Chinese Researchers Study Energy Optimization of the Plug-in Hybrid Electric Bus

Siemens Hybrid Electric Bus being used in Vietnam.  Image courtesy of Siemens.
Siemens Hybrid Electric Bus being used in Vietnam. Image courtesy of Siemens.

Nowadays, the plug-in hybrid electric bus (PHEB) has been widely applied as a transportation method in many cities of China. Compared with conventional buses, better fuel economy might have been achieved, due to the usage of the electric energy from the grid which is relatively less expensive than fossil fuels.

In recent years, a large number of approaches have been adopted in solving the energy management problem, which described via optimal control theory including dynamic programming, fuzzy logic control, Pontryagin Minimum Principle, and Model Predictive Control. Inherently, if those techniques are attempted to be applied online, it is critical to find a control strategy with some kind of driving cycle prediction.

For this purpose, some modeling methods proposed to estimate the fuel consumption cost function with a Markov chain which would give the transition probability of a set of torque demand, meanwhile utilizing the stochastic dynamic programming in solving the cost function.

Considering the characteristics of the driving cycles of city buses, the regularities of the driving cycles might be easily ‘extracted’ from the collected historical data. Obviously, the SDP might be the most appropriate algorithm to implement the optimization of the energy management for the plug-in hybrid electric bus. However, utilizing SDP algorithm to design the optimal energy management strategy also faces two challenges.

First, the cost function of SDP algorithm is constructed through using the basic discrete method, which takes a constant value over each of the discretization intervals. Second, the discretization approach owns the problem of “curse of dimensionality”.

The paper (Sun Y, Chen Z, Yan B J, et al. A learning method for energy optimization of the plug-in hybrid electric bus . Sci China Tech Sci, doi: 10.1007/s11431-015-5852-x) describes an alternative approach for finding control strategy with stochastic Markov model of PHEB energy management, in which the cost function is approximated directly without resorting to discretization. Because the statistical learning method is introduced in this approach, it is not necessary to know all of the parameters in the MDP model. And using the approximate method, it will reduce the burden of the computation of the problem.

The the plug-in hybrid electric bus structure discussed in this paper is a typical single-shaft parallel configuration shown in Figure 1.

Plug-in Hybrid Electric Bus research

This image shows the configuration of the PHEB structure.
©Science China Press

For such a PHEB, a cost function of fuel consumption and electric consumption based on Markov decision process will be presented. Then a learning method is proposed to search for a minimal value of this cost function, and obtain the optimal control strategy simultaneously.

In the proposed method, a simpler function is used for approximating the cost function, and in the process of this method, a linear regression method is adopted which makes the problem much easier to solve. Moreover, sample data is easy to be obtained because PHEBs  run repetitively on a fixed route

The driving cycle for simulation in the research paper starts from Yudong station to Nanping station in Chongqing city, including 32 bus stops. As quantitative perspective, the simulation results with three strategies: CDCS; MDP; andDP show that the energy consumption generated by the proposed MDP strategy is higher than that generated by the standard DP algorithm, but significantly lower than that of CDCS strategy. Furthermore, a test based on a real  plug-in hybrid electric bus was carried out to verify the applicable of the proposed method.

This research was funded by the Natural Science Foundation of China (Grant No. 51275557), and the National Science-technology Support Plan Projects of China (Grant No. 2013BAG14B01).

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This post was prepared by Solar Thermal Magazine staff.

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