Date Published: October 11, 2018
Publisher: Public Library of Science
Author(s): Hongqiang Guo, Jinyong Shangguan, Juan Tang, Qun Sun, Hongting Wu, Rui Xiong.
Additional degrees of freedom existed in dual-motor coupling system bring considerable challenge to the optimal control of electric vehicles. Moreover, the stochastic characteristic of vehicle mass can further increase this challenge. A receding horizon control (RHC) strategy in consideration of stochastic vehicle mass is proposed in this study to respond to this challenge. Aiming at an electric vehicle with dual-motor coupling, a Markov chain is firstly deployed to predict future driving conditions by a formulated state transition probability matrix, based on historical driving cycles in real-world. Then, future required power is predicted by the predicted driving conditions, stochastic vehicle mass and road gradient, where the stochastic vehicle mass is formulated as stochastic variables in different bus stops. Finally, dynamic programming is employed to calculate the optimal vector of the vehicle within the defined prediction horizon, and only the first control values extracted from the optimal control vector are used to execute real-time power distribution control. The simulation results show that the proposed strategy is reasonable and can at least reduce electric consumption by 4.64%, compared with rule-based strategy.
A sharp increase of vehicles is resulting in not only the depletion of oil energy but also the worsening of global environment . Electric vehicles (EVs) characterized by zero-carbon emission and low energy consumption have attracted significant attention all over the world . However, EVs usually have short driving ranges and high cost, compared with conventional vehicles . Therefore, it is of great importance to investigate economical control strategy for EVs .
As state above, the stochastic vehicle mass in different bus stops has strong relationship with the optimal control of the vehicle. To exhaustively evaluate the robustness, application and optimality of the proposed RHC strategy, 46 stochastic variables with respect to bus stops are formulated to describe the stochastic distribution of the vehicle mass in different bus stops. Here, the stochastic vehicle mass is denoted by the mass of passengers, and assuming that the passenger’s mass is around 70kg. Besides, each stochastic variable is designed to 18 levels based on the maximum number of passengers. In this case, the stochastic variables of the bus stops will constitute a huge design space from the viewpoint of design of experiment. To better verify the proposed power distribution strategy, Opt LHD is employed to exhaustively insight into the design space.
This paper proposes an RHC strategy for an EV with dual-motor coupling system in consideration of stochastic vehicle mass. The conclusions are summarized as follows.