Component Sizing of Parallel Hybrid Electric Vehicle Using Optimal Search Algorithm

Kim, Jinseong; Kim, Gisu; Park, Yeong-il

doi:10.1007/s12239-018-0071-7

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Moreover, the system using the hill climbing method performs effectively in the dynamic process with short response time. Kim et al [15] performed component sizing of power sources of parallel hybrid vehicle by applying the golden section search and hill climbing algorithms. The golden section search algorithm was used in selecting a reduction gear ratio that connects the transmission to the electric motor by using the hill climbing search algorithm to find the optimal engine and electric motor sizes.…”

Section: Related Workmentioning

confidence: 99%

Best Path in Mountain Environment based on Parallel Hill Climbing Algorithm

Masadeh¹,

Sharieh²,

Jamal³

et al. 2020

IJACSA

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Heuristic search is a search process that uses domain knowledge in heuristic rules or procedures to direct the progress of a search algorithm. Hill climbing is a heuristic search technique for solving certain mathematical optimization problems in the field of artificial intelligence. In this technique, starting with a suboptimal solution is compared to starting from the base of the hill, and improving the solution is compared to walking up the hill. The optimal solution of the hill climbing technique can be achieved in polynomial time and is an NPcomplete problem in which the numbers of local maxima can lead to an exponential increase in computational time. To address these problems, the proposed hill climbing algorithm based on the local optimal solution is applied to the message passing interface, which is a library of routines that can be used to create parallel programs by using commonly available operating system services to create parallel processes and exchange information among these processes. Experimental results show that parallel hill climbing outperforms sequential methods.

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Section: Related Workmentioning

confidence: 99%

Best Path in Mountain Environment based on Parallel Hill Climbing Algorithm

Masadeh¹,

Sharieh²,

Jamal³

et al. 2020

IJACSA

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“…Reducing the engine size and increasing the motor size is a common way to reduce fossil fuel consumption. 21 Also, improvement in fuel economy (FE) can be achieved by increasing the motor efficiency and reducing the overall vehicle weight. 22 Along with weight reduction, vehicle operating performance parameters are considered here.…”

Section: Introductionmentioning

confidence: 99%

Ecodesigning and improving performance of plugin hybrid electric vehicle in rolling terrain through multi-criteria optimisation of powertrain

Bhattacharjee

Ghosh

Bhola

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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This work presents an ecodesigning and operating performance improvement methodology in series-parallel Plugin hybrid electric vehicle (PHEV) in passenger car category, through optimisation of powertrain, considering gradeability overreaching rolling terrain. Designing involves consideration for power of prime movers and the geometric specification governing gear ratio, which is the teeth number. PHEV performance is measured in terms of various output characteristics, such as, fuel economy, emissions, vehicle weight, battery charge, maximum velocity and maximum acceleration etc. and such output indicators comprising both ecodesign and vehicle operating performance attributes, eleven in all, are considered. For optimisation, the design space is generated using NREL, ADVISOR simulator in accordance with Taguchi’s method. Multi-criteria optimisation is used to converge the aforesaid output indicators into a single one using TOPSIS, MTOPSIS, Grey Relational Analysis and their surrogate assisted evolutionary algorithm (SAEA) based solutions to select the best from. Such design solutions are tested with UDDS driving cycle for performance analysis; reflecting superiority of SAEA based results. However, best values of output indicators are not from a single solution but are spread over these SAEAs. While, gradability is embedded in the model, its variation as supplemental factor, together with total ownership cost, are included, for extended modelling to ascertain the suitability amongst SAEAs. To extend the test for suitability beyond one driving cycle, also a combined one is formed by integrating two other, namely NEDC and 1015Prius with UDDS. The simulation experiment results from combined driving cycle also indicate preference in favour of MTOPSIS-SAEA model, complying upto 25% gradability for rolling terrain, substantially better than the reference model while also ensuring savings in fuel cost by about 60% over the entire ownership period besides reduction in greenhouse gas emissions ranging between 18% and 21%. This solution also helps in lightweighting the vehicle by over 6%.

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“…The authors of ref. [17] successfully downsized the main traction motor in a plugin hybrid vehicle from 120 KW to 30 KW for the harmonised vehicle driving cycle. Not only the component sizing, but also the topology selection could be found in other studies [18], which improves fuel economy by 14.4% and 44.9% in 2 different driving cycles.…”

Section: Introductionmentioning

confidence: 99%

Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance

Bhattacharjee

Ghosh

Bhola

et al. 2019

Energy

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This article presents an optimized series-parallel hybrid powertrain transformed from a conventional vehicle using an analytical approach without changing the original chassis. The proposed approach is based on a many-objective hybrid powertrain model, which aims to optimize the vehicle weight, fuel consumptions, emissions, and performances. Three different Surrogate Assisted Evolutionary Algorithms (SAEAs) are introduced based on the improved Non-dominated Sorting Genetic Algorithm (NSGA III), Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Multi-Objective Genetic Algorithm (MOGA) for optimization of powertrain components. Initially, the powertrain optimization is performed for Urban Dynamometer Driving Schedule (UDDS) driving cycle with 20% road grade. Thereafter, all the obtained Pareto solutions are combined, screened, and 78 best feasible design points are considered depending on the constraints imposed. Subsequently, 15 design points are randomly selected for validation in Federal Test Procedure (FTP) driving cycle without road grade. It is observed that NSGA III reduces the vehicle weight and fuel consumptions by 4.39% and 46.47% respectively. The powertrain energy efficiency is improved by 57.49%, and the engine is downsized by 40%. The contribution of this article is twofold. First, the many-objective simulation model for hybrid powertrain is developed. Second, SAEAs are implemented as optimization techniques for optimal component sizing and promising results are obtained.

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Component Sizing of Parallel Hybrid Electric Vehicle Using Optimal Search Algorithm

Cited by 4 publications

References 4 publications

Best Path in Mountain Environment based on Parallel Hill Climbing Algorithm

Best Path in Mountain Environment based on Parallel Hill Climbing Algorithm

Ecodesigning and improving performance of plugin hybrid electric vehicle in rolling terrain through multi-criteria optimisation of powertrain

Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance

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