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CN112434463A - Energy management system for vehicle hybrid power supply - Google Patents

Energy management system for vehicle hybrid power supply Download PDF

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CN112434463A
CN112434463A CN202011167418.3A CN202011167418A CN112434463A CN 112434463 A CN112434463 A CN 112434463A CN 202011167418 A CN202011167418 A CN 202011167418A CN 112434463 A CN112434463 A CN 112434463A
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lithium battery
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CN112434463B (en
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冯娜
马铁华
陈昌鑫
王晨斌
高伟涛
孟青
牛慧芳
张文
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other DC sources, e.g. providing buffering
    • H02J7/345—Parallel operation in networks using both storage and other DC sources, e.g. providing buffering using capacitors as storage or buffering devices
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2207/00—Indexing scheme relating to details of circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J2207/50—Charging of capacitors, supercapacitors, ultra-capacitors or double layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract

本发明涉及车辆复合电源系统能量管理技术领域，一种车辆复合电源能量管理系统，主要包括复合电源管理单元，模糊逻辑控制器，粒子群优化算法，复合电源管理单元的作用是采集锂电池和超级电容的运行参数处理后将荷电状态值输出到模糊逻辑控制器中，同时接收模糊逻辑控制器的输出信号控制复合电源的输出；模糊逻辑控制器的作用是将输入的锂电池SOC预估值、超级电容SOC和需求功率通过逻辑关系得到超级电容的充放电控制信号；粒子群优化算法的作用是对模糊逻辑控制器隶属度函数的参数进行优化。本发明能够有效减小锂电池的充放电次数，延长锂电池寿命，提高复合电源系统的供电效率。

Figure 202011167418

The invention relates to the technical field of energy management of a vehicle compound power supply system, and a vehicle compound power supply energy management system, which mainly includes a compound power supply management unit, a fuzzy logic controller, and a particle swarm optimization algorithm. After the operating parameters of the capacitor are processed, the state of charge value is output to the fuzzy logic controller, and the output signal of the fuzzy logic controller is received to control the output of the composite power supply; the function of the fuzzy logic controller is to input the estimated value of the lithium battery SOC , the supercapacitor SOC and the required power obtain the supercapacitor's charge and discharge control signal through a logical relationship; the role of the particle swarm optimization algorithm is to optimize the parameters of the membership function of the fuzzy logic controller. The invention can effectively reduce the charging and discharging times of the lithium battery, prolong the life of the lithium battery, and improve the power supply efficiency of the composite power supply system.

Figure 202011167418

Description

一种车辆复合电源能量管理系统A vehicle composite power source energy management system

技术领域technical field

本发明涉及车辆复合电源系统能量管理技术领域，具体涉及一种车辆复合电源能量管理系统。The invention relates to the technical field of energy management of a vehicle composite power supply system, in particular to a vehicle composite power supply energy management system.

背景技术Background technique

随着全球能源危机和环境问题的日益凸显，开发新能源汽车成为汽车产业发展的必然趋势。纯电动汽车采用单一动力电源容易出现续航能力弱、加速动力不足、电池寿命短等缺陷，因此混合动力汽车的研发显得尤为重要。将超级电容与蓄电池相结合作为电动汽车的动力电源，可以充分利用超级电容的快速响应特性，降低蓄电池的充放电频率，以延长蓄电池的使用寿命，增大电动汽车的续驶里程。With the increasingly prominent global energy crisis and environmental problems, the development of new energy vehicles has become an inevitable trend in the development of the automobile industry. Pure electric vehicles using a single power source are prone to defects such as weak endurance, insufficient acceleration power, and short battery life. Therefore, the research and development of hybrid vehicles is particularly important. Combining super capacitors and batteries as the power source of electric vehicles can make full use of the fast response characteristics of super capacitors and reduce the frequency of charging and discharging of batteries, so as to prolong the service life of batteries and increase the driving mileage of electric vehicles.

混合动力汽车的性能与其采用的能量管理策略密切相关，目前最常见的能量管理策略分为两大类，分别是基于规则的能量管理策略和基于优化的能量管理策略。其中，模糊逻辑控制属于模拟人的思维方式制定规则实现能量管理的方法，控制器的隶属度函数和规则的制定基础来源于专家的经验或理论知识，设计简单，易于理解，但容易陷入局部最优的情况。The performance of HEVs is closely related to the energy management strategies adopted. At present, the most common energy management strategies are divided into two categories, namely rule-based energy management strategies and optimization-based energy management strategies. Among them, fuzzy logic control belongs to the method of formulating rules to achieve energy management by simulating the way of thinking of human beings. The basis for formulating the membership function and rules of the controller comes from the experience or theoretical knowledge of experts. The design is simple and easy to understand, but it is easy to fall into the local maximum. excellent situation.

在制定模糊控制规则时，需要考虑电池的SOC值，传统的安时积分法由于SOC初值计算、测量仪器误差、电流和温度导致容量变化等得到SOC值不实时，难以用在实际的车辆动力系统中。When formulating fuzzy control rules, it is necessary to consider the SOC value of the battery. The traditional ampere-hour integration method is not real-time to obtain the SOC value due to the calculation of the initial SOC value, the error of the measuring instrument, and the capacity change caused by the current and temperature, which is difficult to be used in the actual vehicle power. in the system.

发明内容SUMMARY OF THE INVENTION

为了解决上述技术问题，本发明提供了一种车辆复合电源能量管理系统及方法，以解决锂电池荷电状态估计精度不高，锂电池使用寿命短，复合电源动力系统供电效率不高等问题。In order to solve the above technical problems, the present invention provides a vehicle composite power source energy management system and method to solve the problems of low SOC estimation accuracy of lithium batteries, short service life of lithium batteries, and low power supply efficiency of composite power systems.

本发明所采用的技术方案是：一种车辆复合电源能量管理系统，按如下的步骤进行The technical scheme adopted in the present invention is: a vehicle composite power source energy management system, which is carried out according to the following steps

步骤一：建立车辆复合电源动力系统模型；Step 1: Establish a vehicle composite power system model;

建立锂电池电路模型：Build a lithium battery circuit model:

Figure BDA0002745328410000021
Figure BDA0002745328410000021

Figure BDA0002745328410000022
Figure BDA0002745328410000022

UL＝Ubat-ibatRbat U L =U bat -i bat R bat

其中，SOCbat是锂电池实时的荷电状态SOC值；SOCbat.ini是锂电池的初始SOC值；QN为锂电池的额定容量；ibat表示锂电池的充放电电流，在一段时间内的积分累计值表示锂电池已使用容量；Ubat和Rbat分别为锂电池的开路电压和欧姆内阻；Pbat为锂电池的功率，UL是锂电池负载电压，Among them, SOC bat is the real-time state of charge SOC value of the lithium battery; SOC bat.ini is the initial SOC value of the lithium battery; Q N is the rated capacity of the lithium battery; i bat represents the charging and discharging current of the lithium battery, within a period of time The accumulated value of the integral represents the used capacity of the lithium battery; U bat and R bat are the open circuit voltage and ohmic internal resistance of the lithium battery, respectively; P bat is the power of the lithium battery, U L is the load voltage of the lithium battery,

锂电池的负载电压不允许超过开路电压，因此锂电池的最大充放电电流为：The load voltage of the lithium battery is not allowed to exceed the open circuit voltage, so the maximum charge and discharge current of the lithium battery is:

Figure BDA0002745328410000023
Figure BDA0002745328410000023

其中，Imax为锂电池的最大充放电电流，电池的充放电电流ibat在输出前必须与最大充放电电流Imax比较，如果充放电电流超过Imax时，则输出Imax，Among them, I max is the maximum charging and discharging current of the lithium battery, and the charging and discharging current i bat of the battery must be compared with the maximum charging and discharging current I max before output. If the charging and discharging current exceeds I max , then output I max ,

锂电池容量损耗模型采用Arrhenius模型，容量累计损耗为：The lithium battery capacity loss model adopts the Arrhenius model, and the cumulative capacity loss is:

Figure BDA0002745328410000031
Figure BDA0002745328410000031

其中，CRate为电池充放电倍率，

Figure BDA0002745328410000032
i1c为1C充放电电流；R为气体常数，取8.341J/(mol·K)；Tbat为电池温度，单位为K；t(k+1)-t(k)为仿真步长时间间隔，单位为s；Among them, C Rate is the charge and discharge rate of the battery,
Figure BDA0002745328410000032
i 1c is the 1C charge and discharge current; R is the gas constant, which is 8.341J/(mol·K); T bat is the battery temperature, in K; t(k+1)-t(k) is the simulation step time interval , the unit is s;

建立超级电容电路模型Building a supercapacitor circuit model

Figure BDA0002745328410000033
Figure BDA0002745328410000033

Figure BDA0002745328410000034
Figure BDA0002745328410000034

其中，SOCsc是超级电容的荷电状态值，Usc.max和Usc.min分别为超级电容的最大和最小电压，Usc为超级电容的实时电压，Isc为超级电容的充放电电流，Rsc和Psc分别为超级电容的内阻和电功率；Among them, SOC sc is the state of charge value of the supercapacitor, U sc.max and U sc.min are the maximum and minimum voltages of the supercapacitor, respectively, Usc is the real-time voltage of the supercapacitor, and Isc is the charge and discharge current of the supercapacitor , R sc and P sc are the internal resistance and electric power of the supercapacitor, respectively;

建立复合电源系统模型Building a composite power system model

Preq＝Pbat+Psc Preq = Pbat + Psc

其中，Preq为负载需求功率，Pbat和Psc分别为锂电池和超级电容的充放电功率，放电时功率为正，充电时功率为负；Among them, P req is the load demand power, P bat and P sc are the charging and discharging power of the lithium battery and the super capacitor, respectively, the power is positive when discharging, and the power is negative when charging;

步骤二、设计锂电池SOC预估器，预估算法采用贝叶斯-蒙特卡洛法估计得到锂电池的荷电状态SOCbat.e的值，Step 2: Design a lithium battery SOC predictor, and the estimation algorithm adopts the Bayesian-Monte Carlo method to estimate the value of the state of charge SOC bat.e of the lithium battery,

将贝叶斯-蒙特卡洛方法应用于锂电池荷电状态的估计，该方法通过一组具有相关权重的随机样本来近似概率密度函数：A Bayesian-Monte Carlo method is applied to estimate the state of charge of lithium batteries, which approximates the probability density function by a set of random samples with associated weights:

Figure BDA0002745328410000035
Figure BDA0002745328410000035

其中，

Figure BDA0002745328410000036
为锂电池任意k时刻的荷电状态和开路电压所构成的列向量，表示k时刻生成的随机粒子集；Ubat.k表示k时刻锂电池的开路电压，SOCbat.k表示k时刻锂电池的荷电状态；
Figure BDA0002745328410000041
表示在Ubat.k条件下，产生随机粒子集
Figure BDA0002745328410000042
所服从的概率密度函数；
Figure BDA0002745328410000043
是k时刻从概率密度函数
Figure BDA0002745328410000044
表示的分布中提取的第i(i＝1～Ns)个随机粒子集，Ns表示随机粒子集的个数；
Figure BDA0002745328410000045
表示k时刻提取的第i个粒子集的权重；δ(·)表示Dirac函数。in,
Figure BDA0002745328410000036
is the column vector composed of the state of charge and open circuit voltage of the lithium battery at any k time, and represents the random particle set generated at k time; U bat.k represents the open circuit voltage of the lithium battery at k time, and SOC bat.k represents the k time lithium battery. state of charge;
Figure BDA0002745328410000041
Indicates that under the condition of U bat.k , a random particle set is generated
Figure BDA0002745328410000042
The probability density function obeyed;
Figure BDA0002745328410000043
is the k moment from the probability density function
Figure BDA0002745328410000044
the i-th (i=1～N s ) random particle set extracted from the represented distribution, where N s represents the number of random particle sets;
Figure BDA0002745328410000045
represents the weight of the i-th particle set extracted at time k; δ(·) represents the Dirac function.

k时刻的权重

Figure BDA0002745328410000046
以正态分布概率密度函数在k-1时刻的权重
Figure BDA0002745328410000047
的基础上更新，更新规律的推导式为：weight at time k
Figure BDA0002745328410000046
The weight of the normal distribution probability density function at time k-1
Figure BDA0002745328410000047
Based on the update, the derivation of the update rule is:

Figure BDA0002745328410000048
Figure BDA0002745328410000048

其中，Ubat,k和

Figure BDA0002745328410000049
分别为k时刻锂电池开路电压的实测值和模型输出平均值，σ为其标准差。
Figure BDA00027453284100000410
表示在满足粒子集
Figure BDA00027453284100000411
的条件下Ubat.k所服从的概率密度函数，符合正态分布概率密度函数。Among them, U bat,k and
Figure BDA0002745328410000049
are the measured value of the open circuit voltage of the lithium battery at time k and the average output value of the model, and σ is the standard deviation.
Figure BDA00027453284100000410
Indicates that the particle set is satisfied
Figure BDA00027453284100000411
Under the condition of , the probability density function obeyed by U bat.k conforms to the normal distribution probability density function.

对所有粒子的权重进行归一化处理：Normalize the weights of all particles:

Figure BDA00027453284100000412
Figure BDA00027453284100000412

考虑所有粒子总权重后的预估结果可以表示为：The estimated result after considering the total weight of all particles can be expressed as:

Figure BDA00027453284100000413
Figure BDA00027453284100000413

锂电池SOC预估器中执行贝叶斯-蒙特卡洛算法，将产生的粒子集的权重不断的迭代运算，最后通过粒子加权求和的方式，得到锂电池荷电状态的预估值，即为向量

Figure BDA00027453284100000414
的第一个元素，表示为：The Bayesian-Monte Carlo algorithm is executed in the lithium battery SOC predictor, and the weight of the generated particle set is continuously iteratively calculated. Finally, the estimated value of the state of charge of the lithium battery is obtained by the weighted summation of the particles, that is, as a vector
Figure BDA00027453284100000414
The first element of , expressed as:

Figure BDA00027453284100000415
Figure BDA00027453284100000415

步骤三、将不同运行工况下需求功率Preq、锂电池荷电状态预估值SOCbat.e和超级电容的荷电状态SOCsc作为模糊逻辑控制器的输入，采用粒子群优化算法对模糊逻辑控制器的隶属度函数参数进行优化，经过逻辑关系输出超级电容充放电的控制信号比例因子Ksc，进而得到超级电容充放电控制信号Psc＝Ksc·Preq，锂电池充放电控制信号Pbat＝(1-Ksc)·Preq。Step 3: The required power Preq , the estimated state of charge SOC bat.e of the lithium battery and the state of charge SOC sc of the super capacitor under different operating conditions are used as the input of the fuzzy logic controller, and the particle swarm optimization algorithm is used to analyze the fuzzy logic. The membership function parameters of the logic controller are optimized, and the proportional factor K sc of the supercapacitor charging and discharging control signal is output through the logical relationship, and then the supercapacitor charging and discharging control signal P sc =K sc · Preq , and the lithium battery charging and discharging control signal is obtained. P bat =(1-K sc )·P req .

模糊逻辑控制器将输入信号SOCbat.e和SOCsc的模糊子集分别设置为：低L，中M，高H；将Preq和输出信号Ksc模糊子集分别设置为：较小TS，小S，中M，大B，较大TB，模糊逻辑控制器输入输出变量的隶属度函数采用梯形和三角形隶属度函数相结合，The fuzzy logic controller sets the fuzzy subsets of the input signals SOC bat.e and SOC sc as: low L, medium M, and high H; respectively sets the fuzzy subsets of Preq and the output signal K sc as: small TS, Small S, medium M, large B, large TB, the membership function of the input and output variables of the fuzzy logic controller adopts a combination of trapezoidal and triangular membership functions,

三角形隶属度函数的表达式为：The expression of the triangular membership function is:

Figure BDA0002745328410000051
Figure BDA0002745328410000051

梯形隶属度函数的表达式为：The expression of the trapezoidal membership function is:

Figure BDA0002745328410000052
Figure BDA0002745328410000052

隶属度函数曲线的形状由参数a，b，c，d确定，基于步骤三模糊逻辑控制器的隶属度函数，将参数点间的距离进行编码，得到待优化的参数m1到m10，均为实数。The shape of the membership function curve is determined by the parameters a, b, c, and d. Based on the membership function of the fuzzy logic controller in step 3, the distance between the parameter points is coded to obtain the parameters m 1 to m 10 to be optimized. is a real number.

根据粒子群优化算法的思路，考虑锂电池的使用寿命，将优化目标设计为锂电池的容量累计损耗最小，具体的步骤为：According to the idea of particle swarm optimization algorithm, considering the service life of lithium battery, the optimization goal is designed to minimize the cumulative loss of lithium battery capacity. The specific steps are:

(1)确定待优化的粒子群解空间维数d＝10，学习因子c1＝c2＝2，粒子群规模为30，惯性权重ω在2到0.5之间线性下降；(1) Determine the dimension of the particle swarm solution space to be optimized d=10, the learning factor c 1 =c 2 =2, the particle swarm scale is 30, and the inertia weight ω decreases linearly between 2 and 0.5;

(2)初始化粒子群，包括粒子群的大小、随机位置和速度，经验粒子的位置初始值设为图3中优化前的参数位置编码值，其余29个粒子的初始位置值在变化范围内随机生成，迭代次数为50，速度最大值设为0.08；(2) Initialize the particle swarm, including the size, random position and velocity of the particle swarm. The initial position value of the empirical particle is set to the parameter position code value before optimization in Figure 3, and the initial position value of the remaining 29 particles is random within the range of change. Generated, the number of iterations is 50, and the maximum speed is set to 0.08;

(3)根据f(x)计算出每个粒子的对应的适应度值；(3) Calculate the corresponding fitness value of each particle according to f(x);

(4)将每个粒子当前的适应度值与其个体最优适应度值pbest比较，若较好则更新pbest；(4) Compare the current fitness value of each particle with its individual optimal fitness value p best , and update p best if it is better;

(5)将每个粒子当前的适应度值与全局最优适应度值gbest比较，若较好则将该粒子的gbest值更新为全局最优值；(5) Compare the current fitness value of each particle with the global optimal fitness value g best , and if it is better, update the g best value of the particle to the global optimal value;

(6)根据位置更新公式和速度更新公式更新粒子的速度和位置；(6) Update the speed and position of the particle according to the position update formula and the speed update formula;

(7)判断是否达到最大迭代次数，若满足继续步骤(8)，否则返回步骤(2)继续执行；(7) Judging whether the maximum number of iterations is reached, if it is satisfied, continue with step (8), otherwise return to step (2) and continue to execute;

(8)输出整个粒子群的全局最优适应度值gbest，结束寻优操作。(8) Output the global optimal fitness value g best of the entire particle swarm, and end the optimization operation.

将全局最优适应度值gbest对应的隶属度函数参数输出，并将该结果作为新的模糊逻辑控制器的隶属度函数。The membership function parameter corresponding to the global optimal fitness value gbest is output, and the result is used as the membership function of the new fuzzy logic controller.

模糊逻辑控制器的输入输出逻辑关系采用Mamdami模型推理方法，复合电源动力系统模型通过Matlab工作空间向粒子群算法.m文件传递优化目标函数所需要的实时值，粒子群算法将更新的粒子传递到系统模型中用于计算优化目标。The input and output logic relationship of the fuzzy logic controller adopts the Mamdami model inference method. The composite power system model transfers the real-time value required to optimize the objective function to the particle swarm algorithm.m file through the Matlab workspace, and the particle swarm algorithm transfers the updated particles to the Used in the system model to calculate the optimization objective.

本发明的有益效果是：本发明解决了锂电池荷电状态估计精度不高，锂电池使用寿命短，复合电源动力系统供电效率不高等问题。The beneficial effects of the invention are as follows: the invention solves the problems that the estimation accuracy of the state of charge of the lithium battery is not high, the service life of the lithium battery is short, and the power supply efficiency of the composite power source power system is low.

附图说明Description of drawings

图1为本发明的系统总体结构框图；Fig. 1 is the overall structure block diagram of the system of the present invention;

图2为本发明的控制系统图；Fig. 2 is the control system diagram of the present invention;

图3为本发明放电模糊控制器隶属度函数图及待优化参数示意图；Fig. 3 is the membership function diagram of the discharge fuzzy controller and the schematic diagram of the parameters to be optimized according to the present invention;

图4为本发明模糊控制器隶属度函数的粒子群优化流程图；Fig. 4 is the particle swarm optimization flow chart of the membership function of the fuzzy controller of the present invention;

图5为本发明粒子群优化模糊控制器系统执行图。FIG. 5 is an execution diagram of the particle swarm optimization fuzzy controller system of the present invention.

具体实施方式Detailed ways

下面结合附图对本发明作更进一步的说明。显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The present invention will be further described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

本发明的一种车辆复合电源能量管理系统，其系统总体结构如图1所示。本系统主要包括复合电源管理单元，模糊逻辑控制器，粒子群优化算法。复合电源管理单元的作用是采集锂电池和超级电容的运行参数处理后将荷电状态值输出到模糊逻辑控制器中，同时接收模糊逻辑控制器的输出信号控制复合电源的输出；模糊逻辑控制器的作用是将输入的锂电池SOC预估值、超级电容SOC和需求功率通过逻辑关系得到超级电容的充放电控制信号；粒子群优化算法的作用是对模糊逻辑控制器隶属度函数的参数进行优化。复合电源管理单元中，包括锂电池管理单元、超级电容管理单元、锂电池和超级电容，锂电池管理单元的作用是采集锂电池的温度、电流和电压信号，通过SOC预估器得到锂电池荷电状态估计值，接收模糊控制器输出的锂电池的充放电信号，控制锂电池的过充和过放电；超级电容管理单元的作用是采集超级电容的温度、电流和电压信号，接收模糊控制器输出的超级电容充放电信号，控制超级电容的功率输出。控制系统图如图2所示。A vehicle composite power source energy management system of the present invention, the overall structure of the system is shown in FIG. 1 . This system mainly includes composite power management unit, fuzzy logic controller, particle swarm optimization algorithm. The function of the composite power management unit is to collect the operating parameters of the lithium battery and the super capacitor, and then output the state of charge value to the fuzzy logic controller, and at the same time receive the output signal of the fuzzy logic controller to control the output of the composite power supply; the fuzzy logic controller The function is to obtain the charge and discharge control signal of the super capacitor through the input lithium battery SOC estimated value, super capacitor SOC and required power through a logical relationship; the function of the particle swarm optimization algorithm is to optimize the parameters of the membership function of the fuzzy logic controller. . The composite power management unit includes a lithium battery management unit, a super capacitor management unit, a lithium battery and a super capacitor. The function of the lithium battery management unit is to collect the temperature, current and voltage signals of the lithium battery, and obtain the lithium battery charge through the SOC predictor. Electric state estimation value, receive the charge and discharge signal of the lithium battery output by the fuzzy controller, and control the overcharge and overdischarge of the lithium battery; the function of the super capacitor management unit is to collect the temperature, current and voltage signals of the super capacitor, and receive the fuzzy controller. The output supercapacitor charge and discharge signal controls the power output of the supercapacitor. The control system diagram is shown in Figure 2.

本发明的一种车辆复合电源能量管理系统，具体方法步骤如下：A vehicle composite power source energy management system of the present invention, the specific method steps are as follows:

步骤一：建立车辆复合电源动力系统模型；Step 1: Establish a vehicle composite power system model;

所述步骤一建立的车辆复合电源动力系统模型包括：The vehicle composite power system model established in the first step includes:

(1)建立锂电池电路模型：(1) Establish a lithium battery circuit model:

Figure BDA0002745328410000081
Figure BDA0002745328410000081

Figure BDA0002745328410000082
Figure BDA0002745328410000082

UL＝Ubat-ibatRbat U L =U bat -i bat R bat

其中，SOCbat是锂电池实时的荷电状态SOC值；SOCbat.ini是锂电池的初始SOC值；QN为锂电池的额定容量；ibat表示锂电池的充放电电流，在一段时间内的积分累计值表示锂电池已使用容量；Ubat和Rbat分别为锂电池的开路电压和欧姆内阻；Pbat为锂电池的功率，UL是锂电池负载电压。Among them, SOC bat is the real-time state of charge SOC value of the lithium battery; SOC bat.ini is the initial SOC value of the lithium battery; Q N is the rated capacity of the lithium battery; i bat represents the charging and discharging current of the lithium battery, within a period of time The accumulated value of the integral represents the used capacity of the lithium battery; U bat and R bat are the open circuit voltage and ohmic internal resistance of the lithium battery, respectively; P bat is the power of the lithium battery, and U L is the load voltage of the lithium battery.

锂电池的负载电压不允许超过开路电压，因此锂电池的最大充放电电流为：The load voltage of the lithium battery is not allowed to exceed the open circuit voltage, so the maximum charge and discharge current of the lithium battery is:

Figure BDA0002745328410000083
Figure BDA0002745328410000083

其中，Imax为锂电池的最大充放电电流。电池的充放电电流ibat在输出前必须与最大充放电电流Imax比较，如果充放电电流超过Imax时，则输出Imax。Among them, I max is the maximum charge and discharge current of the lithium battery. The charging and discharging current i bat of the battery must be compared with the maximum charging and discharging current I max before outputting, and if the charging and discharging current exceeds I max , then I max is output.

锂电池容量损耗模型采用Arrhenius模型，容量累计损耗为：The lithium battery capacity loss model adopts the Arrhenius model, and the cumulative capacity loss is:

Figure BDA0002745328410000091
Figure BDA0002745328410000091

其中，CRate为电池充放电倍率，

Figure BDA0002745328410000092
i1c为1C充放电电流；R为气体常数，取8.341J/(mol·K)；Tbat为电池温度，单位为K；t(k+1)-t(k)为仿真步长时间间隔，单位为s。Among them, C Rate is the charge and discharge rate of the battery,
Figure BDA0002745328410000092
i 1c is the 1C charge and discharge current; R is the gas constant, which is 8.341J/(mol·K); T bat is the battery temperature, in K; t(k+1)-t(k) is the simulation step time interval , the unit is s.

(2)建立超级电容电路模型：(2) Establish a supercapacitor circuit model:

Figure BDA0002745328410000093
Figure BDA0002745328410000093

Figure BDA0002745328410000094
Figure BDA0002745328410000094

其中，SOCsc是超级电容的荷电状态值，Usc.max和Usc.min分别为超级电容的最大和最小电压，Usc为超级电容的实时电压，Isc为超级电容的充放电电流，Rsc和Psc分别为超级电容的内阻和电功率。Among them, SOC sc is the state of charge value of the supercapacitor, U sc.max and U sc.min are the maximum and minimum voltages of the supercapacitor, respectively, Usc is the real-time voltage of the supercapacitor, and Isc is the charge and discharge current of the supercapacitor , R sc and P sc are the internal resistance and electrical power of the supercapacitor, respectively.

(3)建立复合电源系统模型：(3) Establish a composite power system model:

Preq＝Pbat+Psc Preq = Pbat + Psc

其中，Preq为负载需求功率，Pbat和Psc分别为锂电池和超级电容的充放电功率，放电时功率为正，充电时功率为负。Among them, P req is the load demand power, P bat and P sc are the charging and discharging power of the lithium battery and the super capacitor, respectively, the power is positive when discharging, and the power is negative when charging.

步骤二：设计锂电池SOC预估器，预估算法采用安时积分法与贝叶斯-蒙特卡洛法估计得到锂电池的荷电状态SOCbat.e的值。Step 2: Design a lithium battery SOC predictor, and the estimation algorithm uses the ampere-hour integration method and the Bayesian-Monte Carlo method to estimate the value of the state of charge SOC bat.e of the lithium battery.

所述步骤二中，将贝叶斯-蒙特卡洛方法应用于锂电池荷电状态的估计。该方法通过一组具有相关权重的随机样本来近似概率密度函数：In the second step, the Bayesian-Monte Carlo method is applied to the estimation of the state of charge of the lithium battery. This method approximates the probability density function by a set of random samples with relevant weights:

Figure BDA0002745328410000101
Figure BDA0002745328410000101

其中，

Figure BDA0002745328410000102
为锂电池任意k时刻的荷电状态和开路电压所构成的列向量，表示k时刻生成的随机粒子集；Ubat.k表示k时刻锂电池的开路电压，SOCbat.k表示k时刻锂电池的荷电状态；
Figure BDA0002745328410000103
表示在Ubat.k条件下，产生随机粒子集
Figure BDA0002745328410000104
所服从的概率密度函数；
Figure BDA0002745328410000105
是k时刻从概率密度函数
Figure BDA0002745328410000106
表示的分布中提取的第i(i＝1～Ns)个随机粒子集，Ns表示随机粒子集的个数；
Figure BDA0002745328410000107
表示k时刻提取的第i个粒子集的权重；δ(·)表示Dirac函数。in,
Figure BDA0002745328410000102
is the column vector composed of the state of charge and open circuit voltage of the lithium battery at any k time, and represents the random particle set generated at k time; U bat.k represents the open circuit voltage of the lithium battery at k time, and SOC bat.k represents the k time lithium battery. state of charge;
Figure BDA0002745328410000103
Indicates that under the condition of U bat.k , a random particle set is generated
Figure BDA0002745328410000104
The probability density function obeyed;
Figure BDA0002745328410000105
is the k moment from the probability density function
Figure BDA0002745328410000106
the i-th (i=1～N s ) random particle set extracted from the represented distribution, where N s represents the number of random particle sets;
Figure BDA0002745328410000107
represents the weight of the i-th particle set extracted at time k; δ(·) represents the Dirac function.

k时刻的权重

Figure BDA0002745328410000108
以正态分布概率密度函数在k-1时刻的权重
Figure BDA0002745328410000109
的基础上更新，更新规律的推导式为：weight at time k
Figure BDA0002745328410000108
The weight of the normal distribution probability density function at time k-1
Figure BDA0002745328410000109
Based on the update, the derivation of the update rule is:

Figure BDA00027453284100001010
Figure BDA00027453284100001010

其中，Ubat,k和

Figure BDA00027453284100001011
分别为k时刻锂电池开路电压的实测值和模型输出平均值，σ为其标准差。
Figure BDA00027453284100001012
表示在满足粒子集
Figure BDA00027453284100001013
的条件下Ubat.k所服从的概率密度函数，符合正态分布概率密度函数。Among them, U bat,k and
Figure BDA00027453284100001011
are the measured value of the open circuit voltage of the lithium battery at time k and the average output value of the model, and σ is the standard deviation.
Figure BDA00027453284100001012
Indicates that the particle set is satisfied
Figure BDA00027453284100001013
Under the condition of , the probability density function obeyed by U bat.k conforms to the normal distribution probability density function.

对所有粒子的权重进行归一化处理：Normalize the weights of all particles:

Figure BDA00027453284100001014
Figure BDA00027453284100001014

考虑所有粒子总权重后的预估结果可以表示为：The estimated result after considering the total weight of all particles can be expressed as:

Figure BDA00027453284100001015
Figure BDA00027453284100001015

锂电池SOC预估器中执行贝叶斯-蒙特卡洛算法，将产生的粒子集的权重不断的迭代运算，最后通过粒子加权求和的方式，得到锂电池荷电状态的预估值，即为向量

Figure BDA0002745328410000111
的第一个元素，表示为：The Bayesian-Monte Carlo algorithm is executed in the lithium battery SOC predictor, and the weight of the generated particle set is continuously iteratively calculated. Finally, the estimated value of the state of charge of the lithium battery is obtained by the weighted summation of the particles, that is, as a vector
Figure BDA0002745328410000111
The first element of , expressed as:

Figure BDA0002745328410000112
Figure BDA0002745328410000112

步骤三：基于步骤一建立的车辆复合电源动力系统模型，将不同运行工况下需求功率Preq、锂电池荷电状态预估值SOCbat.e和超级电容的荷电状态SOCsc作为模糊逻辑控制器的输入，采用粒子群优化算法对模糊逻辑控制器的隶属度函数参数进行优化，经过逻辑关系输出超级电容充放电的控制信号比例因子Ksc，进而得到超级电容充放电控制信号Psc。Step 3: Based on the vehicle composite power system model established in step 1, the required power Preq , the estimated state of charge SOC bat.e of the lithium battery and the state of charge SOC sc of the super capacitor under different operating conditions are used as fuzzy logic. For the input of the controller, particle swarm optimization algorithm is used to optimize the membership function parameters of the fuzzy logic controller, and the proportional factor K sc of the supercapacitor charging and discharging control signal is output through the logical relationship, and then the supercapacitor charging and discharging control signal P sc is obtained.

模糊逻辑控制器将输入信号SOCbat.e和SOCsc的模糊子集分别设置为：低L，中M，高H；将Preq和输出信号Ksc模糊子集分别设置为：较小TS，小S，中M，大B，较大TB。模糊逻辑控制器输入输出变量的隶属度函数采用梯形和三角形隶属度函数相结合，其论域和隶属度函数如图3所示。The fuzzy logic controller sets the fuzzy subsets of the input signals SOC bat.e and SOC sc as: low L, medium M, and high H; respectively sets the fuzzy subsets of Preq and the output signal K sc as: small TS, Small S, Medium M, Large B, Large TB. The membership function of the input and output variables of the fuzzy logic controller adopts a combination of trapezoidal and triangular membership functions, and its universe and membership functions are shown in Figure 3.

三角形隶属度函数的表达式为：The expression of the triangular membership function is:

Figure BDA0002745328410000113
Figure BDA0002745328410000113

梯形隶属度函数的表达式为：The expression of the trapezoidal membership function is:

Figure BDA0002745328410000114
Figure BDA0002745328410000114

隶属度函数曲线的形状由参数a，b，c，d确定，基于步骤三模糊逻辑控制器的隶属度函数，将参数点间的距离进行编码，得到待优化的参数m1到m10，均为实数，如图3隶属度函数曲线中的标注所示。The shape of the membership function curve is determined by the parameters a, b, c, and d. Based on the membership function of the fuzzy logic controller in step 3, the distance between the parameter points is coded to obtain the parameters m 1 to m 10 to be optimized. is a real number, as indicated by the labels in the membership function curve in Figure 3.

根据粒子群优化算法的思路，考虑锂电池的使用寿命，将优化目标设计为锂电池的损耗最小，粒子群算法优化隶属度函数的流程如图4所示，具体的步骤为：According to the idea of particle swarm optimization algorithm, considering the service life of lithium battery, the optimization goal is designed to minimize the loss of lithium battery. The flow of particle swarm optimization algorithm to optimize membership function is shown in Figure 4. The specific steps are:

(1)确定待优化的粒子群解空间维数d＝10，学习因子c1＝c2＝2，粒子群规模为30，惯性权重ω在2到0.5之间线性下降；(1) Determine the dimension of the particle swarm solution space to be optimized d=10, the learning factor c 1 =c 2 =2, the particle swarm scale is 30, and the inertia weight ω decreases linearly between 2 and 0.5;

(2)初始化粒子群，包括粒子群的大小、随机位置和速度，经验粒子的位置初始值设为图3中优化前的参数位置编码值，其余29个粒子的初始位置值在变化范围内随机生成，迭代次数为50，速度最大值设为0.08；(2) Initialize the particle swarm, including the size, random position and velocity of the particle swarm. The initial position value of the empirical particle is set to the parameter position code value before optimization in Figure 3, and the initial position value of the remaining 29 particles is random within the range of change. Generated, the number of iterations is 50, and the maximum speed is set to 0.08;

(3)根据f(x)计算出每个粒子的对应的适应度值；(3) Calculate the corresponding fitness value of each particle according to f(x);

(4)将每个粒子当前的适应度值与其个体最优适应度值pbest比较，若较好则更新pbest；(4) Compare the current fitness value of each particle with its individual optimal fitness value p best , and update p best if it is better;

(5)将每个粒子当前的适应度值与全局最优适应度值gbest比较，若较好则将该粒子的gbest值更新为全局最优值；(5) Compare the current fitness value of each particle with the global optimal fitness value g best , and if it is better, update the g best value of the particle to the global optimal value;

(6)根据位置更新公式和速度更新公式更新粒子的速度和位置；(6) Update the speed and position of the particle according to the position update formula and the speed update formula;

(7)判断是否达到最大迭代次数，若满足继续步骤(8)，否则返回步骤(2)继续执行；(7) Judging whether the maximum number of iterations is reached, if it is satisfied, continue with step (8), otherwise return to step (2) and continue to execute;

(8)输出整个粒子群的全局最优适应度值gbest，结束寻优操作。(8) Output the global optimal fitness value g best of the entire particle swarm, and end the optimization operation.

将全局最优适应度值gbest对应的隶属度函数参数输出，并将该结果作为新的模糊逻辑控制器的隶属度函数。The membership function parameter corresponding to the global optimal fitness value gbest is output, and the result is used as the membership function of the new fuzzy logic controller.

进一步，模糊逻辑控制器的输入输出逻辑关系采用Mamdami模型推理方法，规则表如下表所示：Further, the input and output logical relationship of the fuzzy logic controller adopts the Mamdami model inference method, and the rule table is shown in the following table:

Figure BDA0002745328410000131
Figure BDA0002745328410000131

复合电源动力系统模型通过Matlab工作空间向粒子群算法.m文件传递优化目标函数所需要的实时值，粒子群算法将更新的粒子传递到系统模型中用于计算优化目标，系统执行过程如图5所示。The composite power supply power system model transfers the real-time value required to optimize the objective function to the particle swarm algorithm.m file through the Matlab workspace. The particle swarm algorithm transfers the updated particles to the system model for calculating the optimization goal. The system execution process is shown in Figure 5. shown.

模糊逻辑控制器的输出参数为Ksc，超级电容充放电控制信号表示为：The output parameter of the fuzzy logic controller is K sc , and the supercapacitor charging and discharging control signal is expressed as:

Psc＝Ksc·Preq P sc =K sc ·P req

锂电池充放电控制信号表示为：Lithium battery charge and discharge control signal is expressed as:

Pbat＝(1-Ksc)·Preq P bat =(1-K sc )·P req

以上所述，仅为本发明较佳的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明披露的技术范围内，根据本发明的技术方案及其发明构思加以等同替换或改变，都应涵盖在本发明的保护范围之内。The above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited to this. The equivalent replacement or change of the inventive concept thereof shall be included within the protection scope of the present invention.

本发明涉及车辆复合电源系统能量管理技术领域，一种车辆复合电源能量管理系统及方法，主要包括复合电源管理单元，模糊逻辑控制器，粒子群优化算法，复合电源管理单元的作用是采集锂电池和超级电容的运行参数处理后将荷电状态值输出到模糊逻辑控制器中，同时接收模糊逻辑控制器的输出信号控制复合电源的输出；模糊逻辑控制器的作用是将输入的锂电池SOC预估值、超级电容SOC和需求功率通过逻辑关系得到超级电容的充放电控制信号。The invention relates to the technical field of energy management of a vehicle compound power supply system, and a vehicle compound power supply energy management system and method, which mainly include a compound power supply management unit, a fuzzy logic controller, and a particle swarm optimization algorithm. The function of the compound power supply management unit is to collect lithium batteries After processing with the operating parameters of the super capacitor, the state of charge value is output to the fuzzy logic controller, and the output signal of the fuzzy logic controller is received at the same time to control the output of the composite power supply; the function of the fuzzy logic controller is to pre-condition the input lithium battery SOC. The estimated value, supercapacitor SOC and required power obtain the supercapacitor charging and discharging control signal through a logical relationship.

Claims (4)

1. A vehicle hybrid power supply energy management system, characterized by: the method comprises the following steps: establishing a vehicle composite power supply power system model;
establishing a lithium battery circuit model:
Figure FDA0002745328400000011
Figure FDA0002745328400000012
UL＝Ubat-ibatRbat
therein, SOCbatThe real-time SOC value of the lithium battery is obtained; SOCbat.iniIs the initial SOC value of the lithium battery; qNThe rated capacity of the lithium battery; i.e. ibatThe method comprises the steps of representing charge and discharge current of the lithium battery, wherein the integral accumulated value in a period of time represents the used capacity of the lithium battery; u shapebatAnd RbatRespectively the open-circuit voltage and the ohmic internal resistance of the lithium battery; pbatIs the power of a lithium battery, ULIs the load voltage of the lithium battery,
the load voltage of the lithium battery is not allowed to exceed the open circuit voltage, so the maximum charge and discharge current of the lithium battery is:
Figure FDA0002745328400000013
wherein, ImaxIs the maximum charge-discharge current of the lithium battery, the charge-discharge current i of the batterybatMust be equal to the maximum charge-discharge current I before outputmaxIn comparison, if the charging and discharging current exceeds ImaxThen output Imax，
The Arrhenius model is adopted as the lithium battery capacity loss model, and the capacity accumulated loss is as follows:
Figure FDA0002745328400000014
wherein, CRateThe charge-discharge rate of the battery is,
Figure FDA0002745328400000015
i1cis 1C charge-discharge current; r is a gas constant, and 8.341J/(mol. K) is taken; t isbatIs the battery temperature in K; t (k +1) -t (k) is a simulation step time interval with the unit of s;
establishing super capacitor circuit model
Figure FDA0002745328400000021
Figure FDA0002745328400000022
Therein, SOCscIs the state of charge value, U, of the supercapacitorsc.maxAnd Usc.minMaximum and minimum voltages, U, respectively, of the supercapacitorscIs the real-time voltage of a super capacitor, IscIs the charging and discharging current of the super capacitor, RscAnd PscRespectively the internal resistance and the electric power of the super capacitor;
establishing a composite power supply system model
Preq＝Pbat+Psc
Wherein, PreqPower demand for the load, PbatAnd PscThe charging and discharging power of the lithium battery and the super capacitor is respectively, the power is positive during discharging, and the power is negative during charging;
designing a lithium battery SOC predictor, and estimating to obtain the state of charge SOC of the lithium battery by adopting a Bayesian-Monte Carlo method through a prediction algorithmbat.eThe value of (a) is,
applying a bayesian-monte carlo method to the estimation of the state of charge of a lithium battery, the method approximating a probability density function by a set of random samples with associated weights:
Figure FDA0002745328400000023
wherein,
Figure FDA0002745328400000024
representing a random particle set generated at the k moment for a column vector formed by the charge state and the open-circuit voltage of the lithium battery at the any k moment; u shapebat.kRepresents the open-circuit voltage, SOC of the lithium battery at the time kbat.kRepresenting the state of charge of the lithium battery at the moment k;
Figure FDA0002745328400000025
is shown at Ubat.kUnder the condition of generating random particle set
Figure FDA0002745328400000026
A obeyed probability density function;
Figure FDA0002745328400000031
is a function of the probability density at time k
Figure FDA0002745328400000032
I (i-1 to N) extracted from the distribution showns) A random set of particles, NsRepresenting the number of random particle sets;
Figure FDA0002745328400000033
representing the weight of the ith particle set extracted at the time k; δ (·) denotes the Dirac function.
Weight of k time
Figure FDA0002745328400000034
Weight of normally distributed probability density function at k-1 moment
Figure FDA0002745328400000035
Updating on the basis, wherein the derivation formula of the updating rule is as follows:
Figure FDA0002745328400000036
wherein, Ubat,kAnd
Figure FDA0002745328400000037
the measured value and the model output average value of the lithium battery open-circuit voltage at the moment k are respectively, and sigma is the standard deviation of the measured value and the model output average value.
Figure FDA0002745328400000038
Is shown in satisfying the particle set
Figure FDA0002745328400000039
Under the condition of Ubat.kThe obeyed probability density function accords with the normal distribution probability density function.
The weights of all particles are normalized:
Figure FDA00027453284000000310
the estimate after considering the total weight of all particles can be expressed as:
Figure FDA00027453284000000311
executing a Bayesian-Monte Carlo algorithm in the lithium battery SOC predictor, continuously performing iterative operation on the weight of the generated particle set, and finally obtaining a pre-estimated value of the state of charge of the lithium battery in a particle weighted summation mode, namely the vector
Figure FDA00027453284000000312
Is represented as:
Figure FDA00027453284000000313
step three, converting the required power P under different operation conditionsreqLithium battery state of charge (SOC) estimated valuebat.eAnd state of charge SOC of super capacitorscAs the input of the fuzzy logic controller, optimizing membership function parameters of the fuzzy logic controller by adopting a particle swarm optimization algorithm, and outputting a control signal scale factor K for charging and discharging the super capacitor through a logical relationscFurther obtain the charge-discharge control signal P of the super capacitorsc＝Ksc·PreqLithium battery charge and discharge control signal Pbat＝(1-Ksc)·Preq。
2. The vehicle hybrid power supply energy management system of claim 1, wherein: the fuzzy logic controller inputs the signal SOCbat.eAnd SOCscAre set to: low L, medium M, high H; will PreqAnd an output signal KscThe fuzzy subsets are respectively set as: the membership function of the input and output variables of the fuzzy logic controller adopts the combination of trapezoidal and triangular membership functions,
the expression of the triangular membership function is:
Figure FDA0002745328400000041
the expression of the trapezoidal membership function is:
Figure FDA0002745328400000042
the shape of the membership function curve is determined by parameters a, b, c and d, and the distance between parameter points is coded based on the membership function of the fuzzy logic controller in the step three to obtain a parameter m to be optimized1To m10All are real numbers.
3. The vehicle hybrid power supply energy management system of claim 2, wherein: according to the thought of a particle swarm optimization algorithm, the service life of a lithium battery is considered, an optimization target is designed to be the minimum accumulated capacity loss of the lithium battery, and the method comprises the following specific steps:
(1) determining a particle swarm solution space dimension d to be optimized as 10 and a learning factor c1＝c2The particle swarm size is 30, and the inertia weight omega is linearly reduced between 2 and 0.5;
(2) initializing a particle swarm, wherein the particle swarm comprises the size, the random position and the speed of the particle swarm, the initial position value of the empirical particle is set as a parameter position code value before optimization in the graph 3, the initial position values of the rest 29 particles are randomly generated in a variation range, the iteration number is 50, and the maximum speed value is set as 0.08;
(3) calculating a corresponding fitness value of each particle according to f (x);
(4) the current fitness value of each particle is compared with the individual optimal fitness value pbestComparing, if better, updating pbest；
(5) The current fitness value of each particle is compared with the global optimal fitness value gbestIn comparison, if preferred, the g of the particle isbestUpdating the value to a global optimal value;
(6) updating the speed and the position of the particle according to a position updating formula and a speed updating formula;
(7) judging whether the maximum iteration number is reached, if so, continuing the step (8), otherwise, returning to the step (2) to continue execution;
(8) outputting the global optimal fitness value g of the whole particle swarmbestAnd finishing the optimizing operation.
The global optimal fitness value gbestAnd outputting the corresponding membership function parameters, and taking the result as a membership function of the new fuzzy logic controller.
4. A vehicle hybrid power supply energy management system according to claim 3, characterized in that: the input and output logic relation of the fuzzy logic controller adopts a Mamdami model reasoning method, the composite power supply power system model transmits a real-time value required by an optimization objective function to the particle swarm algorithm through a Matlab working space, and the particle swarm algorithm transmits updated particles to the system model for calculating the optimization objective.
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