Influence of machine control strategy on electric vehicle range

Caillard, Pierre; Gillon, F.; Hecquet, Michel; Janiaud, Noëlle

doi:10.1109/ever.2013.6521604

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Saturation and iron losses d eddy current) are taken into ule, mechanical and additional d E as inputs which are obtained rt III-A). If the torque is given, f [13]. The machine model has e element comparison on the s results.…”

Section: Machine Modelmentioning

confidence: 99%

An Optimization Methodology to Pre Design an Electric Vehicle Powertrain

Caillard¹,

Gillon²,

Hecquet³

et al. 2014

2014 IEEE Vehicle Power and Propulsion Conference (VPPC)

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In this paper, a global optimization methodology is described to pre-design an electric vehicle powertrain in order to find the best compromises between components. The modeled system includes a transmission, an electric machine, an inverter and a battery pack. The challenge is to find the dedicated formulations, with the vehicle performance requirements, electric range, and cost calculation that include the whole system without exploding computational time. Bi-objective, range/costs, optimizations with performance constraints are performed to find the potential gain with the system model. Keywords-Electric vehicle powertrain, optimization methodology, electric system design, methodology Notations:Sp: Vehicle Speed (km/h) t: driving cycle time (s) Sl: Road slope dp: number of operating points that represent a portion of a driving cycle pp: number of operation points that represent a performance requirements ng: number of gears of the transmission Tw: required torque at the wheel (Nm) Ωw: wheel speed (rpm) Tm: required machine torque (Nm) Ωm: machine speed (rpm) Tm max : maximum machine achievable torque (Nm) Tm req : required machine torque for performance (Nm) f: stator frequency (Hz) Um: machine voltage (V) Im: machine current (A) cos(φ): machine power factor J: stator current density (A/mm²) Ib: battery current (A) Ub: battery voltage (V) x i : design variable ns: number of cells in series for the battery pack np: number of cells in parallel for the battery pack SOC: battery state of charge

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Section: Machine Modelmentioning

confidence: 99%

An Optimization Methodology to Pre Design an Electric Vehicle Powertrain

Caillard¹,

Gillon²,

Hecquet³

et al. 2014

2014 IEEE Vehicle Power and Propulsion Conference (VPPC)

View full text Add to dashboard Cite

show abstract

“…Saturation and iron losses (hysteresis with Steinmetz and eddy current) are taken into account in addition with joule, mechanical and additional losses. More details for this model can be found in (Caillard et al 2013b). To get a specific operating point, a control strategy is needed to find the associated frequency f (Thanga Raj et al 2009).…”

Section: Focus On the Electric Machine Modelmentioning

confidence: 99%

Mono and bi-Level optimization architectures for powertrain design

Caillard¹,

Gillon²,

Randi

et al. 2016

COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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Purpose The purpose of this paper is to compare two design optimization architectures for the optimal design of a complex device that integrates simultaneously the sizing of system components and the control strategy for increasing the energetic performances. The considered benchmark is a battery electric passenger car. Design/methodology/approach The optimal design of an electric vehicle powertrain is addressed within this paper, with regards to performances and range. The objectives and constraints require simulating several vehicle operating points, each of them has one degree of freedom for the electric machine control. This control is usually determined separately for each point with a sampling or an optimization loop resulting in an architecture called bi-level. In some conditions, the control variables can be transferred to the design optimization loop by suppressing the inner loop to get a mono-level formulation. The paper describes in which conditions this transformation can be done and compares the results for both architectures. Findings Results show a calculation time divided by more than 30 for the mono-level architecture compared to the natural bi-level on the study case. Even with the same models and optimization algorithms, the structure of the problem should be studied to improve the results, especially if computational cost is high. Originality/value The compared architectures bring new guidelines in the field optimal design for electric powertrains. The way to formulate a design optimization with some inner degrees of freedom can have a significant impact on computing time and on the problem understanding

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“…It was modeled with an equivalent circuit on MATLAB to compute the losses and mean torque [5]. The optimization is done on this analytical model, and the obtained geometry is then exported automatically to an FE software ( Fig.…”

Section: Definition Of the Problemmentioning

confidence: 99%

“…They can use the relationship between the parameters in a very complex manner [5]. Moreover, if they can be used for regression, the main use of these algorithms is classification: from a set of characteristic and labels, they are able to cluster data into classes.…”

Section: A General Ideamentioning

confidence: 99%

Modeling Finite-Element Constraint to Run an Electrical Machine Design Optimization Using Machine Learning

2015

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This paper proposes a method to the model constraints from different models to run an optimization over models with different granularities. Through machine learning, the proposed method has proven to be able to accurately map the constraints and minimize the number of call to the model. It handles both continuous and discrete variables and mixes design rules to statistic approach to create a surrogate of the model. Index Terms-Constraint modeling, finite-element (FE) model, machine learning, optimal design, random forest.

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Influence of machine control strategy on electric vehicle range

Cited by 7 publications

References 14 publications

An Optimization Methodology to Pre Design an Electric Vehicle Powertrain

An Optimization Methodology to Pre Design an Electric Vehicle Powertrain

Mono and bi-Level optimization architectures for powertrain design

Modeling Finite-Element Constraint to Run an Electrical Machine Design Optimization Using Machine Learning

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