这本学报最好联系杂志社直接投稿，有人冒充是杂志社编辑，书已经出刊了确一直没收到期刊 电话问编辑那边根本没我的文章 可笑的是冒充谁内编辑的这个人还发了份电子版清样给我，已经与杂志社核实 是骗人的，谨慎等不退款给我再曝光他的QQ号姓名 银行账号，并提交证据到公安机关
1、 题名： 新型双连杆双曲轴内燃机滑块偏转仿真研究作者： 谭理刚;杨靖;龚志辉来源： 《内燃机工程》 ISSN :1000-0925,2005,26(3):57-602、 题名： 直喷式发动机燃油喷射过程的多维模型仿真作者： 刘金武;杨靖;高为国;倪小丹来源： 《系统仿真学报 》ISSN :1004-731X,2004,16(3):525-5293、 题名： 虚拟样机技术在SL1126内燃机设计中的应用研究作者： 易际明;杨靖;张亮峰来源： 《计算机辅助设计与图形学学报》 ISSN :1003-9775,2004,16(7):1016-10194、 题名： 基于案例的SL1126内燃机方案设计作者： 易际明;杨靖;张亮峰来源： 《机械设计》 ISSN :1001-2354,2004,21(12):35-375、题名： 支持Top-Down Design的内燃机参数化建模作者： 易际明;杨靖;张亮峰来源： 《中国制造业信息化》 ISSN :1672-1616,2004,33(3):100-1026、 题名： 直喷式发动机喷雾模型研究进展作者： 刘金武;杨靖;高为国;倪小丹来源： 《内燃机工程》 ISSN :1000-0925,2005,26(1):81-847、 题名： 柴油机的性能改进及缸内工作过程的三维数值模拟作者： 杨靖;肖明伟;崔东晓;邓帮林;周剑来源： 《湖南大学学报. 自然科学版 》ISSN :1000-2472,2006,33(4):50-548、 题名： 内燃机燃烧过程仿真后处理输入文件Ipost的研究作者： 刘金武;杨靖;高为国;倪小丹来源： 《湖南工程学院学报》 自然科学版 ISSN :1671-119X,2003,13(3):34-369、 题名： 关联设计技术及其在内燃机CAD系统中的应用作者： 易际明;朱理;杨靖来源： 《机械设计与研究》 ISSN :1006-2343,2004,20(3):89-90,9510、题名： 基于μC/OS-Ⅱ嵌入式内核的排气分析仪开发研究作者： 谭理刚;杨靖;潘朝辉;龚金科来源：《湖南大学学报》自然科学版 ISSN :1000-2472,2005,32(4):43-4611、题名： CAD系统软件数据交换技术的实现作者： 张亮峰;杨靖;彭浩舸来源：《湖南工程学院学报》自然科学版ISSN :1671-119X,2004,14(4):38-4012、题名： 双连杆内燃机动态仿真作者： 易际明;杨靖来源： 《系统仿真学报》 ISSN :1004-731X,2004,16(12):2780-278213、题名： 提高智能排气分析仪精度的研究作者： 杨靖;潘朝晖;周剑来源： 《内燃机工程》 ISSN :1000-0925,2004,25(2):75-7814、题名： 105系列直喷式柴油机新燃烧系统开发作者： 杨靖;李克;潘朝浑来源： 《内燃机工程》 ISSN :1000-0925,2003,24(6):13-1615、题名： 面向装配的智能变型设计技术及应用研究作者： 易际明;杨靖来源： 《湖南工程学院学报》 自然科学版 ISSN :1671-119X,2005,15(1):25-2916、题名： SL1115单缸双连杆柴油机配气凸轮型线的设计作者： 李蓉;杨靖来源： 《小型内燃机与摩托车》 ISSN :1002-8277,2000,29(2):1917、题名：轻型汽油车改装柴油机后发动机悬置系统和冷却系统的优化作者： 杨靖;肖明伟；崔东晓；邓帮林来源： 《客车技术与研究》 ISSN :1000-2472,2006,28(2):4918、题名： 内燃机燃烧过程仿真计算的双精度系统设计作者： 刘金武;杨靖;倪小丹;黄麓升来源： 《湖南工程学院学报》 自然科学版 ISSN :1671-119X,2004,14(2):40-43
基于嵌入式技术楼宇智能化控制系统*
摘要:为了解决智能楼宇控制点种类和数量多的问题,设计了基于嵌入式技术的智能楼宇控制系统,系统采用MODBUS通
讯协议,485/232总线结构,最大通讯距离达1200m,通过区域控制器与控制模块数目自由组合组成控制网络的方法成功
解决这个问题,效果良好。
关键词:智能楼宇 MODBUS协议 485/232总线 区域控制器
0 引 言
智能楼宇最早出现在美国,我国的智能楼宇起源于
20世纪90年代,楼宇智能化是现代工业高科技的结晶,
是未来“信息高速公路”的主节点,是进入“数字时代”新
兴的产物。所谓楼宇自动化系统是对中央空调系统、通风
系统、给排水系统、照明系统、变配电系统、电梯系统进行
监控。随着高新信息技术和计算机网络技术的高速发展,
对建筑物的结构、系统、服务及管理的最优化组合的要求
越来越高[4]。系统控制的方式由过去的中央集中监控,转
而由高处理能力的现场控制器所取代的集散控制系统,本
文设计的楼宇自动化智能控制系统是专门为楼宇智能化
所设计,同霍尼韦尔、西门子等楼宇控制产品相比结构灵活,
控制简便,并且易于针对个体需求进行软件的二次开发。
1 网络结构
控制系统结构如图1所示,分为三个控制层。上层为
PC远程集中监控,下层为控制模块,中间层为现场区域控
制器。层与层之间通过RS232/485总线联网。
远程集中监控平台主要功能为提供即时的数据显示、
历史数据的保存维护和查询显示、故障报警和故障历史查
询、参数修改和查询。PC远程监控平台为主要人机界面,
所以上位机软件设计体现了如下三个优点:一是将控制网
络WEB化,可以将不同来源、不同格式的信息转变为统一
的格式,供具有统一界面的客户机浏览器浏览,以更好地
适应信息化社会的使用需要;二是建立了基于SQL SERV-
ER数据库的管理信息系统,提高了信息管理的功能;三是
采用开放式设计的网络结构,可以更方便地与其他系统
(如安保系统、消防系统)进行集成。软件基于delphi平台
开发,加载大量图形操作,简单方便。
控制模块包括四种,即数字量输入模块(Digital In-
put)、数字量输出模块(DigitalOutput)、模拟量输入模块
(Analog Input)、模拟量输出模块(AnalogOutput)。控制模
块是控制系统的主要执行机构,即采集数字量信号和模拟
量信号,也输出数字量信号和模拟量信号。因此每种模块
各自拥有单独的控制芯片,既接受现场区域控制器的控制
命令,又需要根据控制命令完成模块的输入输出功能。
中间层现场区域控制器既与PC远程监控平台进行通
讯,接受控制命令并上传实时数据,又通过控制模块采集
数据、执行控制命令。显然,现场区域控制器是整个控制
系统的核心枢纽,其重要性不言而喻,因此整个区域控制
器的软硬件设计无疑成为整个系统的重点和难点。
2 区域控制器
2.1硬件电路
区域控制器硬件电路主要由CPU、上下位机通讯接
口、EEPROM和时钟、键盘和触摸屏、液晶以及数字量/模
拟量输入输出单元组成。硬件结构如图2所示。
区域控制器CPU选用STC89C516RD2,这是一款新一
代抗干扰/高速/低功耗的单片机,指令代码完全兼容传统
8051单片机[1-3]。
区域控制器自身带有一定数目的数字量/模拟量输入
输出单元,可以在智能楼宇控制系统中作为控制模块的补
充,同时也可以使区域控制器单独作为产品配套控制器使
用,灵活多变。
时钟和EEPROM通过I2C总线与区域控制器CPU连
接。I2C总线用两条线(SDA和SCL)在芯片和模块间传
递信息。SDA为串行数据线, SCL为串行时钟线,这两条
线必须用一个上拉电阻与正电源相连,其数据只有在总线
不忙时才可传送。CPU是主设备,时钟和EEPROM是从
设备[9]。
上位机通讯接口由控制器CPU通过SPI总线访问异
步通讯芯片MAX3100来实现。SPI总线采用三线同步接
口。主要特点是可以同时发出和接收串行数据;可以当作
主机或从机工作;提供频率可编程时钟;发送结束中断标
志;写冲突保护;总线竞争保护等;下位机通讯接口以串行
口中断的方式实现半双工通讯。
为了满足多种输入方式,控制器同时带有键盘和触摸
屏,即可以以按键方式键入控制命令,也可以直接点击触
摸屏实现。键盘采用独立式键盘;触摸屏选用电阻式触摸
屏,电阻式触摸屏屏幕主要由两个导电层组成,当手指触
摸屏幕时,两层导电层在触摸点位置就有了接触,电阻发
生变化,在X和Y两个方向上产生信号,然后由触摸屏控
制器侦测到这一接触点并计算出(X,Y)的位置。
2.2软件流程
智能楼宇控制系统所控制的点位种类多样,如温度、
湿度、流量、开关等。硬件电路依据数字量、模拟量以及输
入、输出提供了通用的接口,因此具体识别控制每个点位
则完全由软件完成。现场区域控制器作为整个系统的控
制核心,既要检测自身输入输出单元,完成显示,报警等功
能,又要根据上位机(PC)、控制模块提供信息发出控制决
策。因此软件流程包括初始化、故障检测与处理、控制算
法实现、上下位机通讯等
(图3),初始化包括数值
初始化、中断初始化,通讯
初始化,显示初始化;故障
检测包括通讯故障,反馈
故障,逻辑故障等;控制部
分主要是程序算法的实
现,对输入输出的智能控
制,包括键盘/触摸屏输入
及液晶输出,上位机通讯
即远程PC与区域控制器
通讯,而下位机通讯则是
区域控制器与控制模块之
间通讯[5-6]。
楼宇自动化控制系统
故障种类多样,故障处理方法又各有不同,因此故障的检
测和处理就成为程序设计的一个难点,针对这种情况,程
序采用了查表法(表1),成功的解决了这一难题。
楼宇自动化控制系统
故障种类多样,故障处理方法又各有不同,因此故障的检
测和处理就成为程序设计的一个难点,针对这种情况,程
序采用了查表法(表1),成功的解决了这一难题。
表中分5列,第一列为故障号;第二列为故障处理方
法,如1(停机),2(关机), 3(重启)···;第三列判断是
否联动,如0(否), 1(是),主要判断一些相互有关联的部
分出现故障是否需要同步处理;第四列所谓的报警延时主
要指某一现象视为故障的重复出现时间,目的是为了消除
抖动引起的误报;第五列延迟寄存器则存放报警延时,如
1(0.1秒级延时寄存器), 2(秒级延时), 3(分级延时)。
每条故障都要对应于表中的一条,实际应用中只需填写表
格,快捷方便。
上下位机通讯程序都采用MODBUS通讯协议[7-8],
Modbus协议是应用于电子控制器上的一种通用语言。通
过此协议,控制器相互之间、控制器经由网络(例如以太
网)和其它设备之间可以通信。它已经成为一通用工业标
准。通信时,此协议决定了每个控制器须要知道它们的设
备地址,识别按地址发来的消息,决定要产生何种行动。
如果需要回应,控制器将生成反馈信息并用Modbus协议
发出。控制器通信使用主—从技术,即仅一设备(主设
备)能初始化传输(查询)。其它设备(从设备)根据主设
备查询提供的数据作出相应反应。此系统中当主设备为
上位PC机时,现场区域控制器为从设备,当现场区域控制
器为主设备时,控制模块为从设备。Modbus协议建立了
主设备查询的格式:设备(或广播)地址、功能代码、所有
要发送的数据、一错误检测域。从设备回应消息也由Mod-
bus协议构成,包括确认要行动的域、任何要返回的数据、
和一错误检测域。如果在消息接收过程中发生一错误,或
从设备不能执行其命令,从设备将建立一错误消息并把它
作为回应发送出去。
例如:当主设备(现场区域控制器)发送如表2请求
时,此控制器连接的所有控制模块都接受这请求,但是只
有地址为1的控制模块对此请求应答,其他地址的控制模
块自动丢弃这帧数据,经CRC检验数据正确后,根据功能
码来处理此帧数据,此例中功能码为06,即向此寄存器地
址写寄存器数据,完成后从设备需回应与主机请求相同的
信息。
置区域控制器和各种控制模块数量,结构灵活多变,可以
适应多种输入输出信号,根据用户的实际需求开发控制软
件,真正达到量身定做成为一大特色。本智能控制系统已
经在多个楼宇智能化控制中使用,控制准确,运行稳定;另
外,区域控制器也可单独使用,作为产品配套控制器,成功
应用于除湿机、冷干机、Vocs气体清除装置等。
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化与仪表,2007年2期:37-40
Sensorless torque control scheme of
induction motor for hybrid electric vehicle
Yan LIU 1,2, Cheng SHAO1
(1.Research Institute of Advanced Control Technology, Dalian University of Technology, Dalian Liaoning 116024, China;
2.School of Information Engineering of Dalian University, Dalian Liaoning 116622, China)
Abstract: In this paper, the sensorless torque robust tracking problem of the induction motor for hybrid electric vehicle
(HEV) applications is addressed. Because motor parameter variations in HEV applications are larger than in industrial
drive system, the conventional field-oriented control (FOC) provides poor performance. Therefore, a new robust PI-based
extension of the FOC controller and a speed-flux observer based on sliding mode and Lyapunov theory are developed in
order to improve the overall performance. Simulation results show that the proposed sensorless torque control scheme is
robust with respect to motor parameter variations and loading disturbances. In addition, the operating flux of the motor is
chosen optimally to minimize the consumption of electric energy, which results in a significant reduction in energy losses
shown by simulations.
Keywords: Hybrid electric vehicle; Induction motor; Torque tracking; Sliding mode
1 Introduction
Being confronted by the lack of energy and the increasingly
serious pollution, the automobile industry is seeking
cleaner and more energy-efficient vehicles.A Hybrid Electric
Vehicle (HEV) is one of the solutions. A HEV comprises
both a Combustion Engine (CE) and an Electric Motor
(EM). The coupling of these two components can be in
parallel or in series. The most common type of HEV is the
parallel type, in which both CE and EM contribute to the
traction force that moves the vehicle. Fig1 presents a diagram
of the propulsion system of a parallel HEV [1].
Fig. 1 Parallel HEV automobile propulsion system.
In order to have lower energy consumption and lower pollutant
emissions, in a parallel HEV the CE is commonly
employed at the state (n > 40 km/h or an emergency speed
up), while the electric motor is operated at various operating
conditions and transient to supply the difference in torque
between the torque command and the torque supplied by
the CE. Therefore fast and precise torque tracking of an EM
over a wide range of speed is crucial for the overall performance
of a HEV.
The induction motor is well suited for the HEV application
because of its robustness, low maintenance and low
price. However, the development of a drive system based
on the induction motor is not straightforward because of the
complexity of the control problem involved in the IM. Furthermore,
motor parameter variations in HEV applications
are larger than in industrial drive system during operation
[2]. The conventional control technique ranging from the
inexpensive constant voltage/frequency ratio strategy to the
sophisticated sensorless control schemes are mostly ineffective
where accurate torque tracking is required due to their
drawbacks, which are sensitive to change of the parameters
of the motors.
In general, a HEV operation can be continuing smoothly
for the case of sensor failure, it is of significant to develop
sensorless control algorithms. In this paper, the development
of a sensorless robust torque control system for HEV
applications is proposed. The field oriented control of the induction
motor is commonly employed in HEV applications
due to its relative good dynamic response. However the classical
(PI-based) field oriented control (CFOC) is sensitive to
parameter variations and needs tuning of at least six control
parameters (a minimum of 3 PI controller gains). An improved
robust PI-based controller is designed in this paper,
Received 5 January 2005; revised 20 September 2006.
This work was supported in part by State Science and Technology Pursuing Project of China (No. 2001BA204B01).
Y. LIU et al. / Journal of Control Theory and Applications 2007 5 (1) 42–46 43
which has less controller parameters to be tuned, and is robust
to parameter variation.The variable parameters model
of the motor is considered and its parameters are continuously
updated while the motor is operating. Speed and
flux observers are needed for the schemes. In this paper,
the speed-flux observer is based on the sliding mode technique
due to its superior robustness properties. The sliding
mode observer structure allows for the simultaneous observation
of rotor fluxes and rotor speed. Minimization of the
consumed energy is also considered by optimizing operating
flux of the IM.
2 The control problem in a HEV case
The performance of electric drive system is one of the
key problems in a HEV application. Although the requirements
of various HEV drive system are different, all these
drive systems are kinds of torque control systems. For an
ideal HEV, the torque requested by the supervisor controller
must be accurate and efficient. Another requirement is to
make the rotor flux track a certain reference λref . The reference
is commonly set to a value that generates maximum
torque and avoids magnetic saturation, and is weakened to
limit stator currents and voltages as rotor speed increases.
In HEV applications, however, the flux reference is selected
to minimize the consumption of electrical energy as it is one
of the primary objectives in HEV applications. The control
problem can therefore be stated as the following torque and
flux tracking problems:
min
ids,iqs,we Te(t) − Teref (t), (1)
min
ids,iqs,we λdr(t) − λref (t), (2)
min
ids,iqs,we λqr(t), (3)
where λref is selected to minimize the consumption of electrical
energy. Teref is the torque command issued by the
supervisory controller while Te is the actual motor torque.
Equation (3) reflects the constraint of field orientation commonly
encountered in the literature. In addition, for a HEV
application the operating conditions will vary continuously.
The changes of parameters of the IM model need to be accounted
for in control due to they will considerably change
as the motor changes operating conditions.
3 A variable parameters model of induction
motor for HEV applications
To reduce the elements of storage (inductances), the induction
motor model used in this research in stationary reference
frame is the Γ-model. Fig. 2 shows its q-axis (d-axis
are similar). As noted in [3], the model is identical (without
any loss of information) to the more common T-model in
which the leakage inductance is separated in stator and rotor
leakage [3]. With respect to the classical model, the new
parameters are:
Lm = L2
m
Lr
= γLm, Ll = Lls + γLlr,
Rr = γ2Rr.
Fig. 2 Induction motor model in stationary reference frame (q-axis).
The following basic w−λr−is equations in synchronously
rotating reference frame (d - q) can be derived from the
above model.
⎧⎪
⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪
⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
dλdr
dt
= −ηλdr + (we − wr)λqr + ηLmids,
dλqr
dt
= −(we − wr)λdr − ηλqr + ηLmiqs,
dids
dt
= ηβλdr+βwrλqr−γids+weiqs+
1
σLs
Vds,
diqs
dt
=−βwrλdr+ηβλqr−weids−γiqs+
1
σLs
Vqs,
dwr
dt
= μ(λdriqs − λqrids) −
TL
J
,
dθ
dt
= wr + ηLm
iqs
λdr
= we,
Te = μ(λdriqs − λqrids)
(4)
with constants defined as follows:
μ = np
J
, η = Rr
Lm
, σ = 1−
Lm
Ls
, β =
1
Ll
,
γ = Rs + Rr
Ll
, Ls = Ll + Lm,
where np is the number of poles pairs, J is the inertia of the
rotor. The motor parameters Lm, Ll, Rs, Rr were estimated
offline [4]. Equation (5) shows the mappings between the
parameters of the motor and the operating conditions (ids,
iqs).
Lm = a1i2
ds + a2ids + a3, Ll = b1Is + b2,
Rr = c1iqs + c2.
(5)
4 Sensorless torque control system design
A simplified block diagram of the control diagram is
shown in Fig. 3.
44 Y. LIU et al. / Journal of Control Theory and Applications 2007 5 (1) 42–46
Fig. 3 Control structure.
4.1 PI controller based FOC design
The PI controller is based on the Field Oriented Controller
(FOC) scheme. When Te = Teref, λdr = λref , and
λqr = 0 in synchronously rotating reference frame (d − q),
the following FOC equations can be derived from the equations
(4).
⎧⎪
⎪⎪⎪⎪⎪⎨⎪
⎪⎪⎪⎪⎪⎩
ids = λref
Lm
+ λref
Rr
,
iqs = Teref
npλref
,
we = wr + ηLm
iqs
λref
.
(6)
From the Equation (6), the FOC controller has lower performance
in the presence of parameter uncertainties, especially
in a HEV application due to its inherent open loop
design. Since the rotor flux dynamics in synchronous reference
frame (λq = 0) are linear and only dependent on the
d-current input, the controller can be improved by adding
two PI regulators on error signals λref − λdr and λqr − 0 as
follow
ids = λref
Lm
+ λref
Rr
+ KPd(λref − λdr)
+KId (λref − λdr)dt, (7)
iqs = Teref
npλref
, (8)
we = wr + ηLm
iqs
λref
+ KPqλqr + KIq λqrdt. (9)
The Equation (7) and (9) show that current (ids) can control
the rotor flux magnitude and the speed of the d − q rotating
reference frame (we) can control its orientation correctly
with less sensitivity to motor parameter variations because
of the two PI regulators.
4.2 Stator voltage decoupling design
Based on scalar decoupling theory [5], the stator voltages
commands are given in the form:
⎧⎪
⎪⎪⎨⎪⎪⎪⎩
Uds = Rsids − weσLsiqs = Rsids − weLliqs,
Uqs = Rsiqs + weσLsids + Lm
Lr
weλref
= Rsiqs + weσLsids + weλref .
(10)
Because of fast and good flux tracking, poor dynamics decoupling
performance exerts less effect on the control system.
4.3 Speed-flux observer design
Based on the theory of negative feedback, the design of
speed-flux observer must be robust to motor parameter variations.
The speed-flux observer here is based on the sliding
mode technique described in [6∼8]. The observer equations
are based on the induction motor current and flux equations
in stationary reference frame.
⎧⎪
⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪
⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
d˜ids
dt
= ηβ˜λdr + β ˜ wr˜λqr − γ˜ids +
1
Ll
Vds,
d˜iqs
dt
= −β ˜ wr˜λdr + ηβ˜λqr − γ˜iqs +
1
Ll
Vqs,
d˜λdr
dt
= −η˜λdr − ˜ wr˜λqr + ηLm
˜i
ds,
d˜λqr
dt
= ˜wr˜λ dr − η˜λqr + ηLm
˜i
qs.
(11)
Define a sliding surface as:
s = (˜iqs − iqs)˜λdr − (˜ids − ids)˜λqr. (12)
Let a Lyapunov function be
V = 0.5s2. (13)
After some algebraic derivation, it can be found that when
˜ wr = w0sgn(s) with w0 chosen large enough at all time,
then ˙V = ˙s · s 0. This shows that s will converge to
zero in a finite time, implying the stator current estimates
and rotor flux estimates will converge to their real values
in a finite time [8]. To find the equivalent value of estimate
wr (the smoothed estimate of speed, since estimate wr is a
switching function), the equation must be solved [8]. This
yields:
˜ weq = wr
˜λ
qrλqr + λdr˜λdr
˜λ
2q
r +˜λ2
dr −
η
np
˜λ
qrλdr − λqr˜λdr
˜λ
2q
r +˜λ2
dr
. (14)
The equation implies that if the flux estimates converge to
their real values, the equivalent speed will be equal to the
real speed. But the Equation (14) for equivalent speed cannot
be used as given in the observer since it contains unknown
terms. A low pass filter is used instead,
˜ weq =
1
1 + s · τ
˜ wr. (15)
Y. LIU et al. / Journal of Control Theory and Applications 2007 5 (1) 42–46 45
The same low pass filter is also introduced to the system
input,which guarantees that the input matches the feedback
in time.
The selection of the speed gain w0 has two major constraints:
1) The gain has to be large enough to insure that sliding
mode can be enforced.
2) A very large gain can yield to instability of the observer.
Through simulations, an adaptive gain of the sliding
mode observer to the equivalent speed is proposed.
w0 = k1 ˜ weq + k2. (16)
From Equation (11), the sliding mode observer structure
allows for the simultaneous observation of rotor fluxes.
4.4 Flux reference optimal design
The flux reference can either be left constant or modified
to accomplish certain requirements (minimum current,
maximum efficiency, field weakening) [9,10]. In this paper,
the flux reference is chosen to maximum efficiency at steady
state and is weaken for speeds above rated. The optimal efficiency
flux can be calculated as a function of the torque
reference [9].
λdr−opt = |Teref| · 4Rs · L2r
/L2
m + Rr. (17)
Equation (17) states that if the torque request Teref is
zero, Equation (8) presents a singularity. Moreover, the
analysis of Equation (17) does not consider the flux saturation.
In fact, for speeds above rated, it is necessary to
weaken the flux so that the supply voltage limits are not exceeded.
The improved optimum flux reference is then calculated
as:
⎧⎪
⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪
⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
λref = λdr-opt,
if λmin λdr-opt λdr-rated ·
wrated
wr-actual
,
λref = λmin, if λdr-opt λmin,
λref = λdr-rated ·
wrated
wr-actual
,
if λdr-opt λdr-rated ·
wrated
wr-actual
.
(18)
where λmin is a minimum value to avoid the division by
zero.
4.5 Simulations
The rated parameters of the motor used in the simulations
are given by
Rs = 0.014 Ω, Rr = 0.009 Ω, Lls = 75 H,
Llr = 105 H, Lm = 2.2 mH, Ls = Lls + Lm,
Lr = Llr + Lm, P = 4, Jmot = 0.045 kgm2,
J = Jmot +MR2
tire/Rf, ρair = 1.29, Cd = 0.446,
Af = 3.169 m2, Rf = 8.32, Cr = 0.015,
Rtire = 0.3683 m, M = 3000 kg, wbase = 5400 rpm,
λdr−rated = 0.47 Wb.
Fig.4 shows the torque reference curve that represents
typical operating behaviors in a hybrid electric vehicle.
Fig. 4 The torque reference curve.
Load torque is modeled by considering the aerodynamic,
rolling resistance and road grade forces. Its expression is
given by
TL = Rtire
Rf
(
1
2ρairCdAfv2 +MCr cos αg +M sin αg).
Figures in [5∼8] show the simulation results of the
system of Fig.3 (considering variable motor parameters).
Though a small estimation error can be noticed on the observed
fluxes and speed, the torque tracking is still achieved
at an acceptable level as shown in Figs. [5, 6, 8]. The torque
control over a wide range of speed presents less sensitivity
to motor parameters uncertainty.
Fig.5 presents the d and q components of the rotor flux.
Rotor flux λr is precisely orientated to d-axis because of the
improved PI controllers.
Fig.8 shows clearly the real and observed speed in the
different phases of acceleration, constant and deceleration
speed with the motor control torque of Fig.4. The variable
model parameters exert less influence on speed estimation.
Fig.7 shows the power loss when the rotor flux keeps constant
or optimal state. A significant improvement in power
losses is noticed due to reducing the flux reference during
the periods of low torque requests.
Fig. 5 Motor rotor flux λr.
46 Y. LIU et al. / Journal of Control Theory and Applications 2007 5 (1) 42–46
Fig. 6 Motor torque.
Fig. 7 Power Losses.
Fig. 8 Motor speed.
5 Conclusions
This paper has described a sensorless torque control system
for a high-performance induction motor drive for a
HEV case. The system allows for fast and good torque
tracking over a wide range of speed even in the presence of
motor parameters uncertainty. In this paper, the improved
PI-based FOC controllers show a good performance in the
rotor flux λdr magnitude and its orientation tracking. The
speed-flux observer described here is based on the sliding
mode technique, making it independent of the motor parameters.
Gain adaptation of the speed -flux observer is used to
stabilize the observer when integration errors are present.
