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
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.
