Hardware-in-the-loop test for the design of a hybrid electric bus control system

Author(s):  
Jun Wang ◽  
Qing-nian Wang ◽  
Peng-yu Wang

Hybrid electric vehicles present a promising approach to reduce fuel consumption and carbon dioxide emissions. The core technology of hybrid electric vehicles is an energy management strategy to distribute torque between the engine and the electric motor. This study presents an optimized energy management strategy based on real-time control. The operation platform of the control system is based on the dSPACE/simulator, which is a commercial hardware closed-loop system. First, an energy management strategy is built by using an empirical analysis method. To reduce fuel consumption further and to maintain the balance of the battery state of charge, dynamic programming is introduced to achieve the best fuel economy. Optimal gear shifting and engine torque control rules are then extracted into a rule-based control algorithm. Meanwhile, genetic algorithm is introduced to optimize the mode transition rules and the engine torque under parallel mode through an iterative method by defining a cost function over specific driving cycles. Second, a driving cycle recognition algorithm is built to obtain the optimization result over different driving cycles. The real vehicle model is verified by using a hardware-in-the-loop simulator in a virtual forward-facing simulation environment. The energy management strategy uses a code generation technology in the TTC200 controller to achieve vehicle real-time communication. Simulation results demonstrate that the real-time energy management strategy can coordinate the overall hybrid electric powertrain system to optimize fuel economy over different driving cycles and to maintain the battery state of charge.

2014 ◽  
Vol 45 ◽  
pp. 949-958 ◽  
Author(s):  
Laura Tribioli ◽  
Michele Barbieri ◽  
Roberto Capata ◽  
Enrico Sciubba ◽  
Elio Jannelli ◽  
...  

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Shumin Ruan ◽  
Yue Ma

Precise prediction of future vehicle information can improve the control efficiency of hybrid electric vehicles. Nowadays, most prediction models use previous information of vehicles to predict future driving velocity, which cannot reflect the impact of the driver and the environment. In this paper, a real-time energy management strategy (EMS) based on driver-action-impact MPC is proposed for series hybrid electric vehicles. The proposed EMS consists of two modules: the velocity prediction module and the real-time MPC module. In the velocity prediction module, a long short-term memory (LSTM) neural network model, which is trained by the traffic data derived from a VR-based driving simulator, is adopted to predict the future driving information by using driver action information and current vehicle’s velocity. The obtained future driving velocity is treated as the inputs of the real-time MPC module, which outputs the control variables to act on the underlying controllers of power components by solving a standard quadratic programming (QP) problem. Compared with the rule-based strategy, a 5.6% average reduction of fuel consumption is obtained. The effectiveness of real-time computation of the EMS is validated and verified through a hardware-in-the-loop test platform.


Energy ◽  
2021 ◽  
pp. 122811
Author(s):  
Likang Fan ◽  
Yufei Wang ◽  
Hongqian Wei ◽  
Youtong Zhang ◽  
Pengyu Zheng ◽  
...  

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