Multi-objective optimization of a parallel hybrid electric drive train

Bertram, Christiane; Buecherl, Dominik; Thanheiser, Andreas; Herzog, Hans-Georg

doi:10.1109/vppc.2011.6043154

Cited by 13 publications

(10 citation statements)

References 14 publications

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“…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%

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: Genetic Algorithm (Ga)mentioning

confidence: 99%

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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“…During the sizing process, the maximal power of the ICE, P ICE is changed applying a scaling factor on the torque and the fuel consumption ( ICE =P ICE /P ICE_nom ). It is usually assumed that the power density is constant and lengths proportional to  ICE 1/3 [2]. Mass moments of inertia have units of dimension M.L 2 , this leads to a mass proportional to  ICE and a moment of inertia proportional to  ICE 5/3…”

Section: C Modeling Of the Hybrid Electrical Vehiclementioning

confidence: 99%

“…has a high impact on the operating of the machine. The classical design process usually performed for HEVs consists in sizing energetically the system using coarse models of components such as efficiency maps for the EM [2] [4]. It fixes the constraints on the components (battery number of elements, engine and electrical machine minimum required torque, etc.)…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of the sizing of a hybrid electrical vehicle

Reinbold

Gerbaud

Vinot³

2017

International Journal of Applied Electromagnetics and Mechanics

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Hybrid electrical vehicles involve two sources of energy, usually gasoline and electricity. The energy management determines the power sharing between the internal combustion engine and the electrical machine (EM). It is highly dependent on the driving cycle (i.e. the use of the vehicle). In this context, the optimal sizing of the EM is determined by: the driving cycle, the power-train characteristics (i.e. ratios and physical limitations e.g. maximum torque available) and the energy management. The key idea of this work is to involve the driving cycle and the environment of the electrical machine in a global multi-objective optimization process taking into account an optimal energy management and an accurate model of the EM based on magnetic circuit equivalent model

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“…To solve the combined optimization problem of the sizing and the management of the system a global approach has been proven to be the most efficient for road hybrid vehicle with one engine [8], [9], [10]. The energy management can then be calculated in an optimal manner [11], [12], [13] or with methods close to optimality [14], [7].…”

Section: Introductionmentioning

confidence: 99%

Sizing of a combined series-parallel hybrid architecture for river ship application using genetic algorithm and optimal energy management

Derollepot

Vinot

2019

Mathematics and Computers in Simulation

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A good way to reduce the fuel consumption of a commercial river ship can be the hybridization of the system. Sizing the system related to its actual use and energy management is then a key-point. In this paper a global sizing method based on Genetic Algorithm and dynamic programming is proposed. It allows the sizing of a complex series-parallel architecture on uses cycles which are developed using measurements on real ship. The study shows potential reduction of fuel consumption around 10 %, and proposes some leads to reduce the pollutant emissions.

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Multi-objective optimization of a parallel hybrid electric drive train

Cited by 13 publications

References 14 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

Multi-objective optimization of the sizing of a hybrid electrical vehicle

Sizing of a combined series-parallel hybrid architecture for river ship application using genetic algorithm and optimal energy management

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