串联混合动力汽车能量优化管理策略研究
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摘要
随着能源危机和环境恶化的加剧,对混合动力汽车的研究正如火如荼进行,论文以串联混合动力汽车能量管理策略为研究内容,其主要研究工作如下:
本文以实验建模为主、理论建模为辅的建模方法,建立了串联混合动力汽车前向仿真模型,为能量管理策略的研究提供了必要的仿真平台。并对串联混合动力汽车控制策略的工作原理进行了系统、全面的分析,最后明确了能量管理策略需要解决的问题,即在满足汽车需求功率的前提下,如何分配各能量源的输出功率使得整车的耗油量、排放最少,同时满足电池的荷电状态平衡。本文旨在提出优化效果接近全局优化算法的实时能量管理策略,首先结合动态规划和猴群算法理论获得全局最优控制规则,将这些规则作为预选样本,提出了基于模糊C均值的BP实时能量管理策略。
基于各部件模型及能量管理策略要解决的问题,以发动机效率曲线和电池充放电效率曲线为依据,建立了串联混合动力汽车能量管理最优化数学模型。对于给定工况下的能量管理最优控制问题,通过离散化将一个多阶段决策问题转化为多个单阶段决策问题,并对整个工况的燃油最小化做了机理分析,针对问题的特点,结合动态规划理论,提出了改进的动态规划算法。仿真结果表明,该算法不仅能获得全局最优解,还使得运算时间大大减少。
在建立的整个工况的离散化数学模型基础上,应用新的智能算法—猴群算法,设计了串联混合动力汽车的猴群算法能量管理策略,建立了以发动机输出功率为决策变量、以电池荷电状态平衡为约束的功率分配策略。在原算法的基础上增加了混沌搜索、合作过程和随机扰动机制使之快速收敛。仿真结果表明,该算法能获得全局最优解,可以有效解决串联混合动力汽车的能量管理优化问题。
通过对改进动态规划算法的分析与仿真,将能量管理策略总结为二输入单输出的非线性映射,设计了基于模糊C均值的BP神经网络实时能量管理策略。将全局优化算法结合多个典型工况得到的控制规则作为神经网络的训练样本,采用模糊C均值算法对样本进行预选取,将样本分类,选取每一类中的典型样本离线进行训练,得到与分类数相同个BP神经网络。对于实时进入网络的数据,计算其与各聚类中心的距离,选取距离最近的聚类中心所对应的BP网络输出作为能量管理器的输出。仿真结果表明,基于模糊C均值的BP神经网络能量管理策略不仅能够模拟全局优化算法的控制规则,从而保证混合动力汽车具有很好的燃油经济性,而且还可以实现能量管理策略的实时控制。
As the energy crises and environmental degradation are getting more and more remarkable in the world, researches of hybrid electric vehicles(HEV) are progressing gradually. And this dissertation studies energy management strategy(EMS) for HEV. The main work of this paper is described as follows:
Based on empirical modeling approach with the aid of theoretical modeling, a forward model is presented which provides the necessary simulation platform for the development of EMS. Furthermore, this paper gives a systematical and comprehensive analyses to the working principle of series HEV’s control strategy(CS), and thus the problems of series HEV’s EMS to be resolved are ascertained, that is how to split the instantaneous power required between the two energy sources in order to minimize fuel consumption while the vehicle performs a given driving cycle, at the same time, the state of charge(SOC)of battery is balanced. The aim of the paper is to propose a real-time EMS which closer to global optimation, first combine theories of dynamic programming (DP) and monkey algorithm(MA), we get the control rules of global optimization, the rules are selected as pretreatment samples, then BP neural network based on fuzzy C-means clustering is proposed.
Based on models of all parts and the problems to be solved by EMS, an optimized mathematical model of HEV is established according to the efficiency curve of engine and battery. For a given driving cycle, optimization problem of EM is a multi-stage decision problem, which can be converted to single-stage decision by discretization. Mechanism of minimization the fuel consumption in a given driving cycle is analyzed. Combining the characteristic of the problem and the theory of DP, improved DP(IDP) is presented. Simulation results show that IDP not only can get global optimal solution,but also can greatly reduce the computing time.
Based on discretization mathematical model of whole driving cycle, a new intelligent algorithm, MA, is applied for solving the optimization problem. Output power of engine is used as a decision variable; balance of SOC is used as constraint equation. Chaotic-search, cooperation process and stochastic disturbance are added in original algorithms for fast convergence. The simulation results show that MA can get global optimal solution, and is effective to solve EM optimization problem of series HEV.
Through analysis and simulation of IDP, EMS is abstracted as a nonlinear mapping of two inputs and one output, then BP neural network is designed for real-time EMS on the basis of fuzzy C-means clustering. The global optimum algorithm is adopted in several typical driving cycles, and the principles are taken as training samples for BP neural network. Training samples are classified by fuzzy C-means clustering algorithm, and then typical samples in each class are used for training neural networks off line, thus BP controllers corresponding to each class can be obtained. For the real-time data transmitted into the network, its distance to each cluster center is calculated respectively, and the BP network corresponding to the cluster center with minimal distance to the real-time data is selected, and the output of this BP network is used as the output of EMS. Simulation results show that BP neural network EMS based on fuzzy C-means clustering algorithm not only can simulate the global optimum principles, which can ensure a good fuel economy of HEV, but also can realize real-time control.
本文以实验建模为主、理论建模为辅的建模方法,建立了串联混合动力汽车前向仿真模型,为能量管理策略的研究提供了必要的仿真平台。并对串联混合动力汽车控制策略的工作原理进行了系统、全面的分析,最后明确了能量管理策略需要解决的问题,即在满足汽车需求功率的前提下,如何分配各能量源的输出功率使得整车的耗油量、排放最少,同时满足电池的荷电状态平衡。本文旨在提出优化效果接近全局优化算法的实时能量管理策略,首先结合动态规划和猴群算法理论获得全局最优控制规则,将这些规则作为预选样本,提出了基于模糊C均值的BP实时能量管理策略。
基于各部件模型及能量管理策略要解决的问题,以发动机效率曲线和电池充放电效率曲线为依据,建立了串联混合动力汽车能量管理最优化数学模型。对于给定工况下的能量管理最优控制问题,通过离散化将一个多阶段决策问题转化为多个单阶段决策问题,并对整个工况的燃油最小化做了机理分析,针对问题的特点,结合动态规划理论,提出了改进的动态规划算法。仿真结果表明,该算法不仅能获得全局最优解,还使得运算时间大大减少。
在建立的整个工况的离散化数学模型基础上,应用新的智能算法—猴群算法,设计了串联混合动力汽车的猴群算法能量管理策略,建立了以发动机输出功率为决策变量、以电池荷电状态平衡为约束的功率分配策略。在原算法的基础上增加了混沌搜索、合作过程和随机扰动机制使之快速收敛。仿真结果表明,该算法能获得全局最优解,可以有效解决串联混合动力汽车的能量管理优化问题。
通过对改进动态规划算法的分析与仿真,将能量管理策略总结为二输入单输出的非线性映射,设计了基于模糊C均值的BP神经网络实时能量管理策略。将全局优化算法结合多个典型工况得到的控制规则作为神经网络的训练样本,采用模糊C均值算法对样本进行预选取,将样本分类,选取每一类中的典型样本离线进行训练,得到与分类数相同个BP神经网络。对于实时进入网络的数据,计算其与各聚类中心的距离,选取距离最近的聚类中心所对应的BP网络输出作为能量管理器的输出。仿真结果表明,基于模糊C均值的BP神经网络能量管理策略不仅能够模拟全局优化算法的控制规则,从而保证混合动力汽车具有很好的燃油经济性,而且还可以实现能量管理策略的实时控制。
As the energy crises and environmental degradation are getting more and more remarkable in the world, researches of hybrid electric vehicles(HEV) are progressing gradually. And this dissertation studies energy management strategy(EMS) for HEV. The main work of this paper is described as follows:
Based on empirical modeling approach with the aid of theoretical modeling, a forward model is presented which provides the necessary simulation platform for the development of EMS. Furthermore, this paper gives a systematical and comprehensive analyses to the working principle of series HEV’s control strategy(CS), and thus the problems of series HEV’s EMS to be resolved are ascertained, that is how to split the instantaneous power required between the two energy sources in order to minimize fuel consumption while the vehicle performs a given driving cycle, at the same time, the state of charge(SOC)of battery is balanced. The aim of the paper is to propose a real-time EMS which closer to global optimation, first combine theories of dynamic programming (DP) and monkey algorithm(MA), we get the control rules of global optimization, the rules are selected as pretreatment samples, then BP neural network based on fuzzy C-means clustering is proposed.
Based on models of all parts and the problems to be solved by EMS, an optimized mathematical model of HEV is established according to the efficiency curve of engine and battery. For a given driving cycle, optimization problem of EM is a multi-stage decision problem, which can be converted to single-stage decision by discretization. Mechanism of minimization the fuel consumption in a given driving cycle is analyzed. Combining the characteristic of the problem and the theory of DP, improved DP(IDP) is presented. Simulation results show that IDP not only can get global optimal solution,but also can greatly reduce the computing time.
Based on discretization mathematical model of whole driving cycle, a new intelligent algorithm, MA, is applied for solving the optimization problem. Output power of engine is used as a decision variable; balance of SOC is used as constraint equation. Chaotic-search, cooperation process and stochastic disturbance are added in original algorithms for fast convergence. The simulation results show that MA can get global optimal solution, and is effective to solve EM optimization problem of series HEV.
Through analysis and simulation of IDP, EMS is abstracted as a nonlinear mapping of two inputs and one output, then BP neural network is designed for real-time EMS on the basis of fuzzy C-means clustering. The global optimum algorithm is adopted in several typical driving cycles, and the principles are taken as training samples for BP neural network. Training samples are classified by fuzzy C-means clustering algorithm, and then typical samples in each class are used for training neural networks off line, thus BP controllers corresponding to each class can be obtained. For the real-time data transmitted into the network, its distance to each cluster center is calculated respectively, and the BP network corresponding to the cluster center with minimal distance to the real-time data is selected, and the output of this BP network is used as the output of EMS. Simulation results show that BP neural network EMS based on fuzzy C-means clustering algorithm not only can simulate the global optimum principles, which can ensure a good fuel economy of HEV, but also can realize real-time control.
引文
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