scholarly journals Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles

Energies ◽  
2015 ◽  
Vol 8 (4) ◽  
pp. 3225-3244 ◽  
Author(s):  
Ximing Wang ◽  
Hongwen He ◽  
Fengchun Sun ◽  
Jieli Zhang
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Application Study ◽  
Hybrid Electric

Control and HIL Simulation of Series Hybrid Electric Vehicles Based on Dynamic Programming Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1149-1152
Author(s):  
Liang Zhang ◽  
Bin Jiao ◽  
Xiu Hong Guo
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicles ◽  
Power Flow ◽  
Hybrid Electric Vehicles ◽  
Control Method ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Exhaust Gas Emission ◽  
Hybrid Electric ◽  
Hil Simulation

Hybrid electric vehicles has the potential to save energy consumption and relieve exhaust gas emission while the power flow control techniques are very important for improving a hybrid electric vehicle’s performance. In this paper, the system simulation and control method of series hybrid electric vehicles were proposed. The control method was based on enhancing the energy transfer efficiency based on dynamic programming algorithm. The hardware in the loop (HIL) simulation was constructed containing a real-time driver and controller in the simulation platform, which can be used to evaluate the proposed strategy.

scholarly journals Optimal Control Strategy for Parallel Plug-In Hybrid Electric Vehicles Based on Dynamic Programming

2021 ◽  
Vol 12 (2) ◽  
pp. 85
Author(s):  
Ying Tian ◽  
Jiaqi Liu ◽  
Qiangqiang Yao ◽  
Kai Liu
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Optimal Control Strategy ◽  
Parallel Hybrid ◽  
Hybrid Electric

In this paper, the dynamic programming algorithm is applied to the control strategy design of parallel hybrid electric vehicles. Based on MATLAB/Simulink software, the key component model and controller model of the parallel hybrid system are established, and an offline simulation platform is built. Based on the platform, the global optimal control strategy based on the dynamic programming algorithm is studied. The torque distribution rules and shifting rules are analyzed, and the optimal control strategy is adopted to design the control strategy, which effectively improves the fuel economy of plug-in hybrid electric vehicles. The fuel consumption rate of this parallel hybrid electric vehicle is based on china city bus cycle (CCBC) condition.

Model Predictive Control as an Energy Management Strategy for Hybrid Electric Vehicles

2009 ◽  
Author(s):  
Balaji Sampathnarayanan ◽  
Lorenzo Serrao ◽  
Simona Onori ◽  
Giorgio Rizzoni ◽  
Steve Yurkovich
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
A Priori ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Energy Management Strategy ◽  
Hybrid Electric

The energy management strategy in a hybrid electric vehicle is viewed as an optimal control problem and is solved using Model Predictve Control (MPC). The method is applied to a series hybrid electric vehicle, using a linearized model in state space formulation and a linear MPC algorithm, based on quadratic programming, to find a feasible suboptimal solution. The significance of the results lies in obtaining a real-time implementable control law. The MPC algorithm is applied using a quasi-static simulator developed in the MATLAB environment. The MPC solution is compared with the dynamic programming solution (offline optimization). The dynamic programming algorithm, which requires the entire driving cycle to be known a-priori, guarantees the optimality and is used here as the benchmark solution. The effect of the parameters of the MPC (length of prediction horizon, type of prediction) is also investigated.

scholarly journals A Tool to Enable FPGA-Accelerated Dynamic Programming for Energy Management of Hybrid Electric Vehicles

2020 ◽  
Vol 53 (2) ◽  
pp. 15104-15109
Author(s):  
Frans Skarman ◽  
Oscar Gustafsson ◽  
Daniel Jung ◽  
Mattias Krysander
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Hybrid Electric

scholarly journals Online Energy Management of Plug-In Hybrid Electric Vehicles for Prolongation of All-Electric Range Based on Dynamic Programming

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Zeyu Chen ◽  
Weiguo Liu ◽  
Ying Yang ◽  
Weiqiang Chen
Keyword(s):  
Dynamic Programming ◽  
Energy Saving ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Energy Management Strategy ◽  
Driving Range ◽  
Hybrid Electric

The employed energy management strategy plays an important role in energy saving performance and exhausted emission reduction of plug-in hybrid electric vehicles (HEVs). An application of dynamic programming for optimization of power allocation is implemented in this paper with certain driving cycle and a limited driving range. Considering the DP algorithm can barely be used in real-time control because of its huge computational task and the dependence ona prioridriving cycle, several online useful control rules are established based on the offline optimization results of DP. With the above efforts, an online energy management strategy is proposed finally. The presented energy management strategy concerns the prolongation of all-electric driving range as well as the energy saving performance. A simulation study is deployed to evaluate the control performance of the proposed energy management approach. All-electric range of the plug-in HEV can be prolonged by up to 2.86% for a certain driving condition. The energy saving performance is relative to the driving distance. The presented energy management strategy brings a little higher energy cost when driving distance is short, but for a long driving distance, it can reduce the energy consumption by up to 5.77% compared to the traditional CD-CS strategy.

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