In the micro-grid photovoltaic systems, the random changes of solar radiation enable lead-acid batteries to experience low SOC (State of Charge) or overcharged for periods of time if directly charged with such traditional methods as decreased charging current, which will reduce lifetime of batteries. What’s more, it’s difficult to find a proper reduction coefficient in decreasing charging current. To adapt to the random changes of circumstance and avoid selecting the reduction coefficient, a new fast charging method named decreased charging current based on SOC is proposed to apply into micro-grid photovoltaic systems. It combines batteries’ SOC with the maximum charging voltage to determine the charging rate without strictly selecting reduction coefficient. By close-loop current control strategy and related scheme, the experiment proves the new method is feasible and verifies that, comparing with decreased charging current, the improved method make batteries’ SOC reach 100% in shorter time as well as the temperature of batteries raise more slowly
On basis of traditional battery performance model, paper analyzed the advantage and disadvantage of SOC estimation methods, introduced Adaptive Neuro-Fuzzy Inference Systems which integrated artificial neural network and fuzzy logic have predicted SOC of battery. It’s a battery residual capacity model with more generalization ability, adaptability and high precision. By analyzing the battery charge and discharge process, the key parameters of SOC are determined and the experimental model is modified in MATLAB platform.Experimental results show that the difference of SOC prediction and actual SOC is below 3%.The model can reflect the characteristics curve of the battery. SOC estimation algorithm can meet the requirements for precision. The results have a high practical value
Piezoelectric actuators have been received much attention for the advantages of high precision, no wear and rapid response, etc. However, the intrinsic hysteresis behavior of the piezoelectric materials seriously degraded the output performance of piezoelectric actuators. In this paper, to decrease such nonlinear effects and further improve the output performances of piezoelectric actuators, a modified nonlinear autoregressive moving average with exogenous inputs model, which could describe the rate-dependent hysteresis features of piezoelectric actuators was investigated. In the experiment, the different topologies of the proposed back propagation neural network algorithm were compared and the optimal topology was selected considering both the tracking precision and the structure complexity. The experimental results validated that the modified nonlinear autoregressive moving average with exogenous inputs model featured the hysteresis characteristics description ability with high precision, and the predicted motion matched well with the real trajectory. Then, the initial parameters of the back propagation neural network algorithm were further optimized by particle swarm optimization algorithm. The experimental results also verified that the proposed model based on particle swarm optimization–back propagation neural network algorithm was more accurate than that identified through the conventional back propagation neural network algorithm, and has a better predicting performance.
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