电动汽车的空调系统作为汽车的主要耗电模块,在气温较低或者较高的时候,耗电量大,导致整车续航能力大幅降低。为了提升续航里程,提高汽车座舱舒适性,降低电动汽车的整车能耗,本文通过对模糊比例积分微分(Proportional Integral Derivati...电动汽车的空调系统作为汽车的主要耗电模块,在气温较低或者较高的时候,耗电量大,导致整车续航能力大幅降低。为了提升续航里程,提高汽车座舱舒适性,降低电动汽车的整车能耗,本文通过对模糊比例积分微分(Proportional Integral Derivation,PID)模型预测控制的算法进行研究,在未知空调系统控制逻辑,不改变系统结构的情况下,基于模糊PID模型预测控制,建立汽车热力学模型,并分析了汽车空调系统的能耗参数。基于MATLAB仿真验证结果表明,相较于单独的模糊PID及模型预测控制,模糊PID模型预测控制能耗有明显降低,说明该控制系统在一定程度上降低了整车能耗,有利于提升电动汽车的续航里程,达到了设计标准要求。展开更多
The fuzzy NN predictive control algorithm introduced in this paper uses fuzzy neural network to model the nonlinear MIMO process. Its training method that integrates LS and BP algorithm brings quick convergence. GPC a...The fuzzy NN predictive control algorithm introduced in this paper uses fuzzy neural network to model the nonlinear MIMO process. Its training method that integrates LS and BP algorithm brings quick convergence. GPC algorithm is used as the predictive component. The fuzzy neural network has six layers, including input layer, output layer and four hidden layers. An application to a MIMO nonlinear process(green liquor system of the recovery system in a pulp factory shows that this algorithm has better performance than normal PID algrithm.展开更多
文摘电动汽车的空调系统作为汽车的主要耗电模块,在气温较低或者较高的时候,耗电量大,导致整车续航能力大幅降低。为了提升续航里程,提高汽车座舱舒适性,降低电动汽车的整车能耗,本文通过对模糊比例积分微分(Proportional Integral Derivation,PID)模型预测控制的算法进行研究,在未知空调系统控制逻辑,不改变系统结构的情况下,基于模糊PID模型预测控制,建立汽车热力学模型,并分析了汽车空调系统的能耗参数。基于MATLAB仿真验证结果表明,相较于单独的模糊PID及模型预测控制,模糊PID模型预测控制能耗有明显降低,说明该控制系统在一定程度上降低了整车能耗,有利于提升电动汽车的续航里程,达到了设计标准要求。
文摘The fuzzy NN predictive control algorithm introduced in this paper uses fuzzy neural network to model the nonlinear MIMO process. Its training method that integrates LS and BP algorithm brings quick convergence. GPC algorithm is used as the predictive component. The fuzzy neural network has six layers, including input layer, output layer and four hidden layers. An application to a MIMO nonlinear process(green liquor system of the recovery system in a pulp factory shows that this algorithm has better performance than normal PID algrithm.
