scholarly journals Fuzzy energy management strategy for electric vehicle combining driving cycle construction and air-conditioning load identification

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199438
Author(s):  
Chaofeng Pan ◽  
Yuanxue Tao ◽  
Limei Wang ◽  
Huanhuan Li ◽  
Jufeng Yang
Keyword(s):  
Energy Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Control Strategy ◽  
Air Conditioning ◽  
Management Strategy ◽  
Energy Management Strategy ◽  
Random Load ◽  
Load Fluctuation ◽  
Fuzzy Control Strategy

Energy management strategy is developed by considering the random and air conditioning load fluctuation, which greatly affected the torque control of the electric motor in electric vehicle. Firstly, the vehicle power consumption model is established, based on the influencing factors of electric vehicle energy consumption: random load and air conditioning load. Therefore, driving conditions with random characteristics representing the actual random load are constructed. According to the clustered characteristic parameters, the driving conditions were classified as different driving modes. Secondly, the mode of predicted condition was taken as a variable to evaluate the logic threshold strategy and fuzzy control strategy in which the influence of air conditioning was considered. Finally, under the condition of New European Driving Cycle (NEDC), the proposed management strategy was simulated in software environment, and the hardware in-loop (HIL) test was performed to verify the strategy. The simulation and HIL test results show that the proposed energy management strategy can increase the driving range by considering the load fluctuation of air conditioning. Furthermore, the strategy combining the driving mode prediction can alleviate the decline rate of SOC. And the fuzzy control strategy has better adaptability in complex conditions and lower battery energy consumption rate.

scholarly journals Optimization and Application for Hydraulic Electric Hybrid Vehicle

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 322 ◽  
Author(s):  
Hsiu-Ying Hwang ◽  
Tian-Syung Lan ◽  
Jia-Shiun Chen
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Energy Management ◽  
Economic Performance ◽  
Control Strategy ◽  
Management Strategy ◽  
Hybrid Vehicle ◽  
Management Control ◽  
Energy Management Strategy ◽  
Rule Based

Targeting the application of medium and heavy vehicles, a hydraulic electric hybrid vehicle (HEHV) was designed, and its energy management control strategy is discussed in this paper. Matlab/Simulink was applied to establish the pure electric vehicle and HEHV models, and backward simulation was adopted for the simulation, to get the variation of torque and battery state of charge (SOC) through New York City Cycle of the US Environmental Protection Agency (EPA NYCC). Based on the simulation, the energy management strategy was designed. In this research, the rule-based control strategy was implemented as the energy distribution management strategy first, and then the genetic algorithm was utilized to conduct global optimization strategy analysis. The results from the genetic algorithm were employed to modify the rule-based control strategy to improve the electricity economic performance of the vehicle. The simulation results show that the electricity economic performance of the designed hydraulic hybrid vehicle was improved by 36.51% compared to that of a pure electric vehicle. The performance of energy consumption after genetic algorithm optimization was improved by 43.65%.

Fuel Cell Electric Vehicle Energy Management Strategy Simulation Study

2012 ◽  
Vol 535-537 ◽  
pp. 1597-1600
Author(s):  
Ji Zhang ◽  
Shen Bao Wang
Keyword(s):  
Fuel Cell ◽  
Electric Vehicle ◽  
Energy Management ◽  
Control Strategy ◽  
Management Strategy ◽  
Management Strategies ◽  
Energy Management Strategy ◽  
Hybrid Energy ◽  
Advantages And Disadvantages ◽  
Fuel Cell Electric Vehicle

The advantages and disadvantages for several hybrid energy management strategies were analyzed in this paper. Power follower strategy served as the control strategy for some fuel cell electric vehicle. The control strategy was modeled and simulated in Advisor. The results indicate that the control strategy can manage the multiple energy sources well.

scholarly journals An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yanli Yin ◽  
Yan Ran ◽  
Liufeng Zhang ◽  
Xiaoliang Pan ◽  
Yong Luo
Keyword(s):  
Reinforcement Learning ◽  
Electric Vehicle ◽  
Energy Management ◽  
Control Strategy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Power Demand ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Mild Hybrid

For global optimal control strategy, it is not only necessary to know the driving cycle in advance but also difficult to implement online because of its large calculation volume. As an artificial intelligent-based control strategy, reinforcement learning (RL) is applied to an energy management strategy of a super-mild hybrid electric vehicle. According to time-speed datasets of sample driving cycles, a stochastic model of the driver’s power demand is developed. Based on the Markov decision process theory, a mathematical model of an RL-based energy management strategy is established, which assumes the minimum cumulative return expectation as its optimization objective. A policy iteration algorithm is adopted to obtain the optimum control policy that takes the vehicle speed, driver’s power demand, and state of charge (SOC) as the input and the engine power as the output. Using a MATLAB/Simulink platform, CYC_WVUCITY simulation model is established. The results show that, compared with dynamic programming, this method can not only adapt to random driving cycles and reduce fuel consumption of 2.4%, but also be implemented online because of its small calculation volume.

Research on real-time control strategy of multi-power flow of dual-mode power-split hybrid electric vehicle

2021 ◽  
pp. 095440702110652
Author(s):  
Hui Liu ◽  
Xunming Li ◽  
Lijin Han ◽  
Weida Wang ◽  
Changle Xiang
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Control Strategy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Power Flow ◽  
Energy Management Strategy ◽  
Dual Mode ◽  
Power Split ◽  
Hybrid Electric

With the continuous development of hybrid vehicle control technology, great progress has been made in the research of multi-power flow collaborative control. Due to the internal delay link of each power component, the role of energy storage element, and the limitation of electric power in the whole system, the inevitable delay characteristic of state transfer is caused. Therefore, the speed of multi-power flow control torque coordinated response of hybrid vehicles needs to be improved. The dual-mode power-split hybrid electric vehicle (DMPS-HEV) overall structure and working modes are analyzed, by adopting the combination of theory and experiment method. In order to solve the problem that the power components of dual-mode power-split hybrid electric vehicle cannot follow the optimal control command of the upper energy management strategy quickly due to the engine response delay, thus affecting the control effect of the upper energy management strategy. The research on torque coordination control strategy is carried out, the reference model of electromechanical composite drive is established, and the model reference adaptive coordination control strategy based on Lyapunov stability theory is proposed. The results show that the proposed model reference adaptive torque coordinated control strategy significantly improves the effect of engine response delay on the optimization effect of energy management strategy, and can achieve the control effect of the optimal control strategy of 93.58%. The test platform of the dual-mode power-split hybrid electric vehicle was built. The control system was built based on the rapid control prototype, and the data acquisition system was built based on the NI data acquisition module. The coordinated control strategy of the dual-mode power-split hybrid electric vehicle power system proposed in this paper was verified through the bench test to significantly improve the vehicle fuel economy and the real-time performance of the control strategy, which has a good practical value

scholarly journals Electrified Powertrain with Multiple Planetary Gears and Corresponding Energy Management Strategy

Vehicles ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 341-356
Author(s):  
Daizy Rajput ◽  
Jose M. Herreros ◽  
Mauro S. Innocente ◽  
Joschka Schaub ◽  
Arash M. Dizqah
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Mode Selection ◽  
Optimal Operation ◽  
Fourth Generation ◽  
Selection Strategy ◽  
Energy Management Strategy ◽  
Planetary Gears

Modern hybrid electric vehicles (HEVs) like the fourth generation of Toyota Prius incorporate multiple planetary gears (PG) to interconnect various power components. Previous studies reported that increasing the number of planetary gears from one to two reduces energy consumption. However, these studies did not compare one PG and two PGs topologies at their optimal operation. Moreover, the size of the powertrain components are not the same and hence the source of reduction in energy consumption is not clear. This paper investigates the effect of the number of planetary gears on energy consumption under optimal operation of the powertrain components. The powertrains with one and two PGs are considered and an optimal simultaneous torque distribution and mode selection strategy is proposed. The proposed energy management strategy (EMS) optimally distributes torque demands amongst the power components whilst also controlling clutches (i.e., mode selection). Results show that increasing from one to two PGs reduces energy consumption by 4%.

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