Review of Optimization Strategies for System-Level Design in Hybrid Electric Vehicles

Silvas, Emilia; Hofman, Théo; Murgovski, Nikolce; Etman, L.F.P.; Steinbuch, M Maarten

doi:10.1109/tvt.2016.2547897

Cited by 198 publications

(132 citation statements)

References 149 publications

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“…Electrical drive systems are crucial for the energy efficiency of the whole HEVs and EVs, which require integrated design and optimization, like the in-wheel motor drive. Therefore, design optimization of the whole electrical drive systems has become a promising research topic recently because an optimal system performance cannot be guaranteed by assembling individually optimal components such as motor and inverter into a drive system [21,111]. In other words, the optimal system-level performance does not require each component to be optimal at the component level.…”

Section: Methods and Flowchartmentioning

confidence: 99%

A Review of Design Optimization Methods for Electrical Machines

et al. 2017

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Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines.

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Section: Methods and Flowchartmentioning

confidence: 99%

A Review of Design Optimization Methods for Electrical Machines

et al. 2017

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“…The duty cycle shows the relationship between the power demand and time [26]. Once the duty cycle is obtained, parameters such as peak and average power demand, power transient response, and peak and average energy demand are calculated and used in sizing the powertrain [27]. Duty cycle calculation can be done based on two types of data: "time-at-notch" measurement data and "route simulation data" [26].…”

Section: Duty Cyclementioning

confidence: 99%

A Conceptualized Hydrail Powertrain: A Case Study of the Union Pearson Express Route

Akhoundzadeh

Raahemifar

Panchal

et al. 2019

WEVJ

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A hydrogen rail (hydrail) powertrain is conceptualized in this study, using drive cycles collected from the trains currently working on the Union Pearson Express (UPE) railroad. The powertrain consists of three preliminary different subsystems: fuel cell, battery, and hydrogen storage systems. A backward design approach is proposed to calculate the time-variable power demand based on a "route simulation data" method. The powertrain components are then conceptually sized according to the calculated duty cycle. The results of this study show that 275 kg of hydrogen is sufficient to satisfy the daily power and energy demand of a hydrogen locomotive with drive cycles similar to the ones currently working on the UPE rail route.World Electric Vehicle Journal 2019, 10, 32 2 of 14 specifically for transportation applications. This can also prevent grid fluctuations and improve the flexibility of the electricity grid and help in controlling the intermittency of renewable systems [7][8][9][10][11][12].Hydrogen transportation is a multidimensional issue that can be analyzed from different aspects including, but not limited to, hydrogen production, refueling station infrastructure, powertrain components topology, sizing, and control. Evaluating the infrastructural requirements and energy consumption of such a system is an essential part towards commercialization. In that sense, the high portion of clean electricity, generated by nuclear and renewable sources, in Ontario's supply mix provides a unique opportunity for development of a hydrogen transportation system. Figure 1, shows Ontario electricity supply mix in 2017 [13].World Electric Vehicle Journal 2019, 10, x FOR PEER REVIEW 2 of 14 fluctuations and improve the flexibility of the electricity grid and help in controlling the intermittency of renewable systems [7][8][9][10][11][12].Hydrogen transportation is a multidimensional issue that can be analyzed from different aspects including, but not limited to, hydrogen production, refueling station infrastructure, powertrain components topology, sizing, and control. Evaluating the infrastructural requirements and energy consumption of such a system is an essential part towards commercialization. In that sense, the high portion of clean electricity, generated by nuclear and renewable sources, in Ontario's supply mix provides a unique opportunity for development of a hydrogen transportation system. Figure 1, shows Ontario electricity supply mix in 2017 [13].

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“…Most of them have been utilized to optimally design the component parameters for an outer loop, while a rule-based EMS is nested in an inner loop. However, the optimization results were suboptimal and influenced by the established rules due to the coupling relationship between the component design and EMS [3,7]. To overcome this drawback, another category of methods integrating the evolutionary algorithm with dynamic programming (DP) was proposed to optimize component sizing and EMS simultaneously, and it has been certified to be of significance in improving fuel economy [6,15].…”

mentioning

confidence: 99%

Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

Liu

Zhao

et al. 2019

Processes

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The complicated coupling of component design together with energy management has brought a significant challenge to the design, optimization, and control of plug-in hybrid electric buses (PHEBs). This paper proposes an integrated optimization methodology to ensure the optimum performance of a PHEB with a view toward designing and applications. First, a novel co-optimization method is proposed for redesigning the driveline parameters offline, which combines a nondominated sorting genetic algorithm-II (NSGA-II) with dynamic programming to eliminate the impact of the coupling between the component design and energy management. Within the new method, the driveline parameters are optimally designed based on a global optimal energy management strategy, and fuel consumption and acceleration time can be respectively reduced by 4.71% and 4.59%. Second, a model-free adaptive control (MFAC) method is employed to realize the online optimal control of energy management on the basis of Pontryagin's minimum principle (PMP). Particularly, an MFAC controller is used to track the predesigned linear state-of-charge (SOC), and its control variable is regarded as the co-state of the PMP. The main finding is that the co-state generated by the MFAC controller gradually converges on the optimal one derived according to the prior known driving cycles. This implies that the MFAC controller can realize a real-time application of the PMP strategy without acquiring the optimal co-state by offline calculation. Finally, the verification results demonstrated that the proposed MFAC-based method is applicable to both the typical and unknown stochastic driving cycles, meanwhile, and can further improve fuel economy compared to a conventional proportional-integral-differential (PID) controller.Processes 2019, 7, 477 2 of 23 on a single-objective (or multiobjective) optimization problem of driveline matching, component sizing, topology design, or the EMS [8][9][10][11]. For the predefined single-shaft coaxial parallel plug-in hybrid electric bus (PHEB) in our research, component sizing and the energy management control are extremely significant to fuel economy. To guarantee the optimality of dynamic performance and fuel economy, the driveline design and the EMS should be simultaneously considered, as they are strongly coupled [12]. Previous work has disposed of the combined optimization problem in a bilevel manner, where the outer loop is for the former and the inner loop is for the latter [8,13]. Many optimization algorithms have also been extensively applied to solve this problem, such as a genetic algorithm (GA) [4,14], particle swarm optimization (PSO) [11,15], and simulated annealing (SA) [9,16]. Most of them have been utilized to optimally design the component parameters for an outer loop, while a rule-based EMS is nested in an inner loop. However, the optimization results were suboptimal and influenced by the established rules due to the coupling relationship between the component design and EMS [3,7]. To overcome this drawback, another categ...

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Review of Optimization Strategies for System-Level Design in Hybrid Electric Vehicles

Cited by 198 publications

References 149 publications

A Review of Design Optimization Methods for Electrical Machines

A Review of Design Optimization Methods for Electrical Machines

A Conceptualized Hydrail Powertrain: A Case Study of the Union Pearson Express Route

Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

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