Multiple-objective Real-time Optimum Control Strategy for Fuel Consumption and Emission of Full Hybrid Electric Vehicle

2012 ◽  
Vol 48 (06) ◽  
pp. 83 ◽  
Author(s):  
Datong QIN
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Multiple Objective ◽  
Optimum Control ◽  
Hybrid Electric

scholarly journals Fuzzy Logic Controller for Parallel Plug-in Hybrid Vehicle

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Global Optimization ◽  
Real Time ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Real Time Control ◽  
Switching Strategy ◽  
Time Control ◽  
Hybrid Electric

Control strategies for hybrid electric vehicles are usually aimed at several simultaneous objectives. The primary one is usually the minimization of the vehicle fuel consumption, while also attempting to minimize engine emissions and maintaining or enhancing drivability. Regardless of the topology of the vehicle, the essence of the HEV control problem is the instantaneous management of the power flows from more devices to achieve the overall control objectives. One important characteristic of this generic problem is that the control objectives are mostly integral in nature (fuel consumption and emission per mile of travel), or semi-local in time like drivability, while the control actions are local in time. Furthermore, the control objectives are often subject to integral constraints, such as nominally maintaining the battery state-of-charge (SOC). The global nature of both objectives and constraints do not lend itself to traditional global optimization technique, because the main problem with global optimization index is whole of driving cycle should be predetermined and real time control strategy is not implemented simply. A common method to control of the complex dynamic systems with many uncertainties is designing some different of local controllers each for a specific operating area or determined objects and then designing of a switching strategy through the subsystems to achieve the global objectives of the system. In this research, the control structure has been investigated due to the complexity of hybrid electric vehicle powertrain. From the view point of hierarchy, the switching strategy relates to upper hierarchy and plays the key role in systems operating. Then for each subsystems of hybrid electric vehicle, itself local controller has been designed and after that in order to achieve the operating objectives, switching strategy through subsystems for the real time control strategy has been designed.

scholarly journals Enhanced Q-learning for real-time hybrid electric vehicle energy management with deterministic rule

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1493-1503
Author(s):  
Yang Li ◽  
Jili Tao ◽  
Liang Xie ◽  
Ridong Zhang ◽  
Longhua Ma ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Power Allocation ◽  
Real Time ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Control Policy ◽  
Q Learning ◽  
Hybrid Electric

Power allocation plays an important and challenging role in fuel cell and supercapacitor hybrid electric vehicle because it influences the fuel economy significantly. We present a novel Q-learning strategy with deterministic rule for real-time hybrid electric vehicle energy management between the fuel cell and the supercapacitor. The Q-learning controller (agent) observes the state of charge of the supercapacitor, provides the energy split coefficient satisfying the power demand, and obtains the corresponding rewards of these actions. By processing the accumulated experience, the agent learns an optimal energy control policy by iterative learning and maintains the best Q-table with minimal fuel consumption. To enhance the adaptability to different driving cycles, the deterministic rule is utilized as a complement to the control policy so that the hybrid electric vehicle can achieve better real-time power allocation. Simulation experiments have been carried out using MATLAB and Advanced Vehicle Simulator, and the results prove that the proposed method minimizes the fuel consumption while ensuring less and current fluctuations of the fuel cell.

Research on Modeling and Control Strategy of Parallel Hybrid Electric Back-Loading Compression Sanitation Vehicle

2013 ◽  
Vol 420 ◽  
pp. 355-362
Author(s):  
Rong Yang ◽  
Di Ming Lou ◽  
Pi Qiang Tan ◽  
Zhi Yuan Hu
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Stable Operation ◽  
Parallel Hybrid Electric Vehicle ◽  
Fuel Consumption Reduction ◽  
Consumption Reduction ◽  
Parallel Hybrid ◽  
Hybrid Electric

Establish simulation models of the conventional and parallel hybrid electric back-loading compression sanitation vehicle by AVL CRUISE and MATLAB/Simulink software. Study on control strategy of parallel hybrid electric vehicle based on the work characteristics of back-loading compression sanitation. Results show that: about 24.5% fuel consumption reduction in hybrid modeling compared to the conventional sanitation vehicle under heavy commercial vehicle standard test cycle (C-WTVC, Adapted World Transient Vehicle Cycle), and battery SOC was little changed at 50%. About 32% fuel consumption reduction in hybrid compared to the conventional vehicle under the actual road testing spectrum, and SOC increased about 21.6% relative to the initial state. It controls the engine to work in more stable operation region and reduces engine idle time, but increases engine start-stop times. It also could provide some references for specific engine development of parallel hybrid electric vehicle

scholarly journals Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Haitao Yan ◽  
Yongzhi Xu
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Control ◽  
Time Performance ◽  
Equivalent Fuel ◽  
Hybrid Electric ◽  
Equivalent Fuel Consumption

Energy control strategy is a key technology of hybrid electric vehicle, and its control effect directly affects the overall performance of the vehicle. The current control strategy has some shortcomings such as poor adaptability and poor real-time performance. Therefore, a transient energy control strategy based on terminal neural network is proposed. Firstly, based on the definition of instantaneous control strategy, the equivalent fuel consumption of power battery was calculated, and the objective function of the minimum instantaneous equivalent fuel consumption control strategy was established. Then, for solving the time-varying nonlinear equations used to control the torque output, a terminal recursive neural network calculation method using BARRIER functions is designed. The convergence characteristic is analyzed according to the activation function graph, and then the stability of the model is analyzed and the time efficiency of the error converging to zero is deduced. Using ADVISOR software, the hybrid power system model is simulated under two typical operating conditions. Simulation results show that the hybrid electric vehicle using the proposed instantaneous energy control strategy can not only ensure fuel economy but also shorten the control reaction time and effectively improve the real-time performance.

D2P Real-Time Simulation of Energy Management Strategy for Parallel Hybrid Electric Vehicle

2013 ◽  
Vol 278-280 ◽  
pp. 1704-1707
Author(s):  
Ze Yu Chen ◽  
Guang Yao Zhao
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Power Sources ◽  
Real Time Simulation ◽  
Parallel Hybrid Electric Vehicle ◽  
Time Simulation ◽  
Parallel Hybrid ◽  
Hybrid Electric

For investigating the feasibility of control strategy used in parallel hybrid electric vehicle, a D2P real-time simulation is introduced. A new multi-mode rule-based control strategy is proposed. The strategy is based on splitting the torque from the motor and engine so that these power sources can be operated at high efficiency. A real-time simulation platform is built based on D2P system. Real driver inputs and controller are applied while controlled objects are simulated using the model of parallel hybrid electric system computed in D2P module. Strategy is validated by D2P real-time simulation, which results show that the presented strategy is feasible and effective.

An Energy Management Strategy for Power-split Hybrid Electric Vehicle using Model Predictive Control

2021 ◽  
Vol 18 (4) ◽  
Author(s):  
Muhammad Zahid ◽  
Naseer Ahmad
Keyword(s):  
Model Predictive Control ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Predictive Control ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Specific Fuel Consumption ◽  
Power Split ◽  
Hybrid Electric

To fulfil future demand for energy and to control pollution, a power-split hybrid electric vehicle is a promising solution combining attributes of a conventional vehicle and an electric vehicle. Since energy is available from two subsystems i.e, engine and battery, there is the freedom to manage it optimally. In this work, model predictive control strategy, that has the constraint handling which makes it a better choice over other strategies for efficient energy management of hybrid electric vehicles. A detailed mathematical model of the power split configured hybrid electric vehicle is developed that encompasses the engine, planetary gear, motor/generator, inverter, and battery. An interior-point optimizer based-nonlinear model predictive control strategy is applied to the developed model by incorporation of operational constraints and cost function. The objective is to curtail fuel consumption while the battery’s state of charge should be maintained within predefined limits. The complete developed model was simulated in MATLAB for motor, generator, engine speed, and battery SoC. Computed specific fuel consumption from the proposed MPC during the NEDC and the HWFET cycles are 4.356liters/100km and 2.474 litres/100 km, respectively. These findings are validated by the rule-based strategy of ADVISOR 2003 that provides 4.900 litres/100 km and 3.600 litres/100 km over the NEDC and the HWFET cycles, respectively. This indicates that the proposed MPC shows 11.11 % and 31.26 % improvement in specific fuel consumption in the NEDC and HWFET drive cycles respectively.

Multiple-Objective Optimal Control of Hybrid Electric Vehicle

2013 ◽  
Vol 753-755 ◽  
pp. 1659-1664
Author(s):  
Jun Yan
Keyword(s):  
Optimal Control ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Optimal Control Strategy ◽  
Parallel Hybrid Electric Vehicle ◽  
Hybrid Electric ◽  
Fuel Consumption And Emissions

To reduce the fuel consumption and exhaust (HC, CO) emissions of parallel hybrid electric vehicle, the control strategy of the hybrid electric vehicle is studied in this paper. First it briefly analyzes the structure and working principle of the parallel hybrid electric vehicle drive system. Then a cost function is proposed which explains the fuel consumption and emissions. According to the minimum principle the minimum of the cost function can be got, consequently, the optimal control strategy can be obtained. Furthermore, in order to verify the effectiveness of the optimal control strategy, in MATLAB environment, it establishes a dynamic simulation model for hybrid electric vehicles. Through a comparative study between the optimal control strategy on and the traditional rules control strategy, the results of experiment it reveals that the optimal control strategy can effectively reduces fuel consumption and emissions of hybrid electric vehicles.

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