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Ecodesigning and improving performance of plugin hybrid electric vehicle in rolling terrain through multi-criteria optimisation of powertrain
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering ( IF 1.7 ) Pub Date : 2021-07-03 , DOI: 10.1177/09544070211027531
Debraj Bhattacharjee 1 , Tamal Ghosh 2 , Prabha Bhola 1 , Kristian Martinsen 2 , Pranab Dan 1
Affiliation  

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%.



中文翻译:

多指标优化动力总成生态设计及提升插电式混合动力汽车在起伏地形中的性能

这项工作提出了乘用车类串并联插电式混合动力电动汽车 (PHEV) 的生态设计和运行性能改进方法,通过优化动力系统,考虑爬坡能力越过滚动地形。设计涉及考虑原动机的功率和控制齿轮比的几何规格,即齿数。PHEV 性能是根据各种输出特性来衡量的,例如,燃油经济性、排放、车辆重量、电池电量、最大速度和最大加速度等,包括生态设计和车辆运行性能属性在内的 11 个输出指标是经过考虑的。为了优化,设计空间是根据田口的方法使用 NREL、ADVISOR 模拟器生成的。多标准优化用于使用 TOPSIS、MTOPSIS、灰色关系分析及其基于代理辅助进化算法 (SAEA) 的解决方案将上述输出指标收敛为一个单一的指标,以从中选择最佳的。此类设计方案通过UDDS驱动循环测试进行性能分析;反映了基于 SAEA 的结果的优越性。然而,产出指标的最佳值并非来自单一解决方案,而是分布在这些 SAEA 中。虽然模型中嵌入了可分级性,但它作为补充因素的变化与总拥有成本一起被包括在内,用于扩展建模以确定 SAEA 之间的适用性。为了将适用性测试扩展到一个驾驶周期之外,还通过集成另外两个,即 NEDC 和 1015Prius 与 UDDS 形成一个组合。联合驾驶循环的模拟实验结果也表明偏爱 MTOPSIS-SAEA 模型,符合高达 25% 的起伏地形坡度,明显优于参考模型,同时确保在整个拥有期间节省约 60% 的燃料成本此外,温室气体排放量减少 18% 至 21%。该解决方案还有助于将车辆轻量化 6% 以上。

更新日期:2021-07-04
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