Optimization of the component sizing for a plug-in hybrid electric vehicle using a genetic algorithm

Madanipour, Vahid; Montazeri-Gh, Morteza; Mahmoodi-k, Mehdi

doi:10.1177/0954407015592256

Cited by 33 publications

(25 citation statements)

References 34 publications

(30 reference statements)

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“…More focus on some methods of Region-II is in the sequel. In Region-III, global cost minimum is searched by a systematic step-by-step backward calculation in DDP [33], by evolutionary algorithms [36,39,46], or by linear optimization [85]. These methods are used as benchmark for evaluation and analysis of other methods.…”

Section: Integration To Power Management Methodsmentioning

confidence: 99%

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…Therefore, it proved suitability for unconstrained multi-objective optimization problems in HEVs [36]. Operational constraints are handled by penalty functions to degrade the infeasible solutions and reduce its fitness [36]. In addition, to prevent premature convergence of the solution during the selection phase, simulated annealing (SA) is typically applied to the algorithm [37].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

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Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

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Abstract:In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development.

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Section: Integration To Power Management Methodsmentioning

confidence: 99%

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

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Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

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“…The real-time application of this method is limited due to high computational costs; usually it is only used to find a reference solution to analyze and adjust. The global optimization strategies can also be divided into: Linear Programming [24], Dynamic Programming [25], Genetic Algorithms [26], Optimal Controllers [27] or based on Particles Swarms Optimization (PSO) [28]. On the other hand, real-time optimization strategies apply instant power-handling decisions in order to minimize an objective function.…”

Section: Introductionmentioning

confidence: 99%

Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System

et al. 2019

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Standalone microgrids with photovoltaic (PV) solutions could be a promising solution for powering up off-grid communities. However, this type of application requires the use of energy storage systems (ESS) to manage the intermittency of PV production. The most commonly used ESSs are lithium-ion batteries (Li-ion), but this technology has a low lifespan, mostly caused by the imposed stress. To reduce the stress on Li-ion batteries and extend their lifespan, hybrid energy storage systems (HESS) began to emerge. Although the utilization of HESSs has demonstrated great potential to make up for the limitations of Li-ion batteries, a proper power management strategy is key to achieving the HESS objectives and ensuring a harmonized system operation. This paper proposes a novel power management strategy based on an artificial neural network for a standalone PV system with Li-ion batteries and super-capacitors (SC) HESS. A typical standalone PV system is used to demonstrate and validate the performance of the proposed power management strategy. To demonstrate its effectiveness, computational simulations with short and long duration were performed. The results show a minimization in Li-ion battery dynamic stress and peak current, leading to an increased lifespan of Li-ion batteries. Moreover, the proposed power management strategy increases the level of SC utilization in comparison with other well-established strategies in the literature.

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“…1,2 However, compared with conventional vehicles the driving range of electric vehicles (EVs) are still limited due to the low capacity and the long charging times of today's batteries. [3][4][5] The rangeextended electric vehicle (ReEV) is a clever solution due to its combination of a combustion engine and an electric generator, since both the application range and user acceptance are much greater. 6 However, it is faced with the challenge of more complex noise, vibration and harshness (NVH) behaviors at the same time.…”

Section: Introductionmentioning

confidence: 99%

Electric motor-based crankshaft stop position control to suppress range extender start vibration in electric vehicle

Guo

Han

Wang

et al. 2017

Journal of Low Frequency Noise, Vibration and Active Control

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Range-extended electric vehicles have the most complex noise and vibration problems since certain control strategies often make range extenders (REs) shut down or restart for the sake of better fuel consumption. This paper deals with this uncomfortable riding experience, especially during the range extender start phase. A control-oriented nonlinear model for the start–stop vibration analysis, including range extender mount system, engine–clutch–motor shaft system, engine inertia torque and force, engine friction torque, engine gas torque, engine manifold pressure, electric motor torque, and range extender controller, is thus built. In the developed model, a new estimation method for gas torque is proposed, where the initial crank angle is considered and the relevant equations are simplified. The method has proven to predict gas torque accurately without using a complex calculation process. According to the developed model, the active control method, crankshaft stop position control (CSPC) has been proposed. The crankshaft stop position is analyzed as well as the crankshaft movement with different speeds at top dead center is discussed, which lead to the design of the target curves for crankshaft movement during the stop phase. Based on the set-up model, CSPC is finally applied through the cascade control of the motor to evaluate the control effectiveness. The simulation outcomes demonstrate that CSPC can help the crankshaft to finally stop at the optimal initial crank angle, which effectively lessens the vibration in the next start phase.

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Optimization of the component sizing for a plug-in hybrid electric vehicle using a genetic algorithm

Cited by 33 publications

References 34 publications

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System

Electric motor-based crankshaft stop position control to suppress range extender start vibration in electric vehicle

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