electric power consumption.Focusing on this problem,this paper proposes a control strategy for EAL to participate in the frequency control.For the controller design of the EAL system,the system frequency response model is established and the EAL transfer function model is developed based on the equivalent circuit of EAL.For the problem of load-side frequency measurement error,the frequency estimation method based on Kalman-filtering is designed.To improve the performance of EAL in the frequency control,a fuzzy EAL controller is designed.The testing examples show that the designed Kalman-filter has good performance in de-noising the measured frequency,and the designed fuzzy controller has better performance in stabilizing system frequency than traditional methods.展开更多
This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 A...This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.展开更多
基金funded by Science and Technology Project of State Grid Corporation of China:Research on the Construction and Evaluation Technology of the Data-Driven-based Adjustable Resource Pool of Typical Industrial Enterprises,Grant No.1400-202016386A-0-0-00.
文摘Traditional thermal power units are continuously replaced by renewable energies,of which fluctuations and intermittence impose pressure on the frequency stability of the power system.Electrolytic aluminum load(EAL)accounts for large amount of the local electric loads in some areas.The participation of EAL in local frequency control has huge application prospects.However,the controller design of EAL is difficult due to the measurement noise of the system frequency and the nonlinear dynamics of the EAL’s electric power consumption.Focusing on this problem,this paper proposes a control strategy for EAL to participate in the frequency control.For the controller design of the EAL system,the system frequency response model is established and the EAL transfer function model is developed based on the equivalent circuit of EAL.For the problem of load-side frequency measurement error,the frequency estimation method based on Kalman-filtering is designed.To improve the performance of EAL in the frequency control,a fuzzy EAL controller is designed.The testing examples show that the designed Kalman-filter has good performance in de-noising the measured frequency,and the designed fuzzy controller has better performance in stabilizing system frequency than traditional methods.
文摘This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.
