Regenerative Braking In Electric Vehicles Research Articles

Fuzzy Logic-Based Energy Management System for Regenerative Braking of Electric Vehicles with Hybrid Energy Storage System

Electric vehicles (EVs), which are environmentally friendly, have been used to minimize the global warming caused by fossil fuels used in vehicles and increasing fuel prices due to the decrease in fossil resources. Considering that the energy used in EVs is obtained from fossil resources, it is also important to store and use energy efficiently in EVs. In this context, recovery from a regenerative braking system plays an important role in EV energy efficiency. This paper presents a fuzzy logic-based hybrid storage technique consisting of a supercapacitor (SC) and battery for efficient and safe storage of a regenerative braking system. First, the constraints of the battery to be used in the EV for fuzzy logic control are identified. Then, the fuzzy logic system is created and tested in the ADVISOR and Siemens Simcenter Flomaster programs in the New European Driving Cycle (NEDC) driving cycle. A SC was selected for primary storage to prevent the battery from being continuously charged from regenerative braking, thus reducing its lifetime. In cases where the vehicle consumes more energy than the average energy consumption, energy consumption from the battery is reduced by using the energy stored in the SC, and the SC energy is discharged, making preparations for the energy that will come from the next regenerative braking. Thus, the high current values transferred to the battery during regenerative braking are effectively limited by the SC. In this study, the current values on the battery in the EV with a hybrid storage system decreased by 29.1% in the ADVISOR program and 28.7% in the Simcenter Flomaster program. In addition, the battery generated 46.84% less heat in the hybrid storage system. Thus, the heating and capacity losses caused by this current on the battery were minimized. The presented method provides more efficient energy management for EVs and plays an important role in maintaining battery health.

A Novel Supercapacitor Model Parameters Identification Method Using Metaheuristic Gradient-Based Optimization Algorithms

This paper addresses the critical role of supercapacitors as energy storage systems with a specific focus on their modeling and identification. The lack of a standardized and efficient method for identifying supercapacitor parameters has a definite effect on widespread adoption of supercapacitors, especially in high-power density applications like electric vehicle regenerative braking. The study focuses on parameterizing the Zubieta model for supercapacitors, which involves identifying seven parameters using a hybrid metaheuristic gradient-based optimization (MGBO) approach. The effectiveness of the MGBO method is compared to the existing particle swarm optimization (PSO) and to the following algorithms proposed and developed in this work: ‘modified MGBO’ (M-MGBO) and two PSO variations—one combining PSO and M-MGBO and the other incorporating a local escaping operator (LCEO) with PSO. Metaheuristic- and gradient-based algorithms are both affected by problems associated with locally optimal results and with issues related to enforcing constraints/boundaries on solution values. This work develops the above-mentioned innovations to the MGBO and PSO algorithms for addressing such issues. Rigorous experimentation considering various types of input excitation provides results indicating that hybrid PSO-MGBO and PSO-LCEO outperform traditional PSO, showing improvements of 51% and 94%, respectively, while remaining comparable to M-MGBO. These hybrid approaches effectively estimate Zubieta model parameters. The findings highlight the potential of hybrid optimization strategies in enhancing precision and effectiveness in supercapacitor model parameterization.

Regenerative Braking in Electric Vehicles using BLDC motor with Modified Torque and Adaptive-Neuro-Fuzzy-Control

Regenerative Braking in Electric Vehicles using BLDC motor with Modified Torque and Adaptive-Neuro-Fuzzy-Control

New Advancement of Supercapacitors Technology: a review

Supercapacitors are exciting for researchers due to their ability to store energy very quickly. This makes supercapacitors ideal for applications requiring rapid bursts of power, like regenerative braking in electric vehicles or short-term backup power for electronics. This makes them ideal for low-power devices we wear or carry around (like phones and smartwatches) and even powerful military equipment (like missiles and torpedoes). Scientists are figuring out how to improve SCs by tinkering with the materials they are made from and how those materials interact. These efforts are being done to make SCs a better option than other ways of storing energy, like rechargeable batteries. This article explores the latest findings on how SCs store energy, the materials used to make their electrodes (especially 3D structures that look like paper or fibers), and how they are being used in the real world.

Regenerative Braking of Electric Vehicles Based on Fuzzy Control Strategy

Regenerative braking technology is a viable solution for mitigating the energy consumption of electric vehicles. Constructing a distribution strategy for regenerative braking force will directly affect the energy saving efficiency of electric vehicles, which is a technical bottleneck of battery-powered electric vehicles. The distribution strategy of the front- and rear-axle braking forces of electric vehicles that possess integrated front-wheel-drive arrangements is established based on the Economic Commission of Europe (ECE) regulations, which enables the clarification of the total braking force of the front axle. The regenerative braking torque model of the motor is adjusted to optimize the ratio of motor braking force to the whole front-axle braking force. The regenerative braking process of electric vehicles is influenced by many factors, such as driving speed and braking intensity, so regenerative braking presents characteristics of nonlinearity, time variability, delay, and incomplete models. By considering the impact of fuzzy controllers having better robustness, adaptability, and fault tolerance, a fuzzy control strategy is employed in this paper to accomplish the regenerative braking force distribution on the front axle. A regenerative braking model is created on the Simulink platform using the braking force distribution indicated above, and experiments are run under six specific operating conditions: New European Driving Cycle (NEDC), World Light-Duty Vehicle Test Cycle (WLTC), Federal Test Procedure 72 (FTP-72), Federal Test Procedure 75 (FTP-75), China Light-Duty Vehicle Test Cycle-Passenger (CLTC-P), and New York City Cycle (NYCC). The findings demonstrate that in six typical cycling road conditions, the energy saving efficiency of electric vehicles has greatly increased, reaching over 15%. The energy saving efficiency during the WLTC driving condition reaches 25%, and it rises to 30% under the FTP-72, FTP-75, and CLTC-P driving conditions. Furthermore, under the NYCC road conditions, the energy saving efficiency exceeded 40%. Therefore, our results verify the effectiveness of the regenerative braking control strategy proposed in this paper.

Development of a method for regenerative braking of an electric scooter

<span lang="EN-US">In this work, a method of regenerative braking of an electric scooter was developed. Regenerative braking of electric vehicles is the basis for energy saving cars and environmental protection. A stand was made for the study of a magnets and an ionistor. In the proposed method, an ionistor is additionally connected in parallel to the battery, which charges faster than the battery, so the process of accelerating the electric vehicle after stopping also occurs faster. It should also be noted that if emergency braking is necessary to stop an electric vehicle, the driver can press the brake pedal and stop the car using brake pads. The paper investigates the method of the element base for the vehicle, as well as control. To develop this study, an electric scooter was chosen as an electric vehicle model. The dependences of the electric current on the operating time of the ionistor in three positions are determined. During the actual speed of the electric scooter corresponds to the controlled speed.</span>

Simulation of Electric Vehicle Regenerative Braking Control Strategy Based on Brake Intention Recognition

The development of regenerative braking technology for a pure electric vehicle is the key technology to solving the current limited range of the electric vehicle. Accurately identifying the driver’s braking intent is fundamental to controlling the braking system of an electric vehicle.A brake intent recognition controller is designed in this paper, which is based on a lot of driving data and fuzzy principles. In addition, two typical regenerative brake control strategies are compared. The simulation results of Matlab/Simulink and AMESim show that the SOC of the power battery increases obviously at different braking intensities of 30 km/h. Besides, energy recovery is up to 20.5%, and battery energy increases by 6.5 wh.

Regenerative braking torque compensation control based on improved ADRC

Electro-hydraulic braking (EHB) compensation is an effective method to solve the insufficient regenerative braking of electric vehicles, but the accuracy and speed of executive pressure response are still insufficient. In order to solve this problem, a brake pressure compensation control method based on EHB system is proposed and verified. Firstly, adaptive LuGre friction model and braking dead time compensation are introduced to obtain a more accurate friction model. Then, an electro-hydraulic braking control strategy based on Improved Active Disturbance Rejection Control (IADRC) is proposed. Finally, the effectiveness of the proposed method is verified by simulation experiments and hardware-in-the-loop (HIL) simulation experiments. The test results show that the proposed control method can achieve accurate control of brake pressure compensation in time. The research results promote the research and application of EHB system to a certain extent.

Analysis of Regenerative Braking Strategy for Electric Vehicles Based on Braking Intention

Improved energy recovery during braking is the goal of the distributed drive electric car regenerative braking system. A regenerative braking control strategy for electric vehicles based on braking intention is suggested in order to address various drivers’ braking intents and road adhesion circumstances. An electric vehicle regenerative braking model for distributed drives is created using the MATLAB/Simulink tools. On the basis of FTP-75 operating conditions and open road operating conditions, the efficacy and economy of the control strategy in this study are verified. According to the various road adhesion coefficients, the simulation results demonstrate that the control method in this work can appropriately divide the braking torque of the wheels on both sides of the coaxial wheel. The technique can successfully enhance the regenerative braking recovery of distributed drive electric vehicles as compared to the often employed regenerative braking control method.

Design of LQR-based FLC for the Optimal Regenerative Braking Controlling of Solar PV-based Electric Vehicle System

The article presents an analysis of the optimal control of speed and regenerative braking in an electric vehicle (EV). The presence of the continuous generating power is one of the major challenges for battery charging of electric vehicle. In order to meet the demand of continuous power, electric vehicles are driven by solar PV arrays. Initially, maximum power extraction (MPPT) is assessed from a solar photovoltaic (PV) array using the P&O method. The outputs of the solar PV array are integrated with electric vehicles. Such integration creates the problem of maintaining the power quality. The existing methods also address the issue of optimal speed and torque control with high transients and more settling time. In this article, an electric vehicle is modeled first and then operated in regenerative braking mode. To address the shortcomings of existing methods, the linear quadratic regulator (LQR) method is used; then, fuzzy logic controller (FLC)-based LQR is used for optimal speed and torque control during electric vehicle regenerative braking. In terms of reduced settling time and improved peak overshoot, optimal speed and regenerative torque of electric vehicles are better realized with LQR-based FLCs than with LQR controllers. In addition to this, stopping speed characteristics with various electrical parameters (voltage, current, and flux) have been analyzed with different methods in which LQR-based FLC shows its dominance to attain zero speed in regenerative mode with more sharp characteristics.

Friction Losses During Slip Conditions in Regenerative Braking of Electric Vehicles

The regenerative braking system (RBS) in electric vehicles (EVs) enhances its capability against internal combustion engines (ICE). Antilock braking system (ABS) is widely used in RBS because of its maneuverability and safety. One of the purposes of applying ABS is to avoid slippage. Slip on the braking process, especially hydraulic brakes, results in a difference in time to stop the wheels and the vehicle. It is avoided because it relies on friction between the tires and the road to stop transportation. Timing results showed that ABS with high frequency, above 30 Hz, tended to act like hydraulic brakes. Hydraulic brakes achieved the highest time difference, 8 seconds at 2250 rpm. It increased the distance of 350 m in slipping while the lowest time difference ABS 10 Hz owned, 0.1 seconds at the same rpm. Slips caused losses. These losses could be minimized with low-frequency ABS. It converted the friction between the wheels and the brakes into electrical energy instead of stopping the vehicle. As a result, the 10 Hz ABS had the highest potency stored despite the lowest performance.

Control of BLDC motor and Regenerative Braking in Electric Vehicle

Abstract: With the rising economy, fuel demand also increases. In today's world, fossil fuels are the major contribution to meeting these demands. However, it is not going to be the same scenario in the next decades as the fossil fuels levels are depleting there is a need for an alternative fuel that will satisfy energy requirements and will be a sustainable source in coming years. In the automotive industry petrol and diesel have been major fuels in driving vehicles. Although electric vehicles are going to be replaced by IC engines. Electric vehicles have lower fuel costs as compared to internal combustion engines but because of limited charging stations vehicles are bound to drive within certain distances. With the implementation of regenerative braking in electric vehicles, it is possible to charge batteries when the vehicle is breaking and thus increasing driving distance. This paper presents a simulation of control of BLDC motor with regenerative braking. Keywords: Regenerative Braking, BLDC Motor, Bi-Directional DC-DC Converter, VSI, Electric Drives.

Fault Diagnosis in Regenerative Braking System of Hybrid Electric Vehicles by Using Semigroup of Finite-State Deterministic Fully Intuitionistic Fuzzy Automata.

Regenerative braking is one of the most promising and ecologically friendly solutions for improving energy efficiency and vehicle stability in electric and hybrid electric cars. This research describes a data-driven method for detecting and diagnosing issues in hybrid electric vehicle regenerative braking systems. Early fault identification can help enhance system performance and health. This study is centered on the construction of an inference system for fault diagnosis in a generalized fuzzy environment. For such an inference system, finite-state deterministic fully intuitionistic fuzzy automata (FDFIFA) are established. Semigroup of FDFIFA and its algebraic properties including substructures and structure-preserving maps are investigated. The inference system uses FDFIFA semigroups as variables, and FDFIFA semigroup homomorphisms are employed to illustrate the relationship between variables. The newly established model is then applied to diagnose the possible fault and their nature in the regenerative braking systems of hybrid electric vehicles by modeling the performance of superchargers and air coolers. The method may be used to evaluate faults in a wide range of systems, including autos and aerospace systems.

Regenerative Braking in Electric Vehicle Using Quadratic Gain Bidirectional Converter

The manuscript proposes a quadratic gain bidirectional converter (QGBC). The proposed converter is operated in two different stages—step-up (motoring) and step-down (regenerative braking)—which can be employed in electric vehicle (EV) applications. The converter is operated in the continuous inductor current mode (CICM). For regenerative braking (RB) of permanent magnet brushless DC (PMBLDC) motor, the self-inductance of the motor is exploiting to step up the back electromagnetic force (EMF) of the motor to extract the energy even at low rotor speed. The design parameters of the converter are selected as battery voltage V s = 48 V , output voltage V o = 200 V , output power P o = 1 kW , and switching frequency f s = 20 kHz . The design system is simulated using MATLAB/Simulink. Finally, a 1 kW prototype is developed for validation and performance analysis of the converter. The converter operates at maximum efficiency of 95% during step-up operation of the converter. A DSP microcontroller TMS320F28335 is used to control the switches of the converter in the experimental setup.

Integrated Braking Force Distribution for Electric Vehicle Regenerative Braking System

The automotive industry has made a significant contribution to everyday life by fulfilling society’s mobility needs. Traditionally, electric vehicles (EV) were introduced as an alternative to the traditional internal combustion engine (ICE) to reduce the emission, which improves air quality. The regenerative braking system (RBS) technology is increasing rapidly as an alternative energy-saving solution instead of using the conventional fossil fuel process. In addition, conventional braking creates energy loss because it produces unnecessary heat during braking. Therefore, (RBS) was deliberately designed to solve these drawbacks. Several researchers have found an efficient way to recover regenerative energy, but do not pay enough attention to state-of-the-art (SOC), motor performance and overall performance. This paper designs a new braking force distribution that introduces integrated braking by combining the default ADVISOR and the new parallel braking distribution to improve the SOC battery for three driving cycles. The design of the braking part was based on the braking force distribution of vehicle speed, consisting mainly of friction and regenerative braking ratio allocation in parallel form. The suggested delivery method is evaluated by simulation and shows that the overall performance and battery life are increased. The proposed method was experimentally evaluated using ADVISOR Matlab for the efficiency and final state of the battery.

Design and Analysis of Electrical Braking Torque Limit Trajectory for Regenerative Braking in Electric Vehicles With PMSM Drive Systems

In this article, an electrical braking torque limit trajectory is designed to improve the regenerative braking of electric vehicles based on a regenerative power analysis. Even though electric motors generally regenerate electric energy from kinetic energy, the drive system dissipates electric energy under certain low-speed operation conditions. This phenomenon has been addressed in many previous works and most of them compensate for this power dissipation region by modifying the electrical braking torque limit trajectory of the motor. However, the torque limit trajectory has never been considered based on a regenerative power analysis even though an inaccurate torque limit trajectory causes energy loss to the drive system. In this article, the regenerative power analysis for two major kinds of permanent magnet synchronous motor is performed. Afterward, torque limit trajectories are set based on this analysis so that batteries in electric vehicles can harvest the maximum regenerative energy in any circumstance. To verify the analysis and proposed torque limit trajectories, simulations and experiments are performed.

An Inference-based Prognostic Framework for Health Management of Automotive Systems

This paper presents a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic time-series data. The framework employs proportional hazards model and a soft dynamic multiple fault diagnosis algorithm for inferring the degraded state trajectories of components and to estimate their remaining useful life times. The framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (probabilistic test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via soft dynamic multiple fault diagnosis algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The framework is demonstrated on datasets derived from two automotive systems, namely hybrid electric vehicle regenerative braking system, and an electronic throttle control subsystem simulator. Although the proposed framework is validated on automotive systems, it has the potential to be applicable to a wide variety of systems, ranging from aerospace systems to buildings to power grids.

Fabrication and empirical analysis of graphene dispersion/activated carbon on conductive networks in porous graphite felt supercapacitor

Supercapacitors are a promising candidate for the regenerative braking system due to their inherent fast charging and high-current/temperature tolerance capabilities. However, most supercapacitors suffer from high weight to capacitance ratio, which averts the use of supercapacitors in regenerative braking in electric vehicles. This research proposes a new lightweight supercapacitor electrode fabrication, which consists of networking conductive layers of graphene dispersion and activated carbon in porous graphite felt using conductive ink. The composite electrode showed a specific capacitance up to 170 F g−1, or 13 F g−1 from a completed device in type III deep eutectic solvent electrolyte, which gave the starting voltage of 2.3 V. The submersion fabrication alleviated the fabrication compare to brush method and production time was reduced to under 3 min of physical preparation.

A genetic-fuzzy control method for regenerative braking in electric vehicle

In order to improve the recovery ratio of the regenerative braking energy in electric vehicles, the influence factors on braking energy feedback in electric vehicles were analysed. Then, a parallel braking force distribution model was established, and a fuzzy controller on braking force distribution was designed, in which the inputs were vehicle speed, braking strength, battery SOC, and output was regenerative braking ratio. On the other hand, the implementation of genetic algorithm in optimisation process was studied. Furthermore, the genetic algorithm was used to optimise the fuzzy control rules, and new fuzzy distribution rules of electro-hydraulic braking force were obtained. The experimental results showed that the recoverable energy ratio was increased by 2.7% with the comparison of the optimised distribution rules and the original rules. So, the genetic-fuzzy control method is effective for regenerative braking in electric vehicles.

A genetic-fuzzy control method for regenerative braking in electric vehicle

In order to improve the recovery ratio of the regenerative braking energy in electric vehicles, the influence factors on braking energy feedback in electric vehicles were analysed. Then, a parallel braking force distribution model was established, and a fuzzy controller on braking force distribution was designed, in which the inputs were vehicle speed, braking strength, battery SOC, and output was regenerative braking ratio. On the other hand, the implementation of genetic algorithm in optimisation process was studied. Furthermore, the genetic algorithm was used to optimise the fuzzy control rules, and new fuzzy distribution rules of electro-hydraulic braking force were obtained. The experimental results showed that the recoverable energy ratio was increased by 2.7% with the comparison of the optimised distribution rules and the original rules. So, the genetic-fuzzy control method is effective for regenerative braking in electric vehicles.