CN108284752A - 一种增程式燃料电池电动汽车动力装置及其控制方法 - Google Patents

一种增程式燃料电池电动汽车动力装置及其控制方法 Download PDF

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CN108284752A
CN108284752A CN201810298605.1A CN201810298605A CN108284752A CN 108284752 A CN108284752 A CN 108284752A CN 201810298605 A CN201810298605 A CN 201810298605A CN 108284752 A CN108284752 A CN 108284752A
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output
electric vehicle
fuel cell
input
driving motor
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CN108284752B (zh
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陆玉正
颜森林
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Nanjing Xiaozhuang University
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Nanjing Xiaozhuang University
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Priority to CN201810298605.1A priority Critical patent/CN108284752B/zh
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Priority to US16/753,772 priority patent/US11766942B2/en
Priority to PCT/CN2019/080787 priority patent/WO2019192420A1/zh
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    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
    • B60L15/2045—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed for optimising the use of energy
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Abstract

本发明公开了一种增程式燃料电池电动汽车动力装置及其控制方法，该装置包括驱动电机、双向变流器、斩波器、动力电池、燃料电池、高压储氢罐、电控阀门、控制器、油门踏板和制动踏板；驱动电机的输出通过变速齿轮箱与电动汽车的传动轴连接，驱动电机的输入与双向变流器的交流输出端连接，双向变流器的直流输入端并联动力电池的输出与斩波器的输出，斩波器的输入与燃料电池的电源输出连接，燃料电池的燃料进口通过电控阀门与高压储氢罐的输出连接，控制器的输出分别与电控阀门的控制端口和双向变流器的控制端口连接，控制器输入端分别连接油门踏板和制动踏板的输出信号。控制方法为BP神经网络控制。本发明可实施性高，节能减排效果好。

Description

一种增程式燃料电池电动汽车动力装置及其控制方法
技术领域
本发明涉及一种电动汽车能量管理装置及控制方法，特别涉及一种增程式燃料电池电动汽车动力装置及其控制方法。
背景技术
电动汽车在国际上已经得到了广泛的关注。目前最具有代表性的美国特斯拉电动汽车，已经在全球大部分国家开始销售。电动汽车不排放任何有害气体和温室气体，但是电动汽车最为担心的问题是：一次充电续航能力，以及充电时间的长短以及充电设施的配套等。我国正在积极推动电动汽车的产业，但是电动汽车的快速发展带来的充电问题也相当严重。亚洲电动汽车之父陈清泉院士指出：“电动汽车发展所经历的三个阶段，第一阶段以丰田普锐斯为代表的油电混合电动汽车，目前我国自主研发了该类型的电动汽车；第二阶段是纯电动汽车，目前我国正处于大力发展发展阶段，比亚迪、北汽等推出多款纯电动汽车；第三阶段是燃料电池电动汽车，目前国际上已经商业化产品代表为丰田的Mira”。燃料电池是将燃料中的化学能高效地转化为电能，每辆燃料电池电动汽车就好像一座小型的发电站，可以自己发电，提供车辆所需要的电力，在必要的情况下，也可为其他设备提供电力。
从国际上看，燃料电池电动汽车还存在诸多问题，然而，利用燃料电池作为辅助动力，与充电式电动汽车结合，构成综合能源的电动汽车，是电动汽车发展的一个重要的阶段，利用装机容量较小的燃料电池作为增程器，提升电动汽车的续航能力。增程式电动汽车一般采用串联式拓扑结构，结构相对较简单，是混合动力汽车的一种，其在纯电动汽车的基础上增加一套燃料电池发动机，目的是为了增加汽车的行驶路程，从而有效解决一般纯电动汽车行驶路程较短，续航能力不足的问题。燃料电池发动机作为整车动力***增程器充当备用能源角色，而动力蓄电池作为车辆驱动的主要能源。当动力蓄电池电能不足或输出功率难以满足工况需求时，增程器开始工作，为动力蓄电池充电或直接驱动车辆，从而增加车辆的续驶里程。
发明内容
发明目的：针对现有技术存在的问题和不足，本发明的目的是提供一种增程式燃料电池电动汽车动力装置及其控制方法，在纯电动汽车的基础上增加一套燃料电池***，作为增程器，提升电动汽车的续航能力；另一方面，采用智能控制方案控制燃料电池的工作状态，达到节能减排的效果。
技术方案：本发明提供了一种增程式燃料电池电动汽车动力装置，该装置包括：驱动电机、双向变流器、斩波器、动力电池、燃料电池、高压储氢罐、电控阀门、控制器、油门踏板和制动踏板；
所述驱动电机的输出通过变速齿轮箱与电动汽车的传动轴连接，驱动电机的输入与双向变流器的交流输出端连接，双向变流器的直流输入端并联动力电池的输出与斩波器的输出，斩波器的输入与燃料电池的电源输出连接，燃料电池的燃料进口通过电控阀门与高压储氢罐的输出连接，所述控制器的输出分别与电控阀门的控制端口和双向变流器的控制端口连接，控制器输入端分别连接油门踏板和制动踏板的输出信号。
优选的，所述控制器为BP神经网络控制器，其中，BP神经网络的结构为2-5-2结构，即输入层神经元个数为2，中间层为神经元个数为5，输出层神经元个数为2；输入层为信号传递，输出两个控制量y1和y2。
优选的，所述动力电池为锂离子电池。
本发明还提供了一种增程式燃料电池电动汽车动力装置的控制方法，给定功率Pin为正时，电动汽车工作在行驶状态，为给定功率Pin为负时，电动汽车工作在制动状态，驱动电机的实际输出功率Pout驱动电动汽车，将驱动电机的实际输出功率Pout信号反馈到输入端，与给定功率Pin锦绣比较，构成反馈控制***，Pout信号与Pin的信号输入到比较器中，得到功率误差信号△P，并将功率误差信号△P进行微分处理，得到功率变化率信号dP/dt，将△P与dP/dt经过控制算法得到两个控制量y1和y2，控制量y1控制电控阀门的开度，从而控制燃料电池的输出电功率；控制量y2控制双向变流器的控制端口的PWM信号，从而控制双向变流器的工作方式以及功率大小。
其中，所述控制双向变流器的工作方式包括电动汽车形式状态时的逆变状态和电动汽车工作在制动时的整流状态，逆变状态时，驱动电机运行在电动状态，动力电池给通过双向变流器，将直流逆变为交流，给驱动电机提供电力，整流状态时，驱动电机运行在发电状态，驱动电机将电动汽车的制动时产生的能量转化为电能，通过双向变流器，将驱动电机发出的交流电整流为直流，给动力电池进行充电；当油门踏板有信号产生时，驱动电机运行在发电状态，当制动踏板有信号产生时，驱动电机运行在电动状态。
进一步的，所述控制算法为BP神经网络控制算法，包括以下步骤：
(1)建立BP神经网络结构
BP神经网络的结构为2-5-2结构，即输入层神经元个数i＝1,2，xi对应两个输入变量，输入层为信号传递；
输入变量一：x1：△P；
输入变量一：x2：dP/dt；
中间层为神经元个数j＝1,2,3,4,5；中间层神经元的输入为xj，输出为xj′；
输出层为神经元个数l＝1,2，输出为yl，对应两个控制量y1和y2；
(2)进行BP网络的训练
优选的，所述步骤(2)包括：
(21)前向传播：计算BP神经网络输出
中间层神经元的输入为所有输入的加权之和，即：
中间层神经的输出xj′采用S函数激发xj，得：
则：
输出层神经元的输出为：
BP神经网络第l个输出去与相应理想输出的误差为：
以第p个样本为例，第p个样本的误差性能指标函数为：
(22)反向传播：采用梯度下降法，调整各层间的权值；
输出层与中间层之间的连接权值ωjl学习算法为：
式中η为学习速率，η∈[0,1]；△ωjl为输出层与中间层连接至权值ωjl的变化量；
k+1时刻网络权值为：
ωjl(k+1)＝ωjl(k)+△ωjl (8)；
中间层与输入层之间的连接权值ωij的学习算法为：
式中，
△ωij为中间层与输入层之间的连接权ωij的变化量；
t+1时刻网络权值为：
ωij(k+1)＝ωij(k)+△ωij (10)。
有益效果：与现有技术相比，本发明采用在纯电动汽车的基础上，增加一套燃料电池***，形成轻度混合动力***，增加电动汽车的续航能力，在现有纯电动汽车上进行改造，可实施性高，同时将小容量的燃料电池***运用在车辆上，为将来的燃料电池电动汽车奠定技术基础；且本发明采用智能控制方案控制燃料电池的工作状态，进一步达到节能减排效果。
附图说明
图1是本发明结构示意图；
图2是本发明控制原理图；
图3是本发明增程式高温燃料电池电动汽车能量控制流程图；
图4是本发明的BP神经网络控制结构示意图。
具体实施方式
下面结合附图和具体实施例，进一步阐明本发明的技术方案，应理解这些实施例仅用于说明本发明而不用于限制本发明的范围，在阅读了本发明之后，本领域技术人员对本发明的各种等价形式的修改均落于本申请所附权利要求所限定的范围。
如图1所示，本发明的一种增程式燃料电池电动汽车动力装置包括：驱动电机1、双向变流器2、斩波器3、动力电池4、燃料电池5、高压储氢罐6、电控阀门7、控制器8、油门踏板9和制动踏板10。
所述驱动电机的输出为机械能，通过变速齿轮箱与电动汽车的传动轴连接，驱动电机的输入与双向变流器的交流输出端连接，双向变流器的直流输入端并联动力电池的输出与斩波器的输出，斩波器的输入与燃料电池的电源输出连接，燃料电池的燃料进口与高压储氢罐的输出连接，中间串联电控阀门，电控阀门的控制端口与控制器的输出连接，控制器的输出还连接双向变流器的控制端口，控制双向变流器的工作状态，控制器输入端连接油门踏板、制动踏板的输出信号；所述高压储氢罐设有燃料注入接口，所述动力电池设有充电接口。
所述驱动电机是将电能转化为机械能，用于驱动车辆。
所述双向变流器正向运行是将述动力电池的直流电能逆变为交流，给驱动电机提供交流电能，反向运行时将驱动电机制动时产生的电能，给动力电池充电，实现可再生制动。
所述斩波器是将燃料电池的直流电能变化为双向变流器直流输入端可接受的电压范围。
所述动力电池用于储存电力，可有锂离子电池等构成。
所述燃料电池是将化学能转化为电能，相当于小型的发电装置。
所述高压储氢罐用于储存氢气。
所述电控阀门用于控制氢气的供给量。
所述控制器由高性能芯片及***电路构成，采集油门踏板和制动踏板的信号，控制控阀门和双向变流器的工作状态。控制器可以为TMS320F28335PGFA。
所述油门踏板用于控制车辆速度，该信号送至控制器的输入端。
所述制动踏板用于控制车辆制动过程该信号送至控制器的输入端。
本发明的一种增程式燃料电池电动汽车动力装置的控制方法，如图2所示，其控制方案为给定功率Pin为正时，电动汽车工作在行驶状态，为给定功率Pin为负时，电动汽车工作在制动状态，驱动电机的实际输出功率Pout驱动电动汽车，将驱动电机的实际输出功率Pout信号反馈到输入端，与给定功率Pin进行比较，构成反馈控制***，Pout信号与Pin的信号输入到比较器中，得到功率误差信号△P，并将功率误差信号△P进行微分处理，得到功率变化率信号dP/dt，将△P与dP/dt经过控制算法得到两个控制量y1和y2，控制量y1控制电控阀门的开度，从而控制燃料电池的输出电功率；控制量y2控制双向变流器的控制端口的PWM(脉宽调制)信号，从而控制双向变流器的工作方式以及功率大小，增程式高温燃料电池电动汽车能量控制流程图如图3所示。
所述控制双向变流器的工作方式包括电动汽车形式状态时的逆变状态和电动汽车工作在制动时的整流状态，逆变状态时，驱动电机运行在电动状态，动力电池给通过双向变流器，将直流逆变为交流，给驱动电机提供电力，整流状态时，驱动电机运行在发电状态，驱动电机将电动汽车的制动时产生的能量转化为电能，通过双向变流器，将驱动电机发出的交流电整流为直流，给动力电池进行充电；当油门踏板有信号产生时，驱动电机运行在发电状态，当制动踏板有信号产生时，驱动电机运行在电动状态。
上述控制算法采用BP神经网络控制，利用BP神经网络学习能力，学习驱动电机与动力电池、燃料电池以及驾驶员的动作之间的关系，能够快速、准确地做出相应的控制信号，从而控制驱动电机；
如图4所示，BP神经网络的结构为2-5-2结构，即输入层神经元个数i＝1,2，xi对应两个输入变量，输入层为信号传递；
输入变量一：x1：△P
输入变量一：x2：dP/dt
中间层为神经元个数j＝1,2,3,4,5；中间层神经元的输入为xj，输出为xj′；
输出层为神经元个数l＝1,2，输出为yl，对应两个控制量y1和y2；
BP神经网络结构建立好，下一步需要进行BP网络的训练，具体的训练过程为：
正向传播是输入信号从输入层经中间层传向输出层，若输出层得到了期望的输出，则学习算法结束；否则，转向反向传播；
具体的网络学习算法为：
(1)前向传播：计算网络输出
中间层神经元的输入为所有输入的加权之和，即：
中间层神经的输出xj′采用S函数激发xj，得：
则：
输出层神经元的输出为：
BP神经网络第l个输出去与相应理想输出的误差为：
以第p个样本为例，第p个样本的误差性能指标函数为：
(2)反向传播：采用梯度下降法，调整各层间的权值；
输出层与中间层之间的连接权值ωjl学习算法为：
式中η为学习速率，η∈[0,1]；
k+1时刻网络权值为：
ωjl(k+1)＝ωjl(k)+△ωjl (8)；
中间层与输入层之间的连接权值ωij的学习算法为：
其中，
t+1时刻网络权值为：
ωij(k+1)＝ωij(k)+△ωij (10)；
基于上述步骤(1)与步骤(2)，完成控制器结构设计与学习算法设计，可实现BP神经网络控制，所需要的样本可以从实验中获得。在车辆进行测试时，记录大量的样本数据，可用作训练样本。神经网络控制器设计好后，并不能直接使用，需要样本数据进行训练和学习，燃料材料使用。设计好的控制方法，需要样本进行训练，样本数据的来源：在车辆实际测试过程中得到样本。

Claims (7)

1.一种增程式燃料电池电动汽车动力装置，其特征在于，该装置包括：驱动电机(1)、双向变流器(2)、斩波器(3)、动力电池(4)、燃料电池(5)、高压储氢罐(6)、电控阀门(7)、控制器(8)、油门踏板(9)和制动踏板(10)；
所述驱动电机的输出通过变速齿轮箱与电动汽车的传动轴连接，驱动电机的输入与双向变流器的交流输出端连接，双向变流器的直流输入端并联动力电池的输出与斩波器的输出，斩波器的输入与燃料电池的电源输出连接，燃料电池的燃料进口通过电控阀门与高压储氢罐的输出连接，所述控制器的输出分别与电控阀门的控制端口和双向变流器的控制端口连接，控制器输入端分别连接油门踏板和制动踏板的输出信号。
2.根据权利要求1所述的一种增程式燃料电池电动汽车动力装置，其特征在于：所述控制器为BP神经网络控制器，其中，BP神经网络的结构为2-5-2结构，即输入层神经元个数为2，中间层为神经元个数为5，输出层神经元个数为2；输入层为信号传递，输出两个控制量y1和y2。
3.根据权利要求1所述的一种增程式燃料电池电动汽车动力装置，其特征在于：所述动力电池为锂离子电池。
4.一种增程式燃料电池电动汽车动力装置的控制方法，其特征在于：给定功率Pin为正时，电动汽车工作在行驶状态，为给定功率Pin为负时，电动汽车工作在制动状态，驱动电机的实际输出功率Pout驱动电动汽车，将驱动电机的实际输出功率Pout信号反馈到输入端，与给定功率Pin进行比较，构成反馈控制***，Pout信号与Pin的信号输入到比较器中，得到功率误差信号△P，并将功率误差信号△P进行微分处理，得到功率变化率信号dP/dt，将△P与dP/dt经过控制算法得到两个控制量y1和y2，控制量y1控制电控阀门的开度，从而控制燃料电池的输出电功率；控制量y2控制双向变流器的控制端口的PWM信号，从而控制双向变流器的工作方式以及功率大小。
5.根据权利要求4所述的一种增程式燃料电池电动汽车动力装置的控制方法，其特征在于，所述控制双向变流器的工作方式包括电动汽车形式状态时的逆变状态和电动汽车工作在制动时的整流状态，逆变状态时，驱动电机运行在电动状态，动力电池给通过双向变流器，将直流逆变为交流，给驱动电机提供电力，整流状态时，驱动电机运行在发电状态，驱动电机将电动汽车的制动时产生的能量转化为电能，通过双向变流器，将驱动电机发出的交流电整流为直流，给动力电池进行充电；当油门踏板有信号产生时，驱动电机运行在发电状态，当制动踏板有信号产生时，驱动电机运行在电动状态。
6.根据权利要求4所述的一种增程式燃料电池电动汽车动力装置的控制方法，其特征在于，所述控制算法为BP神经网络控制算法，包括以下步骤：
(1)建立BP神经网络结构
BP神经网络的结构为2-5-2结构，即输入层神经元个数i＝1,2，xi对应两个输入变量，输入层为信号传递；
输入变量一：x1：△P；
输入变量一：x2：dP/dt；
中间层为神经元个数j＝1,2,3,4,5；中间层神经元的输入为xj，输出为xj′；
输出层为神经元个数l＝1,2，输出为yl，对应两个控制量y1和y2；
(2)进行BP网络的训练。
7.根据权利要求6所述的一种增程式燃料电池电动汽车动力装置的控制方法，其特征在于，所述步骤(2)包括：
(21)前向传播：计算BP神经网络输出
中间层神经元的输入为所有输入的加权之和，即：
中间层神经的输出xj′采用S函数激发xj，得：
则：
输出层神经元的输出为：
BP神经网络第l个输出去与相应理想输出的误差为：
以第p个样本为例，第p个样本的误差性能指标函数为：
(22)反向传播：采用梯度下降法，调整各层间的权值；
输出层与中间层之间的连接权值ωjl学习算法为：
式中η为学习速率，η∈[0,1]；△ωjl为输出层与中间层连接至权值ωjl的变化量；
k+1时刻网络权值为：
ωjl(k+1)＝ωjl(k)+△ωjl (8)；
中间层与输入层之间的连接权值ωij的学习算法为：
式中，△ωij为中间层与输入层之间的连接权ωij的变化量；
t+1时刻网络权值为：
ωij(k+1)＝ωij(k)+△ωij (10)。
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