NSGA-II multi-objectives optimization algorithm for energy management control of hybrid electric vehicle

2018 ◽  
Vol 233 (4) ◽  
pp. 1023-1034 ◽  
Author(s):  
Tao Deng ◽  
Chunsong Lin ◽  
Junlin Luo ◽  
Bingqu Chen
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Optimization Algorithm ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Management Control ◽  
Evaluation Index ◽  
Nsga Ii ◽  
Hybrid Electric ◽  
The Impact

The currently existing energy management control optimization for hybrid electric vehicle (HEV) mainly focuses on fuel economy. Apart from this, there has been some consideration of the impact of emissions, but almost no attention has been paid to drivability performance. Therefore, from the point of view of multi-objectives optimization, the influences of fuel economy, emission and drivability performance on the energy management are comprehensively considered for a parallel HEV. The energy management control parameters and driveline parameters are selected to be optimized parameters. Then, the NSGA-II (Fast Non-dominated Sorting Genetic Algorithm-II) algorithm is proposed to solve the multi-objectives optimization problem. Furthermore, the multi-objectives optimization method for HEV energy management control is established and comparatively simulated with the parallel electric assist control strategy. The results show that the evaluation index of drivability decreases by 27.12% from the maximum and the average enhancement effect of optimization falls by 20.84%. The evaluation index of fuel economy declines by 22.30% from the maximum and the average index drops by 20.26%. The comprehensive index of emission performance descends by 11.33% from the maximum. The proposed multi-objectives optimization algorithm has good convergence and distribution, and obtains more Pareto optimal solution sets, which can provide more selectivity in building HEV energy management control strategies.

scholarly journals Modeling and energy management strategy research of a power-split hybrid electric vehicle

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096262
Author(s):  
Yupeng Zou ◽  
Ruchen Huang ◽  
Xiangshu Wu ◽  
Baolong Zhang ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Research Work ◽  
Energy Management Strategy ◽  
Power Sources ◽  
Power Split ◽  
Hybrid Electric ◽  
The Impact

A power-split hybrid electric vehicle with a dual-planetary gearset is researched in this paper. Based on the lever analogy method of planetary gearsets, the power-split device is theoretically modeled, and the driveline simulation model is built by using vehicle modeling and simulation toolboxes in MATLAB. Six operation modes of the vehicle are discussed in detail, and the kinematic constraint behavior of power sources are analyzed. To verify the rationality of the modeling, a rule-based control strategy (RB) and an adaptive equivalent consumption minimization strategy (A-ECMS) are designed based on the finite state machine and MATLAB language respectively. In order to demonstrate the superiority of A-ECMS in fuel-saving and to explore the impact of different energy management strategies on emission, fuel economy and emission performance of the vehicle are simulated and analyzed under UDDS driving cycle. The simulation results of the two strategies are compared in the end, shows that the modeling is rational, and compared with RB strategy, A-ECMS ensures charge sustaining better, enables power sources to work in more efficient areas, and improves fuel economy by 8.65%, but significantly increases NOx emissions, which will be the focus of the next research work.

scholarly journals Energy Management Strategy Design and Simulation Validation of Hybrid Electric Vehicle Driving in an Intelligent Fleet

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1516
Author(s):  
Xin Ye ◽  
Fei Lai ◽  
Zhiwei Huo
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Combination Method ◽  
Driving Safety ◽  
System Efficiency ◽  
Hybrid Electric ◽  
The Impact

This paper proposes a combination method of longitudinal control and fuel management for an intelligent Hybrid Electric Vehicle (HEV) fleet. This method can reduce the fuel consumption while maintaining the distance and speed for each vehicle in the fleet. An HEV system efficiency model was established to simulate the impact of different working modes. Based on the principle of optimal vehicle system efficiency, the energy management control strategy of HEV was designed. Then, the driver model of the piloting vehicle and the following vehicle was built by using an intelligent fuzzy control method. Finally, the intelligent fleet model and energy matching model of HEV were integrated with the simulation platform that was developed based on MATLAB/Simulink/Stateflow. The validity of the energy matching strategy of HEV under the principle of optimal system efficiency was verified by simulation results, and the purpose of improving the driving safety, traffic efficiency, and fuel economy of the fleet was achieved. Comparing with the conventional control strategy, the proposed method saved 7.79% of fuel for the HEV fleet. Meanwhile, the distance ranges between the vehicles were from 12 meters to 15 meters, which improved the driving safety, passing rate, and fuel economy.

Energy Management and Control of a Hybrid Electric Vehicle With an Integrated Low Temperature Combustion (LTC) Engine

2014 ◽  
Author(s):  
Ali Solouk ◽  
Mahdi Shahbakhti ◽  
Mohammad J. Mahjoob
Keyword(s):  
Low Temperature ◽  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Low Temperature Combustion ◽  
Si Engine ◽  
Hcci Engine ◽  
Temperature Combustion ◽  
Hybrid Electric

Low Temperature Combustion (LTC) provides a promising solution for clean energy-efficient engine technology which has not yet been utilized in Hybrid Electric Vehicle (HEV) engines. In this study, a variant of LTC engines, known as Homogeneous Charge Compression Ignition (HCCI), is utilized for operation in a series HEV configuration. An experimentally validated dynamic HCCI model is used to develop required engine torque-fuel consumption data. Given the importance of Energy Management Control (EMC) on HEV fuel economy, three different types of EMCs are designed and implemented. The EMC strategies incorporate three different control schemes including thermostatic Rule-Based Control (RBC), Dynamic Programming (DP), and Model Predictive Control (MPC). The simulation results are used to examine the fuel economy advantage of a series HEV with an integrated HCCI engine, compared to a conventional HEV with a modern Spark Ignition (SI) engine. The results show 12.6% improvement in fuel economy by using a HCCI engine in a HEV compared to a conventional HEV using a SI engine. In addition, the selection of EMC strategy is found to have a strong impact on vehicle fuel economy. EMC based on DP controller provides 15.3% fuel economy advantage over the RBC in a HEV with a HCCI engine.

Study the Impact of Driver’s Behavior on the Energy Efficiency of Hybrid Electric Vehicles

2013 ◽  
Author(s):  
Lei Feng ◽  
Bo Chen
Keyword(s):  
Energy Loss ◽  
Electric Vehicle ◽  
Simulation Study ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Fuel Efficiency ◽  
Aggressive Driving ◽  
Driving Cycles ◽  
Hybrid Electric ◽  
The Impact

This paper investigates the impact of driver’s behavior on the fuel efficiency of a hybrid electric vehicle (HEV) and its powertrain components, including engine, motor, and battery. The simulation study focuses on the investigation of power request, power output, energy loss, and operating region of powertrain components with the change of driver’s behavior. It is well known that a noticeable difference between the sticker number fuel economy and actual fuel economy will happen when a driver drives aggressively. To simulate aggressive driving, the input driving cycles are scaled from the baseline driving cycles to increase the level of acceleration/deceleration. With scaled aggressive driving cycles, the simulation result shows a significant change of HEV equivalent fuel economy. In addition, the high power demands of aggressive driving cause engine to operate within a higher fuel rate region. Furthermore, the engine is started and shut down frequently due to the large instantaneous power request peaks, which result in high energy loss. The simulation study of the impact of aggressive driving on the HEV fuel efficiency is conducted for a power-split hybrid electric vehicle using powertrain simulation and analysis software Autonomie developed by Argonne National Laboratory. The performance of the major powertrain components is analyzed when the HEV operates at different level of aggressiveness. The simulation results provide useful information to identify the major factors that need to be included in the vehicle control design to improve the fuel efficiency of HEVs under aggressive driving.

Hybrid Electric Vehicle Energy Management and Control Based on Battery Energy Balance

2013 ◽  
Vol 273 ◽  
pp. 764-767
Author(s):  
Bin Yan ◽  
Yan Qing Hu ◽  
Ting Yan ◽  
Pei Pei Ma ◽  
Lin Yang
Keyword(s):  
Energy Balance ◽  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Power Efficiency ◽  
Hybrid Electric Vehicle ◽  
Real Time Control ◽  
Energy Management Strategy ◽  
Hybrid Electric ◽  
Battery Energy

Hybrid electric vehicle has better power and economy than conventional vehicle attributed to power efficiency range is optimized by battery energy. So making battery energy balance not only can ensure hybrid power system operate normally, but also is the key role in meeting vehicle drivability and improving fuel economy effectively. This paper analyze of the regenerating and using of battery energy. Real-time control and global optimization is used to adjust energy management strategy, the adaptive control strategy also introduced to making energy power balance on the basis of maximum fuel economy in the driving cycle.

Mode shift map design and integrated energy management control of a multi-mode hybrid electric vehicle

2017 ◽  
Vol 204 ◽  
pp. 476-488 ◽  
Author(s):  
Weichao Zhuang ◽  
Xiaowu Zhang ◽  
Daofei Li ◽  
Liangmo Wang ◽  
Guodong Yin
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Management Control ◽  
Mode Shift ◽  
Shift Map ◽  
Map Design ◽  
Hybrid Electric ◽  
Multi Mode
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