Implementation of Optimized Regenerative Braking in Energy Efficient Driving Strategies

Pusztai, Zoltán; Kőrös, Péter; Szauter, Ferenc; Friedler, Ferenc

doi:10.3390/en16062682

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…In this application, the optimization of the propulsion system of the 2022 competition vehicle of the SZEnergy student team of the Széchényi István University is presented. For a detailed description of the vehicle, see [4,28]. This drive system consists of a PMSM (SZEvol) with a rated power of 1 kW and a gear belt drive with a 3.27 gear ratio.…”

Section: The Applicationmentioning

confidence: 99%

Investigating the Effect of Gear Ratio in the Case of Joint Multi-Objective Optimization of Electric Motor and Gearbox

Istenes,

Polák

2024

Energies

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In this paper, a software framework is presented through an application that is able to jointly optimize an electric motor and a gearbox for the design of a drive system for electric vehicles. The framework employs a global optimization method and uses both analytical and finite element method (FEM) models to evaluate the objective functions. The optimization process is supported by a statistical surrogate model, which allows a large reduction of runtime. An earlier version of this framework was only suitable for electric motor optimization. In the application presented in a previous paper, the motor of a belt-driven electric drive system was optimized. In this paper, the optimization of the same drive system is shown, but now with a combined optimization of a gear drive and motor. The objective functions of optimization are minimizing the total loss energy and the weight of the drive system. The optimization results are compared with previous results to demonstrate the further potential of joint optimization.

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Section: The Applicationmentioning

confidence: 99%

Investigating the Effect of Gear Ratio in the Case of Joint Multi-Objective Optimization of Electric Motor and Gearbox

Istenes,

Polák

2024

Energies

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“…This change could be made due to the application of a validated FEM model. The track characteristics of TT Circuit Assen were considered in the optimization, which was made according to the problem formulation of the Max TRQ method [13,14].…”

Section: The Application Of Optimization Frameworkmentioning

confidence: 99%

Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy

et al. 2023

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In this paper, a multi-objective optimization framework for electric motors and its validation is presented. This framework is suitable for the optimization of design variables of electric motors based on a predetermined driving strategy using MATLAB R2019b and Ansys Maxwell 2019 R3 software. The framework is capable of managing a wide range of objective functions due to its modular structure. The optimization can also be easily parallelized and enhanced with surrogate models to reduce the runtime. The framework is validated by manufacturing and measuring the optimized electric motor. The method’s applicability for solving electric motor design problems is demonstrated via the validation process. A test application is also presented, in which the operating points of a predetermined driving strategy provide the input for the optimization. The kriging surrogate model is used in the framework to reduce the runtime. The results of the optimization and the framework’s benefits and drawbacks are discussed through the provided examples, in addition to displaying the properly applicable design processes. The optimization framework provides a ready-to-use tool for optimizing electric motors based on the driving strategy for single- or multi-objective purposes. The applicability of the framework is demonstrated by optimizing the electric motor of a world recorder energy-efficient race vehicle. In this application, the optimization framework achieved a 2% improvement in energy consumption and a 9% increase in speed at a rated DC voltage, allowing the motor to operate at desired working points even with low battery voltage.

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Implementation of Optimized Regenerative Braking in Energy Efficient Driving Strategies

Cited by 2 publications

References 19 publications

Investigating the Effect of Gear Ratio in the Case of Joint Multi-Objective Optimization of Electric Motor and Gearbox

Investigating the Effect of Gear Ratio in the Case of Joint Multi-Objective Optimization of Electric Motor and Gearbox

Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy

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