Braking System Of Electric Vehicle Research Articles

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

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.

Study on multi-closed loop control of electro-mechanical braking for electric vehicles based on clamping force

In order to improve the clamping force control accuracy of electro-mechanical braking system of electric vehicles, a multi-closed loop control strategy of electro-mechanical braking based on clamping force is proposed. A detailed EMB mathematical model is established. The sliding mode speed controller and improved fuzzy PID clamping force controller are designed, and the joint simulation model of the speed the clamping force controller is established, and simulation experiments are used to verify the effectiveness of the control strategy. Comparative analysis of three simulation conditions, the maximum adjustment time of the proposed control strategy is 0.254 s and the maximum overshoot is 0.45%. The results of research show that the control strategy designed in this paper can quickly and stably reach the target value of clamping force, has a strong anti-interference capability, has some reference value in the electric vehicle braking control.

Optimizing Energy Harvesting: A Gain-Scheduled Braking System for Electric Vehicles with Enhanced State of Charge and Efficiency

Recycling braking energy is crucial in increasing the overall energy efficiency of an electric vehicle. Regenerative braking system (RBS) technology makes a significant contribution, but it is quite challenging to design an optimal braking force distribution while ensuring vehicle stability and battery health. In this study, a parallel-distribution braking system that transfers as much energy as possible from the wheel to the battery was investigated. An integrated braking force distribution with gain-scheduling super-twisting sliding mode control (GSTSMC) was proposed to capture the maximum kinetic energy during braking and convert it into electrical energy. Parallel friction and regenerative braking ratios dominate the design of the braking component, which is based on the speed of the vehicle. A GSTSMC was implemented and incorporated into the vehicle dynamics model developed in the ADVISOR environment. Simulation was utilized to rigorously validate the efficacy of the proposed control strategy, ensuring its potential to perform optimally in practical applications. Consideration was given to the vehicle’s slip ratio on dry asphalt to maintain vehicle stability. Simulation results were used to validate the performance of the proposed design in terms of the state of charge (SOC), transmitted energy, motor efficiency, battery temperature, and slip ratio. Based on the results, the proposed control strategy is capable of increasing the SOC value to 54%, overall efficiency to 25.98%, energy transmitted to 14.27%, and energy loss to 87 kJ while considering the vehicle’s speed-tracking ability, battery temperature, and stability.

Review of Regenerative Braking System for Electric Vehicle

Abstract: Electric vehicles (EVs) are becoming increasingly popular due to their zero emission environmental friendliness and lower cost of operation compared to conventional vehicles running on fossil fuels like Petrol & Diesel. Fossil fuels are the depleting fuel resources, detrimental to the environment due to greenhouse gases they produce. Many countries have set the target to reduce use of fossil fuels run vehicles by 50% by 2050* and increase the use environment friendly vehicles such as Electric Vehicles, hybrid Vehicles and Hydrogen Fuel Cell operated Vehicles which would constitute a major part of vehicles on road.

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

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.

Neural Inverse Optimal Control of a Regenerative Braking System for Electric Vehicles

This paper presents the development of a neural inverse optimal control (NIOC) for a regenerative braking system installed in electric vehicles (EVs), which is composed of a main energy system (MES) including a storage system and an auxiliary energy system (AES). This last one is composed of a supercapacitor and a buck–boost converter. The AES aims to recover the energy generated during braking that the MES is incapable of saving and using later during the speed increase. To build up the NIOC, a neural identifier has been trained with an extended Kalman filter (EKF) to estimate the real dynamics of the buck–boost converter. The NIOC is implemented to regulate the voltage and current dynamics in the AES. For testing the drive system of the EV, a DC motor is considered where the speed is controlled using a PID controller to regulate the tracking source in the regenerative braking. Simulation results illustrate the efficiency of the proposed control scheme to track time-varying references of the AES voltage and current dynamics measured at the buck–boost converter and to guarantee the charging and discharging operation modes of the supercapacitor. In addition, it is demonstrated that the proposed control scheme enhances the EV storage system’s efficacy and performance when the regenerative braking system is working. Furthermore, the mean squared error is calculated to prove and compare the proposed control scheme with the mean squared error for a PID controller.

Research on composite braking control strategy of battery electric vehicle based on road surface recognition

Concerning the issue of low energy recovery in composite braking of battery electric vehicles, a composite braking control strategy based on road recognition is proposed. The structure of the composite braking system of electric vehicles is analyzed, and the road surface identifier is designed by fuzzy algorithm to track the peak adhesion coefficient of the road surface to obtain the maximum brake braking force. In addition, fuzzy inference is used to modify the proportional coefficient of the motor braking force to the total braking force so as to maximize the energy recovery, and CRUISE and MATLAB are used for joint simulation analysis. The results show that the proposed control strategy not only ensures the safety and stability of the vehicle braking, but also has larger motor braking torque, smaller braking distance and shorter braking time. Under New European Driving Cycle(NEDC), Federal Test Procedure (FTP) and Extra Urban Driving Cycle (EUDC), the battery state of charge ( SOC) values are increased by 3.66%, 1.89%, and 0.85% respectively, and the energy recovery is increased by 169.7, 633.2, and 37.1 kJ respectively.

Study on Analysis of Improvement of Deceleration Control Characteristic Using PI Control in Power Regenerative Braking System for Electric Vehicle

Study on Analysis of Improvement of Deceleration Control Characteristic Using PI Control in Power Regenerative Braking System for Electric Vehicle

Effective Regenerative Braking System for Electric Vehicle

To improve driving ability of electric vehicle. Regenerative braking system (RBS) are an effective method of recovering the energy released and at the same time reducing the exhaust and brake emissions of vehicle . this method is based on the principle of converting the kinetic energy created by mechanical energy of the motor into electrical energy and the converted electrical energy is stored in battery for later use. These system provide economic benefits via fuel saving . This use also contribute to a clean environment and renewable energy source Keywords — Regenerative brake, Energy, Vehicle, Emission ,Fuel Saving ,Clean air, Power saving

Research on particle swarm optimization algorithm of electromechanical hybrid braking control strategy based on road surface recognition

ABSTRACT Concerning the issue of low energy recovery efficiency of compound braking in Battery electric vehicles, an electromechanical composite braking control strategy based on road recognition and particle swarm optimization algorithm is proposed, which is different from the braking force control strategy of front and rear wheels with fixed ratio distribution before optimization. By analyzing the structure and working principle of the compound braking system of electric vehicle, a road surface identifier based on fuzzy algorithm is designed to track the peak adhesion coefficient of the road surface to obtain the maximum braking force of the brake. In addition, particle swarm optimization (PSO) is used to modify the motor braking force, so as to maximize the efficiency of energy recovery, and CRUISE and MATLAB are used in simulation environment to carry out joint simulation analysis. The results show that compared with the control strategy before optimization, the proposed control strategy not only ensures the vehicle braking stability but also has shorter braking distance, shorter braking time, larger motor braking torque, and slower decrease of battery State of Charge (SOC) value. Under the cycle condition of New European Driving Cycle (NEDC), Federal Test Procedure (FTP) and Extra Urban Driving Cycle (EUDC), the State of Charge of the battery increased by 1.98%, 2.1%, and 0.5%, respectively.

Research on parallel braking control of distributed drive electric vehicles

For distributed drive electric vehicles, according to the actual needs of the composite braking system of electric vehicles, the braking safety is the mainstay, and the energy recovery is carried out at the same time, and the distributed parallel braking control strategy is proposed. The strategy adopts a hierarchical control structure, with the upper control aiming at brake safety and the lower control aiming at brake energy recovery. The designed parallel brake control policy takes into account the distribution of braking force in the front and rear axles, as well as constraints such as braking strength, battery SOC value, and motor. Finally, a simulation analysis is carried out to verify the braking efficiency and energy recovery effect.

An adaptive fuzzy sliding‐mode control for regenerative braking system of electric vehicles

SummaryThis article proposes a novel fuzzy sliding‐mode control scheme under an adaptive control strategy for energy management mechanism in electric vehicles that are subject to a regenerative braking system. The effectiveness of the fuzzy logic controller is to adjust the sliding mode parameters according to the slip ratio tracking error between the optimal slip ratio and the actual slip ratio. Specifically, the proposed torque distribution strategy can integrate the best battery condition and energy recovery efficiency under the practical constraints through this control method by fixing the pneumatic braking torque and motor torque. Finally, an electric vehicle model is established in the Simulink environment to verify the applicability of the proposed control algorithm.

Intelligent anti-lock braking system of electric vehicle with the possibility of mixed braking using fuzzy logic

This article discusses the work of an intelligent anti-lock braking system (ABS), in which electric machines and friction brakes act as actuators. Combining several actuators at one control object is a difficult task. One of the possible ways to solve it is the use of a control system based on fuzzy logic. The paper compares two tracking algorithms for controlling the actuators of the anti-lock braking system: - slipping control using mixed braking, with a control system based on fuzzy logic; - slipping control by means of braking by one actuator - friction brakes. Research results have shown that the use of mixed braking allows one to increase the performance of this system in various driving conditions.

Efficiency Optimization and Control Strategy of Regenerative Braking System with Dual Motor

The regenerative braking system of electric vehicles can not only achieve the task of braking but also recover the braking energy. However, due to the lack of in-depth analysis of the energy loss mechanism in electric braking, the energy cannot be fully recovered. In this study, the energy recovery problem of regenerative braking using the independent front axle and rear axle motor drive system is investigated. The accurate motor model is established, and various losses are analyzed. Based on the principle of minimum losses, the motor control strategy is designed. Furthermore, the power flow characteristics in electric braking are analyzed, and the optimal continuously variable transmission (CVT) speed ratio under different working conditions is obtained through optimization. To understand the potential of dual-motor energy recovery, a regenerative braking control strategy is proposed by optimizing the dynamic distribution coefficient of the dual-electric mechanism and considering the restrictions of regulations and the I curve. The simulation results under typical operating conditions and the New York City Cycle (NYCC) proposed conditions indicate that the improved strategy has higher joint efficiency. The energy recovery rate of the proposed strategy is increased by 1.18% in comparison with the typical braking strategy.

Simulation research on regenerative braking control strategy of electric vehicle

In view of the shortage of cruising range of current electric vehicles, the research on the regenerative braking system of electric vehicles has been carried out. Aiming at improving energy utilization rate, combined with the working characteristics of electric motor and battery, a regenerative braking control strategy based on braking intensity is proposed. The control strategy model is built in MATLAB/Simulink firstly, and then the control strategy is verified by the integrated simulation of AVL CRUISE and MATLAB. The simulation results show that the energy utilization rate of the proposed control strategy under custom mild and moderate braking conditions can reach 34.8% and 21.5% respectively.

Modification of Existing Regenerative Braking System for Electric Vehicle

Zero emissions producing and propel the vehicle wheels with own battery energy possible only by Electric vehicles. Energy conversion progression completes with little amount of heat lost only. These advantages influences internationally made the electrical vehicle as the new generation transport for the automobile engineering. Electric vehicles incorporated with regenerative braking system. However, electrical automobile on a solitary charge assortment meaningfully less than the motorized automobile. By this system, reuse energy about on fifth of the energy generally lost through put on the brakes. Lack of a serious impact on the development and popularization of electric vehicle, to overcome this hurdle by involving principle of energy regaining method in design of electric mobility operative manner. The mechanism of electric motor’s braking method encompassing converts parts of kinetic dynamisms of automobile as electric power while braking. This electric power passes to the battery for further battery charges and electric mobility mileage increases compared to conventional engines. When driving in decelerating the inertia of the vehicle wheels through the transmission of energy to pass through to the motor, to control electrical engineering with the generating electricity a way work refreshes for power battery and achieve the regeneration of braking energy. The power developed in the course of the motor braking torque remain used over transmission of the steering wheel brake, consequential in braking power. The innovative regenerative braking with kinetic energy regenerative system (KERS) saved more energy than normal regenerative braking. The life of the KERS more than the steering wheel brake system. The KERS and normal regenerative pressure is 11.94% and 4.95% respectively, Hence, KERS system more efficient than normal regenerative system

Boosting Energy Density of an All Solid-State Asymmetric Supercapacitor Based on rGO/Ni(OH)2 and Activated Carbon Via Model Guided Design

Supercapacitors are one of the promising energy storage devices because of their long life span and high power density, which leads to their advantageous application in regenerative braking system of electric vehicles (EVs). The major challenge that supercapacitors facing is their relatively lower energy density. There are two possible routes to overcome this shortcoming: first, increasing the capacitance by selecting electrode materials with a higher specific area, such as activated carbon (AC); second, enlarging the operable potential window by choosing two electrodes of different materials, forming the so called asymmetric supercapacitors (ASCs). Despite ASCs do enlarge the operable voltage window, most of the aqueous ASCs still suffer from the decomposition of water, limiting the maximum operation voltage to around 1.6 V. Capacitors employing aqueous electrolyte are also prone to leakage, leading to safety concerns. The gel polymer electrolyte (GPE), on the other hand, has higher electrochemical stability compared to aqueous ones. Their solid-state characteristics prevent the electrochemical system from leakage, which is advantageous for supercapacitors employed in portable electronics or EVs. In this work, we aim to construct an asymmetric supercapacitor, where rGO/Ni(OH)2 and AC are used as electrode materials and a LiClO4–based gel polymer is employed as the electrolyte, to achieve enhanced energy density. Cyclic voltammetry is first conducted on rGO/Ni(OH)2 and AC half-cells with GPEs to identify the operational voltage window, and the former is selected as the positive electrode and the latter as the negative electrode to optimize the utilization of electrical double layer capacitance (EDLC) of the system. With limited redox reactions involved, the cell can not only have longer cycle life but also endure higher current densities. The assembled ASC is further examined by galvanostatic charge-discharge (GCD) experiments. GCD examination is conducted with an upper voltage limit of 3 V and under a current density of 2 mA/cm2, and the results are shown in Figure 1. Figure 1 (a) shows charge/discharge curves of the first five and the last five of the recorded 10,000 cycles of our ASC, and the highly similar voltage-time behavior after the first cycle indicates high electrochemical reversibility of this ASC. Figure 1 (b) shows that the coulombic efficiency remains close to 100% after the initial activation cycles. The specific capacitance, energy density and power density of this ASC system are measured to be 2 F/g, 61.6Wh/kg, and 1800 W/kg, respectively. By exerting excess faradaic reaction of the rGO/Ni(OH)2 electrode, this ASC system can potentially have enhanced specific capacitance. We propose to conduct multi-physics simulations to better match the two electrodes when faradaic reaction is involved, as well as optimize the operation window of the ASC. COMSOL software is employed to simulate charge/discharge profiles under pseudo capacitive conditions, with the physical parameters measured by experiments. With the simulation guided design of the ASC, we anticipate to systematically enhance the functionality of this electrochemical device for future application such as in EVs. Figure 1

Safety Ensurence Research on the Regenerative Braking System of Electric Vehicle

The safety problem is the primary factor that should be viewed in the regenerative braking system design of vehicles. To ensure braking safety and battery safety of the electrical vehicles (EVs) a regenerative braking system contain us two fuzzy logic controllers is designed in the paper. In the system, one controller includes slip coefficient, vehicle speed and the driver’s brake requirement to ensure braking security and the other takes battery State of Charge (SOC) and temperature as inputs to assure battery safety. Then, two proportional coefficients [Formula: see text] and [Formula: see text] satisfying the safety needs are introduced into the braking system. At last, the simulation model is established in the simulation software-ADVISOR (ADvanced VehIcle SimulatOR). Through simulation, the results verify that more energy can be regenerated from braking under the conditions of ensuring braking and batteries safety.

A Neural Network Control Strategy for Composite Braking System of Electric Vehicle

The composite braking system is significant to improve the total energy efficiency of electric vehicles by recycling braking energy. A neural network controller for the CBS is proposed to improve the energy regeneration rate and braking stability of EVs in this paper. The regenerative and hydraulic braking torques were optimized offline by using Downhill-Simplex method, and the NN model related to the driving state parameters were trained. Compared with a parallel braking strategy, the energy economy of NN is improved by 4.64% than the parallel controller’s in 3 NEDC cycles, and performs more closely to the I curve in a specific braking condition with a decreasing braking severity.

Energy recovery based on pedal situation for regenerative braking system of electric vehicle

ABSTRACTEnergy recovery is a key technology to improve energy efficiency and extend driving range of electric vehicle. It is still a challenging issue to maximise energy recovery. We present an energy recovery mode (mode A) which recovers braking energy under all situations that accelerator pedal (AP) is lifted, brake pedal (BP) is depressed, as well as AP and BP are released completely; and propose a control strategy of regenerative braking based on driver's intention identified by a fuzzy recognition method. Other two modes: (1) recovery braking energy only the BP is depressed (mode B), (2) no energy recovery, have been studied to compare with mode A. Simulations are carried out on different adhesion conditions. Recovered energy and driving range are also obtained under FTP75 driving cycle. Road test is implemented to validate simulation results. Results show that mode A can improve energy recovery by almost 15.8% compared with mode B, and extend driving range by almost 8.81% compared with mode B and 20.39% with the mode of no energy recovery; the control strategy of regenerative braking can balance energy recovery and braking stability. The proposed energy recovery mode provides a possibility to achieve a single-pedal design of the electric vehicle.