当然可以了。虽然这个学报只是专科学报，但是学报对文章的审核还是非常严格的，实行三审制，而且是双月刊，知网全文收录，希望可以帮到你。
参考文献要注意采用规范化的著录格式，同时要注意在供内部交流的刊物上发表的文章和内部使用的资料，尤其是不宜公开的资料，均不能作为参考文献著录。下面是我为大家整理的一些2017电子商务参考文献，供大家参阅。
2017电子商务文献
篇一：电子商务 论文 参考文献
[1] 宋义秋,魏亚楠. 浅谈电子商务带来的 企业管理 的变革[J]. 科技 资讯, 2009,(03) .
[2] 林鲁生. 谈电子商务企业组织结构设计[J]. 商业时代, 2009,(12) .
[3] 周星. 浅析电子商务的发展趋势[J]. 科技创新导报, 2009,(07) .
[4] 徐晓雨. 电子商务——传统企业管理现代化的加速器[J]. 辽宁省交通高等专科学校学报, 2008,(01) .
[5] 王尊民. 电子商务促进企业管理现代化[J]. 现代商业, 2008,(08) .
[6] 周海兵. 电子商务时代的企业管理变革[J]. 时代经贸(下旬刊), 2008,(11) .
[7] 石正喜. 电子商务对企业管理的影响及应对策略[J]. 商场现代化, 2007,(27) .
[8] 李莉. 论电子商务环境下企业管理新模式[J]. 企业经济, 2008,(05) .
[9] 吴靳. 电子商务对企业 财务管理 的影响[J]. 会计 之友(中旬刊), 2009,(03) .
[10] 林震. 论电子商务对企业物流管理的影响[J]. 现代商贸工业, 2009,(09) .
篇二： 电子商务论文 参考文献
[1]赵晓津。 计算机 安全技术在电子商务中的应用探讨[J]。硅谷，2014(4)：140-141。
[2]雷殷睿。 网络安全 技术在电子商务中的融合[J]。计算机光盘软件与应用，2014(4)：162-163。
[3]余佩颖。 微信 电子商务模式探讨[J]。软件，2013(10)：124-125。
[4]邵泽云。数字签名技术在电子商务中的应用研究[J]。农业网络信息，2014(3)：83-85。
[5]齐赫。基于物联网技术下的电子商务发展策略研究[J]。计算机光盘软件与应用，2014(5)：41-42。
[6]汪顺。计算机技术用于电子商务的研究[J]。电子技术与软件工程，2014(5)：232。
[7]李珣。移动支付进击的微信PK无力应对的支付宝[J]。记者观察，2014(4)：56-57。
[8]吕廷杰编著。移动电子商务[M]。北京：电子工业出版社，2011。
篇三：电子商务论文参考文献
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篇四：电子商务论文参考文献
[1] 文化 .传统企业的电子商务化[J].广东 财经 学院院报，2006(3)
[2]刘珍.传统企业发展电子商务的风险分析及对策研究[M].优秀硕博论文,2005(6).
[3]杨洪涛.电子商务对消费者需求的影响与企业营销策略[J].中国科技信息,2005(6).
[4]多琦.基于电子商务的顾客满意信息 收集 与评价系统设计的研究[D].哈尔滨理工大学,2003(2)
[5]赵冬梅.电子商务市场价格离散问题研究[D].中国农业大学,2005(4)
[6]杨坚.电子商务网站典型案例评析[M].西安电子科技大学出版社，2005(5)
[7]刘海,孙浩.海尔家居体现海尔竞合战略[N].新华日报，2006(2)
[8]尤齐钧.八大难题困扰中国家具业[N].中国建材报, 2008 (3)
[9]贾玮.2007 家具业变局之年[N].中国建材报, 2007 (12)
[10]王坤.家具行业创新之路迫在眉睫[N].中国财经报,2008(23)
[11]陶海音,肖青.湖南家具业亟待创新[N].湖南日报, 2006(2)
篇五：电子商务论文参考文献
[1] 黄崇珍, 杜蓉.电子商务下第三方物流研究[J] 信息技术, 2004 年11月,第28 卷 第11期
[2] 崔介何主编，《电子商务与物流》，中国物资出版社，2002年4月第一版
[3] 张晓燕.对中国B2C 电子商务发展思路的探索[J]商场现代化.2005 年9 月(中),总第443 期.
[4] 张铎,林自葵.电子商务与现代物流[M].北京: 北京大学出版社, 2002.
[5] 谭清美,王子龙，城市物流对经济的拉动作用研究——以江苏南京为例，工业技术经济，2004年01期
[6] 王健,方佳林，美、日、欧现代物流发展的比较与启示，东北亚论坛，2005年02期
[7] 王淑琴,陈峻,王炜，城市现代物流系统布局规划研究——以扬州市为例，规划师，2005年02期
[8] 梁燕君，《电子商务物流新旧模式之比较》，商品储运与养护，2009年第五期
[9] 王文斌,马祖军,武振业，现代物流业与区域经济发展，经济体制改革，2002年01期
[10] 李辉民，现代物流的形成趋势与对策，集装箱化，2009年04期
[11] 汪鸣,冯浩，我国现代物流业发展政策及建议，宏观经济研究，2010年05期
[12] 张林红,陈家源，新世纪我国航运企业物流运作模式的探讨，世界海运，2011年05期
[13] 王成钢,陈登斌.B2C电子商务配送系统建设[M].长沙：湖南师范大学出版,2008.
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[15] 常连玉,陈海燕.B2C电子商务配送模式的思考[J].物流技术 .2010(8).
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沥青碎石封层技术的施工工艺与施工装备运用
摘 要：随着我国城镇的迅速发展，道路建设也变成了一项十分重要的工程项目，其中对于原有道路的改建工作则是道路建设的重要组成部分，目前国内对于破坏严重的路面对采用碎石化施工技术，也就是对原路面进行破碎，使其成为柔性路面的基层，然后加上柔性面层，最终达到加强路面的目的。
该文将从沥青碎石封层施工入手，对施工过程当中需要注意的施工手段进行讨论，并对其质量检查提出自己相应的思考，最终通过相关施工装备的研究为沥青碎石封层技术的使用提供了一些参考依据。
关键词：沥青碎石 封层 施工
1 沥青碎石封层施工工艺
良好的沥青碎石封层不仅能够有效地恢复路面的功能，还可以防止路面渗水，提高路面的.抗滑能力，最终到达节约成本，增加道路使用寿命的目标。
所以说，沥青碎石封层的施工工艺就十分重要，其直接决定着整体施工质量的好坏，下面我们针对沥青碎石封层施工工艺进行讨论。
1.1 施工准备
碎石封层施工前，应彻底清除旧路面表面的泥土、杂物，并使旧路面表面矿料外露，对旧路面表面松散、坑槽的应予处理，同时旧路面表面进行粗糙度处理。
对于路况指数PCI低于沥青路面标注的，可以铺筑封层;对于路况指数PIC符合标准的，但是其中土质松散、过于光滑等情况的也可以进行封层处理。
针对一些年限已久，表面老化、透水但是强度系数符合规定值，这时候也可采取封层工艺进行施工处理。
1.2 施工工艺
首先在封层前，对顶面进行检查，发现有破损的部位要进行补洒，为防止施工完成的封层遭污染，沥青在施工旧路面前应选择合适的喷洒时间，喷洒采用同步封层车进行，同步封层车喷嘴的轴线应与路面垂直，并保证所有喷嘴的角度一致，同时保证洒布管的高度，尽量使同一地点能够接受到两个或三个喷洒嘴喷洒的沥青。
其次为了确保沥青膜不受到摊铺机的损坏，需在沥青膜上洒布碎石，对于碎石要用水清洗保持洁净，采取同步碎石封层机撒布碎石，这里需要注意撒布均匀;为了保证撒布均匀性，集料与沥青同时撒布，数量按室内确定的最佳撒布量计，集料覆盖率在 60%左右。
集料撒布全部在沥青未凝固之前完成。
撒布车在启动阶段不能出现重叠或者漏撒的现象，若出现的话要及时采取人工作业的方式将多余的碎石清扫干净。
最后，铺设完毕之后要进行碾压施工，集料撒布后即用轮胎压路机均匀碾压2～3遍，确保集料与沥青牢固粘结。
碾压时每次碾压重叠 1/3轮宽，碾压要求两侧到边，确保有效压实宽度。
碾压顺序由路肩侧到中分带侧依次碾压。
除此之外，封层施工的气温不能低于15 ℃，风力不能高于2级，大风以及下雨天气严禁施工。
1.3 施工质量
沥青碎石封层施工当中，对于施工质量的检验也是十分重要的一项工作，对沥青碎石封层施工项目，除公路施工企业进行检查外，相关建立单位也应针对沥青碎石封层施工进行质量检查与认定，一般检查对象有厚度、平整度、宽度和沥青用量。
其中厚度要达到设计厚度，通常采取钻芯的检测方法;平整度采取直尺测量，其检测点每100 m设置1点;对于沥青用量采取抽提方式，质量要求要求为浓稠度±0.5%。
2 沥青碎石封层施工装备运用
在合适的施工技术前提下，要配备合适的人、机才能具体地实施施工工艺，针对沥青封层施工我们从以下集中施工装备来进行讨论。
2.1 沥青技术指标
实际沥青碎石封层施工当中，相应的技术指标与规定值一般都具有严格的规定：针入度(25 ℃，100 g，5 s)规定值为30～60 mm，针入度指数PI规定值最小为0，延度5 ℃，5 cm/min为25 cm，软化点TR&B为70 ℃，动力粘度135 ℃规定值为3 Pa・s，闪点规定值最小为230 ℃，溶解度为99%，弹性恢复25 ℃规定值为75%。
除此之外RTFOT后残留物的质量损失、针入度和延度三相指标规定值也分别为±1.0%、65%和15 cm。
2.2 仪器及机械设备
沥青碎石封层施工过程中需要的仪器分别为：电子天平、砂石标注筛、延度仪、针入度仪、针入度仪、软化点仪和试盘，其目的分别为称量、筛分，沥青三大指标和检测晒布量。
除了上述仪器之外，沥青碎石封层施工还需要用到相应的机械设备，分别为同步碎石封层车、轮胎压路机、小松装载机和森林灭火鼓风机，去作用分别为洒布碎石、沥青，碾压路面、装料以及清洁除尘。
对于施工装备运用，要抓好现场管理，坚持文明施工，设置安全防护标志。
保障人身、机械和器材的安全，尤其是上公路的机动车辆必须限速行驶，不侵道、不抢行，做到文明礼让，弯道鸣笛，严防交通事故的发生。
当进行碎石封层作业时，在上下游过渡区内设置移动式标志车或配备交通管制人员，且顺着交通流方向设置安全设施。
当作业完成后，应顺着交通流方向撤除为撒铺作业而设置的有关安全设施，恢复正常交通，锥形交通路标布设间距宜为10～20 m，与路中心线平行，距离作业区边缘20～30 cm。
通过上述对于沥青碎石封层施工施工机械进行分析与统计，采用表格形式对整个路面的各个机械使用情况、施工检查状况进行规律性、统计性的分析，是为研究封层施工装备的运用做出准备，同时也为装备的优化配置提供了理论上的支持。
3 结语
沥青碎石封层施工技术在公路施工中不仅具有良好的防水效果，同时对防水层强度的增高、抗温性的提高都具有很强的改善能力，对减轻路面基层受到的冲刷破坏和沥青面层的破坏都具备很实际的作用。
因此对沥青碎石封层施工工艺进行要求，一方面能够使施工工艺不断简化，提高工作效率，同时通过对施工装备的运用可以节约施工材料，达到降低工程造价、使性价比进一步提高。
综上所述，只有清楚地了解沥青碎石封层技术的施工工艺，才能保证沥青和碎石在最短时间能完成结合，确保路面具有足够的结合强度，而通过对装备运用的研究，更进一步地保证了沥青和石料有足够的结合，从而确保路面具有严密的防水密度，总之只有通过更深入地研究、积累经验、不断改进，才能使沥青碎石封层得到更广泛地应用。
参考文献
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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.
