Article(id=1148011755101479492, tenantId=1146029695717560320, journalId=1146119989267898375, issueId=1149298831252079541, articleNumber=null, orderNo=null, doi=10.7654/j.issn.2097-1974.20240210, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1595865600000, receivedDateStr=2020-07-28, revisedDate=1607529600000, revisedDateStr=2020-12-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1751636931442, onlineDateStr=2025-07-04, pubDate=1713974400000, pubDateStr=2024-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1751636931442, onlineIssueDateStr=2025-07-04, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1751636931442, creator=13701087609, updateTime=1751636931442, updator=13701087609, issue=Issue{id=1149298831252079541, tenantId=1146029695717560320, journalId=1146119989267898375, year='2024', volume='47', issue='2', pageStart='1', pageEnd='106', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1751943794309, creator=13701087609, updateTime=1754895895552, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1161680841353552315, tenantId=1146029695717560320, journalId=1146119989267898375, issueId=1149298831252079541, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1161680841353552316, tenantId=1146029695717560320, journalId=1146119989267898375, issueId=1149298831252079541, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=63, endPage=68, ext={EN=ArticleExt(id=1148011755386692186, articleId=1148011755101479492, tenantId=1146029695717560320, journalId=1146119989267898375, language=EN, title=Research on Load Compensation Strategy of Power Unit of Series Hybrid Electric Vehicle, columnId=1154067058611446771, journalTitle=Missiles and Space Vehicles, columnName=Launch Support, runingTitle=null, highlight=null, articleAbstract=

When the power unit of the series hybrid vehicle is overloaded suddenly, the engine torque load rate would be too high, which would lead to abnormally operation. In order to solve this problem, a load compensation strategy based on fuzzy control is designed. By controlling the torque of generator, the engine torque load rate could be regulated, so that avoiding the engine working near the critical condition. The simulation and test results show that the strategy improves the working characteristics of the power unit effectively, makes it operates stable and smoothly until achieve the expected operating point, improves the robustness of the power unit.

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为解决串联式混合动力车辆动力单元突加重载时发动机转矩负荷率过高导致运行异常的问题,设计了一种基于模糊控制的载荷补偿策略,通过控制发电机转矩来调节发动机转矩负荷率,避免发动机在临界条件工作。仿真和试验结果表明,该策略有效改善了动力单元工作特性,使其可以稳定平滑地过渡到预期工作点,提高了动力单元工作的鲁棒性。

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赵宏志(1996—),男,助理工程师,主要研究方向为电力驱动技术。

骆志伟(1980—),男,研究员,主要研究方向为电力电子及电机传动。

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赵宏志(1996—),男,助理工程师,主要研究方向为电力驱动技术。

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赵宏志(1996—),男,助理工程师,主要研究方向为电力驱动技术。

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骆志伟(1980—),男,研究员,主要研究方向为电力电子及电机传动。

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骆志伟(1980—),男,研究员,主要研究方向为电力电子及电机传动。

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串联式混合动力车辆动力单元载荷补偿策略研究
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赵宏志 , 骆志伟
导弹与航天运载技术 | 发射技术 2024,47(2): 63-68
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导弹与航天运载技术 | 发射技术 2024, 47(2): 63-68
串联式混合动力车辆动力单元载荷补偿策略研究
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赵宏志, 骆志伟
作者信息
  • 北京航天发射技术研究所,北京,100076
  • 赵宏志(1996—),男,助理工程师,主要研究方向为电力驱动技术。

    骆志伟(1980—),男,研究员,主要研究方向为电力电子及电机传动。

Research on Load Compensation Strategy of Power Unit of Series Hybrid Electric Vehicle
Hongzhi ZHAO, Zhiwei LUO
Affiliations
  • Beijing Institute of Space Launch Technology,Beijing,100076
出版时间: 2024-04-25 doi: 10.7654/j.issn.2097-1974.20240210
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为解决串联式混合动力车辆动力单元突加重载时发动机转矩负荷率过高导致运行异常的问题,设计了一种基于模糊控制的载荷补偿策略,通过控制发电机转矩来调节发动机转矩负荷率,避免发动机在临界条件工作。仿真和试验结果表明,该策略有效改善了动力单元工作特性,使其可以稳定平滑地过渡到预期工作点,提高了动力单元工作的鲁棒性。

动力单元  /  模糊控制  /  载荷补偿  /  发动机  /  鲁棒性

When the power unit of the series hybrid vehicle is overloaded suddenly, the engine torque load rate would be too high, which would lead to abnormally operation. In order to solve this problem, a load compensation strategy based on fuzzy control is designed. By controlling the torque of generator, the engine torque load rate could be regulated, so that avoiding the engine working near the critical condition. The simulation and test results show that the strategy improves the working characteristics of the power unit effectively, makes it operates stable and smoothly until achieve the expected operating point, improves the robustness of the power unit.

power unit  /  fuzzy control  /  load compensation  /  engine  /  robustness
赵宏志, 骆志伟. 串联式混合动力车辆动力单元载荷补偿策略研究. 导弹与航天运载技术, 2024 , 47 (2) : 63 -68 . DOI: 10.7654/j.issn.2097-1974.20240210
Hongzhi ZHAO, Zhiwei LUO. Research on Load Compensation Strategy of Power Unit of Series Hybrid Electric Vehicle[J]. Missiles and Space Vehicles, 2024 , 47 (2) : 63 -68 . DOI: 10.7654/j.issn.2097-1974.20240210
串联式混合动力车辆(Series Hybrid Electric Vehicle, SHEV)一般由智能动力单元(Intelligent Power Unit, IPU)和动力电池共同为驱动电机提供能源, IPU控制性能的优劣将直接影响整车的行驶能力[1]
针对IPU输出功率控制问题, 国内外学者进行了大量研究。如按照IPU工作方式, 文献[2]提出了开关式和负载跟随式两种控制策略。文献[3]设计了一种转速和转矩耦合$\mathrm{{PI}}$控制器,以此达到快速调整输出功率的要求。文献[4]中基于PID控制对IPU转速和功率阶跃响应特性进行了试验研究。文献[5]和文献[6]提出了一种鲁棒变增益的控制来实现对发动机和输出电压的控制。基于系统中发动机与发电机直接通过机械装置连接的强耦合特性, 文献[7]简化了IPU均值模型并对发动机的转速和发电机的输出功率提出了一种解耦控制算法。
本文从动力单元载荷突变的角度出发,考虑IPU在突加重载时的功率输出问题。IPU在运行过程中通常以发动机外特性曲线作为工作点选取的依据, 而在动力单元突加重载时, 发动机会承受很大的反向转矩, 其工作点会接近甚至超出外特性曲线决定的运行区域, 此时发动机的转矩负荷率可能达到甚至超过100%,从而引发发动机带载能力不足的问题,影响了动力单元的工作, 严重时发动机会直接熄火, 对串联式混合动力车辆的行驶性能产生较大影响。
为了解决上述问题, 本文设计了一种基于模糊控制的载荷补偿策略, 该策略可以有效防止发动机在动力单元突加重载过程中因转矩负荷率过高而导致带载能力不足, 搭建了动力单元的仿真模型, 对有、无载荷补偿策略进行对比分析,最终通过试验进行了验证。
IPU系统架构如图1所示, 发动机与发电机同轴连接, ECU控制器控制发动机在转速控制模式下工作, 发电机由发电机控制器控制, 在转矩控制模式下工作, IPU控制器接收到整车需求功率后, 分解为转速值和转矩值, 将转速、转矩指令分别发送给ECU和发电机控制器,实现动力单元输出功率控制[8],其具体计算公式为
${P}_{\text{电 }}= {P}_{\text{轴 }}\cdot {\eta }_{\text{发电机 }}$
${P}_{\text{轴 }}= \frac{N \cdot {T}_{\mathrm{e}}}{9550}$
式中${P}_{\text{电 }}$为整车需求电功率;${P}_{\text{轴 }}$为动力单元轴功率;${\eta }_{\text{发电机 }}$为发电机及控制器效率;$N$为发动机转速;${T}_{\mathrm{e}}$为发电机转矩。
按照发动机在IPU中的运行特性, 搭建发动机的仿真模型如图2所示。
该模型为四输入三输出模型, 输入为经控制策略计算得出的发动机给定转速、发电机转矩、所处环境的大气压及温度, 输出为经过补偿与逻辑合成的转矩限值、实际转速、转矩负荷率。给定转速与实际转速的差值经过$\mathrm{{PI}}$控制器和惯性环节得到输出转矩,它们的参数由发动机出厂试验数据来确定。然后经过转速转矩功能模块, 根据发动机外特性曲线求出实际转速下的理论转矩限值, 再经过气压修正与温度修正, 得出实际转矩限值, 最后经过输出控制模块, 发电机转矩与实际转矩限值相比即可得到发动机的转矩负荷率, 若实际转矩限值小于发电机转矩值, 则转速会降低,发动机熄火, 输出转矩变为0 ,反之则正常输出。
为简化永磁同步发电机(Permanent Magnet
Synchronous Generator, PMSG)模型,假设如下[9]:
a)不考虑铁心饱和、磁滞损耗和涡流现象;
b)磁芯的电阻率无限大;
c)电机三相绕组是完全对称的, 忽略边缘效应;
d)不计高次谐波与齿槽效应影响, 每相绕组中感应电动势均为正弦波。
PMSG定子电压和磁链方程:
$\left\{\begin{array}{l}{\mathbf{u}}_{\mathrm{s}}= {\mathbf{R}}_{\mathrm{s}}{\mathbf{i}}_{\mathrm{s}}+ \frac{\mathrm{d}{\mathbf{\psi }}_{\mathrm{s}}}{\mathrm{d}t}\\ e =\frac{\mathrm{d}{\mathbf{\psi }}_{\mathrm{s}}}{2}\end{array}\right.$
${\mathbf{\psi }}_{\mathrm{s}}= {\mathbf{L}}_{\mathrm{s}}{\mathbf{i}}_{\mathrm{s}}+ {\mathbf{\psi }}_{\mathrm{f}}$
PMSG转矩方程:
${T}_{\mathrm{e}}= \frac{3}{2}{N}_{\mathrm{p}}{\mathbf{\psi }}_{\mathrm{s}}\times {\mathbf{i}}_{\mathrm{s}}$
式中${\mathbf{u}}_{\mathrm{s}}$为定子电压矢量;${\mathbf{R}}_{\mathrm{s}}$为定子电阻矩阵;${\mathbf{i}}_{\mathrm{s}}$为定子电流矢量;${\psi }_{\mathrm{s}}$为定子磁链矢量;${\mathbf{L}}_{\mathrm{s}}{\mathbf{i}}_{\mathrm{s}}$为电枢磁链矢量;${\psi }_{\mathrm{f}}$为转子磁链矢量;${N}_{\mathrm{p}}$为电机的极对数。
经过CLARKE变换和PARK变换后,转化为在$d$-$q$坐标系下的数学方程,再采用${i}_{d}= 0$的电流矢量控制策略, 此时的电磁转矩表达式为
${T}_{\mathrm{e}}= \frac{3}{2}{N}_{\mathrm{p}}{\mathbf{\psi }}_{\mathrm{f}}{\mathbf{i}}_{\mathrm{s}}= \frac{3}{2}{N}_{\mathrm{p}}{\mathbf{\psi }}_{\mathrm{f}}{i}_{q}$
故有:${i}_{\mathrm{s}}= {i}_{q}= \frac{{T}_{\mathrm{e}}}{\frac{3}{2}{N}_{\mathrm{p}}{\psi }_{\mathrm{f}}}$
因此,可以直接输入转矩值,通过调节交轴电流${i}_{q}$来控制发电机[10]。PMSG控制系统原理示意如图3所示。
载荷补偿策略主要是为了解决动力单元突加负载导致的发动机转矩负荷率过高, 进而熄火的问题。在IPU系统中, 发电机与发动机的转速方向相同, 转矩方向相反,发动机是原动机,发电机将机械能转换为电能输出, 可通过调节发动机转速及发电机转矩来控制动力单元的输出功率。但是由于IPU系统的各模块之间是复杂的非线性关系, 难以建立精确的数学模型, 且发电机与发动机的控制是由各自的控制器完成, 其动态调节过程是IPU控制器无法直接参与的, 因此本文引入模糊控制方式, 在原有目标转矩的基础上, 加入载荷补偿, 使得IPU可在这种严重非线性的情况下满足快速且高效的调节需求。载荷补偿模糊控制系统如图4所示。
本文设计的模糊控制策略输入分别为发动机的转矩负荷率、目标转矩与实际转矩的差值, 输出为传输给发电机的转矩调节量。主要控制策略为: 通过判断转矩负荷率与转矩差值, 逐步调节工作点, 在转矩负荷率反馈正常的情况下,达到目标转矩值。
对于实际的输入变量, 首先进行尺度变换, 将其变换到要求的论域范围内, 量化后的值作为模糊推理系统的输入。对经模糊推理系统得到的输出变量同样进行量化,将其变换到实际的变化范围内。
以转矩差值为例,若实际的差值为${e}^{* }$,其变化范围为$\left\lbrack {{e}^{* }{}_{\min },{e}^{* }{}_{\max }}\right\rbrack$,若要求的论域为$\left\lbrack {{e}_{\min },{e}_{\max }}\right\rbrack$,采用线性变换, 则变换后的差值计算公式为
$ e =\frac{{e}_{\max }+ {e}_{\min }}{2}+ k\left({{e}^{* }- \frac{{e}^{* }{}_{\max }+ {e}^{* }{}_{\min }}{2}}\right)$
式中$e$为经尺度变换后的值;$k$为比例因子,$k =$$\frac{{e}_{\max }- {e}_{\min }}{{e}_{\max }^{* }- {e}_{\min }^{* }}$
本文设计的模糊控制器转矩负荷率输入论域为$\left\lbrack {0,1}\right\rbrack$,转矩差值与模糊控制器输出论域为$\lbrack - 1$, 1]。根据上述公式,转矩差值的实际输入值${e}^{* }$经尺度变换后的值$e$变为$\left\lbrack {-1,1}\right\rbrack$范围内的某一数值,作为模糊推理系统的输入量。
选定3个模糊子集:{适中,较大,极大}= {mf1, mf2, mf3}, 用于覆盖转矩负荷率输入论域$\left\lbrack {0,1}\right\rbrack$; 选定3个模糊子集:{负,零,正}$=\{\mathrm{{mf}}1$, mf2, mf3}, 用于覆盖转矩差值与模糊控制器输出论域$\left\lbrack {-1,1}\right\rbrack$。按照模糊语言值划分论域,得到各输入输出的隶属函数。
本文根据分析推理法, 为了仿真方便, 考虑并结合实际经验, 选择较为典型的三角形函数和梯形函数。
转矩负荷率低于0.95时, 发动机可正常工作, 判定处于适中区域; 位于0.95与0.98之间时, 认为发动机转矩负荷率较大; 当高于0.98时, 转矩负荷率处于极大状态。转矩差值与转矩调节量则根据实际正负号判断所属模糊子集, 在此基础上, 利用三角形函数和梯形函数, 经多次仿真试验适当调节重叠区域, 得到的输入输出的隶属函数如图5所示。
本文基于历史数据与工程经验, 设计模糊控制规则。在转矩负荷率处于适中位置时, 根据转矩差值设置转矩调节量;当转矩负荷率较高时,转矩调节量不允许被增加; 当转矩负荷率极高时, 转矩调节量应适当减小。以此类推, 便可得到全部9条控制规则, 如表1所示。
经模糊推理得到的转矩调节量为$\mathrm{{mf}}1$$\mathrm{{mf}}2$$\mathrm{{mf}}3$中的一个模糊量, 采用重心法对输出模糊量进行解模糊运算。重心的计算公式为
${\Delta T}= \frac{{\int }_{a}^{b}\left({\Delta {T}^{* }}\right){u}_{c}\left({\Delta {T}^{* }}\right)\mathrm{d}\left({\Delta {T}^{* }}\right)}{{\int }_{a}^{b}{u}_{c}\left({\Delta {T}^{* }}\right)\mathrm{d}\left({\Delta {T}^{* }}\right)} $
式中${\Delta T}$为转矩调节量精确值;$\Delta {T}^{* }$为转矩调节量模糊值;$a, b$分别为转矩调节量$\Delta {T}^{* }$所在论域范围的上下限值,该限值按不同转速下功率变化率为${20}\mathrm{\;{kW}}/\mathrm{s}$计算得出;$c$$\Delta {T}^{* }$的模糊集合;${u}_{c}\left({\Delta {T}^{* }}\right)$$\Delta {T}^{* }$对应的隶属函数。
根据上述对发动机和发电机模型的分析, 以及IPU载荷补偿策略的设计, 搭建了动力单元的仿真模型, 如图6所示。
为测试动力单元发电功率的动态调节能力, 初始给定功率为$0\mathrm{\;{kW}}, t = 1\mathrm{\;s}$时突加到满载${70}\mathrm{\;{kW}}$,动力单元仿真模型怠速转速为${1300}\mathrm{r}/\mathrm{{min}}$,初始转矩为0, 系统采样周期为${7.5}\times {10}^{-5}\mathrm{\;s}$
图7为动力单元未加载荷补偿策略的仿真波形,图8为动力单元加入载荷补偿策略的仿真波形。从仿真结果可以看出, 在没有载荷补偿时, 突加负载使得发动机转矩负荷超过了自身载荷力, 发动机转速逐渐下降, 最终熄火; 加入载荷补偿策略后, 突加负载时, 载荷补偿模块检测到转矩负荷率过高, 动态降低了发电机的转矩指令, 抑制了转矩负荷率的上升趋势,当负荷率降低到适中区域(即mf1覆盖区域)时, 再次调节转矩指令, 这时发动机的转速已上升, 发动机的载荷能力提高, 使得IPU系统正常工作, 满足整车功率需求。
整个动态调节过程中,转矩负荷率超调$\sigma ={3.9}\%$,转矩动态调节时间$\tau ={0.75}\mathrm{\;s}$,系统功率静态误差${e}_{\mathrm{{ss}}}= {0.34}\%$
为验证载荷补偿策略的有效性, 在动力单元试验台上进行试验研究。试验台由柴油发动机、发电机、 发电机控制器、发电机组控制器、冷却系统、附件等组成。
柴油发动机采用潍柴WP3Q130E50柴油发动机, 发电机采用精进电动TM5019B永磁高压电机, 额定功率为${70}\mathrm{\;{kW}}$
动力单元控制系统由机组控制器、柴油发动机ECU、发电机控制器、机组传感器及线束、冷却系统控制模块、远程控制及通信组成。机组控制器与ECU之间采用CAN总线通信, 实现机组启停控制, 油压、 水温、转速等信号监视, 机组参数采集、上传及保护, 控制合、分闸。图9为试验台照片。
突加额定功率${70}\mathrm{\;{kW}}$,载荷补偿策略对比试验结果如图10所示。在未加入载荷补偿时, 由于突加负载, 转矩指令会突然增大, 发动机在速度上升时转矩负荷率长时间保持在100%,发动机载荷能力不足, 转速逐渐下降, 最终熄火。加入载荷补偿后, 从图10中可以明显看出, 在给定发电机转矩指令时, 充分考虑发动机的转矩负荷率大小, 当转矩负荷率处于较大值时, 会适当降低发电机转矩, 使得功率平稳过渡到所需功率点处。
本文针对串联式混合动力汽车动力单元突加载荷引入的工作稳定性恶化问题,提出了一种载荷补偿策略。结合动力单元工作特点,设计模糊控制补偿策略, 根据转矩负荷率与转矩差值求取发电机转矩指令。仿真和试验结果表明, 载荷补偿策略改善了动力单元工作特性, 提升了动力单元的载荷自适应能力。
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doi: 10.7654/j.issn.2097-1974.20240210
  • 接收时间:2020-07-28
  • 首发时间:2025-07-04
  • 出版时间:2024-04-25
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  • 收稿日期:2020-07-28
  • 修回日期:2020-12-10
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    北京航天发射技术研究所,北京,100076
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