scholarly journals Energy Management Strategy Implementation for Hybrid Electric Vehicles Using Genetic Algorithm Tuned Pontryagin’s Minimum Principle Controller

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Aishwarya Panday ◽  
Hari Om Bansal
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Management Strategy ◽  
Minimum Principle ◽  
Hybrid Vehicles ◽  
State Of Charge ◽  
Energy Management Strategy ◽  
Power Split ◽  
Hybrid Electric ◽  
State Of Charge Estimation

To reduce apace extraction of natural resources, to plummet the toxic emissions, and to increase the fuel economy for road transportation, hybrid vehicles are found to be promising. Hybrid vehicles use batteries and engine to propel the vehicle which minimizes dependence on liquid fuels. Battery is an important component of hybrid vehicles and is mainly characterized by its state of charge level. Here a modified state of charge estimation algorithm is applied, which includes not only coulomb counting but also open circuit voltage, weighting factor, and correction factor to track the run time state of charge efficiently. Further, presence of battery and engine together needs a prevailing power split scheme for their efficient utilization. In this paper, a fuel efficient energy management strategy for power-split hybrid electric vehicle using modified state of charge estimation method is developed. Here, the optimal values of various governing parameters are firstly computed with genetic algorithm and then fed to Pontryagin’s minimum principle to decide the threshold power at which engine is turned on. This process makes the proposed method robust and provides better chance to improve the fuel efficiency. Engine efficient operating region is identified to operate vehicle in efficient regions and reduce fuel consumption.

scholarly journals Optimization of the fuel economy and emissions for plug in hybrid electric recreational boat energy management strategy using genetic algorithm

2019 ◽  
Vol 10 (2) ◽  
pp. 792
Author(s):  
Jabar Siti Norbakyah ◽  
Abdul Rahman Salisa
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Conventional Vehicle ◽  
Cycle Simulation ◽  
Water Transportation ◽  
Hybrid Electric

<span>Today, the transportation sector has undergone a change from conventional vehicle to hybrid electric vehicle especially land-based with the aim to reduce fuel consumption and emissions. However, water transportation is also one of the contributors of excessive use of fuel and emissions. Therefore, water transport needs changes as it has been done on land transport, especially cars. In this paper, plug in hybrid electric recreational boat (PHERB) is introduced. PHERB is a special model because in PHERB powertrain configuration, it only needed one EM compared to existing configuration with energy management strategy (EMS).  In this work, the optimal EMS for PHERB are presented via genetic algorithm (GA). To estimate the fuel economy and emissions, the model of PHERB is employed numerically in the MATLAB/SIMULINK environment with a special EMS using Kuala Terengganu (KT) river driving cycle. Simulation result of PHERB optimization using GA improve to 15% for KT river driving cycles without violating the PHERB performance.</span>

scholarly journals Cloud Model-Based Energy Management Strategy for Parallel Hybrid Vehicles

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaolan Wu ◽  
Guifang Guo ◽  
Zhifeng Bai
Keyword(s):  
Energy Management ◽  
Management Strategy ◽  
Combustion Engine ◽  
Cloud Model ◽  
Hybrid Vehicles ◽  
State Of Charge ◽  
Energy Management Strategy ◽  
Control Scheme ◽  
Parallel Hybrid ◽  
Simulation Results

Using the uncertain conversion capacity between the expressions of quantitative and qualitative concept in the cloud model, an energy management strategy based on cloud model is developed for parallel hybrid vehicles (PHVs). By the driver input and the state of charge (SOC) of the energy storage, a set of rules are developed to effectively determine the torque split between the internal combustion engine (ICE) and the electric motor. An analysis of the simulation results is conducted using ADVISOR in order to verify the effectiveness of the proposed control strategy. It is confirmed that the control scheme can be used to improve fuel economy and emission of the hybrid vehicles.

scholarly journals A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Pei Li ◽  
Jun Yan ◽  
Qunzhang Tu ◽  
Ming Pan ◽  
Jinhong Xue
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Fuel Consumption ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Optimization Method ◽  
Energy Management Strategy ◽  
Quantum Genetic Algorithm ◽  
Hybrid Electric

The performance and fuel consumption of hybrid electric vehicle heavily depend on the EMS (energy management strategy). This paper presents a novel EMS for a series hybrid electric rescue vehicle. Firstly, considering the working characteristics of engine and battery, the EMS combining logic threshold and fuzzy control is proposed. Secondly, a fuzzy control optimization method based on IQGA (improved quantum genetic algorithm) is designed to achieve better fuel efficiency. Then, the modeling and simulation are completed by using MATLAB/Simulink; the results demonstrate that the fuel consumption can be decreased by 5.17% after IQGA optimization and that the optimization effect of IQGA is better than that of GA (genetic algorithm) and QGA (quantum genetic algorithm). Finally, the HILS (hardware in loop simulation) platform is constructed with dSPACE; the HILS experiment shows that the proposed EMS can effectively improve the vehicle working efficiency, which can be applied to practical application.

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