行驶工况自适应的PHEV能量在线实时优化控制研究
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摘要
燃油经济性是衡量混合动力电动汽车(Hybrid Electric Vehicle, HEV)性能的一个重要指标。与传统汽车相比,HEV具有再生制动功能,其驱动电机在制动期间工作于发电状态,回收制动能量并将回收的能量存储到能量存储单元,以供未来车辆驱动或车载电子设备使用,从而提高车辆的燃油经济性。此外,HEV至少由两种动力源(一般为发动机和电机/电池)驱动,通过适当的控制策略使两种或多种动力源都工作在高效率区间,显著提高整车的燃油经济性。由HEV发展而来的插电式混合动力电动汽车(Plug-in Hybrid Electric Vehicle, PHEV)是汽车产业由传统汽油机汽车向纯电动汽车(Battery-powered Electric Vehicle, BEV)发展的过渡产品,在电池技术尚未达到BEV驾驶要求之前的很长一段时间将发挥重要作用,具有非常大的发展潜力。
目前,HEV上的制动系统为液压制动,一般按一定比例对前后轴制动力进行分配,这样导致前后轮不能同时刹住,从而给车辆的稳定和安全带来隐患；另外,由于制动力无法精确控制,不能使再生制动能量得到最优利用。HEV普遍采用基于经验的逻辑门限控制对前轴再生制动与摩擦制动力矩进行控制,由于依赖于工程经验,门限值的选择比较困难。在驱动能量控制方面,因HEV电池容量较小且无外接充电装置,因此只能工作在CS (Charge-Sustaining)模式。而PHEV除了继承HEV的优点外,还搭载有较大容量电池及外接充电装置,在能量控制方面除了CS模式外还能工作在CD (Charge-Depleting)模式,即仅靠电池驱动车辆的纯电动模式。在PHEV上使用的能量控制策略主要为基于规则(Rule-Based, RB)的CD-CS控制策略,即先使用电池驱动PHEV,当电池充电状态(State of Charge, SOC)下降到一定的水平,再启动发动机配合电池一起工作,此时的能量控制与HEV一样。RB控制策略不能使电池和发动机工作在最优的工作区域,尤其是在CD模式下,当电池电流较大时,电池的内部损耗也会比较大,从而影响电池的效率导致燃油经济性下降。
为了解决上述问题,提高PHEV的燃油经济性,本论文进行了如下研究工作：
针对液压制动系统制动力矩不能得到精确控制从而影响到不能获得最大的再生制动能量问题,提出了一种新的PHEV结构,该结构采用电子机械制动(Electronic Mechanical Brake, EMB)系统对每个车轮提供制动力矩,制动力矩可精确控制。论文分析了新的PHEV结构的工作原理,建立了整车动力学模型。构建了相应的实验平台,通过实验测量得到了发动机、电动/发电机和锂离子电池的特性曲线,以实验数据为基础,建立了发动机、电动/发电机和锂离子电池的计算机仿真模型,为后续工作奠定了基础。
针对目前的EMB系统在高温环境下永磁电机会产生“退磁”的现象,从而影响系统性能的问题,论文提出一种新型的EMB结构为PHEV提供制动力矩。进一步,对EMB系统的防抱死功能进行了研究。为解决ABS系统中常用的滑模控制算法存在“颤抖”问题,在路面识别的基础上提出了基于切换增益模糊调节的滑模控制算法的ABS的控制。提出了一种新的车身速度观测器设计方案,解决了车身速度在车轮打滑或抱死时不能准确测量的问题。
由于EMB系统制动力矩可精确控制,本论文按照理想制动力分配曲线(Ⅰ曲线)将制动力分配到前后轴,并应用模糊逻辑控制策略取代基于经验的逻辑门限控制策略,对前轴再生制动力矩和摩擦制动力矩进行精确控制,从而实现了再生制动力矩的最大程度地利用。仿真实验结果表明该方法可回收更多的再生制动能量。
分别采用动态规划(Dynamic Programming, DP)和二次规划(Quadratic Program, QP)方法对工况已知(UDDS、HFEDS、US06、SC03和冷UDDS工况)的PHEV进行能量离线优化,找出整个工况下的能量全局最优分配方案；比较分析了DP和QP优化方法的优缺点,为全局优化方法的选择提供了依据。
为了解决采用DP和QP优化方法需要工况已知的问题,提出了工况预测算法。以美国环保局(EPA)提供的乘用车19种类型行驶工况为分类标准,提出了基于径向基神经网络(Radial Basis Function Neural Network, RBFNN)的工况预测算法。在工况预测的基础上,提出利用DP优化结果训练得到的RBFNN和利用QP优化结果训练得到的RBFNN两种PHEV能量在线实时控制策略,利用工况预测算法和能量优化控制的集成方法对PHEV的能量进行控制。与其它能量控制方法相比,仿真结果表明该方法使燃油经济性得到了明显的提高。
为了验证本文提出的能量实时优化控制策略的可行性和有效性,在参数优化匹配的基础上将纯电动汽车改装成串联式PHEV,并将基于DP优化结果的能量实时优化控制策略下载到dSPACE中作为实车的能量控制策略。实车实验结果表明,在高速档、中速档、低速档和混合档工况下应用本文提出的能量实时优化控制策略PHEV的燃油经济性均有较大幅度的提高,分别为11.6%、13.1%、13.5%和10.4%。此实车实验结果表明,本文提出的能量在线实时优化控制策略可行、有效,为商用PHEV的能量在线实时优化控制提供了新的方法。
Fuel economy is an important indicator to measure the performance of Hybrid Electric Vehicle (HEV). Compared with traditional vehicles, HEV has the function of regenerative braking. Its driving motor is in generator mode during braking, recycling the braking energy, storing the energy in the energy storage unit, so as to offer energy for the vehicle driver or the vehicle electronic equipment in the future, which can improve the fuel economy of vehicles. In addition, HEV is driven by at least two sources of power (usually engine and electric motor/battery), and through appropriate control strategy, the two or multiple power sources are working in a range of high efficiency, significantly improving the vehicle fuel economy. Derived from HEV, Plug-in Hybrid Electric Vehicle (PHEV) is a transitional product from the traditional gasoline vehicles to a pure electric vehicle (Battery-powered Electric Vehicle BEV). It will play an important role and have a very large potential in a long time before the battery reaches the BEV driving requirements.
The braking system of HEV is the hydraulic brake, usually allocating the braking force between the front and rear axles according to a certain percentage, the consequence of which is that the front and rear wheels can not be stopped at the same time, and this is harmful to the vehicle stability and security. Moreover, the regenerative braking energy can not be made to get the optimal use because the braking force can not be precisely controlled. The HEV universally adopts experience-based logic threshold control on the front axle regenerative braking and friction braking torque distribution. The choice of threshold is difficult because it depends on the engineering experience. As to the driving energy control, HEV battery capacity is small and has no external charging device, so it can only work in CS (Charge-Sustaining) Mode. However, in addition to inheriting the advantages of the HEV, PHEV, equipped with a larger capacity battery and an external charging device, can work in the CS mode as well as the CD (Charge-Depleting) Mode, which means, it is a pure electric mode of battery-powered-alone vehicles. The energy control strategy used in PHEV is the CSCD control strategy which is mainly based on rules (Rule-Based. RB); it means to use the battery to drive PHEV first, then when the battery state of charge (SOC) drops to a certain level, the engine can be started, working together with the battery. At this point, the energy control is the same as the one of HEV. RB control strategy can not make the battery and the engine work in the optimal area, especially in the CD mode. When the battery current is large, the battery internal losses will be relatively large, thus affecting the efficiency of the battery, resulting in decreased fuel economy.
In order to solve these problems to improve the PHEV fuel economy, the research of this paper focus on the following aspects:
Because the braking torque in the hydraulic brake system can not be precisely controlled, causing the vehicles not obtaining the largest regenerative braking energy, this paper puts forward a new structure of PHEV----the structure of using the system of Electronic Mechanical Brake (EMB) to provide braking torque to each wheel, precisely controlling the braking torque. The paper analyzes the principles of the new PHEV structure, establishing the vehicle dynamics model. This paper builds the corresponding experimental platform, getting the characteristic curve of the engine, electric/generator and lithium-ion battery by experimental measurements; based on experimental data, the computer simulation model of the engine, electric/generator and lithium-ion battery is also established in this model. This model contributes a lot to the follow-up research.
To solve the problem that the permanent magnet motor of the EMB system produces the phenomenon of the demagnetization under high temperature, thus affecting the performance of system, this paper proposes a new EMB structure to provide braking torque to the PHEV. Furthermore, this paper also studies the function of the anti-lock braking of the EMB system. There is "trembling" in the sliding mode control algorithm commonly used in the ABS system, therefore on the basis of the road surface recognition, this paper brings forward the ABS of sliding mode control based on fuzzy switching gain adjustment. A new design of vehicle body speed observer is also proposed to solve the problem that the vehicle body speed can not be accurately measured when the wheels are skidding or locking.
Because the EMB braking torque can be precisely controlled, this paper uses ideal braking force distribution method (I-curve) to distribute braking force to front and rear axle, and fuzzy logic control strategy is used to replace the experience-based logic threshold control strategy, the greatest degree of regenerative braking torque can be achieved by the precise allocation between the front axle regenerative braking torque and friction braking torque. The simulation results show that this method can recycle more regenerative braking energy.
By using dynamic programming (DP) and quadratic programming (QP), the energy offline of the PHEV whose operating conditions are already known (UDDSn HFEDS、US06、SC03and cold UDDS) is optimized; the energy globally optimal allocation scheme is found out; the advantages and disadvantages of the DP and QP optimization methods are comparative analyzed; and a basis is provided for the choice of the global optimization method.
Using the driving cycles of the19types of passenger cars of the U.S. Environmental Protection Agency as the classification criteria, this paper proposes driving cycle prediction algorithm based on Radial Basis Function Neural Network (RBFNN). On the basis of the driving cycle forecast, two PHEV energy online and realtime control strategies are proposed—one is the RBFNN resulting from using the QP to optimize the results of training; the other is RBFNN resulting from using the QP to optimize the results of training. By using the driving cycle prediction algorithm and energy optimization of the integrated control of the driving cycles, the PHEV energy can be controlled. Compared with other energy control method, the simulation results show that this method can significantly improve the fuel economy.
In order to verify the feasibility and effectiveness of the energy online and realtime optimization of the control strategy proposed in this paper, pure electric vehicle is converted into a series PHEV on the basis of the parameter optimization; the online and realtime optimal energy control strategy based on the results of the DP optimization is downloaded to the dSPACE as a energy control strategy of a real car. The real vehicle test results show that the online and realtime optimal energy control strategy proposed in this paper in high speed, medium speed, low speed and mixed speed driving clcye, PHEV fuel economy have dramatically increased----11.6%,13.1%,13.5%and10.4%respectively. The real vehicle test results show that the energy online and realtime optimal control strategy proposed in this paper is feasible and effective, providing a new approach of online and realtime optimal energy control strategy for commercial PHEV.
目前,HEV上的制动系统为液压制动,一般按一定比例对前后轴制动力进行分配,这样导致前后轮不能同时刹住,从而给车辆的稳定和安全带来隐患；另外,由于制动力无法精确控制,不能使再生制动能量得到最优利用。HEV普遍采用基于经验的逻辑门限控制对前轴再生制动与摩擦制动力矩进行控制,由于依赖于工程经验,门限值的选择比较困难。在驱动能量控制方面,因HEV电池容量较小且无外接充电装置,因此只能工作在CS (Charge-Sustaining)模式。而PHEV除了继承HEV的优点外,还搭载有较大容量电池及外接充电装置,在能量控制方面除了CS模式外还能工作在CD (Charge-Depleting)模式,即仅靠电池驱动车辆的纯电动模式。在PHEV上使用的能量控制策略主要为基于规则(Rule-Based, RB)的CD-CS控制策略,即先使用电池驱动PHEV,当电池充电状态(State of Charge, SOC)下降到一定的水平,再启动发动机配合电池一起工作,此时的能量控制与HEV一样。RB控制策略不能使电池和发动机工作在最优的工作区域,尤其是在CD模式下,当电池电流较大时,电池的内部损耗也会比较大,从而影响电池的效率导致燃油经济性下降。
为了解决上述问题,提高PHEV的燃油经济性,本论文进行了如下研究工作：
针对液压制动系统制动力矩不能得到精确控制从而影响到不能获得最大的再生制动能量问题,提出了一种新的PHEV结构,该结构采用电子机械制动(Electronic Mechanical Brake, EMB)系统对每个车轮提供制动力矩,制动力矩可精确控制。论文分析了新的PHEV结构的工作原理,建立了整车动力学模型。构建了相应的实验平台,通过实验测量得到了发动机、电动/发电机和锂离子电池的特性曲线,以实验数据为基础,建立了发动机、电动/发电机和锂离子电池的计算机仿真模型,为后续工作奠定了基础。
针对目前的EMB系统在高温环境下永磁电机会产生“退磁”的现象,从而影响系统性能的问题,论文提出一种新型的EMB结构为PHEV提供制动力矩。进一步,对EMB系统的防抱死功能进行了研究。为解决ABS系统中常用的滑模控制算法存在“颤抖”问题,在路面识别的基础上提出了基于切换增益模糊调节的滑模控制算法的ABS的控制。提出了一种新的车身速度观测器设计方案,解决了车身速度在车轮打滑或抱死时不能准确测量的问题。
由于EMB系统制动力矩可精确控制,本论文按照理想制动力分配曲线(Ⅰ曲线)将制动力分配到前后轴,并应用模糊逻辑控制策略取代基于经验的逻辑门限控制策略,对前轴再生制动力矩和摩擦制动力矩进行精确控制,从而实现了再生制动力矩的最大程度地利用。仿真实验结果表明该方法可回收更多的再生制动能量。
分别采用动态规划(Dynamic Programming, DP)和二次规划(Quadratic Program, QP)方法对工况已知(UDDS、HFEDS、US06、SC03和冷UDDS工况)的PHEV进行能量离线优化,找出整个工况下的能量全局最优分配方案；比较分析了DP和QP优化方法的优缺点,为全局优化方法的选择提供了依据。
为了解决采用DP和QP优化方法需要工况已知的问题,提出了工况预测算法。以美国环保局(EPA)提供的乘用车19种类型行驶工况为分类标准,提出了基于径向基神经网络(Radial Basis Function Neural Network, RBFNN)的工况预测算法。在工况预测的基础上,提出利用DP优化结果训练得到的RBFNN和利用QP优化结果训练得到的RBFNN两种PHEV能量在线实时控制策略,利用工况预测算法和能量优化控制的集成方法对PHEV的能量进行控制。与其它能量控制方法相比,仿真结果表明该方法使燃油经济性得到了明显的提高。
为了验证本文提出的能量实时优化控制策略的可行性和有效性,在参数优化匹配的基础上将纯电动汽车改装成串联式PHEV,并将基于DP优化结果的能量实时优化控制策略下载到dSPACE中作为实车的能量控制策略。实车实验结果表明,在高速档、中速档、低速档和混合档工况下应用本文提出的能量实时优化控制策略PHEV的燃油经济性均有较大幅度的提高,分别为11.6%、13.1%、13.5%和10.4%。此实车实验结果表明,本文提出的能量在线实时优化控制策略可行、有效,为商用PHEV的能量在线实时优化控制提供了新的方法。
Fuel economy is an important indicator to measure the performance of Hybrid Electric Vehicle (HEV). Compared with traditional vehicles, HEV has the function of regenerative braking. Its driving motor is in generator mode during braking, recycling the braking energy, storing the energy in the energy storage unit, so as to offer energy for the vehicle driver or the vehicle electronic equipment in the future, which can improve the fuel economy of vehicles. In addition, HEV is driven by at least two sources of power (usually engine and electric motor/battery), and through appropriate control strategy, the two or multiple power sources are working in a range of high efficiency, significantly improving the vehicle fuel economy. Derived from HEV, Plug-in Hybrid Electric Vehicle (PHEV) is a transitional product from the traditional gasoline vehicles to a pure electric vehicle (Battery-powered Electric Vehicle BEV). It will play an important role and have a very large potential in a long time before the battery reaches the BEV driving requirements.
The braking system of HEV is the hydraulic brake, usually allocating the braking force between the front and rear axles according to a certain percentage, the consequence of which is that the front and rear wheels can not be stopped at the same time, and this is harmful to the vehicle stability and security. Moreover, the regenerative braking energy can not be made to get the optimal use because the braking force can not be precisely controlled. The HEV universally adopts experience-based logic threshold control on the front axle regenerative braking and friction braking torque distribution. The choice of threshold is difficult because it depends on the engineering experience. As to the driving energy control, HEV battery capacity is small and has no external charging device, so it can only work in CS (Charge-Sustaining) Mode. However, in addition to inheriting the advantages of the HEV, PHEV, equipped with a larger capacity battery and an external charging device, can work in the CS mode as well as the CD (Charge-Depleting) Mode, which means, it is a pure electric mode of battery-powered-alone vehicles. The energy control strategy used in PHEV is the CSCD control strategy which is mainly based on rules (Rule-Based. RB); it means to use the battery to drive PHEV first, then when the battery state of charge (SOC) drops to a certain level, the engine can be started, working together with the battery. At this point, the energy control is the same as the one of HEV. RB control strategy can not make the battery and the engine work in the optimal area, especially in the CD mode. When the battery current is large, the battery internal losses will be relatively large, thus affecting the efficiency of the battery, resulting in decreased fuel economy.
In order to solve these problems to improve the PHEV fuel economy, the research of this paper focus on the following aspects:
Because the braking torque in the hydraulic brake system can not be precisely controlled, causing the vehicles not obtaining the largest regenerative braking energy, this paper puts forward a new structure of PHEV----the structure of using the system of Electronic Mechanical Brake (EMB) to provide braking torque to each wheel, precisely controlling the braking torque. The paper analyzes the principles of the new PHEV structure, establishing the vehicle dynamics model. This paper builds the corresponding experimental platform, getting the characteristic curve of the engine, electric/generator and lithium-ion battery by experimental measurements; based on experimental data, the computer simulation model of the engine, electric/generator and lithium-ion battery is also established in this model. This model contributes a lot to the follow-up research.
To solve the problem that the permanent magnet motor of the EMB system produces the phenomenon of the demagnetization under high temperature, thus affecting the performance of system, this paper proposes a new EMB structure to provide braking torque to the PHEV. Furthermore, this paper also studies the function of the anti-lock braking of the EMB system. There is "trembling" in the sliding mode control algorithm commonly used in the ABS system, therefore on the basis of the road surface recognition, this paper brings forward the ABS of sliding mode control based on fuzzy switching gain adjustment. A new design of vehicle body speed observer is also proposed to solve the problem that the vehicle body speed can not be accurately measured when the wheels are skidding or locking.
Because the EMB braking torque can be precisely controlled, this paper uses ideal braking force distribution method (I-curve) to distribute braking force to front and rear axle, and fuzzy logic control strategy is used to replace the experience-based logic threshold control strategy, the greatest degree of regenerative braking torque can be achieved by the precise allocation between the front axle regenerative braking torque and friction braking torque. The simulation results show that this method can recycle more regenerative braking energy.
By using dynamic programming (DP) and quadratic programming (QP), the energy offline of the PHEV whose operating conditions are already known (UDDSn HFEDS、US06、SC03and cold UDDS) is optimized; the energy globally optimal allocation scheme is found out; the advantages and disadvantages of the DP and QP optimization methods are comparative analyzed; and a basis is provided for the choice of the global optimization method.
Using the driving cycles of the19types of passenger cars of the U.S. Environmental Protection Agency as the classification criteria, this paper proposes driving cycle prediction algorithm based on Radial Basis Function Neural Network (RBFNN). On the basis of the driving cycle forecast, two PHEV energy online and realtime control strategies are proposed—one is the RBFNN resulting from using the QP to optimize the results of training; the other is RBFNN resulting from using the QP to optimize the results of training. By using the driving cycle prediction algorithm and energy optimization of the integrated control of the driving cycles, the PHEV energy can be controlled. Compared with other energy control method, the simulation results show that this method can significantly improve the fuel economy.
In order to verify the feasibility and effectiveness of the energy online and realtime optimization of the control strategy proposed in this paper, pure electric vehicle is converted into a series PHEV on the basis of the parameter optimization; the online and realtime optimal energy control strategy based on the results of the DP optimization is downloaded to the dSPACE as a energy control strategy of a real car. The real vehicle test results show that the online and realtime optimal energy control strategy proposed in this paper in high speed, medium speed, low speed and mixed speed driving clcye, PHEV fuel economy have dramatically increased----11.6%,13.1%,13.5%and10.4%respectively. The real vehicle test results show that the energy online and realtime optimal control strategy proposed in this paper is feasible and effective, providing a new approach of online and realtime optimal energy control strategy for commercial PHEV.
引文
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