Particle Swarm Optimization for Operational Parameters of Series Hybrid Electric Vehicle

Wang, Zhancheng; Huang, Bufu; Liu, Weimin; Xu, Yangsheng

doi:10.1109/robio.2006.340289

Cited by 17 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because vehicle usage depends on many factors including geographic area (North America, Asia, or Europe), type of lane (urban or motorway), and also vehicle category (private car or truck), a significant number of driving cycles have been considered for describing the diversity of the observed driving conditions. With regard to their consequent applications, driving cycles are most often applied to implement a power control strategy for optimization purposes [4]- [6].…”

Section: Nomenclaturementioning

confidence: 99%

“…The US06 Supplemental Federal Test Procedure was then developed to address the shortcomings with the last FTP-75 test cycle in the representation of aggressive, high speed, and/or high acceleration driving behavior, rapid speed fluctuations, and driving behavior following start-up. The cycle represents a 12.8-km route with an average speed of 78 km/h, a maximum speed of 129.2 km/h, and a duration of 596 s. In [4], Wang implements a power management strategy where parameters are optimized over both standard cycles, FTP-75 and the Highway Fuel Economy Cycle (HWFET) developed by the United States Environmental Protection Agency, for fuel economy and pollutant emissions limitation. Many other standard cycles have been derived from the combination of already approved standards [10], [11] according to the vehicle structure (parallel/full hybrid), the vehicle category (heavy/light duty), or else the kind of route (city, intercity, suburban, etc.)…”

Section: B Review Of Recent Developments In Terms Of Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Simulation of Real-World Vehicle Missions Using a Stochastic Markov Model for Optimal Powertrain Sizing

Souffran

Miègeville

Guérin

2012

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Growing concerns about petroleum supplies and air pollution have spurred increased interest and research into hybrid electric vehicles (HEVs). While standard driving cycles are commonly used for the purpose of propulsion chain optimization, the issue of how representative they actually are is questioned. This paper proposes a novel methodology for modeling real-world vehicle missions by considering the stochastic characteristics of the driving pattern and the dependence among the variables of the mission, i.e., vehicle speed, acceleration, and road gradient. The modeling procedure is based on a Markov matrix formulation, and a simulation algorithm is implemented to generate an unlimited number of stochastic mission profiles. Two kinds of mission natures have been modeled and discussed so as to stress the mission impact on the energy consumption according to the propulsion chain sizing. The approach is then validated using the architecture of a series HEV powered by a diesel generator and batteries. The results on fuel consumption are presented, and the benefit of the proposed method over conventional approaches is argued.Index Terms-Automotive applications, battery, diesel engine, drive cycle, fuel consumption, hybrid electric vehicle (HEV), Markov processes, power system modeling, power system simulation, sizing, stochastic systems.

show abstract

Section: Nomenclaturementioning

confidence: 99%

Section: B Review Of Recent Developments In Terms Of Applicationsmentioning

confidence: 99%

Simulation of Real-World Vehicle Missions Using a Stochastic Markov Model for Optimal Powertrain Sizing

Souffran

Miègeville

Guérin

2012

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…Also, to implement the global constraint, the authors developed a nonlinear penalty function in terms of battery SOC deviation from its desired value. Literature speaks that real-time optimization techniques like ECMS [6,7], model predictive control (MPC) [8][9][10], Neural Network (NN) [11,12], particle swarm optimization (PSO) [13][14][15], and Pontryagin's minimum principle (PMP) [16,17] are used extensively. Table 1 compares different real-time strategies with its pros and cons.…”

Section: Introductionmentioning

confidence: 99%

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

Panday

Bansal

2016

International Journal of Vehicular Technology

View full text Add to dashboard Cite

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.

show abstract

“…Derivative-free methods, such as genetic algorithms [6][7][8][9][10][11] or particle swarm optimization [12][13][14] have been proven to be a suitable approach to solve the HEV design optimization problem. However, most of these methods convert the multi-objective optimization problem into a single objective optimization problem by allocating weights to each of the objective functions (a priori methods).…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms

Fang

Qin

et al. 2011

Energies

View full text Add to dashboard Cite

Compared to conventional vehicles Hybrid Electric Vehicles (HEVs) provide fairly high fuel economy with lower emissions. To enhance HEV performance in terms of fuel economy and emissions, and ensure user satisfaction with driving performance, the need for simultaneous optimization for the main parameters of powertrain components and control system is inevitable. However, this problem is challenging due to the large amount of coupling design parameters, conflicting design objectives and nonlinear constraints. Considering the defect of the methods which convert multi-objective optimization problems into single-objective ones, a comprehensive methodology based on the non-dominated sorting genetic algorithms II (NSGA II) to achieve parameter optimization for powertrain components and control system simultaneously and successfully find the Pareto-optimal solutions set is presented in this paper. A case simulation is carried out and simulated by ADVISOR, The simulation results show that this method can produce many Pareto-optimal solutions and a satisfactory solution can be selected by decision-makers according to their requirements. The results demonstrate the effectiveness of the algorithms proposed in this paper.

show abstract

Particle Swarm Optimization for Operational Parameters of Series Hybrid Electric Vehicle

Cited by 17 publications

References 13 publications

Simulation of Real-World Vehicle Missions Using a Stochastic Markov Model for Optimal Powertrain Sizing

Simulation of Real-World Vehicle Missions Using a Stochastic Markov Model for Optimal Powertrain Sizing

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

Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms

Contact Info

Product

Resources

About