scholarly journals Impact of Different Driving Cycles and Operating Conditions on CO2 Emissions and Energy Management Strategies of a Euro-6 Hybrid Electric Vehicle

Energies ◽  
2017 ◽  
Vol 10 (10) ◽  
pp. 1590 ◽  
Author(s):  
Claudio Cubito ◽  
Federico Millo ◽  
Giulio Boccardo ◽  
Giuseppe Di Pierro ◽  
Biagio Ciuffo ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Co2 Emissions ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Management Strategies ◽  
Operating Conditions ◽  
Driving Cycles ◽  
Hybrid Electric

scholarly journals Research on energy management strategy considering battery life for plug-in hybrid electric vehicle

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879776 ◽  
Author(s):  
Jianjun Hu ◽  
Zhihua Hu ◽  
Xiyuan Niu ◽  
Qin Bai
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Battery Life ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Comprehensive Test ◽  
Hybrid Electric

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.

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.

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