串联混合动力汽车建模与能源管理系统控制策略研究
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摘要
随着石油的短缺和污染的不断加剧,环保和高效越来越成为大众关注的焦点,大量的汽车制造商和科研工作者都将注意力转移到了更加节能、更加环保和使用新能源的新型汽车的制造和研究上。而混合动力汽车既具有电动汽车的优点,也具有传统汽车的优点,经济性好,排放性好,行驶里程长,因此,成为了新能源汽车的研究重点。
而在混合动力汽车中,为了能够充分开发其中增加动力电池所带来的提高汽车性能的潜能,为其开发使用合适控制策略的能量管理系统是至关重要的。随着人们对混合动力汽车的兴趣越来越高和其市场的不断增加,混合动力汽车能量管理系统的开发变的更为重要,因为能量管理系统能够根据驾驶员的驾驶意图,合理地控制混合动力汽车中的发动机、动力电池和电机协调工作,优化各个部件的输出,实现汽车中能量的合理分配,达到提高经济性和排放性等汽车性能的目的。
目前,对于串联式混合动力汽车能量管理系统中控制策略的研究大多是基于经验的,通过设定一些控制门限来实现能量的分配,不能使混合动力汽车的性能达到最优。因此,本文中首先对开发的串联式混合动力汽车进行了前向式建模,然后,在此模型的基础上,使用动态规划优化算法和等效燃油消耗最小优化算法对整车中能量的分配进行优化,从而使整车的性能达到最优。这样就找到了串联式混合动力汽车理论上的最佳性能和最优的控制方法,避免了对控制经验的依赖,也为其它控制策略的开发提供了理论依据,能够设计出更加有效的整车控制器。
本文所做的主要工作和结论如下:
为了能够对所研究项目中开发的串联式混合动力汽车的整车性能进行准确的分析,开发出好的整车能源管理策略,使得能量分配能够更优化,实现好的整车动力性和经济性的开发目标,本文对串联式混合动力汽车进行了建模研究,主要采用以实验建模法和理论建模法相结合的方法。对于混合动力汽车的主要部件发动机、动力电池和电机,分别从试验中得到各自相应的主要特性曲线,通过查表实现主要变量之间的计算。另外,还通过合理简化建立了车轮模型、汽车动力学模型、驱动桥模型、汽车附件模型、驾驶员模型和整车控制器模型,从而完成了整车的建模,为以后的整车性能研究和控制策略的开发提供了完整的平台。
为了模型建立的需要,本文对混合动力汽车中的主要动力部件进行了台架试验,得到了各个模型所需要的各种性能曲线和参数。之后,通过对比整车的试验数据和对模型所进行的仿真得到的数据,发现两者有很好地一致性,验证了所建立模型的正确性,能够将其应用到对整车的控制策略的开发中。
通过分析串联式混合动力汽车的结构特点和工作特性,本文选择了两种优化算法来对整车的能量分配和性能进行优化,一种是动态规划优化算法,它是一种全局性优化方法,能够得到优化对象的最优解;另一种是等效燃油消耗最小优化算法,它是一种局部优化算法,能够得到瞬时最优解。针对两种优化算法的特点,本文找到了它们在串联式混合动力汽车性能优化中的应用方法,并分别对两种优化方法的应用过程进行了分析。由于在串联式混合动力汽车中存在着两个能量源(发动机和动力电池),如何对两者所输出的能量进行合理的分配,从而使得整车性能最优,是优化算法和控制策略开发的重点。鉴于此,本文提出了采用更能反映动力电池能量水平的指标能量状态SOE来作为控制策略的输入参数,从而更加方便进行能量分配。
为了对动态规划优化算法和等效燃油消耗最小优化算法的优化性能进行更为细致的研究,本文基于建立的串联式混合动力汽车模型对两种优化方法进行了仿真试验。从仿真试验的结果可以发现,在已知整个汽车标准循环的状态后,动态规划优化方法能够找到全局最优控制方法,实现整车的最佳性能,因此,可以将动态规划优化得到的结果作为其它控制策略的参考依据和目标。
与动态规划优化方法不同,等效燃油消耗最小优化方法不需要提前知道整个汽车循环的状态数据,它所要优化的是汽车运行中每一时刻实现最优性能的控制方法,因此,它是一种能够在汽车控制器中实际应用的控制方法,只不过需要的计算量较大,目前难以实现。从仿真结果可以看出,虽然等效燃油消耗最小优化方法是一种局部优化方法,但由于其使得汽车运行每一时刻都实现了最优控制,因此其最终优化得到的整车性能接近于动态规划的优化结果,且要优于传统的基于规则的混合动力汽车控制方法。另外,在仿真中发现,对于不同的汽车行驶循环,等效燃油消耗最小优化方法对应着不同的充放电等效因子,但只要汽车实际行驶路况和得到等效因子的汽车行驶循环近似,整车性能就不会有太大变化。因此,在实际应用中可以先优化得到对应不同汽车行驶路况的等效因子(如郊区、城市和高速),并采用预测模型预测路况,从而采用不同的等效因子,从而使汽车总能达到接近最优的性能。
同时本文基于PowerPC566单片机为所开发的串联式混合动力汽车设计了整车控制器,并将模糊逻辑控制方法应用到该控制器中,实现整车的能量分配。在模糊控制策略的设计过程中,确定了整车的需求功率、动力电池能量状态SOE和发电机输出功率为模糊控制器的输入输出参数。确定了它们的论域和相应的隶属度函数,并根据以往的控制经验和两种优化方法得到的结果制定模糊规则,设计出了模糊控制器,得到模糊控制表,存储在控制器的存储器中进行查表控制。通过在试验中对比模糊控制器控制得到的整车性能和能量分配方法和在优化算法仿真中得到的结果,调整模糊控制器中的模糊规则,使得模糊控制器的控制效果逐渐接近最优。最后,通过在底盘测功机上的汽车标准循环的试验验证了所设计的整车控制器的控制效果,结果证明其能够有效地控制串联式混合动力汽车整车运行,能够较大地提高串联式混合动力汽车的燃油经济性,使动力电池始终保持在其高效区域内运行。
With the oil shortage and increasing pollution, environmental protection and efficient is becoming more and more important to us. Now, a lot of car manufacturers and researchers are turning their attention to vehicles with new energy, which are higher efficiency and cleaner. The hybrid electric vehicles have both the advantages of electric vehicles and traditional vehicles, such as: good economy, low emissions and long driving range. Therefore, hybrid electric vehicles become the research focus of new energy vehicles. Energy management is of fundamental importance in hybrid electric vehicles, for exploiting the advantages deriving from the availability of a rechargeable energy buffer. With the increasing interest in hybrid electric vehicles and their future commercial availability, energy management is becoming even more important, since energy management system can control engine, battery and motor reasonablely according to the driver’s driving intention, optimize the output of each power component, to achieve a reasonable distribution of energy in hybrid electric vehicles, and then improve economy and emission.
Currently, in the development of energy management system for the series hybrid electric vehicle, control strategies are mostly based on experience, by setting the threshold to control energy distribution, but can’t achieve optimal performance of hybrid electric vehicle. Therefore, in this dissertation a foreword model is firstly built for a series hybrid electric vehicle, then, based on the model, distribution of energy is optimized using dynamic programming (DP) and equivalent fuel consumption minimization strategy (ECMS), so that the optimal vehicle performances are achieved. Then the best performance in theory and optimal control methods are found for a series hybrid electric vehicle, so avoid dependence on the control experience, and provide a theoretical basis for the development of other control strategies. Finally a more effective vehicle controller can be designed. This major work done and conclusions in this dissertation are as follows:
In order to analyze the performances of the series hybrid electric vehicle which is developed in a project, and develop energy management strategies, so that the energy distribution to optimized to achieve good power and economy, a foreword model is built for the series hybrid electric vehicle in this dissertation, mainly using a modeling method which combines experimental and theoretical modeling methods. For the main components of hybrid electric vehicle (engine, battery and motor), the corresponding feature curves are respectively obtained from the experiments, then the main variables can be calculated using look-up tables. In addition, some models are simplified reasonably, such as: the wheel model, vehicle dynamics model, driving axle model, vehicle accessories model, driver model and vehicle controller model, then the vehicle model is completed for the development of performance and control strategy of the vehicle to provide a complete platform in the future. For the need of building models, in this dissertation, the main power components of the series hybrid electric vehicle are tested using a test bench, the performance curves and parameters for each model are obtained through the tests. Then by comparing the test results and simulation results, good agreements can be found between them, and this show the correctness of the established model, and the models can be applied for the development of the vehicle control strategy.
By analyzing the structural features and operating characteristics of the series hybrid electric vehicle, two optimization algorithms are selected to optimize the energy distribution and performances of the vehicle in this dissertation, and one is dynamic programming optimization algorithm, which is a global optimization method, and can obtain the optimal solution of the optimized object; the other is equivalent fuel consumption minimization optimization algorithm, which is a local optimization algorithm, and can obtain the instantaneous optimal solution. Based on the characteristics of the both optimization algorithms, reasonable methods are found to use them to optimize the performances of series hybrid electric vehicle in this dissertation, and the applied processes of both optimization methods are analyzed. In the series hybrid electric vehicle, there are two energy sources (engine and battery), how to distribute energy outputs for both sources and obtaining optimal vehicle performances is the focus of optimization algorithm and control strategy development. In view of this, the state of energy (SOE), which can reflect the level of battery power status, is presented in this dissertation as an input parameter of control strategy, which is more convenient for energy distribution.
In order to do deeper research for the performance of optimization of the dynamic programming and equivalent fuel consumption minimization algorithm, based on the model of the series hybrid electric vehicle, simulation tests are done in this dissertation. The simulation results show that dynamic programming optimization method can find the global optimal control methods to achieve optimal vehicle performances if the states of the vehicle in the vehicle driving cycles can be known in advance. Therefore, dynamic programming can provide the global optimal solution in a numerical way and represents a benchmark for the other strategies.
Unlike the dynamic programming optimization method, ECMS does not require knowledge of the entire driving cycle in advance, and can optimize the vehicle performances at each time, so is implementable on-line. However, large amount of calculation is needed, so it is difficult to achieve in controller level. The simulation results show that, although the optimal method of equivalent fuel consumption minimization algorithm is a local optimization method, but because of it letting vehicle achieve the optimal control at every moment, so the ultimate vehicle performance is close to the dynamic planning optimization results, and better than the traditional rule-based hybrid electric vehicle control method. In addition, the simulation results show that for different vehicle driving cycles, the corresponding charge and discharge equivalent factors of equivalent fuel consumption minimization algorithm are different. But as long as vehicle driving situation is similar to the vehicle driving cycle in which equivalent factors are get, then there will be little change in vehicle performances. Therefore, in practical applications the corresponding charge and discharge equivalent factors can be obtained by optimizing different vehicle driving cycles (such as rural, urban and highway) in advance, and predict driving situation by a forecasting model, and use corresponding equivalent factors, so that the vehicle performances can reach close to the optimal results all the time.
Based on PowerPC566 singlechip a vehicle control unit is developed for the series hybrid electric vehicle, in which fuzzy logic control method is applied to achieve energy distribution. In the design process of fuzzy control strategy the vehicle's power requirement, battery state of energy (SOE) and the generator output power is determined as the input and output parameters of the fuzzy controller. The domains and their corresponding membership functions of the parameters are determined, and based on previous control experience and the results of two optimization methods fuzzy rules are made and fuzzy controller is designed, then, fuzzy control table is got and stored in the controller's memory, after that, the fuzzy controller can control the vehicle by using look-up table control. By comparing the results of fuzzy controller in the test and optimization algorithms in the simulation, adjust the fuzzy rules in fuzzy controller to make the control effect of fuzzy controller gradually close to the optimal. Finally, the series hybrid electric vehicle is tested on a vehicle test bench, and the control effect of the fuzzy controller has proved its ability to effectively control the series hybrid electric vehicle, and can greatly improve the economy, at the same time the battery can be always maintained at its highly efficient operation region.
而在混合动力汽车中,为了能够充分开发其中增加动力电池所带来的提高汽车性能的潜能,为其开发使用合适控制策略的能量管理系统是至关重要的。随着人们对混合动力汽车的兴趣越来越高和其市场的不断增加,混合动力汽车能量管理系统的开发变的更为重要,因为能量管理系统能够根据驾驶员的驾驶意图,合理地控制混合动力汽车中的发动机、动力电池和电机协调工作,优化各个部件的输出,实现汽车中能量的合理分配,达到提高经济性和排放性等汽车性能的目的。
目前,对于串联式混合动力汽车能量管理系统中控制策略的研究大多是基于经验的,通过设定一些控制门限来实现能量的分配,不能使混合动力汽车的性能达到最优。因此,本文中首先对开发的串联式混合动力汽车进行了前向式建模,然后,在此模型的基础上,使用动态规划优化算法和等效燃油消耗最小优化算法对整车中能量的分配进行优化,从而使整车的性能达到最优。这样就找到了串联式混合动力汽车理论上的最佳性能和最优的控制方法,避免了对控制经验的依赖,也为其它控制策略的开发提供了理论依据,能够设计出更加有效的整车控制器。
本文所做的主要工作和结论如下:
为了能够对所研究项目中开发的串联式混合动力汽车的整车性能进行准确的分析,开发出好的整车能源管理策略,使得能量分配能够更优化,实现好的整车动力性和经济性的开发目标,本文对串联式混合动力汽车进行了建模研究,主要采用以实验建模法和理论建模法相结合的方法。对于混合动力汽车的主要部件发动机、动力电池和电机,分别从试验中得到各自相应的主要特性曲线,通过查表实现主要变量之间的计算。另外,还通过合理简化建立了车轮模型、汽车动力学模型、驱动桥模型、汽车附件模型、驾驶员模型和整车控制器模型,从而完成了整车的建模,为以后的整车性能研究和控制策略的开发提供了完整的平台。
为了模型建立的需要,本文对混合动力汽车中的主要动力部件进行了台架试验,得到了各个模型所需要的各种性能曲线和参数。之后,通过对比整车的试验数据和对模型所进行的仿真得到的数据,发现两者有很好地一致性,验证了所建立模型的正确性,能够将其应用到对整车的控制策略的开发中。
通过分析串联式混合动力汽车的结构特点和工作特性,本文选择了两种优化算法来对整车的能量分配和性能进行优化,一种是动态规划优化算法,它是一种全局性优化方法,能够得到优化对象的最优解;另一种是等效燃油消耗最小优化算法,它是一种局部优化算法,能够得到瞬时最优解。针对两种优化算法的特点,本文找到了它们在串联式混合动力汽车性能优化中的应用方法,并分别对两种优化方法的应用过程进行了分析。由于在串联式混合动力汽车中存在着两个能量源(发动机和动力电池),如何对两者所输出的能量进行合理的分配,从而使得整车性能最优,是优化算法和控制策略开发的重点。鉴于此,本文提出了采用更能反映动力电池能量水平的指标能量状态SOE来作为控制策略的输入参数,从而更加方便进行能量分配。
为了对动态规划优化算法和等效燃油消耗最小优化算法的优化性能进行更为细致的研究,本文基于建立的串联式混合动力汽车模型对两种优化方法进行了仿真试验。从仿真试验的结果可以发现,在已知整个汽车标准循环的状态后,动态规划优化方法能够找到全局最优控制方法,实现整车的最佳性能,因此,可以将动态规划优化得到的结果作为其它控制策略的参考依据和目标。
与动态规划优化方法不同,等效燃油消耗最小优化方法不需要提前知道整个汽车循环的状态数据,它所要优化的是汽车运行中每一时刻实现最优性能的控制方法,因此,它是一种能够在汽车控制器中实际应用的控制方法,只不过需要的计算量较大,目前难以实现。从仿真结果可以看出,虽然等效燃油消耗最小优化方法是一种局部优化方法,但由于其使得汽车运行每一时刻都实现了最优控制,因此其最终优化得到的整车性能接近于动态规划的优化结果,且要优于传统的基于规则的混合动力汽车控制方法。另外,在仿真中发现,对于不同的汽车行驶循环,等效燃油消耗最小优化方法对应着不同的充放电等效因子,但只要汽车实际行驶路况和得到等效因子的汽车行驶循环近似,整车性能就不会有太大变化。因此,在实际应用中可以先优化得到对应不同汽车行驶路况的等效因子(如郊区、城市和高速),并采用预测模型预测路况,从而采用不同的等效因子,从而使汽车总能达到接近最优的性能。
同时本文基于PowerPC566单片机为所开发的串联式混合动力汽车设计了整车控制器,并将模糊逻辑控制方法应用到该控制器中,实现整车的能量分配。在模糊控制策略的设计过程中,确定了整车的需求功率、动力电池能量状态SOE和发电机输出功率为模糊控制器的输入输出参数。确定了它们的论域和相应的隶属度函数,并根据以往的控制经验和两种优化方法得到的结果制定模糊规则,设计出了模糊控制器,得到模糊控制表,存储在控制器的存储器中进行查表控制。通过在试验中对比模糊控制器控制得到的整车性能和能量分配方法和在优化算法仿真中得到的结果,调整模糊控制器中的模糊规则,使得模糊控制器的控制效果逐渐接近最优。最后,通过在底盘测功机上的汽车标准循环的试验验证了所设计的整车控制器的控制效果,结果证明其能够有效地控制串联式混合动力汽车整车运行,能够较大地提高串联式混合动力汽车的燃油经济性,使动力电池始终保持在其高效区域内运行。
With the oil shortage and increasing pollution, environmental protection and efficient is becoming more and more important to us. Now, a lot of car manufacturers and researchers are turning their attention to vehicles with new energy, which are higher efficiency and cleaner. The hybrid electric vehicles have both the advantages of electric vehicles and traditional vehicles, such as: good economy, low emissions and long driving range. Therefore, hybrid electric vehicles become the research focus of new energy vehicles. Energy management is of fundamental importance in hybrid electric vehicles, for exploiting the advantages deriving from the availability of a rechargeable energy buffer. With the increasing interest in hybrid electric vehicles and their future commercial availability, energy management is becoming even more important, since energy management system can control engine, battery and motor reasonablely according to the driver’s driving intention, optimize the output of each power component, to achieve a reasonable distribution of energy in hybrid electric vehicles, and then improve economy and emission.
Currently, in the development of energy management system for the series hybrid electric vehicle, control strategies are mostly based on experience, by setting the threshold to control energy distribution, but can’t achieve optimal performance of hybrid electric vehicle. Therefore, in this dissertation a foreword model is firstly built for a series hybrid electric vehicle, then, based on the model, distribution of energy is optimized using dynamic programming (DP) and equivalent fuel consumption minimization strategy (ECMS), so that the optimal vehicle performances are achieved. Then the best performance in theory and optimal control methods are found for a series hybrid electric vehicle, so avoid dependence on the control experience, and provide a theoretical basis for the development of other control strategies. Finally a more effective vehicle controller can be designed. This major work done and conclusions in this dissertation are as follows:
In order to analyze the performances of the series hybrid electric vehicle which is developed in a project, and develop energy management strategies, so that the energy distribution to optimized to achieve good power and economy, a foreword model is built for the series hybrid electric vehicle in this dissertation, mainly using a modeling method which combines experimental and theoretical modeling methods. For the main components of hybrid electric vehicle (engine, battery and motor), the corresponding feature curves are respectively obtained from the experiments, then the main variables can be calculated using look-up tables. In addition, some models are simplified reasonably, such as: the wheel model, vehicle dynamics model, driving axle model, vehicle accessories model, driver model and vehicle controller model, then the vehicle model is completed for the development of performance and control strategy of the vehicle to provide a complete platform in the future. For the need of building models, in this dissertation, the main power components of the series hybrid electric vehicle are tested using a test bench, the performance curves and parameters for each model are obtained through the tests. Then by comparing the test results and simulation results, good agreements can be found between them, and this show the correctness of the established model, and the models can be applied for the development of the vehicle control strategy.
By analyzing the structural features and operating characteristics of the series hybrid electric vehicle, two optimization algorithms are selected to optimize the energy distribution and performances of the vehicle in this dissertation, and one is dynamic programming optimization algorithm, which is a global optimization method, and can obtain the optimal solution of the optimized object; the other is equivalent fuel consumption minimization optimization algorithm, which is a local optimization algorithm, and can obtain the instantaneous optimal solution. Based on the characteristics of the both optimization algorithms, reasonable methods are found to use them to optimize the performances of series hybrid electric vehicle in this dissertation, and the applied processes of both optimization methods are analyzed. In the series hybrid electric vehicle, there are two energy sources (engine and battery), how to distribute energy outputs for both sources and obtaining optimal vehicle performances is the focus of optimization algorithm and control strategy development. In view of this, the state of energy (SOE), which can reflect the level of battery power status, is presented in this dissertation as an input parameter of control strategy, which is more convenient for energy distribution.
In order to do deeper research for the performance of optimization of the dynamic programming and equivalent fuel consumption minimization algorithm, based on the model of the series hybrid electric vehicle, simulation tests are done in this dissertation. The simulation results show that dynamic programming optimization method can find the global optimal control methods to achieve optimal vehicle performances if the states of the vehicle in the vehicle driving cycles can be known in advance. Therefore, dynamic programming can provide the global optimal solution in a numerical way and represents a benchmark for the other strategies.
Unlike the dynamic programming optimization method, ECMS does not require knowledge of the entire driving cycle in advance, and can optimize the vehicle performances at each time, so is implementable on-line. However, large amount of calculation is needed, so it is difficult to achieve in controller level. The simulation results show that, although the optimal method of equivalent fuel consumption minimization algorithm is a local optimization method, but because of it letting vehicle achieve the optimal control at every moment, so the ultimate vehicle performance is close to the dynamic planning optimization results, and better than the traditional rule-based hybrid electric vehicle control method. In addition, the simulation results show that for different vehicle driving cycles, the corresponding charge and discharge equivalent factors of equivalent fuel consumption minimization algorithm are different. But as long as vehicle driving situation is similar to the vehicle driving cycle in which equivalent factors are get, then there will be little change in vehicle performances. Therefore, in practical applications the corresponding charge and discharge equivalent factors can be obtained by optimizing different vehicle driving cycles (such as rural, urban and highway) in advance, and predict driving situation by a forecasting model, and use corresponding equivalent factors, so that the vehicle performances can reach close to the optimal results all the time.
Based on PowerPC566 singlechip a vehicle control unit is developed for the series hybrid electric vehicle, in which fuzzy logic control method is applied to achieve energy distribution. In the design process of fuzzy control strategy the vehicle's power requirement, battery state of energy (SOE) and the generator output power is determined as the input and output parameters of the fuzzy controller. The domains and their corresponding membership functions of the parameters are determined, and based on previous control experience and the results of two optimization methods fuzzy rules are made and fuzzy controller is designed, then, fuzzy control table is got and stored in the controller's memory, after that, the fuzzy controller can control the vehicle by using look-up table control. By comparing the results of fuzzy controller in the test and optimization algorithms in the simulation, adjust the fuzzy rules in fuzzy controller to make the control effect of fuzzy controller gradually close to the optimal. Finally, the series hybrid electric vehicle is tested on a vehicle test bench, and the control effect of the fuzzy controller has proved its ability to effectively control the series hybrid electric vehicle, and can greatly improve the economy, at the same time the battery can be always maintained at its highly efficient operation region.
引文
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