随着电动汽车(electric vehicle,EV)普及度的不断提高,工业园区内的EV用户日益增多,其充放电行为给园区综合能源系统(park integrated energy system,PIES)的规划运行带来极大挑战。文中提出考虑EV充放电意愿的PIES双层优化调度。首先,...随着电动汽车(electric vehicle,EV)普及度的不断提高,工业园区内的EV用户日益增多,其充放电行为给园区综合能源系统(park integrated energy system,PIES)的规划运行带来极大挑战。文中提出考虑EV充放电意愿的PIES双层优化调度。首先,基于动态实时电价、电池荷电量、电池损耗补偿、额外参与激励等因素建立充放电意愿模型,在此基础上得到改进的EV充放电模型;然后,以PIES总成本最小和EV充电费用最小为目标建立双层优化调度模型,通过Karush-Kuhn-Tucker(KKT)条件将内层模型转化为外层模型的约束条件,从而快速稳定地实现单层模型的求解;最后,进行仿真求解,设置3种不同场景,对比所提模型与一般充放电意愿模型,验证了文中所提引入EV充放电意愿模型的PIES双层优化调度的有效性和可行性。展开更多
利用光学显微镜(Optical microscope,OM)、X射线衍射仪(X-ray diffractometer,XRD)、扫描电子显微镜(Scanning electron microscope,SEM)、能谱分析仪(Energy dispersive spectrometer,EDS)、透射电镜(Transmission electron microscope...利用光学显微镜(Optical microscope,OM)、X射线衍射仪(X-ray diffractometer,XRD)、扫描电子显微镜(Scanning electron microscope,SEM)、能谱分析仪(Energy dispersive spectrometer,EDS)、透射电镜(Transmission electron microscope,TEM)和电子万能试验机等手段对传统砂型重力铸造方法制备的EV31A合金的显微组织和性能进行了深入研究。实验结果表明:铸态EV31A合金的显微组织由α-Mg和α-Mg+Mg_(41)(Nd_(0.9)Gd_(0.1))_(5)共晶相组成,由于稀土元素含量较少,共晶反应发生在α-Mg结晶后期,表现为除少量固溶于α-Mg基体中,含稀土元素的Mg_(41)(Nd_(0.9)Gd_(0.1))_(5)相主要分布在α-Mg的晶界处。Mg_(41)(Nd_(0.9)Gd_(0.1))_(5)相为bcc结构,晶格常数为0.285 nm,呈鱼骨状,尺寸在数十微米。合金中α-Mg基体的晶粒尺寸为56.8μm。EV31A铸态合金的抗拉强度、屈服强度和延伸率分别为207 MPa、124 MPa和7.0%。合金具有优异力学性能主要归因于Zr元素所带来的细晶强化,Gd、Nd、Zr等溶质原子的固溶强化,共晶Mg_(41)(Nd_(0.9),Gd_(0.1))_(5)相所带来的第二相强化等多种强化方式的协同作用。展开更多
Electric Vehicle (EV) adoption is rapidly increasing, necessitating efficient and precise methods for predicting EV charging requirements. The early and precise prediction of the battery discharging status is helpful ...Electric Vehicle (EV) adoption is rapidly increasing, necessitating efficient and precise methods for predicting EV charging requirements. The early and precise prediction of the battery discharging status is helpful to avoid the complete discharging of the battery. The complete discharge of the battery degrades its lifetime and requires a longer charging duration. In the present work, a novel approach leverages the Edge Impulse platform for live prediction of the battery status and early alert signal to avoid complete discharging. The proposed method predicts the actual remaining useful life of batteries. A powerful edge computing platform utilizes Tensor Flow-based machine learning models to predict EV charging needs accurately. The proposed method improves the overall lifetime of the battery by the efficient utilization and precise prediction of the battery status. The EON-Tuner and DSP processing blocks are used for efficient results. The performance of the proposed method is analyzed in terms of accuracy, mean square error and other performance parameters.展开更多
文摘随着电动汽车(electric vehicle,EV)普及度的不断提高,工业园区内的EV用户日益增多,其充放电行为给园区综合能源系统(park integrated energy system,PIES)的规划运行带来极大挑战。文中提出考虑EV充放电意愿的PIES双层优化调度。首先,基于动态实时电价、电池荷电量、电池损耗补偿、额外参与激励等因素建立充放电意愿模型,在此基础上得到改进的EV充放电模型;然后,以PIES总成本最小和EV充电费用最小为目标建立双层优化调度模型,通过Karush-Kuhn-Tucker(KKT)条件将内层模型转化为外层模型的约束条件,从而快速稳定地实现单层模型的求解;最后,进行仿真求解,设置3种不同场景,对比所提模型与一般充放电意愿模型,验证了文中所提引入EV充放电意愿模型的PIES双层优化调度的有效性和可行性。
文摘Electric Vehicle (EV) adoption is rapidly increasing, necessitating efficient and precise methods for predicting EV charging requirements. The early and precise prediction of the battery discharging status is helpful to avoid the complete discharging of the battery. The complete discharge of the battery degrades its lifetime and requires a longer charging duration. In the present work, a novel approach leverages the Edge Impulse platform for live prediction of the battery status and early alert signal to avoid complete discharging. The proposed method predicts the actual remaining useful life of batteries. A powerful edge computing platform utilizes Tensor Flow-based machine learning models to predict EV charging needs accurately. The proposed method improves the overall lifetime of the battery by the efficient utilization and precise prediction of the battery status. The EON-Tuner and DSP processing blocks are used for efficient results. The performance of the proposed method is analyzed in terms of accuracy, mean square error and other performance parameters.
