电动汽车再生制动控制算法研究

以"在满足车辆制动性能要求、保证车辆制动稳定性的前提下,最大限度地回收再生制动能量"为原则,对电动汽车再生制动力与制动器制动力的分配算法进行研究,得到车辆制动时制动力的控制算法,最后以某电动车辆为例进行仿真分析。制动力分配算法对车辆再生制动和机械制动的分配规律的制定具有较好的参考作用。     

混合动力电动汽车制动系统回馈特性仿真

为了研究混合动力电动汽车(HEV)回馈制动特性,建立了用于城市公交的混合动力电动汽车复合制动系统的仿真模型,提出了回馈制动控制策略,分析了复合制动系统的工作过程,并探讨影响电动汽车制动系统可靠、安全和高效的主要因素,研究电动汽车复合制动系统优化途径。研究结果表明:回馈制动最低车速限值越小,制动能量回收率越大;从回收电动汽车能量角度分析,回馈制动比例应有一个有效范围值;在各种循环工况下,具有回馈制动功能时混合动力电动汽车城市客车单位里程的能量消耗可降低10%~25%。     

基于电机最优回馈转矩曲线的制动控制策略

本文中首先基于电机等效电路模型,分析了车用内置式永磁同步电机的耗能制动状态和回馈制动状态;然后根据电机矢量控制原理,对控制电流指令进行解析,并经试验数据的验证;接着计算得到永磁同步电机最优回馈转矩曲线,并据此提出一种制动回馈能量最优的串联制动控制策略。最后针对某P4并联混合动力商用车,仿真分析了在C?WTVC、CHTC?TT循环工况和试验采集到的某段省道工况下,并联制动和所提出的串联最优制动控制策略下的百公里油耗和制动回收能量。结果表明,与并联制动控制相比,基于电机最优回馈转矩曲线的串联制动控制策略可降低油耗,并回收更多的制动能量,实现制动回收能量和燃油经济性的提升。     

并联混合动力客车再生制动仿真研究

建立了并联式混合动力汽车动力学模型,并对纯电机制动模式和机电混合模式混合动力汽车能量再生制动进行了仿真。仿真结果表明：对于纯电机模式,制动效能低,能量回收率达29％;对于机电混合制动模式,制动效能高,能量回收率仅2％。     

Co-operative control for regenerative braking and friction braking to increase energy recovery without wheel lock

This paper presents a regenerative braking co-operative control algorithm to increase energy recovery without wheel lock. Considering the magnitude of the braking force available between the tire and road surface, the control algorithm was designed for the regenerative braking force at the front wheel and friction braking force at the rear wheel to be increased following the friction coefficient line. The performance of the proposed regenerative braking co-operative control algorithm was evaluated by the hardware in the loop simulation (HILS) with an electronic wedge brake on its front wheels and an electronic mechanical brake on its rear wheels. The HILS results showed that a proper braking force on the front and rear wheels on a low μ road prevented the lock of the front wheels that was connected to the motor, and maintained the regenerative braking and increased energy recovery.     

串联式复合电源再生制动系统恒流控制

再生制动技术可以有效回收车辆制动能量，是提高电动汽车续驶里程的重要途径，超级电容具有高功率密度、高效率的特点，利用蓄电池-超级电容组成的复合电源作为电动汽车的储能装置可以改善电池工作状态，提高电池寿命及可靠性，并提高能量回收率。目前使用复合电源（蓄电池-超级电容）进行再生制动的电动汽车多采用并联形式，针对此类状况，基于无源串联复合电源结构设计其再生制动系统，其主要由电机、超级电容组、整流桥和控制器组成。在控制策略上，采用电压反馈恒定电流制动方式，基于脉冲宽度调制（PWM）控制，在制动过程中根据电动汽车车速与超级电容端电压实时调节PWM的占空比以实现目标制动电流恒定。在MATLAB/Simulink平台上建立再生制动系统仿真模型，验证所提控制策略的有效性，并利用某电动汽车对所设计系统进行滑行、制动等试验。研究结果表明：相比有源并联式复合电源，该系统不需要DC/DC转换器，结构及控制简单，该系统能够较好地实现制动能量回收，所采用的控制策略能够有效地实现恒电流制动，电制动减速度稳定，同时具有较高的能量回收率。     

插电式混合动力汽车再生制动控制策略

由于再生制动控制策略直接影响了插电式混合动力汽车（PHEV）的经济性,文章提出了一种基于理想制动力分配的再生制动控制策略,这种策略能在保证制动稳定性的同时,尽可能多地回收制动能量,在Simulink平台上建立再生制动控制策略模型,并嵌入到Cruise软件中进行仿真。仿真结果表明,此模型相比没有制动能量回收的PHEV和传统汽车,都有效地提高了经济性,验证了再生制动控制策略的合理性。     

直流无刷电机再生制动系统试验台的设计与验证

基于再生制动理论,以电动汽车用轮毂电机为主体,搭建了直流无刷电机再生制动试验台。以回收能量最大化为目标,提出了相应的再生制动控制策略。结合电机的输出特性和工作原理,利用SIMULINK软件建立了再生制动系统模型。通过模型仿真和试验台试验结果的对比分析,验证了再生制动试验台设计方案的可行性和控制策略的合理性。     

分布式驱动电动汽车回馈制动失效的液压补偿控制

分布式驱动电动汽车各驱动轮转速和转矩可以单独精确控制,便于实现整车动力学控制和制动能量回馈,从而提升车辆的主动安全性和行驶经济性。但车辆在回馈制动过程中,一旦1台电机突发故障,其他电机产生的制动力矩将对整车形成附加横摆力矩,从而造成车辆失稳,此时虽可通过截断异侧对应电机制动力矩输出来保证行驶方向,但会使车辆制动力大幅衰减或丧失,同样不利于行车安全。为了解决此问题,提出并验证一种基于电动助力液压制动系统的制动压力补偿控制方法,力图有效保证整车制动安全性。以轮毂电机驱动汽车为例,首先建立了整车动力学模型以及轮毂电机模型,通过仿真验证了回馈制动失效的整车失稳特性以及电机转矩截断控制的不足;然后,建立了电动助力液压制动系统模型,并通过原理样机的台架试验验证了模型的准确性;接着,基于滑模控制算法设计了制动压力补偿控制器,并在单侧电机再生制动失效后的转矩截断控制基础上完成了液压制动补偿控制效果仿真验证;最后,通过实车试验证明了所提控制方法的有效性和实用性。研究结果表明：在分布式驱动电动汽车单侧电机再生制动失效工况下,通过异侧电机转矩截断控制和制动系统的液压主动补偿,能够使车辆快速恢复稳定行驶并满足制动强度需求。     

Combined control of a regenerative braking and antilock braking system for hybrid electric vehicles

Most parallel hybrid electric vehicles (HEV) employ both a hydraulic braking system and a regenerative braking system to provide enhanced braking performance and energy regeneration. A new design of a combined braking control strategy (CBCS) is presented in this paper. The design is based on a new method of HEV braking torque distribution that makes the hydraulic braking system work together with the regenerative braking system. The control system meets the requirements of a vehicle longitudinal braking performance and gets more regenerative energy charge back to the battery. In the described system, a logic threshold control strategy (LTCS) is developed to adjust the hydraulic braking torque dynamically, and a fuzzy logic control strategy (FCS) is applied to adjust the regenerative braking torque dynamically. With the control strategy, the hydraulic braking system and the regenerative braking system work synchronously to assure high regenerative efficiency and good braking performance, even on roads with a low adhesion coefficient when emergency braking is required. The proposed braking control strategy is steady and effective, as demonstrated by the experiment and the simulation.     

Vehicle Dynamic Control for In-Wheel Electric Vehicles Via Temperature Consideration of Braking Systems

?Vehicle dynamic control (VDC) systems play an important role with regard to vehicle stability and safety when turning. VDC systems prevent vehicles from spinning or slipping when cornering sharply by controlling vehicle yaw moment, which is generated by braking forces. Thus, it is important to control braking forces depending on the driving conditions of the vehicle. The required yaw moment to stabilize a vehicle is calculated through optimal control and a combination of braking forces used to generate the calculated yaw moment. However, braking forces can change due to frictional coefficients being affected by variations in temperature. This can cause vehicles to experience stability problems due an improper yaw moment being applied to the vehicle. In this paper, a brake temperature estimator based on the finite different method (FDM) was proposed with a friction coefficient estimator in order to solve this problem. The developed braking characteristic estimation model was used to develop a VDC cooperative control algorithm using hydraulic braking and the regenerative braking of an in-wheel motor. Performance simulations of the developed cooperative control algorithm were performed through cosimulation with MATLAB/Simulink and CarSim. From the simulation results, it was verified that vehicle stability was ensured despite any changes in the braking characteristics due to brake temperatures.     

能量回收式电涡流缓速器制动策略研究

文章结合电涡流缓速器和再生制动能量回收技术的优点,提出了能量回收式电涡流缓速器制动补偿策略。利用再生制动系统提供的制动力矩为电涡流缓速器在持续制动过程中的制动力矩热衰退予以补偿。以GB12676-2014政策法规为验证标准,车辆在满载情况下在7%的坡道上保持以30km/h的车速匀速行驶5km为仿真目标,对某商用车型进行仿真分析。验证了该策略使得实际产生的总制动力矩始终能满足驾驶员的制动需求,可以延缓电涡流缓速器温升,保障车辆行车安全。     

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

ABSTRACT

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

Cooperative regenerative braking control for front-wheel-drive hybrid electric vehicle based on adaptive regenerative brake torque optimization using under-steer index

In this study, cooperative regenerative braking control of front-wheel-drive hybrid electric vehicle is proposed to recover optimal braking energy while guaranteeing the vehicle lateral stability. In front-wheel-drive hybrid electric vehicle, excessive regenerative braking for recuperation of the maximum braking energy can cause under-steer problem. This is due to the fact that the resultant lateral force on front tire saturates and starts to decrease. Therefore, cost function with constraints is newly defined to determine optimum distribution of brake torques including the regenerative brake torque for improving the braking energy recovery as well as the vehicle lateral stability. This cost function includes trade-off relation of two objectives. The physical meaning of first objective of cost function is to maximize the regenerative brake torque for improving the fuel economy and that of second objective is to increase the mechanical-friction brake torques at rear wheels rather than regenerative brake torque at front wheels for preventing front tire saturation. And weighting factor in cost function is also proposed as a function of under-steer index representing current state of the vehicle lateral motion in order to generalize the constrained optimization problem including both normal and severe cornering situation. For example, as the vehicle approaches its handling limits, adaptation of weighting factor is possible to prioritize front tire saturation over increasing the recuperation of braking energy for driver safety and vehicle lateral stability. Finally, computer simulation of closed loop driver-vehicle system based on Carsim? performed to verify the effectiveness of adaptation method in proposed controller and the vehicle performance of the proposed controller in comparison with the conventional controller for only considering the vehicle lateral stability. Simulation results indicate that the proposed controller improved the performance of braking energy recovery as well as guaranteed the vehicle lateral stability similar to the conventional controller.     

并行再生制动系统控制器的设计与开发

分析了混合动力汽车再生制动系统的特点及其应用前景,提出了一种基于并行控制的再生制动控制策略;针对某款并联式混合动力轿车,采用并行再生制动控制策略,进行了制动控制器的软硬件开发;搭建了硬件在环仿真试验系统对控制器进行了硬件在环仿真验证,并对控制器进行了实车测功机试验和实车道路试验。试验结果表明:该控制器运行稳定、可靠,整车平均制动能量回收效率达15%左右,显著提高了汽车的能源利用效率。     

Improvement of drivability and fuel economy with a hybrid antiskid braking system in hybrid electric vehicles

When braking on wet roads, Antilock Braking System (ABS) control can be triggered because the available brake torque is not sufficient. When the ABS system is active, for a hybrid electric vehicle, the regenerative brake is switched off to safeguard the normal ABS function. When the ABS control is terminated, it would be favorable to reactivate the regenerative brake. However, recurring cycles from ABS to motor regenerative braking could occur. This condition is felt to be unpleasant by the driver and has adverse effects on driving stability. In this paper, a novel hybrid antiskid braking system using fuzzy logic is proposed for a hybrid electric vehicle that has a regenerative braking system operatively connected to an electric traction motor and a separate hydraulic braking system. This control strategy and the method for coordination between regenerative and hydraulic braking are developed. The motor regenerative braking controller is designed. Control of regenerative and hydraulic braking force distribution is investigated. The simulation and experimental results show that vehicle braking performance and fuel economy can be improved and the proposed control strategy and method are effective and robust.     

模糊PID控制的电动汽车再生制动系统变换器的研究

提出了利用超级电容作为储能元件实现电动汽车再生制动的能量回收方案,分析了电动汽车控制系统的双向DC/DC变换器和电机驱动器的驱动降压电路、制动升压电路,设计了该控制系统的模糊自整定PID控制器。通过仿真研究表明,在车辆驱动降压变换时,模糊自整定PID控制的超级电容器在150 A左右的大电流放电情况下,超级电容仍能维持2.5 s的指定电压输出,车辆在额定功率下工作,通过降压变换,超级电容储存的能量迅速供给电机,有效提高了驱动电流,改善了起动及加速性能,有效增加了续驶里程。在制动升压变换时,模糊自整定PID控制的超级电容器电流基本跟随指令值上下波动,超级电容电压从120 V不断上升,使得该电容器的储能能力得到充分利用,实现了高水平的能量回收。     

一种电动汽车制动能量回收系统研究

文章以某款纯电动车制动能量回收系统为研究对象,首先,设计一种电液助力系统,阐述其结构方案和工作原理,接着基于该电液助力系统开展纯电动车串行制动能量回收系统设计研究,包括结构方案、控制方案、电气方案;实现在某款纯电动车产品上的搭载应用开发,结果表明,基于该电液助力系统的纯电动车能量回收系统,实现车辆在制动或减速阶段,机械-液压制动力与电机回馈制动力实时协调,最大限度地回收制动能量,并且获得较好的制动稳定性和“踏板感”,单个ECE循环工况经济性贡献率最高达28.9%。     

Design of Regenerative Anti-Lock Braking System Controller for 4 In-Wheel-Motor Drive Electric Vehicle with Road Surface Estimation

This paper presents a regenerative anti-lock braking system control method with road detection capability. The aim of the proposed methodology is to improve electric vehicle safety and energy economy during braking maneuvers. Vehicle body longitudinal deceleration is used to estimate a road surface. Based on the estimation results, the controller generates an appropriate braking torque to keep an optimal for various road surfaces wheel slip and to regenerate for a given motor the maximum possible amount of energy during vehicle deceleration. A fuzzy logic controller is applied to fulfill the task. The control method is tested on a four in-wheel-motor drive sport utility electric vehicle model. The model is constructed and parametrized according to the specifications provided by the vehicle manufacturer. The simulation results conducted on different road surfaces, including dry, wet and icy, are introduced.     

Wheel slip control with torque blending using linear and nonlinear model predictive control

Modern hybrid electric vehicles employ electric braking to recuperate energy during deceleration. However, currently anti-lock braking system (ABS) functionality is delivered solely by friction brakes. Hence regenerative braking is typically deactivated at a low deceleration threshold in case high slip develops at the wheels and ABS activation is required. If blending of friction and electric braking can be achieved during ABS events, there would be no need to impose conservative thresholds for deactivation of regenerative braking and the recuperation capacity of the vehicle would increase significantly. In addition, electric actuators are typically significantly faster responding and would deliver better control of wheel slip than friction brakes. In this work we present a control strategy for ABS on a fully electric vehicle with each wheel independently driven by an electric machine and friction brake independently applied at each wheel. In particular we develop linear and nonlinear model predictive control strategies for optimal performance and enforcement of critical control and state constraints. The capability for real-time implementation of these controllers is assessed and their performance is validated in high fidelity simulation.