A new control algorithm of regenerative braking management for energy efficiency and safety enhancement of electric vehicles

Salari, Ali Hosseini; Mirzaeinejad, Hossein; Mahani, M. Fooladi

doi:10.1016/j.enconman.2022.116564

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…Jiang et al [71] reported that their energy recovery efficiency reached 55%. In the NEDC, Jiang et al [71] achieved an energy recovery efficiency of 55.3%, followed by Salari et al [80], Wager et al [81], Xiao et al [86], Mondal et al [84], Yin et al [70], and Xu et al [92]. Although our energy recovery efficiency is not as high as that of Jiang et al [71], it is better than others.…”

Section: Comparison Of Energy Recovery Performancementioning

confidence: 49%

A Logic Threshold Control Strategy to Improve the Regenerative Braking Energy Recovery of Electric Vehicles

Yin,

Ma,

Zhang

et al. 2023

Sustainability

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With increasing global attention to climate change and environmental sustainability, the sustainable development of the automotive industry has become an important issue. This study focuses on the regenerative braking issues in pure electric vehicles. Specifically, it intends to elucidate the influence of the braking force distribution of the front and rear axles on access to energy recovery efficiency. Combining the I curve of a pure electric vehicle and the boundary line of the Economic Commission of Europe (ECE) regulations, the braking force distribution relationship between the front and rear axles is formulated to satisfy braking stability. The maximum regenerative braking force of the motor is determined based on the motor torque characteristics and battery charging power, and the regenerative braking torque is optimized by combining the constraints of the braking strength, battery state of charge (SOC), and vehicle speed. Six road working conditions are built, including the New European Driving Cycle (NEDC), the World Light-Duty Vehicle Test Cycle (WLTC), Federal Test Procedure 72 (FTP-72), Federal Test Procedure 75 (FTP-75), the China Light-Duty Vehicle Test Cycle—Passenger (CLTC-P), and the New York City Cycle (NYCC). The efficiency of the regenerative braking strategy is validated by using the Simulink/MATLAB simulation. The simulation results show that the proposed dynamic logic threshold control strategy can significantly improve the energy recovery effect of electric vehicles, and the energy recovery efficiency can be improved by at least 25% compared to the situation without regenerative braking. Specifically, under the aforementioned road working conditions, the braking energy recovery efficiency levels are 27.69%, 42.18%, 49.54%, 47.60%, 49.28%, and 51.06%, respectively. Moreover, the energy recovery efficiency obtained by the current dynamic logic threshold is also compared with other published results. The regenerative braking control method proposed in this article makes the braking control of electric vehicles more precise, effectively reducing energy consumption and improving the driving range of electric vehicles.

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Section: Comparison Of Energy Recovery Performancementioning

confidence: 49%

A Logic Threshold Control Strategy to Improve the Regenerative Braking Energy Recovery of Electric Vehicles

Yin,

Ma,

Zhang

et al. 2023

Sustainability

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“…In this paper, the nonlinear predictive control method will be used to design the control system. [24][25][26][27] In this method, the nonlinear response of the system is predicted by the Taylor series at the next time interval, and then the control law is achieved in a way that the predicted tracking error reaches a minimum. In the following, predictive-based optimal nonlinear control laws for tracking the optimal longitudinal slip of the front and rear wheels are presented.…”

Section: Nonlinear Predictive Controllermentioning

confidence: 99%

Instantaneous optimum wheel slip estimation of anti-lock braking system based on extremum seeking algorithm and fuzzy method

Deylaghian

Mirzaeinejad

2023

Proceedings of the Institution of Mechanical Engineers, Part K:

Self Cite

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The anti-lock braking system (ABS) adjusts the longitudinal wheel slip at its optimum value to achieve the maximum braking forces. The highest braking force capacity happens at a specific slip value and depends on the friction coefficient between the tire and the road, vertical tire force, and vehicle speed. Hence, using a fixed value for the desired longitudinal slip is not appropriate. To solve this problem, the instantaneous optimum wheel slip is determined via the sliding mode-based extremum seeking algorithm in combination with the fuzzy method to achieve the maximum possible brake deceleration. Then the nonlinear prediction-based controller is designed to find the braking torque by adjusting the longitudinal slip in the calculated desired value. In addition, a nonlinear half-vehicle model considering pitch dynamics is developed and validated with the Carsim software. The main contribution of the present work involves the combination of the optimal nonlinear predictive control method with the fuzzy extremum seeking algorithm to design a wheel slip controller. Additionally, the pitch dynamics has been taken into account in the design of the control system. The performance of the designed control system is investigated through conducted simulations in Matlab/Simulink software environment. The obtained results show an enhancement in the braking performance along with a considerable reduction in the stopping distance.

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“…A PI was used to adjust the dynamics of a bidirectional buck-boost in a regenerative braking system with a supercapacitor as an auxiliary energy device in [14]. In [15], the mileage of an EV has been increased by implementing a nonlinear predictive control strategy with two stages to regulate the torque needed during braking. In addition, the optimization of the torque improves the energy conservation during braking.…”

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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A new control algorithm of regenerative braking management for energy efficiency and safety enhancement of electric vehicles

Cited by 10 publications

References 22 publications

A Logic Threshold Control Strategy to Improve the Regenerative Braking Energy Recovery of Electric Vehicles

A Logic Threshold Control Strategy to Improve the Regenerative Braking Energy Recovery of Electric Vehicles

Instantaneous optimum wheel slip estimation of anti-lock braking system based on extremum seeking algorithm and fuzzy method

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

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