Component sizing optimization of plug-in hybrid electric vehicles

Wu, Xiaolan; Cao, Binggang; Li, Xueyan; Xu, Jun; Ren, Xiaolong

doi:10.1016/j.apenergy.2010.08.018

Cited by 128 publications

(62 citation statements)

References 14 publications

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“…Given in the covariance matrix of the state estimation uncertainty P k and the measurement sensitivity matrix H k , the measurement noise model is properly controlled by the measurement noise covariance, R k , which is an important value in determining the gain (K k ) of the Kalman filter, as expressed in Eqs. (7) and (8). The value of K k is inversely related to the value of R k .…”

Section: Review Of the Nstmentioning

confidence: 99%

“…In recent times, the demand for lithium rechargeable cells with high energy density and high discharging voltage, having application in electric-powered applications such as electric vehicles (EVs), hybrid electric vehicles (HEVs) and energy storage system (ESS) has gradually increased [1][2][3][4][5][6][7][8][9][10][11]. Half of the price of these electricpowered applications is related to the battery charge.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO₂/LiFePO₄/LiNiMnCoO₂Cells

Lee¹,

Park²,

Kim³

2016

Journal of Electrical Engineering and Technology

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-Lithium rechargeable cells are used in many industrial applications, because they have high energy density and high power density. For an effective use of these lithium cells, it is essential to build a reliable battery management system (BMS). Therefore, the state of charge (SOC) estimation is one of the most important techniques used in the BMS. An appropriate modeling of the battery characteristics and an accurate algorithm to correct the modeling errors in accordance with the simplified model are required for practical SOC estimation. In order to implement these issues, this approach presents the comparative analysis of the SOC estimation performance using equivalent electrical circuit modeling (EECM) and noise suppression technique (NST) in three representative LiCoO 2 /LiFePO 4 /LiNiMnCoO 2 cells extensively applied in electric vehicles (EVs), hybrid electric vehicles (HEVs) and energy storage system (ESS) applications. Depending on the difference between some EECMs according to the number of RC-ladders and NST, the SOC estimation performances based on the extended Kalman filter (EKF) algorithm are compared. Additionally, in order to increase the accuracy of the EECM of the LiFePO 4 cell, a minor loop trajectory for proper OCV parameterization is applied to the SOC estimation for the comparison of the performances among the compared to SOC estimation performance.

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Section: Review Of the Nstmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO₂/LiFePO₄/LiNiMnCoO₂Cells

Lee¹,

Park²,

Kim³

2016

Journal of Electrical Engineering and Technology

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“…The on-board charger, battery (Batt), electric machine (EM), Inverters (INV), Transmission (Trn), DC/DC converter and wheel have all been modeled using data recorded from the Smart Move 2 Vehicle Trial Program. A detailed description of this process and validation of the models has been published previously in [13,15] and will therefore not be repeated here. For each powertrain component the model execution equations have been listed.…”

Section: Design Of a Scalable Phev Model To Support System Optimizationmentioning

confidence: 99%

“…The vehicle's controller limits the top speed of the vehicle to 100 km/h. The range of the vehicle, published by the manufacturer over the new European driving cycle (NEDC) cycle, is 135 km [15].…”

Section: A2 Trial Programmementioning

confidence: 99%

The Novel Application of Optimization and Charge Blended Energy Management Control for Component Downsizing within a Plug-in Hybrid Electric Vehicle

2012

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Abstract:The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO 2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%-9% being realizable.Keywords: PHEV; battery; energy management; equivalent consumption minimization strategy (ECMS); component sizing; optimization 4895Number of cells in series Vehicle velocity (km/h) Slope of the road (-) Power split ratio Equivalence ratio for battery fuel consumption (-)

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“…Wu et al study in [20] a component sizing optimization of plug-in hybrid electric vehicles. This paper describes a methodology for the optimization of PHEVs component sizing using parallel chaos optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Techno-economic design of hybrid electric vehicles using multi objective optimization techniques

Dimitrova

Maréchal

2015

Energy

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Highlights:• Multi objective optimization is applied for optimal hybrid electric powertrain designs.• A holistic approach for optimal design and control strategies is presented on vehicle usages.• Hybrid electric vehicles can reach large range of efficiency -26% to 45%.• Hybrid electric vehicle can emit very low CO 2 emissions -30 g/km for D class vehicles. Abstract:The improvement of the efficiency of vehicle energy systems promotes an active search to find innovative solutions during the design process. Engineers can use computer-aided processes to find automatically the best design solutions. This kind of approach named "multi-objective optimization" is based on genetic algorithms. The idea is to obtain simultaneously a population of possible design solutions corresponding to the most efficient energy system definition for a vehicle. These solutions will be optimal from technical and economic point of view. In this article this kind of "genetic intelligence" is tested for the holistic design of the optimal vehicle powertrain solutions and their optimal operating strategies.The methodology is applied on D class hybrid electric vehicles, in order to define the powertrain configurations, to estimate the cost of the powertrain equipment and to show the environmental impact of the technical choices. The optimal designs and operating strategies are researched for different vehicle usages -normalized, urban and long way driving. Introduction:

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Component sizing optimization of plug-in hybrid electric vehicles

Cited by 128 publications

References 14 publications

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO₂/LiFePO₄/LiNiMnCoO₂Cells

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO₂/LiFePO₄/LiNiMnCoO₂Cells

The Novel Application of Optimization and Charge Blended Energy Management Control for Component Downsizing within a Plug-in Hybrid Electric Vehicle

Techno-economic design of hybrid electric vehicles using multi objective optimization techniques

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Component sizing optimization of plug-in hybrid electric vehicles

Cited by 128 publications

References 14 publications

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO2/LiFePO4/LiNiMnCoO2Cells

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO2/LiFePO4/LiNiMnCoO2Cells

The Novel Application of Optimization and Charge Blended Energy Management Control for Component Downsizing within a Plug-in Hybrid Electric Vehicle

Techno-economic design of hybrid electric vehicles using multi objective optimization techniques

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Product

Resources

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Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO₂/LiFePO₄/LiNiMnCoO₂Cells

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO₂/LiFePO₄/LiNiMnCoO₂Cells