Optimal fractional controller design methodology for electric drive train

Ganeshaperumal, D.; Muthukumar, N.; Ramkumar, K.; Srinivasan, Seshadhri; Subathra, B.

doi:10.1504/ijehv.2016.080731

Cited by 10 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A FOPID controller tuned with Real coded genetic algorithm (RGA) and Bio-geography based optimisation (BBO) was used for a BLDC for an electric vehicle. The performance comparison of FOPID with PID controller showed 50% improvement in transient response performance [87].…”

Section: Fractional Order Pid Control Of Brushless DC Motor Drivesmentioning

confidence: 97%

Fractional Order Control of Power Electronic Converters in Industrial Drives and Renewable Energy Systems: A Review

Warrier

Shah

2021

IEEE Access

View full text Add to dashboard Cite

The power electronics industry is undergoing a revolution driven by an industry 4.0 perspective, with smart and green/hybrid energy management systems being the requirement of the future. There is a need to highlight the potential of fractional order control in power electronics for the highly efficient systems of tomorrow. This paper reviews the developments in fractional order control in power electronics ranging from stand-alone power converters, industrial drives and electric vehicles to renewable energy systems and management in smart grids and microgrids. Various controllers used in power electronics such as the fractional order PI/PID (FOPI/FOPID) and fractional-order sliding mode controllers have been discussed in detail. This review indicates that the plug-and-play type of intelligent fractional order systems needs to be developed for our sustainable future. The review also points out that there is tremendous scope for the design of modular fractional-order intelligent controllers. Such controllers can be embedded into power converters, resulting in smart power electronic systems that contribute to the faster and greener implementation of industry 4.0 standards. INDEX TERMS Fractional calculus, power electronic converters, fractional order control, industrial drives, electric vehicles, renewable energy applications, smart grids and microgrids, industry 4.0.

show abstract

Section: Fractional Order Pid Control Of Brushless DC Motor Drivesmentioning

confidence: 97%

Fractional Order Control of Power Electronic Converters in Industrial Drives and Renewable Energy Systems: A Review

Warrier

Shah

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…However, their performances could be diminished by improper tuning and load‐torque disturbances. To overcome this, optimisation techniques, such as a genetic algorithm [11], particle swarm optimisation [12], fractional‐order controller [13] etc., have been used for tuning PID controllers. Similarly, methods based on fuzzy or its variants [14], neural networks [15], and hybridisation approaches (e.g.…”

Section: Introductionmentioning

confidence: 99%

Real‐time extremum seeking controller for brushless DC hub motors in electric vehicles

Ramaraj

Dharmaraj

Srinivasan

et al. 2020

IET Electric Power Applications

Self Cite

View full text Add to dashboard Cite

This study presents an extremum seeking‐proportional–integral and derivative (ES‐PID) controller design for brushless direct current motors and its implementation in electric vehicles. The ES‐PID controller aims to simultaneously maintain a speed set‐point and reduce torque ripples in the presence of load‐torque disturbances. The proposed ES‐PID combines the simplicity of a PID controller with an extremum seeking approach, a model‐free optimisation approach, thereby resulting in an optimal controller that can be realised on simple hardware (with limited computing power and memory). In addition, a theoretical analysis of the proposed ES‐PID control scheme in terms of stability and convergence is also presented. Performance evaluation of the proposed ES‐PID controller using both simulations and hardware experiments are presented. The results clearly illustrate the ability of the controller to track speed set‐point and reduce torque ripples in the presence of load‐torque variations.

show abstract

“…Example topics cover the lightening of the vehicular structure employing composite and non-conventional materials (Belingardi et al, 2010;Borazjani and Belingardi, 2017) and incentive systems for reducing the aggressiveness of the driving style (Alvarez et al, 2014;Ferreira et al, 2014;Knowles et al, 2012). Particularly, optimisation of the powertrain design and operation currently represents a fundamental procedure for extending the range of BEVs (Danapalasingam, 2013;Ganeshaperumal et al, 2016;Srinivasa Rao et al, 2014;Urbina Coronado et al, 2014;.…”

Section: Introductionmentioning

confidence: 99%

Comparing battery electric vehicle powertrains through rapid component sizing

Anselma

Belingardi

2019

IJEHV

View full text Add to dashboard Cite

The current transition from internal combustion engine vehicles to battery electric vehicles (BEVs) requires the development and implementation of novel design methodologies. This paper introduces an original design procedure for battery electric light vehicle powertrains based on rapid brute force optimisation. At first, a large design space is generated by sweeping the relevant design parameters, specifically the motor size, the number of gears and the transmission ratios. Analytical modelling for electric vehicle powertrains is then presented and combined with lookup tables for the actual system components. The feasible candidate designs are identified by introducing evaluation criteria for the transmission ratios according to the vehicle and electric machine data considered. Subsequently, these candidate designs are evaluated in terms of both launching performance and energy consumption by adopting an efficiency based gear-shifting strategy. Optimal designs can finally be graphically identified through a Pareto frontier analysis as effectively demonstrated in the case of four actual vehicles of different classes. The methodology introduced efficiently addresses the non-linear multidimensional optimisation problem of electric vehicle powertrain design.

show abstract

Optimal fractional controller design methodology for electric drive train

Cited by 10 publications

References 0 publications

Fractional Order Control of Power Electronic Converters in Industrial Drives and Renewable Energy Systems: A Review

Fractional Order Control of Power Electronic Converters in Industrial Drives and Renewable Energy Systems: A Review

Real‐time extremum seeking controller for brushless DC hub motors in electric vehicles

Comparing battery electric vehicle powertrains through rapid component sizing

Contact Info

Product

Resources

About