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.

A novel hybrid energy management strategy of a diesel-electric hybrid ship based on dynamic programing and model predictive control

2022 ◽  
pp. 147509022110689
Author(s):  
Yupeng Yuan ◽  
Mingshuang Chen ◽  
Jixiang Wang ◽  
Wanneng Yu ◽  
Boyang Shen
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Control Strategy ◽  
Management Strategy ◽  
Energy Management Strategy ◽  
Threshold Control ◽  
Rule Based ◽  
Dynamic Programing ◽  
Simulation Results

The energy-saving characteristics of diesel-electric series hybrid ships largely depend on their energy management strategy. In this paper, a strategy that combines dynamic programing and model predictive control (DP-MPC) is proposed to solve the energy management problems of diesel-electric hybrid ships. The DP-MPC strategy has considered some typical working conditions of a ship, and the corresponding influence of white noise disturbance on the control strategy was studied. The simulation results show that the DP-MPC strategy has an excellent anti-interference capability. The control performance of the DP-MPC strategy is then further analyzed and compared with the rule-based logic threshold control strategy. The simulation results show that the proposed DP-MPC strategy can save 2.5% of the fuel consumption and has a better anti-interference capability than the rule-based control strategy.

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.

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.

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