An Investigation into Regenerative Braking Control Strategy for Hybrid Electric Vehicle

Energy regeneration during braking is an important technique for hybrid electric vehicle (HEV) to improve their fuel economy and extend their driving range. Due to the effect of regenerative braking torque which is added by electric motor, the braking torque distribution between front and rear axles should be changed and the control logic of anti-lock braking system (ABS) ought to be adjusted according to the regenerative braking torque. This paper put forward a braking control strategy for hybrid electric vehicle; the control strategy is implemented with eight DOFs (Degree-of-Freedom) nonlinear vehicle forward simulation model which is built under the environment of Matlab/Simulink. Based on target wheel slip ratio, a fuzzy logic approach was applied to maintain the optimal target slip ratio so that best compromise between hydraulic torque and regenerative torque can be obtained for the vehicle.

Regenerative braking control for hybrid electric vehicles under decelerating condition

The operating mode of a single shaft hybrid electric vehicle （SSHEV） in which the electric motor exerts negative torque on the shaft to imitate engine braking is analyzed. The method of determining the quantity of regenerative braking torque is proposed with the premise that the braking intensity required by the driver is satisfied. On this basis, factors that affect torque generated by the motor are listed, and how the battery＇ s temperature and state of charge （ SOC ） restrict and correct the braking torque is expounded. Finally, road test results show that the motor＇ s constant power or constant torque control is an effective way to recover the mechanical energy during decelerating.

Design and Analysis of Electro-mechanical Hybrid Anti-lock Braking System for Hybrid Electric Vehicle Utilizing Motor Regenerative Braking

Braking on low adhesion-coefficient roads, hybrid electric vehicle＇s motor regenerative torque is switched off to safeguard the normal anti-lock braking system （ABS） fimction. When the ABS control is terminated, the motor regenerative braking is readmitted. Aiming at avoiding permanent cycles from hydraulic anti-lock braking to motor regenerative braking, a novel electro-mechanical hybrid anti-lock braking system using fuzzy logic is designed. Different from the traditional single control structure, this system has a two-layered hierarchical structure, The first layer is responsible for harmonious adjustment or interaction between regenerative system and anti-lock braking system. The second layer is responsible for braking torque distribution and adjustment. The closed-loop simulation model is built. Control strategy and method for coordination between regenerative and anti-lock braking are developed. Simulation braking on low adhesion-coefficient roads with fuzzy logic control and real vehicle braking field test are presented. The results from simulating analysis and experiment show braking performance of the vehicle is perfect, harmonious coordination between regenerative and anti-lock braking function, significant amount of braking energy can be recovered and the proposed control strategy and method are effective.

A Novel Braking Control Strategy for Hybrid Electric Buses Based on Vehicle Mass and Road Slope Estimation

Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles,which is prominently proved by many studies.To achieve better dynamic stable performance and higher energy recovery efficiency,an effective braking control strategy for hybrid electric buses(HEB)based on vehicle mass and road slope estimation is proposed in this paper.Firstly,the road slope and the vehicle mass are estimated by a hybrid algorithm of extended Kalman filter(EKF)and recursive least square(RLS).Secondly,the total braking torque of HEB is calculated by the sliding mode controller(SMC),which uses the information of brake intensity,whole vehicle mass,and road slope.Finally,comprehensively considering driver’s braking intention and regulations of the Economic Commission for Europe(ECE),the optimal proportional relationship between regenerative braking and pneumatic braking is obtained.Furthermore,related simulations and experiments are carried out on the hardware-in-the-loop test bench.Results show that the proposed strategy can effectively improve the braking performance and increase the recovered energy through precise control of the braking torque.

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS), which uses an equivalence factor to define the control strategy and the power train component torque split. An equivalence factor that is optimal for a single drive cycle can be found offline with a priori knowledge of the drive cycle. The RBF-ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN. For the majority of drive cycles examined, the RBF-ANN implementation is shown to produce fuel economy values that are within ±2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF-ANN is that it does not require a priori drive cycle knowledge and is able to be implemented in real-time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF-ANN could be improved to produce better results across a greater array of driving conditions.

Electro-mechanical Braking Method in Hybrid Electric Vehicles Based on Feedback Control Theory

In this paper, the hybrid electric vehicle braking process is researched, by using variables consists of HEV speed, motor speed, and state of charge established, fimctions of mechanical braking force, regenerative braking force and efficiency of energy recovery are constructed, and the control goal is to maximization the energy recovery efficiency. Under the feedback control strategy, with the constrain condition of braking strength and braking stability, combining experiments in ADVISOR, in different experiments of different working conditions, we can see that in UDDS Cycle, the regenerative braking efficiency is the best. What＇s more, compared with strategies in ADVISOR, strategy proposed in this paper is obviously better.

Series-parallel Hybrid Vehicle Control Strategy Design and Optimization Using Real-valued Genetic Algorithm

Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle, few researches about the series-parallel hybrid electric vehicle have been revealed because of its complex co nstruction and control strategy. In this paper, a series-parallel hybrid electric bus as well as its control strategy is revealed, and a control parameter optimization approach using the real-valued genetic algorithm is proposed. The optimization objective is to minimize the fuel consumption while sustain the battery state of charge, a tangent penalty function of state of charge（SOC） is embodied in the objective function to recast this multi-objective nonlinear optimization problem as a single linear optimization problem. For this strategy, the vehicle operating mode is switched based on the vehicle speed, and an ＂optimal line＂ typed strategy is designed for the parallel control. The optimization parameters include the speed threshold for mode switching, the highest state of charge allowed, the lowest state of charge allowed and the scale factor of the engine optimal torque to the engine maximum torque at a rotational speed. They are optimized through numerical experiments based on real-value genes, arithmetic crossover and mutation operators. The hybrid bus has been evaluated at the Chinese Transit Bus City Driving Cycle via road test, in which a control area network-based monitor system was used to trace the driving schedule. The test result shows that this approach is feasible for the control parameter optimization. This approach can be applied to not only the novel construction presented in this paper, but also other types of hybrid electric vehicles.

The Combination of Two Control Strategies for Series Hybrid Electric Vehicles

With most countries paying attention to the environment protection, hybrid electric vehicles have become a focus of automobile research and development due to the characteristics of energy saving and low emission. Power follower control strategy(PFCS) and DC-link voltage control strategy are two sorts of control strategies for series hybrid electric vehicles(HEVs). Combining those two control strategies is a new idea for control strategy of series hybrid electric vehicles. By tuning essential parameters which are the defined constants under DClink voltage control and under PFCS, the points of minimum mass of equivalent fuel consumption(EFC) corresponding to a series of variables are marked for worldwide harmonized light vehicles test procedure(WLTP). The fuel economy of series HEVs with the combination control schemes performs better compared with individual control scheme. The results show the effects of the combination control schemes for series HEVs driving in an urban environment.

ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES

Energy management strategy (EMS) is the core of the real-time controlalgorithm of the hybrid electric vehicle (HEV). A novel EMS using the logic threshold approach withincorporation of a stand-by optimization algorithm is proposed. The aim of it is to minimize theengine fuel consumption and maintain the battery state of charge (SOC) in its operation range, whilesatisfying the vehicle performance and drivability requirements. The hybrid powertrain bench testis carried out to collect data of the engine, motor and battery pack, which are used in the EMS tocontrol the powertrain. Computer simulation model of the HEV is established in the MATLAB/Simulinkenvironment according to the bench test results. Simulation results are presented for behaviors ofthe engine, motor and battery. The proposed EMS is implemented for a real parallel hybrid carcontrol system and validated by vehicle field tests.

Operation control strategy of series-parallel hydraulic hybrid vehicle

A series-parallel hydraulic hybrid system applied to public buses is put torwaro, ano parameters of key components are analyzed and determined. Energy management strategy based on logic thresh- old is designed which is aimed at efficient operation of the overall system considering the operational characteristic of the components and taking the curves of engine, hydraulic pump/motor and hydrau- lic pump as the main design basis; regenerative control strategy which makes regenerative brake sys- tem and frictional brake system work harmoniously is designed to raise recovery rate of regenerative brake energy. System dynamic modeling and simulation results show that the energy control strategy designed here is able to adapt system to changes of working condition and switch the operating mode reasonably. The regenerative braking control strategy is effective in raising the utilization of energy and improving fuel economy.

Composite Braking Control Strategy for Electromechanical-Hydraulic Vehicles

Short driving ranges and low braking energy recovery efficiencies are two recognized technical bottlenecks to be overcome in electric vehicles. In this paper, a novel electromechanical-hydraulic coupling system is proposed and integrated as a powertrain for electric vehicles, which can assist the electric vehicle to fully utilize its braking energy. The hydraulic regenerative braking force and electric regenerative braking force can provide all the braking needs using the medium and small braking intensities. Furthermore, an improved compound brake control strategy based on the braking force distribution is proposed and simulated. The results show that under the premise of ensuring braking stability, the electromechanical-hydraulic coupling driving electric vehicle can adapt to various working conditions with excellent energy-saving results. The hydraulic accumulator recovery efficiency is above 99%, and the state of charge consumption rate of the battery pack can be reduced by more than 9%. More importantly, the proposed hybrid power system can significantly improve the driving range and energy efficiency, as well as reduce the consumers' mileage anxiety in electric vehicles.

Regenerative Braking Algorithm for an ISG HEV Based on Regenerative Torque Optimization

A novel regenerative braking algorithm based on regenerative torque optimization with emulate engine compression braking (EECB) was proposed to make effective and maximum use of brake energy in order to improve fuel economy.The actual brake oil pressure of driving wheel which is reduced by the amount of the regenerative braking force is supplied from the electronic hydraulic brake system.Regenerative torque optimization maximizes the actual regenerative power recuperation by energy storage component,and EECB is a useful extended type of regenerative braking.The simulation results show that actual regenerative power recuperation for the novel regenerative braking algorithm is more than using conventional one,and life-span of brake disks is prolonged for the novel algorithm.

Evaluation of regenerative braking based on single-pedal control for electric vehicles

More than 25%of vehicle kinetic energy can be recycled under urban driving cycles.A single-pedal control strategy for regenerative braking is proposed to further enhance energy efficiency.Acceleration and deceleration are controlled by a single pedal,which alleviates driving intensity and prompts energy recovery.Regenerative braking is theoretically analyzed based on the construction of the single-pedal system,vehicle braking dynamics,and energy conservation law.The single-pedal control strategy is developed by considering daily driving conditions,and a single-pedal simulation model is established.Typical driving cycles are simulated to verify the effectiveness of the single-pedal control strategy.A dynamometer test is conducted to confirm the validity of the simulation model.Results show that using the single-pedal control strategy for electric vehicles can effectively improve the energy recovery rate and extend the driving range under the premise of ensuring safety while braking.The study lays a technical foundation for the optimization of regenerative braking systems and development of single-pedal control systems,which are conducive to the promotion and popularization of electric vehicles.

Driving and Braking Control of PM Synchronous Motor Based on Low-resolution Hall Sensor for Battery Electric Vehicle

Resolvers are normally employed for rotor positioning in motors for electric vehicles, but resolvers are expensive and vulnerable to vibrations. Hall sensors have the advantages of low cost and high reliability, but the positioning accuracy is low. Motors with Hall sensors are typically controlled by six-step commutation algorithm, which brings high torque ripple. This paper studies the high-performance driving and braking control of the in-wheel permanent magnetic synchronous motor （PMSM） based on low-resolution Hall sensors. Field oriented control （FOC） based on Hall-effect sensors is developed to reduce the torque ripple. The positioning accuracy of the Hall sensors is improved by interpolation between two consecutive Hall signals using the estimated motor speed. The position error from the misalignment of the Hall sensors is compensated by the precise calibration of Hall transition timing. The braking control algorithms based on six-step commutation and FOC are studied. Two variants of the six-step commutation braking control, namely, half-bridge commutation and full-bridge commutation, are discussed and compared, which shows that the full-bridge commutation could better explore the potential of the back electro-motive forces （EMF）, thus can deliver higher efficiency and smaller current ripple. The FOC braking is analyzed with the phasor diagrams. At a given motor speed, the motor turns from the regenerative braking mode into the plug braking mode if the braking torque exceeds a certain limit, which is proportional to the motor speed. Tests in the dynamometer show that a smooth control could be realized by FOC driving control and the highest efficiency and the smallest current ripple could be achieved by FOC braking control, compared to six-step commutation braking control. Therefore, FOC braking is selected as the braking control algorithm for electric vehicles. The proposed research ensures a good motor control performance while maintaining low cost and high reliability.

Energy-Optimal Braking Control Using a Double-Layer Scheme for Trajectory Planning and Tracking of Connected Electric Vehicles

Most researches focus on the regenerative braking system design in vehicle components control and braking torque distribution,few combine the connected vehicle technologies into braking velocity planning.If the braking intention is accessed by the vehicle-to-everything communication,the electric vehicles(EVs)could plan the braking velocity for recovering more vehicle kinetic energy.Therefore,this paper presents an energy-optimal braking strategy(EOBS)to improve the energy efficiency of EVs with the consideration of shared braking intention.First,a double-layer control scheme is formulated.In the upper-layer,an energy-optimal braking problem with accessed braking intention is formulated and solved by the distance-based dynamic programming algorithm,which could derive the energy-optimal braking trajectory.In the lower-layer,the nonlinear time-varying vehicle longitudinal dynamics is transformed to the linear time-varying system,then an efficient model predictive controller is designed and solved by quadratic programming algorithm to track the original energy-optimal braking trajectory while ensuring braking comfort and safety.Several simulations are conducted by jointing MATLAB and CarSim,the results demonstrated the proposed EOBS achieves prominent regeneration energy improvement than the regular constant deceleration braking strategy.Finally,the energy-optimal braking mechanism of EVs is investigated based on the analysis of braking deceleration,battery charging power,and motor efficiency,which could be a guide to real-time control.

Real-time optimization power-split strategy for hybrid electric vehicles

Energy management strategies based on optimal control theory can achieve minimum fuel consumption for hybrid electric vehicles, but the requirement for driving cycles known in prior leads to a real-time problem. A real-time optimization power-split strategy is proposed based on linear quadratic optimal control. The battery state of charge sustainability and fuel economy are ensured by designing a quadratic performance index combined with two rules. The engine power and motor power of this strategy are calculated in real-time based on current system state and command, and not related to future driving conditions. The simulation results in ADVISOR demonstrate that, under the conditions of various driving cycles, road slopes and vehicle parameters, the proposed strategy significantly improves fuel economy, which is very close to that of the optimal control based on Pontryagin's minimum principle, and greatly reduces computation complexity.

Design and optimization of equivalent consumption minimization strategy for 4WD hybrid electric vehicles incorporating vehicle connectivity

This paper presents an optimized equivalent consumption minimization strategy(ECMS) for four-wheel-drive(4 WD) hybrid electric vehicles(HEVs) incorporating vehicle connectivity. In order to be applicable to the 4 WD architecture, the ECMS is designed based on a rule-based strategy and used under the condition that a certain propulsion mode is activated. Assuming that a group of 4 WD HEVs are connected and position information can be shared with each other, we formulate a decentralized model predictive control(MPC) framework that compromises fuel efficiency, mobility, and inter-vehicle distance to optimize the velocity profile of each individual vehicle. Based on the optimized velocity profile, an optimization problem considering both fuel economy and battery state of charge(SOC) sustainability is formulated to optimize the equivalent factors(EFs) of the ECMS for HEVs over an appropriate time window. MATLAB User Datagram Protocol(UDP) is used in the codes run on multiple computers to simulate the wireless communication among vehicles, which share position information via UDP-based communication, and dSPACE is used as a software-in-the-loop platform for the simulation of the optimized ECMS. Simulation results validate the control effectiveness of the proposed method.

Critical Speeds of Electric Vehicles for Regenerative Braking

Efficient regenerative braking of electric vehicles(EVs)can enhance the efficiency of an energy storage system(ESS)and reduce the system cost.To ensure swift braking energy recovery,it is paramount to know the upper limit of the regenerative energy during braking.Therefore,this paper,based on 14 typical urban driving cycles,proposes the concept and principle of confidence interval of“probability event”and“likelihood energy”proportion of braking.The critical speeds of EVs for braking energy recovery are defined and studied through case studies.First,high-probability critical braking speed and high-energy critical braking speed are obtained,compared,and analyzed,according to statistical analysis and calculations of the braking randomness and likelihood energy in the urban driving cycles of EVs.Subsequently,a new optimized ESS concept is proposed under the frame of a battery/ultra-capacitor(UC)hybrid energy storage system(HESS)combined with two critical speeds.The battery/UC HESS with 9 UCs can achieve better regenerative braking performances and discharging performances,which indicates that a minimal amount of UCs can be used as auxiliary power source to optimize the ESS.After that,the efficiency regenerative braking model,including the longitudinal dynamics,motor,drivetrain,tire,and wheel slip models,is established.Finally,parameters optimization and performance verification of the optimized HESS are implemented and analyzed using a specific EV.Research results emphasize the significance of the critical speeds of EVs for regenerative braking.

Optimal Energy Management for a Complex Hybrid Electric Vehicle:Tolerating Power-loss of Motor

The energy management may perform well under normal conditions, but may lead to poor behavior under abnormal situations. To tackle this problem, an optimal control strategy called rule-based equivalent fuel consumption minimization strategy （RECMS） is developed for a new complex hybrid electric vehicle （CHEV）. It optimizes the energy efficiency and drive performance to cater for normal and power-loss operations of the tractive motor. Firstly, the strategy formulates a novel objective function based on the equivalent fuel concept. By accounting for the actual fuel cost, the equivalent fuel cost for the electric machines and virtual fuel cost for the drivability, the cost function is obtained. Furthermore, some penalty factors are presented to optimize the performance target. Finally, experiments for a practical CHEV are performed to validate a simulation model. Then simulations are carried out for both rule-based and RECMS. The results show that the optimal energy management is working well.

Shift scheduling strategy development for parallel hybrid construction vehicles

The shift scheduling system of the transmission has an important effect on the dynamic and economic performance of hybrid vehicles. In this work, shift scheduling strategies are developed for parallel hybrid construction vehicles. The effect of power distribution and direction on shift characteristics of the parallel hybrid vehicle with operating loads is evaluated, which must be considered for optimal shift control. A power distribution factor is defined to accurately describe the power distribution and direction in various parallel hybrid systems. This paper proposes a Levenberg-Marquardt algorithm optimized neural network shift scheduling strategy. The methodology contains two objective functions, it is a dynamic combination of a dynamic shift schedule for optimal vehicle acceleration, and an energy-efficient shift schedule for optimal powertrain efficiency. The study is performed on a test bench under typical operating conditions of a wheel loader. The experimental results show that the proposed strategies offer effective and competitive shift performance.