The proposed solution addresses the issues of high overshoot and long response time in traditional PID control methods for complex nonlinear heating systems with large inertia and pure lag. The Improved coati Optimiza...
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ISBN:
(纸本)9798350372694;9798350372700
The proposed solution addresses the issues of high overshoot and long response time in traditional PID control methods for complex nonlinear heating systems with large inertia and pure lag. The Improved coati Optimization algorithm (Icoa-PID) introduces an enhanced controller. Firstly, the heating system's primary and secondary piping transfer function models are established. Secondly, the coati Optimization algorithm (coa) has been improved in four aspects: the introduction of the chaotic mapping mechanism, nonlinear inertial weighting factors, t-distribution variation, and the coati alarmist mechanism, and the test is carried out using the CEC2022 function set, which performs well in solving most of them in dimension 20;meanwhile, the data are plotted in boxplots. It can be observed that Icoa has both the smallest mean height and the smallest interquartile range (IQR). These verify that the Icoa is able to converge more rapidly, with a greater degree of accuracy and stability than other algorithms. Finally, the simulation experiments were conducted, the step response plot demonstrated that the rise time of the Icoa-PID controller was reduced by 5 seconds in comparison to the pre-improvement period, and by approximately 10 seconds in comparison to the DBO-PID controller. Additionally, the amount of overshoot was reduced by 2% in comparison to the pre-improvement period;the fitness value curves indicate that the fitness value of the Icoa-PID controller is consistently minimized, indicating that it identifies the most optimal set of PID parameters. the error curve exhibits a rapid and pronounced decline, indicating the optimization of parameters to achieve both dynamic responsiveness and static accuracy within the system. The aforementioned results substantiate that the IOCA-PID control methodology is more effective in meeting the indoor thermal demand demands of winter users compared to other algorithmic controllers, and thus possesses certain practical relevance fo
Accurate temperature estimation models for lithium (Li)-ion batteries are critical for timely identification of and response to thermal runaway effects to ensure battery safety. In this paper, a hybrid data-driven app...
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Accurate temperature estimation models for lithium (Li)-ion batteries are critical for timely identification of and response to thermal runaway effects to ensure battery safety. In this paper, a hybrid data-driven approach incorporating thermoelectric equivalent model (TEM) is proposed to predict the temperature of Li-ion batteries under different state of health (SOH) based on measured data. The proposed TEM model consists of an electrical equivalent circuit model (EECM) and a thermal equivalent circuit modeling (TECM). The electrical model is a second-order RC equivalent circuit model, and the thermal model is a first-order thermal model, which interacts with parameters such as state of charge (SOC) and internal resistance to improve the accuracy of the model. In order to solve the problem that the model part is susceptible to measurement errors, a data-driven model using Kalman filter (KF) combined bidirectional gated recursive unit (BiGRU) and Transformer is proposed to ensure high accuracy in predicting the temperature. The output of the TEM is used as the input to the data-driven part to obtain the implied relationship between the temperature and parameters. The experimental results confirm the high accuracy of the hybrid model in estimating the battery temperature. The maximum temperature prediction error of the Li-ion battery was 0.3423 degrees C with a predicted root mean square error (RMSE) of 0.1266 under different SOH conditions.
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