scholarly journals Design of an Adaptive Power Management Strategy for Range Extended Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1610 ◽  
Author(s):  
Jen-Chiun Guan ◽  
Bo-Chiuan Chen ◽  
Yuh-Yih Wu
Keyword(s):  
Fuel Consumption ◽  
Power Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Consumption Rate ◽  
Fuel Consumption Rate ◽  
Power Management Strategy ◽  
Equivalent Fuel ◽  
Adaptive Power ◽  
Equivalent Fuel Consumption

The cruising distance of the range extended electric vehicle (REEV) can be further extended using a range extender, which consists of an engine and a generator, i.e., a genset. An adaptive power management strategy (PMS) based on the equivalent fuel consumption minimization strategy (ECMS) is proposed for the REEV in this paper. The desired trajectory of the state of charge (SOC) is designed based on the energy-to-distance ratio, which is defined as the difference between the initial SOC and the minimum allowable SOC divided by the remaining travel distance, for discharging the battery. A self-organizing fuzzy controller (SOFC) with SOC feedback is utilized to modify the equivalence factor, which is defined as the fuel consumption rate per unit of electric power, for tracking the desired SOC trajectory. An instantaneous cost function, that consists of the fuel consumption rate of the genset and the equivalent fuel consumption rate of the battery, is minimized to find the optimum power distribution for the genset and the battery. Dynamic programming, which is a global minimization method, is employed to obtain the performance upper bound for the target REEV. Simulation results show that the proposed algorithm is adaptive for different driving cycles and can effectively increase the fuel economy of the thermostat control strategy (TCS) by 11.1% to 16%. The proposed algorithm can also reduce average charging/discharging powers and low SOC operations for possibly extending the battery life and increasing the battery efficiency, respectively. An experiment of the prototype REEV on a chassis dynamometer is set up with the proposed algorithm implemented on a real-time controller. Experiment results show that the proposed algorithm can increase the fuel economy of the TCS by 7.8% for the tested driving cycle. In addition, the proposed algorithm can reduce the average charge/discharge powers of TCS by 7.9% and 11.7%, respectively.

Investigating the effect of engine noise on power management strategy of a hybrid electric vehicle

2017 ◽  
Vol 232 (10) ◽  
pp. 1287-1296 ◽  
Author(s):  
Mojtaba Delkhosh ◽  
Masoud Aliramezani ◽  
Mahdi Khadem Nahvi
Keyword(s):  
Fuel Consumption ◽  
Power Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Combustion Noise ◽  
Speed Ratio ◽  
Noise Emission ◽  
Engine Noise ◽  
Power Management Strategy ◽  
Hybrid Electric

Hybrid electric vehicles (HEVs) have been developed as a promising way to decrease the fuel consumption and emissions of conventional vehicles. Although the noise emission of HEVs is generally lower than that of conventional vehicles, it is still an issue, especially in urban transportation. In this paper, a power management strategy is developed to minimize the annoying noise of the engine for an HEV. This is a modified version of the strategy that was originally established based on the speed ratio of continuously variable transmission (CVT) as the control parameter (CVT-based strategy). The engine combustion noise is assessed using the experimental data of the in-cylinder pressure. Also, the engine brake specific fuel consumption (bsfc) is defined from the experiments. The bsfc and noise data are implemented in the power management strategy. The proposed strategy offers a better performance in terms of reducing engine noise and fuel consumption in comparison with an electric assist control strategy (EACS). On the other hand, the proposed strategy results in a lower level of engine noise than the original CVT-based strategy at the expense of slightly increasing the fuel consumption. For instance, the noise level (dB) in an urban dynamometer driving cycle using the proposed strategy is 49% lower than the case of CVT-based strategy, while the vehicle FC is about 1.1% more than the CVT-based case.

scholarly journals Support vector machine–based driving cycle recognition for dynamic equivalent fuel consumption minimization strategy with hybrid electric vehicle

2018 ◽  
Vol 10 (11) ◽  
pp. 168781401881102
Author(s):  
QIN Shi ◽  
Duoyang Qiu ◽  
Lin He ◽  
Bing Wu ◽  
Yiming Li
Keyword(s):  
Support Vector Machine ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Driving Cycle ◽  
Support Vector ◽  
Equivalent Fuel ◽  
Hybrid Electric ◽  
Equivalent Fuel Consumption

For a great influence on the fuel economy and exhaust, driving cycle recognition is becoming more and more widely used in hybrid electric vehicles. The purpose of this study is to develop a method to identify the type of driving cycle in real time with better accuracy and apply the driving cycle recognition to minimize the fuel consumption with dynamic equivalent fuel consumption minimization strategy. The support vector machine optimized by the particle swarm algorithm is created for building driving cycle recognition model. Furthermore,the influence of the two parameters of window width and window moving velocity on the accuracy is also analyzed in online application. A case study of driving cycle in a medium-sized city is introduced based on collecting four typical driving cycle data in real vehicle test. A series of characteristic parameters are defined and principal component analysis is used for data processing. Finally, the driving cycle recognition model is used for equivalent fuel consumption minimization strategy with a parallel hybrid electric vehicle. Simulation results show that the fuel economy can improve by 9.914% based on optimized support vector machine, and the fluctuations of battery state of charge are more stable so that system efficiency and batter life are substantially improved.

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