Q-Learning-based fuzzy energy management for fuel cell/supercapacitor HEV

2022 ◽  
pp. 014233122110694
Author(s):  
Jili Tao ◽  
Ridong Zhang ◽  
Zhijun Qiao ◽  
Longhua Ma
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Learning Strategy ◽  
Fuzzy Controller ◽  
Management Strategies ◽  
Energy Management Strategy ◽  
Q Learning ◽  
Driving Pattern ◽  
Fuzzy Energy

A novel fuzzy energy management strategy (EMS) based on improved Q-learning controller and genetic algorithm (GA) is proposed for the real-time power split between fuel cell and supercapacitor of hybrid electric vehicle (HEV). Different from driving pattern recognition–based method, Q-Learning controller takes actions by observing the driving states and compensates to fuzzy controller, that is, no need to know the driving pattern in advance. Aimed to prolong the fuel cell lifetime and decrease its energy consumption, the initial values of Q-table are optimized by GA. Moreover, to enhance the environment adaptation capability, the learning strategy of Q-learning controller is improved. Two adaptive energy management strategies have been compared, and simulation results show that current fluctuation can be reduced by 6.9% and 41.5%, and H2 consumption can be saved by 0.35% and 6.08%, respectively. Meanwhile, state of charge (SOC) of supercapacitor is sustained within the desired safe range.

scholarly journals Multi-objective Parameters Optimization for HEV Based on improved Particle Swarm Algorithm

2019 ◽  
Vol 118 ◽  
pp. 02005
Author(s):  
Ying Ai ◽  
Yuanjie Gao ◽  
dongsheng Liu
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Energy Management Strategy ◽  
Multi Objective

Hybrid electric vehicle fuel consumption and emissions are closely related to its energy management strategy. A fuzzy controller of energy management using vehicle torque request and battery state of charge (SOC) as inputs, engine torque as output is designed in this paper foe parallel hybrid electric vehicle. And a multi-objective mathematical function which purpose on maximize fuel economy and minimize emissions is also established, in order to improve the adaptive ability and the control precision of basic fuzzy controller, this paper proposed an improved particle swarm algorithm that based on dynamic learning factor and adaptive inertia weight to optimize the control parameters. Simulation results based on ADVISOR software platform show that the optimized energy management strategy has a better distribution of engine and motor torque, which helps to improved the vehicle’s fuel economy and exhaust emission performance.

scholarly journals An Online Energy Management Strategy for a Fuel Cell/Battery Vehicle Considering the Driving Pattern and Performance Drift Impacts

2019 ◽  
Vol 68 (12) ◽  
pp. 11427-11438 ◽  
Author(s):  
Mohsen Kandidayeni ◽  
Alvaro Omar Macias Fernandez ◽  
Arash Khalatbarisoltani ◽  
Loic Boulon ◽  
Sousso Kelouwani ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Management Strategy ◽  
Energy Management Strategy ◽  
Driving Pattern ◽  
And Performance

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.

scholarly journals Examination on Classification of EVs and Energy Management Strategies of HEV

2019 ◽  
Vol 9 (1S) ◽  
pp. 261-266
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Management Strategies ◽  
Transport Sector ◽  
Energy Management Strategy ◽  
Power Sources ◽  
Power Train ◽  
Range Anxiety

In recent days, the demand for petroleum and emission of pollutant gases continuously increase. This necessitates the electrification power train which replaces Internal Combustion Engine (ICE). Despite pure electric vehicles or Battery Electric Vehicle (EV) reduce the greenhouse gas emissions, there are some major hurdles for EVs to overcome before they totally relieve ICE vehicles form transport sector such as range anxiety, battery storage, economic fall down due to automobile industries, etc. This necessitates Hybrid Electric vehicle (HEV) which combines two different power sources to propel the vehicle. One of the challenges in HEV is how to control the power coming from the two different sources such as battery and ICE. The prime goal of an Energy Management Strategy (EMS) is to manage energy flow such that fuel consumption and emissions are minimized without affecting the vehicle’s performance. In this paper, the different structures of power train and energy management strategies are analysed.

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