An Efficient Regenerative Braking System Based on Battery- Ultracapacitor for Electric Vehicles

Indragandhi, V.; Selvamathi, R.; Gunapriya, D.; Balagurunathan, B; Suresh, Gokul; Chitra, A.

doi:10.1109/i-pact52855.2021.9696557

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Different control methodologies and regenerative braking architectures have been investigated and implemented in the last years. In [8], a design of the regenerative braking for EV with help of an ultra-capacitor pack and battery is developed where the objective is to save the wasted energy during braking. Results illustrate that the additional super-capacitor pack improves the efficiency of the regenerative braking in comparison with the standalone battery system.…”

Section: Differentmentioning

confidence: 99%

Neural Sliding mode control of a regenerative braking system for electric vehicles

Canul

Djilali

Ruz-Hernández

et al. 2022

JID

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This paper summarizes the work done on the development of a Neural Sliding Mode Controller (NSMC) for a regenerative braking system used in an electric vehicle (EV), which is composed of a Main Energy System (MES) and an Auxiliary Energy System (AES). This last one contains a buck-boost converter and a super-capacitor. The AES aims to recover the energy generated during braking that the MES cannot retrieve and use later during acceleration. A neural identifier trained with the Extended Kalman Filter (EKF) has been used to estimate the buck-boost converter real dynamics and to build up the NSMC, which is implemented to regulate the voltage and current dynamics in the AES. Simulation results, illustrate the effectiveness of the proposed control scheme to track time-varying references of the AES voltage and current dynamics measured at the buck-boost converter and ensure the charging and discharging operation modes of the super-capacitor. In addition, the proposed control scheme enhances the EV storage system efficiency and performance, when the regenerative braking system is employed.

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Section: Differentmentioning

confidence: 99%

Neural Sliding mode control of a regenerative braking system for electric vehicles

Canul

Djilali

Ruz-Hernández

et al. 2022

JID

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“…The combination of a supercapacitor and battery bank as an energy supply for an EV has demonstrated an improvement in regenerative braking performance, saving energy and increasing efficiency [5].…”

Section: Introductionmentioning

confidence: 99%

Neural Sliding Mode Control of a Buck-Boost Converter Applied to a Regenerative Braking System for Electric Vehicles

Ruz-Hernandez,

Garcia-Hernandez,

Ruz Canul

et al. 2024

WEVJ

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This paper presents the design and simulation of a neural sliding mode controller (NSMC) for a regenerative braking system in an electric vehicle (EV). The NSMC regulates the required current and voltage of the bidirectional DC-DC buck–boost converter, an element of the auxiliary energy system (AES), to improve the state of charge (SOC) of the battery of the EV. The controller is based on a recurrent high-order neural network (RHONN) trained using the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) as the tools to train the neural networks to obtain a higher SOC in the battery. The performance of the controller with the two training algorithms is compared with a proportional integral (PI) controller illustrating the differences and improvements obtained with the EKF and the UKF. Furthermore, robustness tests considering Gaussian noise and varying of parameters have demonstrated the outcome of the NSMC over a PI controller. The proposed controller is a new strategy with better results than the PI controller applied to the same buck–boost converter circuit, which can be used for the main energy system (MES) efficiency in an EV architecture.

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Emerging Intelligent Bidirectional Charging Strategy Based on Recurrent Neural Network Accosting EMI and Temperature Effects for Electric Vehicle

et al. 2022

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Transportation is currently advancing towards Electric Vehicle (EV) technology. This paper presents a brief and systematic analysis of the real-time issues obtained in Electric Vehicles (EVs) due to the various ranges of energy storage devices. In general, EV energy management system is integrated with power electronic circuits for effective power conversion and reliable operation. Some issues are addressed while using the batteries in EV systems such as charging time, efficiency of battery, and raw materials. Not only battery issues but also real-time non-technical issues and operational issues are also discussed in this paper. During energy conversion with power electronic circuits, the system attains extreme temperature levels which in turn reduces the performance of the system. To maintain an optimum temperature level, it is important to study the temperature effect of the system at the most prior levels. Due to the adaption of power electronic components, some extent of noise is generated, technically treated as Electromagnetic Interference (EMI), as system capacity increases the EMI content also improved proportionately. Therefore, to mitigate the EMI effect, the low pass filter-based EMI filter is introduced in the system such that the noise level is suppressed. Bidirectional Charging System (BCS) is one of the emerging technologies in EV to obtain autonomous power supply systems in the form of Vehicle to Grid (V2G), Grid to Vehicle (G2V), and Vehicle to Load (V2L). To know the behaviour of BCS the proposed RNN controller is employed and is compared with ANN bidirectional charging model. BCS charging system with RNN controller has better dynamic response to exchange the power via DC/DC converter and AC/DC converter as compared to ANN controller.

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An Efficient Regenerative Braking System Based on Battery- Ultracapacitor for Electric Vehicles

Cited by 6 publications

References 8 publications

Neural Sliding mode control of a regenerative braking system for electric vehicles

Neural Sliding mode control of a regenerative braking system for electric vehicles

Neural Sliding Mode Control of a Buck-Boost Converter Applied to a Regenerative Braking System for Electric Vehicles

Emerging Intelligent Bidirectional Charging Strategy Based on Recurrent Neural Network Accosting EMI and Temperature Effects for Electric Vehicle

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