The present paper deals with a real-time implementation of a novel Fuzzy logic energy management strategy (EMS), applied to a battery–super capacitor hybrid energy system and associated with a permanent magnet synchronous motor (PMSM) which emulates the traction part of an electric vehicle (EV). On the sources side, the fuzzy logic supervisor acts in a smart way to permute smoothly between the various operations modes via an efficient power frequency splitting. In addition, it permits a quite regulation of both the DC bus and the super-capacitor (SC) voltages regardless of the speed profile variations to ensure an optimal power flow to the load and to keep the SC operation in a safe voltage range while providing or absorbing power in transients. On the traction side, a second order sliding mode control called ‘super-twisting’ (ST), associated with a space vector modulation (SVM) strategy is applied to ensure …

The present paper deals with a real-time implementation of a novel Fuzzy logic energy management strategy (EMS), applied to a battery–super capacitor hybrid energy system and associated with a permanent magnet synchronous motor (PMSM) which emulates the traction part of an electric vehicle (EV). On the sources side, the fuzzy logic supervisor acts in a smart way to permute smoothly between the various operations modes via an efficient power frequency splitting. In addition, it permits a quite regulation of both the DC bus and the super-capacitor (SC) voltages regardless of the speed profile variations to ensure an optimal power flow to the load and to keep the SC operation in a safe voltage range while providing or absorbing power in transients. On the traction side, a second order sliding mode control called ‘super-twisting’ (ST), associated with a space vector modulation (SVM) strategy is applied to ensure …

The present paper deals with a real-time implementation of a novel Fuzzy logic energy management strategy (EMS), applied to a battery–super capacitor hybrid energy system and associated with a permanent magnet synchronous motor (PMSM) which emulates the traction part of an electric vehicle (EV). On the sources side, the fuzzy logic supervisor acts in a smart way to permute smoothly between the various operations modes via an efficient power frequency splitting. In addition, it permits a quite regulation of both the DC bus and the super-capacitor (SC) voltages regardless of the speed profile variations to ensure an optimal power flow to the load and to keep the SC operation in a safe voltage range while providing or absorbing power in transients. On the traction side, a second order sliding mode control called ‘super-twisting’ (ST), associated with a space vector modulation (SVM) strategy is applied to ensure …

Multilevel thresholding is a widely used technique to perform image segmentation. It consists of dividing an input image into several distinct regions by finding the optimal thresholds according to a certain objective function. In this work, we generalize the use of the SSIM quality measure as an objective function to solve the multilevel thresholding problem using empirically tuned swarm intelligence algorithms. The experimental study we have conducted shows that our approach, producing near-exact solutions, is more effective compared to the state-of-the-art methods. Moreover, we show that the computation complexity has been significantly reduced by adopting a shared-memory parallel programming paradigm for all the algorithms we have implemented.

Multilevel thresholding is a widely used technique to perform image segmentation. It consists of dividing an input image into several distinct regions by finding the optimal thresholds according to a certain objective function. In this work, we generalize the use of the SSIM quality measure as an objective function to solve the multilevel thresholding problem using empirically tuned swarm intelligence algorithms. The experimental study we have conducted shows that our approach, producing near-exact solutions, is more effective compared to the state-of-the-art methods. Moreover, we show that the computation complexity has been significantly reduced by adopting a shared-memory parallel programming paradigm for all the algorithms we have implemented.

Multilevel thresholding is a widely used technique to perform image segmentation. It consists of dividing an input image into several distinct regions by finding the optimal thresholds according to a certain objective function. In this work, we generalize the use of the SSIM quality measure as an objective function to solve the multilevel thresholding problem using empirically tuned swarm intelligence algorithms. The experimental study we have conducted shows that our approach, producing near-exact solutions, is more effective compared to the state-of-the-art methods. Moreover, we show that the computation complexity has been significantly reduced by adopting a shared-memory parallel programming paradigm for all the algorithms we have implemented.