Braking Strength Research Articles

Regenerative braking fault compensation control of distributed electric vehicle considering random wheel fault degree

Distributed drive electric vehicles can reduce range anxiety through regenerative braking. However, if the wheel motor torque output fails, it will form an additional yaw moment to the vehicle, causing instability, or deviation and threatening its safety. To solve this problem, the research object is an electric vehicle driven by a four-wheel hub motor. A braking force compensation distribution strategy for front and rear axles is proposed, which combines electronic hydraulic braking (EHB) system compensation control and deviation auxiliary control. Firstly, a fault detection module is established, and the motor’s output torque is estimated by designing a torque observer to obtain the fault degree information of the motor. Secondly, to fully use the motor’s regenerative braking force, the fault-free and faulty electro-hydraulic braking force distribution strategies are designed in the coordinated distribution layer of the electro-hydraulic braking system. The corresponding electro-hydraulic braking force compensation method is selected according to the fault degree of the regenerative braking function, the position of the faulty wheel, and the braking strength. Then, a deviation auxiliary controller is designed based on the model predictive control, and the intervention time of the auxiliary controller is determined according to the vehicle’s state. Finally, the control method is verified based on CarSim/Simulink co-simulation and hardware-in-the-loop (HIL) platform. The test results show that the designed control method can effectively compensate for the regenerative braking failure of random wheel and ensure the braking safety of the vehicle.

Energy recovery of four-wheel hub motor driven vehicle in steering-braking condition

The factors that affect the efficiency of braking energy recovery is a crucial and worthy research problem for the four-wheel hub motor driven (FWHMD) electric vehicle (EV). This paper presents an energy classification statistical method by integrating the direct current (DC) of the power battery and considering the efficiency of the permanent magnet synchronous motor (PMSM). According to the characteristics of DC, the energy recovered and consumed by the motor during regenerative braking is divided into total recovered energy, net recovered energy and dissipated energy. Then, the influence of steering resistance on braking energy recovery of the steering braking is analyzed by using the proposed energy statistical method and combining Carsim and Matlab/Simulink co-simulation. Firstly, a closed-loop coupling model combined with the PMSM and vehicle dynamics is established. Secondly, the braking energy recovery only the PMSM engaged is simulated for different brake strengths under steering-braking conditions, also the stability of the vehicle is analyzed and the recovery energy is counted. Lastly, the energy recovered is compared with the recovery energy by longitudinal braking under the same braking strength. The results show that the energy recovery of the steering regenerative braking conditions with braking strengths of 0.1 and 0.2 is 4.10% and 1.90% less than that of the longitudinal braking conditions with the same braking strengths, respectively, and the steering resistance is constant. The reduction of the recovery energy is only related to the braking time. Meanwhile, the research in this paper provides a reference significance for the research of braking energy recovery strategies for FWHMD vehicles.

A Multi-Source Braking Force Control Method for Electric Vehicles Considering Energy Economy

Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking performance. In this paper, a multi-source braking force system and its control strategy are proposed with the aim of enhancing braking strength, safety, and energy economy during the braking process. Firstly, an ENMPC (explicit nonlinear model predictive control)-based braking force control strategy is proposed to replace the traditional ABS strategy in order to improve braking strength and safety while providing a foundation for the participation of the drive motor in ABS (anti-lock braking system) regulation. Secondly, a grey wolf algorithm is used to rationally allocate mechanical and electrical braking forces, with power consumption as the fitness function, to obtain the optimal allocation method and provide potential for EMB (electro–mechanical brake) optimization. Finally, simulation tests verify that the proposed method can improve braking strength, safety, and energy economy for different road conditions, and compared to other methods, it shows good performance.

Integrated control of anti-lock and regenerative braking for in-wheel-motor-driven electric vehicles

The in-wheel motor is increasingly used in electric vehicles due to the significantly improved controllability, response capability, and energy recovery efficiency based on this technology. However, the independent control of in-wheel motors will lead to braking torque distribution problems, especially in a situation where anti-lock braking systems (ABS) are triggered, which may cause the braking energy to be unrecoverable without the coordinated control between anti-lock and RB for two in-wheel motor-driven electric vehicles based on the RB efficiency map. Control-oriented wheel dynamics and slip ratio models of the system are generated. A sliding mode intervention of regenerative braking (RB) control. This paper presents an integrated algorithm to realize the control-based ABS controller is designed to prevent the wheels from locking and to maintain the slip ratio within a desired level, and the stability and robustness of the controller to uncertainties and disturbances are discussed. Moreover, the braking strength of the driver is calculated and divided into different modes to derive a dynamic braking torque distribution to improve the energy recovery efficiency. The hardware-in-the-loop simulation results show that the recovered energy of the proposed strategy under ABS-triggered maneuver is increased by 52.9% than that of the Proportional, Integral, and Derivative controller and can effectively improve the braking performance and stability.

Coordination control between vibration suppression and energy regeneration for vehicles driven by in-wheel motors under regenerative braking condition

In this paper, focusing on regenerative braking condition, a coordination control method is proposed to achieve the vibration suppression and braking energy recovery of vehicles driven by In-Wheel Switch Reluctance Motor (IWSRM). Firstly, a vehicle vertical dynamic model, including SRM drive system, vehicle braking system, and quarter SRM-suspension system, is established based on the analysis of the generating and vibration characteristics of IWSRM. Then, after analyzing the effects of eccentricity and electromagnetic excitation, the vertical unbalanced radial force is used as control signal in design of the control method. Moreover, for ensuring the recovered energy and reduce vertical vibration, the optimal key control parameters varying with the vehicle speed and braking strength are obtained by genetic algorithm. To achieve verification of the control method, the several vehicle performance indexes are selected as observation objects, which are analyzed and compared to other control methods, and the results indicate the control method can effectively weaken the vertical acceleration and improve the vehicle ride comfort without significantly decreasing regenerative braking energy. Finally, a hardware in the loop experiment is conducted, and the effectiveness and real-time ability of the proposed method can be verified.

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

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.

Braking Intention Identification Strategy of Electric Loader Based on Fuzzy Control

As a widely-used construction machinery, the electric loader has the potential to recover braking energy due to its large mass and frequent starts and stops. Identifying braking intention accurately is the foundation of braking energy recovery. The typical braking condition of an electric loader is analyzed; the braking intention is divided into sliding brake, mild braking, moderate braking, and emergency braking. A large number of braking data were collected under different braking intentions, which are used as the basis for fuzzy control variable partitioning, fuzzy controller parameter setting and fuzzy control rule formulation. The control strategies of deceleration intention identification based on accelerator pedal, braking intention identification based on brake pedal and sliding brake intention identification are proposed in this paper, respectively. This paper takes the hydraulic brake pressure as a feedback parameter, even if there is hysteresis or failure based on the intention of pedal identification, the system can still provide sufficient braking strength to ensure the braking safety. The AMESim(R12)-Matlab/Simulink co-simulation model and prototype are built to verify the feasibility of the control strategy under different braking intention identification. The results show that the braking strength under stronger braking intention can satisfy the braking demand whether at a speed of 2 m/s or 12 m/s, which ensures the safety of emergency braking, and the electric loader can provide stable braking strength under different braking intention and different speed. This has good braking stability.

Electro-hydraulic composite stability control for regenerative braking failure of in-wheel motors drive electric vehicle

The in-wheel motor simplifies the structure of drive system and improves dynamic performance of vehicle. However, the regenerative braking failure of unilateral motor could greatly reduce braking strength and cause the instability of vehicle. The existing control methods are difficult to guarantee the braking performance and driving stability at the same time. To resolve this fail-safe problem, an electro-hydraulic composite stability control method is proposed and verified. Firstly, the vehicle model, the in-wheel motor model, and the hydraulic braking system model are built. Secondly, the electro-hydraulic composite stability control method is proposed and a stability controller based on model predictive control algorithm is designed to solve the problems of model uncertainty and external interference in the control process. Thirdly, the control effect of proposed method is compared with torque truncation control, hydraulic compensation control, and yaw stability control. Finally, the electro-hydraulic composite stability control method proposed in this paper is verified and tested on the real vehicle. The results show that the combination of the torque truncation control, the hydraulic compensation control, and yaw stability control could improve the driving stability of vehicle and meet the demand of braking intensity.

Research on Regenerative Braking Control Strategy for Single-Pedal Pure Electric Commercial Vehicles

In recent years, with the increasing severity of energy and environmental issues, countries have vigorously developed the new energy automotive industry. To reduce the difficulty of driver operation and increase endurance mileage, this article proposes a regenerative braking control strategy for a single-pedal pure electric commercial vehicle. Firstly, the single-pedal control system’s hierarchical approach was designed to contain the driver’s intention analysis and torque calculation layers. After identifying the driver’s intention, a logic threshold method was used to determine the braking pattern. Then, a fuzzy theory was used, with road gradient, braking strength, and speed as input parameters, and the ratio coefficient of braking force as the output parameter. A hybrid regenerative braking strategy was formulated based on the ideal distribution curve. Finally, the proposed strategy was verified through simulation and a constant-speed car-following experiment. The constant-speed car-following experiment results show that the maximum optimization rate of energy consumption provided by the proposed single-pedal regenerative braking control strategy is 5.81%, and the average optimization rate is 4.33%. This strategy can effectively reduce energy consumption and improve the economic performance of single-pedal pure electric commercial vehicles.

Single-Wheel Failure Stability Control for Vehicle Equipped with Brake-by-Wire System

In order to solve the problem of vehicle stability control after a single-wheel brake failure in the brake-by-wire system, a control strategy of braking force redistribution with a yaw moment is proposed to ensure the braking efficiency and stability of vehicles. In this strategy, a two-layer architecture is adopted. In the upper layer control, a fault factor is introduced to represent the real-time failure degree of the wheel, and the driver’s braking intention is perceived through the pedal travel and pedal speed of the driver. The braking force redistribution algorithm of the remaining three wheels is designed based on the wheel failure degree and braking intensity. In the lower control, according to the state parameters of the vehicle, the additional yaw moment, which controls the yaw rate and the sideslip angle of the vehicle, is calculated by using the sliding mode control theory, and the yaw moment is reasonably allocated to the normal wheel. By using MATLAB/Simulink and Carsim co-simulation, different braking strength and failure types are selected for simulation analysis. The simulation results show that the proposed control strategy can improve the braking efficiency and stability of the vehicle under different braking conditions.

Research on Adaptive Distribution Control Strategy of Braking Force for Pure Electric Vehicles

The actual driving conditions of electric vehicles (EVs) are complex and changeable. Limited by road adhesion conditions, it is necessary to give priority to ensuring safety, taking into account the energy recovery ratio of the vehicle during braking to obtain better braking quality. In this work, an electric vehicle with an EHB (electro-hydraulic braking) system whose braking force adaptive distribution control strategy is studied. Firstly, the vehicle dynamics model, including seven degrees of freedom, tire, drive motor, main reducer, battery pack, and braking system, was constructed, which is attributed to the vehicle configuration and braking system scheme. Second, based on curve I and ECE regulations, the adaptive braking force distribution control strategy was formulated by taking the maximum regenerative braking torque as the inflection point, the synchronous adhesion coefficient as the desired point, and the battery SOC, road adhesion coefficient, and braking strength as the threshold. Finally, the vehicle dynamics simulation model was built on the Matlab/Simulink platform, and the simulation results verified the feasibility of the proposed braking force adaptive allocation control strategy. The research shows that the adaptive distribution control strategy can better adapt to the complex and variable driving conditions of the vehicle by combining the inflection point and the desired point. The braking energy recovery ratios of the vehicle under the NEDC and NYCC cycle conditions on a high adhesion road are 52.62% and 47.45%. The braking force distribution curve is close to curve I under the low adhesion extreme road.

A Methodology for Stochastic Simulation of Head Impact on Windshields

In accidents involving cars with pedestrians, the impact of the head on structural parts of the vehicle presents a significant risk of injury. If the head hits the windshield, the injury is highly influenced by glass fracture. In pedestrian protection tests, a head form impactor is shot on the windshield while the resultant acceleration at the centre of gravity of the head is measured. To assess the risk of fatal or serious injury, a head injury criterion (HIC) as an explicit function of the measured acceleration can be determined. The braking strength of glass, which has a major impact on the head acceleration, however, is not deterministic but depends on production-related microcracks on the glass surface as well as on the loading rate. The aim of the present paper is to show a pragmatic method for how to include the stochastic failure of glass in crash and impact simulations. The methodology includes a fracture mechanical model for the strain rate-dependent failure of glass, an experimental determination of the glass strength for the different areas of a windshield (surface, edge, and screen-printing area), a statistical evaluation of the experimental data, and a computation of an HIC probability distribution by stochastic simulation.

A novel coordinated control strategy of regenerative braking and anti-lock system for electric vehicles

Regenerative braking is one of the core technologies to improve the economy of pure electric vehicles. To realize the improvement of the energy recovery effect under conventional braking conditions and the coordinated control between regenerative braking and anti-lock system under emergency braking conditions of pure electric vehicles, this paper studies a front and rear axle braking force distribution method with the actual road adhesion coefficient and braking strength as the premise, while using fuzzy control to distribute the front axle electromechanical braking force. The sliding mode controller with double power convergence law is designed to enhance the stability of the anti-lock system. Finally, the braking coordination control strategy is proposed by combining the designed regenerative braking and anti-lock control. Through simulations and experiment, it has been proved that the control strategy is effective and can effectively improve the economy of pure electric vehicles.

Energy management strategy for hydraulic hybrid system with compound accumulator based on dynamic programming

The compound accumulator is an energy storage device composed of one large accumulator and one small accumulator. Compared with traditional single-accumulator hydraulic hybrid vehicles, hydraulic hybrid vehicles based on compound accumulator can switch the working timing of large and small accumulators according to the characteristics of different working conditions, more braking energy can be recovered on the premise of providing enough braking strength. This article is aimed at hydraulic hybrid vehicles based on compound accumulator. First, the key components of the hydraulic system were matched, then according to the engine fuel consumption curve, the engine’s high-efficiency work area is divided, and formulated a rule-based energy management strategy based on the principle of adjusting the engine to work in a high-efficiency area. The internal pressure of the compound accumulator is selected as the state variable, the ratio of the torque of the hydraulic pump/motor to the required torque of the vehicle is the control variable, and the optimal fuel economy of the entire cycle is the objective function, and the mathematical model is established using the dynamic programming algorithm, and compiled a program in MATLAB to obtain the optimal torque distribution relationship between the engine and the hydraulic pump/motor and the optimal working timing of the large and small accumulators. Simulation results show that compared with the traditional hydraulic hybrid vehicle with single accumulator based on initial rules and the hydraulic hybrid vehicle with compound accumulator based on initial rules, the hybrid vehicle with compound accumulator based on improved rules can save fuel by 11.84[Formula: see text] and 5.96[Formula: see text] respectively. The fuel saving potential of the compound accumulator structure and the effectiveness of the improved rule control strategy based on dynamic programming algorithm are verified.

A new braking torque distribution strategy based on braking actuator characteristics and a command signal for a blended braking system

Braking torque distribution strategy is not only related to the tracking performance of the blended braking system in commanding the braking signal, but is also related to the energy utilization efficiency of the blended braking system. In this paper, a new braking torque distribution strategy is designed for a blended braking system consisting of a motor braking system and a friction braking system. In contrast to the classic braking torque distribution strategy, which adjusts the braking torque distribution ratio based on the braking strength, the newly designed braking torque distribution strategy adjusts the braking torque that is allocated to the motor braking system and friction braking system, according to the command braking torque signal, the braking actuators’ dynamic characteristics and the motor's electrical characteristics. In addition, a three-layer controller is designed to ensure the execution of the proposed braking torque distribution strategy and the stability of the blended braking system. Compared to the classic distribution strategy, based on braking strength, numerical simulation results show that the braking energy recovery of the blended braking system has been improved by 16% in normal braking conditions and has been improved by 84% in emergency braking conditions under the new braking torque distribution strategy.

Braking energy management strategy for electric vehicles based on working condition prediction

To improve the mileage capacity of electric vehicles (EVs), a dual-motor front-wheel-drive EV is considered as the research object. Through experiments with actual vehicles, data from four typical working conditions are collected; a C4.5 decision tree algorithm is developed to train a working condition recognition model. The long short termmemory neural network is used to train four deep-learning working condition prediction models, and the particleswarm algorithm is used to optimize their structural parameters. The braking strength, demand torque, and demand speed are determined based on the predicted working conditions. Based on four common braking energy recovery control strategies, front- and rear-wheel braking force distribution strategies are formulated according to the changes in braking strength. The maximum regenerative braking torque and remaining mechanical braking torque provided by the front wheels are optimized. The Seagull Optimization Algorithm is used to optimize the torque distribution of the dual motors on the front wheels and improve the working efficiency of the motors. In the experimental conditions, the recovered energy at 100 km is 2.6 kWh; the energy recovery rate is 19.1%, and the power consumption ratio is reduced by 15.8%, improving the EV cruising range.

From sport to eco: A case study of driver inputs on electric vehicle efficiency

This study focuses on driver-induced changes to vehicle energy and emissions performance by altering drivetrain configurations. It defines and examines the motivations for the use of drive modes in plug-in hybrid electric vehicles (PHEVs). Drive modes are defined as user-selectable drivetrain configurations that allow drivers to change vehicle performance aspects such as altering its propulsion status from electric to gas, changing the steering responsiveness, suspension stiffness, and regenerative braking strength. Despite the direct impact they have on the vehicle’s energy usage, greenhouse gas emissions, and local air emissions, the impact of drive modes are not traditionally measured as part of the vehicle’s performance and certification tests. The impact of drive mode choice can explain a portion of the discrepancy between PHEVs expected and on road performance as a recent study by the authors found that the use of certain modes can result in a 15–30% increase in total energy usage in a vehicle traveling at freeway speeds.This is the first study to examine the motivations for the use of these drive modes and provides an overview of PHEV drive modes, classifying them into two main mode categories (propulsion adjustment and driver experience) and eight different mode types based on their intended purpose. This is followed by a logistic regression of 11 indicators for mode usage taken from a survey of over 4,500 PHEV owning households, showing that gender and the number of long-distance trips were the most commonly associated with usage across mode types.

Simulation Research on Regenerative Braking Control Strategy of Hybrid Electric Vehicle

This paper proposes a double layered multi parameters braking energy recovery control strategy for Hybrid Electric Vehicle, which can combine the mechanical brake system with the motor brake system in the braking process to achieve higher energy utilization efficiency and at the same time ensure that the vehicle has sufficient braking performance and safety performance. The first layer of the control strategy proposed in this paper aims to improve the braking force distribution coefficient of the front axle. On the basis of following the principle of braking force distribution, the braking force of the front axle and the rear axle is reasonably distributed according to the braking strength. The second layer is to obtain the proportional coefficient of regenerative braking, considering the influence of vehicle speed, braking strength, and power battery state of charge (SOC) on the front axle mechanical braking force and motor braking force distribution, and a three-input single-output fuzzy controller is designed to realize the coordinated control of mechanical braking force and motor braking force of the front axle. Finally, the AMESim and Matlab/Simulink co-simulation model was built; the braking energy recovery control strategy proposed in this paper was simulated and analyzed based on standard cycle conditions (the NEDC and WLTC), and the simulation results were compared with regenerative braking control strategies A and B. The research results show that the braking energy recovery rate of the proposed control strategy is respectively 2.42%, 18.08% and 2.56%, 16.91% higher than that of the control strategies A and B, which significantly improves the energy recovery efficiency of the vehicle.

Energy-Efficient Braking Torque Distribution Strategy of Rear-Axle Drive Commercial EV Based on Fuzzy Neural Network

<div class="section abstract"><div class="htmlview paragraph">Regenerative braking is identified as an essential step toward extending cruising mileage for electric vehicle (EV). Braking energy recovery strategies usually focus on passenger EV and commercial EV is ignored. In this paper, an energy-efficient braking torque distribution strategy is proposed for a rear-axle drive commercial EV to improve braking energy recovery and safety. Firstly, the braking force distribution curve is determined referring to the EU braking law for commercial vehicle and the ideal braking distribution curve. Secondly, a novel braking torque distribution strategy is established adopting fuzzy control algorithm, where the ratio between hydraulic braking torque and regenerative braking torque is updated instantaneously according to vehicle velocity, braking strength and state of charge of battery. Then, the corresponding controller is synthesized on ideal braking condition and several classic cycles. To further enhance the performance of the controller, a neural network based framework is established to optimize the membership function in fuzzy controller. Simulations on ideal braking condition demonstrate the controller can always meet emergency braking needs. For the standard cycles, including NEDC and WLTC, the energy-efficient strategy based on fuzzy control can recover up to 18.88% and 16.56% of energy under NEDC and WLTC cycles, and on this basis, the optimized strategy based on adaptive neuro-fuzzy control can improve energy recovery by 2.84% and 3.6% under these two cycles. The developed braking torque distribution strategy can potentially be embedded in real-time supervisory systems to realize the energy saving and increase the cruising mileage for commercial EV.</div></div>

Regenerative Braking Control Strategy of Electric Vehicles Based on Braking Stability Requirements

Electric vehicles are effective way to solve energy and environmental problems, but the promotion and application of electric vehicles are suppressed by their limited endurance range seriously. The regenerative braking technology is an important method to increase the endurance range of the electric vehicle. During the braking process, the kinetic energy of the electric vehicle can be converted into electric energy and stored in the energy source device with the regenerative braking system, so the endurance range of the electric vehicle can be increased accordingly. In order to increase the efficiency of energy recovery, a regenerative braking strategy with the optimization distribution algorithm is proposed in this paper, and the braking forces of the front and rear axles are distributed optimally with variable ratios based on the braking strength. With the optimal braking force distribution ratio and related constraint conditions, the regenerative braking control strategy was designed to meet the braking stability and the maximum braking energy recovery. And then a simulation model of the braking control strategy was built with MATLAB/Simulink software, and the simulation tests on UDDS and NEDC cycle conditions were done to verify the effectiveness of the designed regenerative braking control strategy. Compared with the control strategy of ADVISOR software, the braking energy recovery efficiency was improved more than 51.9 % while maintaining the braking stability.