scholarly journals Research on Energy Management Method of Plug-In Hybrid Electric Vehicle Based on Travel Characteristic Prediction

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6134
Author(s):  
Yangyang Ma ◽  
Pengyu Wang ◽  
Tianjun Sun
Keyword(s):  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Intelligent Transportation System ◽  
Future Trend ◽  
Traffic Information ◽  
Reference Trajectory ◽  
Characteristics Analysis ◽  
Planning Algorithm ◽  
Management Method ◽  
Hybrid Electric

In the research on energy management methods of plug-in hybrid electric vehicles, it is expected that a future trend will be to optimize energy management using the information provided by the global positioning system (GPS) and intelligent transportation system (ITS), which is relatively scarce in current research. This study proposes a PHEV energy management method based on travel characteristic prediction. Firstly, this study processes the historical travel data of a certain driver obtained by GPS and ITS and uses the established Markov trajectory prediction model based on key points to predict the trajectory and mileage. Then, on the basis of characteristics analysis of historical travel data, while considering traffic information to form a target cycle, the driving cycles are classified and identified based on traffic information predictions. Then, according to the reasonable SOC allocation range of the four typical cycles, the planning algorithm of the SOC reference trajectory is determined and verified. Finally, based on the previous work, an A-ECMS energy management method based on travel characteristic prediction is established. By comparing different energy management methods, the developed energy management method based on travel characteristic prediction can reasonably utilize power batteries. The fuel saving is about 8.95% higher than the rule-based energy management method, which can effectively improve the whole vehicle’s fuel economy and optimization ability.

Energy management strategy of intelligent plug-in split hybrid electric vehicle based on deep reinforcement learning with optimized path planning algorithm

2021 ◽  
pp. 095440702110368
Author(s):  
Shengguang Xiong ◽  
Yishi Zhang ◽  
Chaozhong Wu ◽  
Zhijun Chen ◽  
Jiankun Peng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Planning Algorithm ◽  
Hybrid Electric ◽  
Path Planning Algorithm

Energy management is a fundamental task and challenge of plug-in split hybrid electric vehicle (PSHEV) research field because of the complicated powertrain and variable driving conditions. Motivated by the foresight of intelligent vehicle and the breakthroughs of deep reinforcement learning framework, an energy management strategy of intelligent plug-in split hybrid electric vehicle (IPSHEV) based on optimized Dijkstra’s path planning algorithm (ODA) and reinforcement learning Deep-Q-Network (DQN) is proposed to cope with the challenge. Firstly, a gray model is used to predict the traffic congestion of each road and the length of each road calculated in the traditional Dijkstra’s algorithm (DA) is modified for path planning. Secondly, on the basis of the predicted velocity of each road, the planned velocity is constrained by the vehicle dynamics to ensure the driving security. Finally, the planning information is inputted to DQN to control the working mode of IPSHEV, so as to achieve energy saving of the vehicle. The simulation results show the optimized path planning algorithm and proposed energy management strategy is feasible and effective.

Energy management of plug-in hybrid electric vehicle based on trip characteristic prediction

2020 ◽  
Vol 234 (8) ◽  
pp. 2239-2259
Author(s):  
Pengyu Wang ◽  
Jinke Li ◽  
Yuanbin Yu ◽  
Xiaoyong Xiong ◽  
Shijie Zhao ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Global Positioning System ◽  
Hybrid Electric Vehicle ◽  
Intelligent Transportation System ◽  
State Of Charge ◽  
Positioning System ◽  
Driving Cycles ◽  
Global Positioning ◽  
Hybrid Electric

In the research regarding plug-in hybrid electric vehicle energy management strategies, the use of global positioning system and intelligent transportation system information to optimize control strategy will be the future trend, and this is relatively scarce in the existing researches. Therefore, an adaptive energy management strategy of plug-in hybrid electric vehicle based on trip characteristic prediction was investigated in this paper, and the main achievement is to suggest a way to determine the reference state of charge for control strategy using global positioning system or intelligent transportation system information. First, given the historical driving data of a driver by global positioning system, the important location points of the commuting routes were discovered. Second, a Markov trajectory prediction model based on the key points was established to predict and identify the driving routes. As such, the trip characteristics, such as information of mileage and driving cycles, were collected. Then, five typical driving cycles were extracted. According to the trip characteristic information, the optimal battery state of charge consumption regulation of plug-in hybrid electric vehicle was realized using a dynamic programming algorithm. This algorithm was applied to the research of state of charge trajectory planning algorithm. Moreover, an adaptive equivalent consumption minimization strategy based on state of charge planning trajectory was developed. The comparison of different control strategies proved that the developed strategy uses battery power reasonably and reduces fuel consumption of the vehicle.

Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

2021 ◽  
pp. 095440702110297
Author(s):  
Hui Liu ◽  
Rui Liu ◽  
Riming Xu ◽  
Lijin Han ◽  
Shumin Ruan
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Control Strategy ◽  
Power Distribution ◽  
Hybrid Electric Vehicle ◽  
Hierarchical Control ◽  
Management Problem ◽  
Energy Management Strategy ◽  
Dual Mode ◽  
Hybrid Electric

Energy management strategies are critical for hybrid electric vehicles (HEVs) to improve fuel economy. To solve the dual-mode HEV energy management problem combined with switching schedule and power distribution, a hierarchical control strategy is proposed in this paper. The mode planning controller is twofold. First, the mode schedule is obtained according to the mode switch map and driving condition, then a switch hunting suppression algorithm is proposed to flatten the mode schedule through eliminating unnecessary switch. The proposed algorithm can reduce switch frequency while fuel consumption remains nearly unchanged. The power distribution controller receives the mode schedule and optimizes power distribution between the engine and battery based on the Radau pseudospectral knotting method (RPKM). Simulations are implemented to verify the effectiveness of the proposed hierarchical control strategy. For the mode planning controller, as the flattening threshold value increases, the fuel consumption remains nearly unchanged, however, the switch frequency decreases significantly. For the power distribution controller, the fuel consumption obtained by RPKM is 4.29% higher than that of DP, while the elapsed time is reduced by 92.53%.

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