Electrification of heavy duty vehicles(HDVs)is critical to realization of the target of carbon neutralization in the future.For most HDVs,the influence of road slope on vehicle power usually cannot be ignored due to s...Electrification of heavy duty vehicles(HDVs)is critical to realization of the target of carbon neutralization in the future.For most HDVs,the influence of road slope on vehicle power usually cannot be ignored due to significant road slope variation during long driving mileages.In order to design the powertrain system for electrified HDVs effectively,it is necessary to construct representative driving cycles with road slope information.There are two difficulties for this task.(1)Road slope measuring devices are usually costly.A cheaper yet effective method for measuring road slope needs to be developed.(2)A 3D(three dimension)Markov chain method is usually utilized for constructing cycles with velocity and road slope.This method is complex and time consuming,and needs to be improved.In this paper,a 2D(two dimension)Markov chain method for addressing these issues is proposed.A road slope observation is designed based on normal GPS(Global Positioning System)signals and a high order Butterworth filter.The effectiveness of the method is validated.Driving velocity and road slope are collected and observed for the area between Beijing and Zhangjiakou in northern China.Representative cycles with road slope are constructed using a 2D Markov chain method and a matching algorithm based on average speed.With the introduced technology,three representative driving cycles with road slope for urban,suburban and highway routes are designed.Statistic results on vehicle power show that,the representative driving cycles are effective with relative errors less than 4%compared to the real driving conditions.These driving cycles will be utilized in designing electric HDVs,such as hydrogen fuel cell vehicles in the future.展开更多
This paper addresses the issues of channel estimation in a Multiple-Input/Multiple-Output (MIMO) system. Markov Chain Monte Carlo (MCMC) method is employed to jointly estimate the Channel State Information (CSI) and t...This paper addresses the issues of channel estimation in a Multiple-Input/Multiple-Output (MIMO) system. Markov Chain Monte Carlo (MCMC) method is employed to jointly estimate the Channel State Information (CSI) and the transmitted signals. The deduced algorithms can work well under circumstances of low Signal-to-Noise Ratio (SNR). Simulation results are presented to demonstrate their effectiveness.展开更多
We discuss the incomplete semi-iterative method (ISIM) for an approximate solution of a linear fixed point equations x=Tx+c with a bounded linear operator T acting on a complex Banach space X such that its resolvent h...We discuss the incomplete semi-iterative method (ISIM) for an approximate solution of a linear fixed point equations x=Tx+c with a bounded linear operator T acting on a complex Banach space X such that its resolvent has a pole of order k at the point 1. Sufficient conditions for the convergence of ISIM to a solution of x=Tx+c, where c belongs to the range space of R(I-T) k, are established. We show that the ISIM has an attractive feature that it is usually convergent even when the spectral radius of the operator T is greater than 1 and Ind 1T≥1. Applications in finite Markov chain is considered and illustrative examples are reported, showing the convergence rate of the ISIM is very high.展开更多
This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method...This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method is efficient.展开更多
载荷外推作为载荷谱编制的重要技术手段,当前研究缺乏对于载荷外推总体方法的全面梳理、马尔可夫稳态分布的求解方法适应性不够、缺乏不同非参频次外推方法的比较与选用原则,导致不便生成高精度载荷谱以支撑装备性能设计。围绕坦克在高...载荷外推作为载荷谱编制的重要技术手段,当前研究缺乏对于载荷外推总体方法的全面梳理、马尔可夫稳态分布的求解方法适应性不够、缺乏不同非参频次外推方法的比较与选用原则,导致不便生成高精度载荷谱以支撑装备性能设计。围绕坦克在高机动和极限工况下的载荷谱编制问题,基于某坦克行进间身管位移数据样本,分别使用基于雨流矩阵及核密度估计的非参数外推法、基于马尔可夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)的信号重构法以及Metropolis-Hastings(简称MH)直接采样法进行了载荷频次外推,并针对MCMC的信号重构法提出了一种改良马尔可夫稳态分布的求解方法。应用所提出的频次-极值相结合的载荷外推总体方法对坦克身管位移进行了频次扩充与极值预测,并结合实车试验结果验证了方法的准确性。研究结果表明:改良的马尔可夫稳态分布求解方法是有效的;在样本长度足够、外推精度要求不甚高的情况下,MH直接采样法可作为一种新的频次外推方法;运用频次-极值相结合的载荷外推总体方法所得结果精度较高;形成的频次外推法选用原则对于载荷谱编制过程中的方法选择具有一定的指导意义。研究工作为装备载荷谱的高质量编制提供了成熟的技术路线和参考。展开更多
基金This work was supported by Toyota Motor Corporation(TMC)in the Tsinghua-Toyota Joint Research Center for Hydrogen Energy and Fuel Cell Technology of Vehicles(TTFC-2019-0)National Natural Science Foundation of China(Nos.52022050 and 52002210).
文摘Electrification of heavy duty vehicles(HDVs)is critical to realization of the target of carbon neutralization in the future.For most HDVs,the influence of road slope on vehicle power usually cannot be ignored due to significant road slope variation during long driving mileages.In order to design the powertrain system for electrified HDVs effectively,it is necessary to construct representative driving cycles with road slope information.There are two difficulties for this task.(1)Road slope measuring devices are usually costly.A cheaper yet effective method for measuring road slope needs to be developed.(2)A 3D(three dimension)Markov chain method is usually utilized for constructing cycles with velocity and road slope.This method is complex and time consuming,and needs to be improved.In this paper,a 2D(two dimension)Markov chain method for addressing these issues is proposed.A road slope observation is designed based on normal GPS(Global Positioning System)signals and a high order Butterworth filter.The effectiveness of the method is validated.Driving velocity and road slope are collected and observed for the area between Beijing and Zhangjiakou in northern China.Representative cycles with road slope are constructed using a 2D Markov chain method and a matching algorithm based on average speed.With the introduced technology,three representative driving cycles with road slope for urban,suburban and highway routes are designed.Statistic results on vehicle power show that,the representative driving cycles are effective with relative errors less than 4%compared to the real driving conditions.These driving cycles will be utilized in designing electric HDVs,such as hydrogen fuel cell vehicles in the future.
文摘This paper addresses the issues of channel estimation in a Multiple-Input/Multiple-Output (MIMO) system. Markov Chain Monte Carlo (MCMC) method is employed to jointly estimate the Channel State Information (CSI) and the transmitted signals. The deduced algorithms can work well under circumstances of low Signal-to-Noise Ratio (SNR). Simulation results are presented to demonstrate their effectiveness.
基金Project1 990 1 0 0 6 supported by National Natural Science Foundation of China,Doctoral Foundation of China,Chi-na Scholarship council and Laboratory of Computational Physics in Beijing of Chinathe second author is also supportedby the State Major Key
文摘We discuss the incomplete semi-iterative method (ISIM) for an approximate solution of a linear fixed point equations x=Tx+c with a bounded linear operator T acting on a complex Banach space X such that its resolvent has a pole of order k at the point 1. Sufficient conditions for the convergence of ISIM to a solution of x=Tx+c, where c belongs to the range space of R(I-T) k, are established. We show that the ISIM has an attractive feature that it is usually convergent even when the spectral radius of the operator T is greater than 1 and Ind 1T≥1. Applications in finite Markov chain is considered and illustrative examples are reported, showing the convergence rate of the ISIM is very high.
文摘This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method is efficient.
文摘载荷外推作为载荷谱编制的重要技术手段,当前研究缺乏对于载荷外推总体方法的全面梳理、马尔可夫稳态分布的求解方法适应性不够、缺乏不同非参频次外推方法的比较与选用原则,导致不便生成高精度载荷谱以支撑装备性能设计。围绕坦克在高机动和极限工况下的载荷谱编制问题,基于某坦克行进间身管位移数据样本,分别使用基于雨流矩阵及核密度估计的非参数外推法、基于马尔可夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)的信号重构法以及Metropolis-Hastings(简称MH)直接采样法进行了载荷频次外推,并针对MCMC的信号重构法提出了一种改良马尔可夫稳态分布的求解方法。应用所提出的频次-极值相结合的载荷外推总体方法对坦克身管位移进行了频次扩充与极值预测,并结合实车试验结果验证了方法的准确性。研究结果表明:改良的马尔可夫稳态分布求解方法是有效的;在样本长度足够、外推精度要求不甚高的情况下,MH直接采样法可作为一种新的频次外推方法;运用频次-极值相结合的载荷外推总体方法所得结果精度较高;形成的频次外推法选用原则对于载荷谱编制过程中的方法选择具有一定的指导意义。研究工作为装备载荷谱的高质量编制提供了成熟的技术路线和参考。
