Regenerative Braking Torque Research Articles

Cooperative optimization of energy recovery and braking feel based on vehicle speed prediction under downshifting conditions

Regenerative braking can effectively recover vehicle kinetic energy, but its energy conversion efficiency is low under low-speed conditions, and there is also the problem of premature exit from energy recovery due to insufficient reverse electromotive force. The cooperation of gearbox gears can increase the speed range for the motor to recover energy, but unreasonable shifting will cause fluctuations in braking force and affect the consistency of the braking feel. Therefore, this paper aims to collaboratively optimize energy recovery and braking force fluctuations during gear shifting. Firstly, based on the model of braking system and transmission, the influence of shift strategy on braking impact and energy recovery is studied. In view of the challenge of determining a shift strategy with uncertain target braking speeds, a speed prediction model reconstructed by the support vector regression (SVR) model and the hybrid nonlinear autoregressive neural network (NAR) is proposed. On the basis of NAR-SVR speed prediction, the coupling effect of braking impact force and energy recovery efficiency is considered, and the collaborative optimization of regenerative braking torque and shift time is solved through a multi-objective cuckoo search algorithm. The hardware-in-the-loop test results verified that under high-speed conditions, the braking energy recovery rate of the proposed strategy was increased by 47.06 %, and the peak braking impact was reduced by 61.4 %. This research can provide a reference for the brake downshift optimization strategy and regenerative braking research of vehicles with non-decoupled electro-hydraulic composite braking systems.

Optimization and control of the brushless DC motor speed for a Battery Electric Vehicle (BEV) using Fuzzy-grasshopper optimization regenerative braking system

Transportation electrification is at the core of the possible solutions to many challenges the world is currently facing. Efficient vehicle electrification has the potential to simultaneously reduce greenhouse gasses emissions and to tackle range anxiety issues. Among different strategies for advancements in Battery Electric Vehicle (BEV) efficiency, enhancing Regenerative Braking (RB) capabilities is an area with opportunities. As RB faces different impediments, upgrades in safety, efficiency, and/or battery quality of life are usually accompanied with further strain in energy management schemes, limiting RB performance. Power Electronics (PE) improvements are among the options that have the potential to benefit RB and overall efficiency. This work proposes a method to improves RB through brushless DC Motor. As the EV speed drops, the fuzzy-grasshopper optimization regulates the regenerative braking torque to maintain the braking system's dependability. The fuzzy-grasshopper optimization controller also provides real-time fulfillment of the brake distribution force between the front wheel, rear wheel, and subsection of the regenerative forces and mechanical. At the conclusion of the run, the estimated SOC for smooth roads was 23.72%, compared to the measured SOC for smooth roads of 20.71%, and the simulated FL-RB SOC is 21.25%. At the rough road's conclusion, the measured SOC was 14.8%, the SOC simulated with the FL-RB was 15.98%, whereas without the FL-RB, the predicted SOC was 20.72%.

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.

Integrated control of braking-yaw-roll stability under steering-braking conditions

Sharp steering-braking at a high speed exposes sport utility vehicles with high gravity centers and narrow wheel tracks to the risks of tire locking, sideslip and rollover. To avoid these risks and ensure braking safety, yaw stability and roll stability upon steering-braking, a braking-yaw-roll stability integrated control strategy was proposed, which consists of a supervisor, an upper and a lower controller for the front and rear axle independent drive electric vehicle. In the supervisor, a nonlinear vehicle predictive model was constructed and four control modes were proposed according to the vehicle status and rollover indexes. The weight coefficients between braking force, yaw stability and roll stability are determined dynamically by the control mode and output to the upper controller. The upper controller used a nonlinear model predictive control to determine the longitudinal braking force distribution of the four wheels. And in the lower controller, the regenerative braking torque and friction braking torque of each wheel were distributed. Finally, simulation verifications were carried out on the high and low adhesion roads. The results show that the control strategy proposed in this study can effectively prevent the vehicle from rollover while ensuring braking safety and yaw stability.

Design and performance analysis of braking system in an electric vehicle using adaptive neural networks

Research article emphasizes on the impact of braking concepts considering regenerative braking system and energy consumption aspects in electric vehicles through a new perspective. The electric vehicle system is modeled and simulated using the MATLAB/Simulink software. A dataset is developed using the virtual simulation environment created by co-simulation using the MATLAB/Simulink and the IPG Carmaker software. This dataset is also used in a neural network model based on adaptive neuro fuzzy logic and the system performance is analyzed. Parameters considered for training the neural network are the brake pedal displacement, braking change rate and the need for brake application. The highlight of this study is the focus on a front wheel driven electric vehicle, which uses a standard drive cycle input to validate the model. The significant parameters evaluated in this study include the braking effects, kinetic energy, regenerative braking torque, battery state of the charge and the motor torque. The torque generation and its intended braking force requirements based on the acceleration, deceleration and braking conditions are the notable observations. The regenerative capability of this proposed system design is also illustrated along with the surface plots based on the training dataset. Investigation and analysis reveal that, the battery state of charge could be revived throughout the drive with a steady and stable increase. Transitions of motor torques between tractive and regenerative phases are also illustrated and explained for clarity and brevity.

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.

A μ‐H∞ control strategy for decreasing torque fluctuation of electro‐hydraulic integrated braking system in mode switching

AbstractDue to the difference of time‐domain response between hydraulic braking and regenerative braking, as well as changes of equivalent parameters and operating parameters during braking mode switching, it is liable to cause torque fluctuation, which affects braking safety and vehicle ride comfort. First, the uncertainties of vehicle load and frictional coefficient model are investigated. Second, the hybrid system theory is applied to provide state transfer condition for mode switching strategy. Finally, the control strategy that utilizes regenerative braking torque to compensate for difference of the required braking torque is designed, and a new μ‐H∞ control algorithm through D‐K iteration is presented to improve the robust performance. The proposed μ‐H∞ control strategy is examined under various braking situations, and the results indicate that (1) the μ‐H∞ controller have the advantage of robustness performance, the amplitude of regenerative braking is decreased by 6.14%, and the steady‐state error of hydraulic braking is decreased by 5.26% over the H∞, and (2) under the braking mode switching, the designed compensation control strategy has the performance of fast and accurate tracking of the desired torque, and the steady‐state error does not exceed 3.5%.

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.

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.

Study on Braking Energy Recovery Control Strategy for Four-Axle Battery Electric Heavy-Duty Trucks

Regenerative braking can extend the driving range and reduce PM emissions from abrasion for battery electric heavy-duty trucks (BETs). The composite braking control strategy including torque distribution and dynamic coordinated control for the four-axle BET equipped with the electromechanical braking system is studied. A segmented torque distribution strategy is proposed to maximize energy recovery while ensuring braking stability. The simulation results reveal that the strategy shows better comprehensive braking performance than the two benchmark strategies, and the energy recovery rate in different load states under CHTC-D is above 40%. The proposed coordinated control strategy takes advantage of regenerative braking’s rapid response and precise control to compensate for torque deviations caused by the hysteresis of friction braking. For two common braking mode transition conditions, regenerative braking torque correction and advance of the mode switching timing are adopted to enable the motor to obtain the torque compensation ability. This method leads to a slight loss of braking energy, and the maximum torque deviation during the mode switching process is suppressed to less than 1.4 kN·m, and the jerk and braking distance is reduced accordingly, which is of great importance in improving driving comfort and braking safety.

Regenerative Braking Strategy of Dual-Motor EV Considering Energy Recovery and Brake Stability

The dual-motor EV (Electric Vehicle) is increasingly favored by manufacturers for its excellent performance in terms of power and economy. How to further reduce its energy consumption and make full use of the dual-motor energy recovery is an important support to improve the overall vehicle economy and realize the “dual carbon” strategy. For the dual-motor EV architecture, the motor model, power battery loss model and vehicle longitudinal braking force model are established and the energy recovery-dominated regenerative braking torque distribution (RBD) rule of the dual motors is designed. Based on genetic algorithm (GA) theory and taking into account SOC, vehicle speed and braking intensity, a regenerative-braking torque optimization method is proposed that integrates energy recovery and braking stability. The braking intensity of 0.3 and the initial vehicle speed of 90 km/h are selected for verification. Compared with the rule method, the energy recovery and stability are improved by 22.8% and 4.8%, respectively, under the genetic algorithm-based and energy recovery-dominated regenerative-braking torque distribution (GA-RBD) strategy. A variety of conditions are selected for further strategy validation and the result shows that compared with the rule-based method, both energy recovery and braking stability are improved as braking speed and braking intensity increase under the GA-RBD strategy.

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

A new generation of electric vehicles with in-wheel motor technology has been introduced and developed. Increasing system efficiency, eliminating mechanical intermediaries, and achieving regenerative braking torque with better performance are the motivations to seek to improve this technology. To the best knowledge of authors, no solution has been introduced to enhance such system efficiency while all constraints and limitations, especially the battery health and safety issues have been considered. In order to properly manage the energy and use the maximum possible braking capacity of the motor’s regenerative mode, accurate instantaneous information of the maximum possible braking torque capacity is required. In the present study, a half-car model with five degrees of freedom has been developed by considering a vehicle equipped with two in-wheel motors on the rear axle as a sample vehicle. The braking strategy has been designed using a two-stage nonlinear predictive controller. The first stage will reach the appropriate pressure for the brake fluid lines. Then, in the second stage, the proper amount of electric regenerative torque is obtained using the electronic braking force distribution (EBD) function and considering all constraints. The amount of regenerative torque is calculated by considering the system constraints using the Karush–Kuhn–Tucker conditions (KKT). Finally, the designed strategy is examined from the perspective of safety and vehicle mileage capability. The results show that optimal braking can be achieved, and the braking stopping distance can be reduced by utilizing the designed controller and the proposed model. Also, the amount of regenerated energy to the battery can be increased during braking by using the proposed braking strategy and the designed control system compared with the relevant studies. The proposed strategy enhanced the vehicle mileage by more than 24 percent while considering all constraints and limitations.

Pipe architecture optimization of the parallel Hydraulic-pneumatic hybrid system using a hydraulic transformer

The contradiction between the input power fluctuating caused by the working condition’s randomness and the fixed energy recovery power of the energy storage system caused by the energy storage units’ fixed power density has arisen in many fields. The battery-ultracapacitor energy storage system and the parallel hydraulic-pneumatic hybrid system have been proved feasible to solve this contradiction in electric vehicles and heavy vehicles respectively. A hydraulic transformer with a function of regulating the pressure ratio between the input circuit and the load circuit could be a potential solution to the contradiction in the parallel hydraulic-pneumatic hybrid system. However, there were two load circuits (hydraulic and pneumatic) in the parallel hydraulic-pneumatic hybrid system which proposed challenges for the implantation of hydraulic transformer into the system and regulation of the pressure ratio between the input circuit and the two load circuits. To solve this problem, a hydraulic transformer with two variable displacement hydraulic power units was used to form a hydraulic transformer, the different connection methods of hydraulic transformer ports and each circuit, and the corresponding pressure proportional relationship were investigated. Based on above analysis, a new pressure adjustable parallel hydraulic-pneumatic hybrid system is proposed in this paper. The implantation of the hydraulic transformer into the system and the proportional regulation of the pressure in each circuit were achieved. Simulation models for parallel hydraulic-pneumatic hybrid system and the pressure adjustable parallel hydraulic-pneumatic hybrid system were built, and the simulation results were compared and analyzed. The regenerative braking torque could be varied from 14433.9 Nm to 168.56 Nm by adjusting the displacement factor ratio of the hydraulic transformer; the regenerative braking torque fluctuation of the PAPHPH system can be reduced by 65.61% when the displacement coefficient ratio (εB/εA) is 0.5; Stable work of the air compressor was achieved.

Analysis of Slip rate Control Technology of Electric Vehicle Based on Sliding Mode Algorithm

With the increasingly prominent environmental and energy problems, it is very important to strengthen the research of new energy vehicles. In the research process of new energy vehicles, the safety of electric vehicles during driving is extremely important. In the braking process of electric vehicles, wheel slip control is an important factor to ensure the safety of electric vehicles. In the process of slip rate control, it is necessary to identify the wheel slip rate under different road surfaces, so as to complete the design of the slip rate controller on the basis of mastering the wheel friction characteristics. In this research, we mainly consider the maximum braking torque limit of motor output, scientifically distribute regenerative braking and hydraulic braking torque, complete the design of slip rate controller based on sliding mode algorithm, and introduce saturation function to solve the buffeting problem in road friction adaptive slip rate control method. Put forward the core of slip rate control strategy of electric vehicle. Through research, it is determined that the reasonable division of regenerative braking and mechanical braking based on slip rate can improve the safety of vehicle braking and ensure the efficiency of energy recovery. Therefore, in the process of electric vehicle slip rate control, the regenerative braking control method based on slip rate closed-loop control has application value.

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.

Novel regenerative braking method for transient torsional oscillation suppression of planetary-gear electrical powertrain

Electric regenerative braking systems have drawn much attention in the trends of energy-saving and emission-reduction requirements for electric vehicles. However, the significant differences between regenerative braking transmission chains and the traditional ones have lead to increasing oscillation and noise of electrical powertrain. With the application of planetary-gear transmission chains, adopting an advanced regenerative braking method becomes practical. This paper develops a novel regenerative braking method for transient torsional oscillation suppression of planetary-gear electrical powertrain (PGEP), which uses an angle-varying mesh stiffness-considered transmission model and a genetic algorithm-based method for allocation of electric regenerative braking torque. Simulations are conducted with various angle-varying mesh stiffness and backlash to compare the transient torsional oscillation-suppression performance of PGEP during regenerative braking transition process. For an initial speed of 80 km/h, simulation results show that the proposed torsional oscillation-considered electric regenerative braking torque allocating method performs the best comparing with the traditional methods, which has a 55% reduction in vehicle jerk and 36% improvement in torsional oscillation of PGEP. The AVL experiment results also demonstrated that torsional oscillation-considered regenerative braking method has considerable benefits in both transient torsional oscillation suppression of PGEP as well as enhancement of driving comfort.

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>

Multi-objective optimal computer-aided engineering of hydraulic brake systems for electrified road vehicles

Electrification of road vehicle powertrains involves significant re-design and re-sizing of foundation brakes. Both the usage and the size of hydraulic brake systems can particularly be reduced thanks to the contribution of regenerative braking torque supplied by electric motors, thus reducing cost and weight. In this work, a multi-objective optimal computer-aided engineering (CAE) methodology is proposed that allows designing hydraulic brake systems for electrified road vehicles. The achievement of requirements imposed by braking maneuvers concerning safety standards for brake systems represents a feasibility constraint for the design candidates within the proposed CAE workflow. A particle swarm optimisation (PSO) algorithm allows efficiently exploring the design space. Minimising the overall size of the hydraulic brake system and maximising the electrical energy recovered in the battery on average in everyday driving conditions represent the optimisation targets retained. A case study considering a battery electric vehicle (BEV) powertrain layout suggests the effectiveness and the flexibility of the developed CAE methodology in promptly identifying and assessing sub-optimal design alternatives for both front-wheel drive (FWD) and rear-wheel drive (RWD) arrangements. Furthermore, compared with the RWD option, the FWD powertrain layout appears considerably favorable for a BEV in terms of electrical energy recovering capability during braking.

Optimal Anti-Lock Braking Control With Nonlinear Variable Voltage Charging Scheme for an Electric Vehicle

An optimal anti-lock braking control strategy using nonlinear variable voltage charging scheme for an electric-wheel vehicle is developed with aim of improving energy recovery efficiency on the premise of vehicle safety under the critical braking situation. A variable voltage charging control scheme is proposed with its operational principles to obtain maximum energy recovery as well as to balance the energy differences of each battery cell during the anti-lock braking. Given the nonlinearity of in-wheel motor, the nonlinear variable voltage charging control law is obtained on the basis of parameters fitting via experimental data. An ideal regenerative braking torque calculated by the nonlinear variable voltage charging control law is set as one of the disturbance vectors and an optimal sliding mode–anti-lock braking control strategy is developed to provide optimal hydraulic pressure for anti-lock braking system. In this manner, the torque allocation process is omitted by using a compensation control of the hydraulic braking torque. Simulation results corroborate the effectiveness of the proposed optimal anti-lock braking control strategy with higher energy recovery efficiency.

Integrated Regenerative Braking System and Anti-Lock Braking System for Hybrid Electric Vehicles & Battery Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">This paper describes development of an integrated regenerative braking system and anti-lock brake system (ABS) control during an ABS event for hybrid and electric vehicles with drivelines containing a single electric motor connected to the axle shaft through an open differential. The control objectives are to recuperate the maximum amount of kinetic energy during an ABS event, and to provide no degraded anti-lock control behavior as seen in vehicles with regenerative braking disabled. The paper first presents a detailed control system analysis to reveal the inherent property of non-zero regenerative braking torque control during ABS event and explain the reason why regenerative braking torque can increase the wheel slip during ABS event with existing regenerative braking control strategies. Then, the regenerative brake control problem during ABS events is formulated with a unified control system architecture where the regenerative braking torque is coordinated with the friction braking torque of ABS system. An integrated closed loop based wheel slip control including both regenerative braking control loop and friction braking control loop during ABS event, referred to as RBS-ABS event control, is developed. The maximum regenerative braking is achieved and optimal vehicle braking performances and vehicle stability are maintained during ABS event. Finally, simulation tests are provided to illustrate RBS-ABS event control as an effective solution to satisfy desired wheel slip with the same level of stop distance in comparison with that of ABS control only while performing energy recuperation.</div></div>