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.
营销策划论文参考文献
导语：营销策划论文的参考文献有哪些呢?营销策划有助于企业的可持续的发展。下面是我分享的营销策划论文的参考文献，欢迎阅读!
[1] 段小明，胡波，郑兴华，解晋东. 化妆品市场现状及发展趋势分析[J]. 日用化学品科学. 2013(11)
[2] 董维维，庄贵军. 中国营销渠道中关系营销导向对企业关系型治理的影响[J]. 管理学报. 2013(10)
[3] 杨惠. 国外品牌轴承在中国市场的渠道管理浅析[J]. 市场周刊(理论研究). 2013(05)
[4] 李杨. 营销渠道理论综述[J]. 经营管理者. 2013(06)
[5] 石凯鸣. 内外超市企业竞争力差异的比较分析[J]. 现代营销(学苑版). 2012(10)
[6] 刘小莲. 我国企业品牌国际化经营战略策略探索[J]. 长春教育学院学报. 2012(06)
[7] 瞿莉娜. 现代企业营销渠道体系的整合与创新途径[J]. 现代营销(学苑版). 2012(05)
[8] 郭华山，赵毅. 国内外化妆品市场观察[J]. 日用化学品科学. 2012(04)
[9] 杨柏超. 我国化妆品行业网络营销问题和对策探析[J]. 现代商贸工业. 2012(03)
[10] 赵陈婷，岳彩周，陈岳峰. 本土化妆品连锁路在何方[J]. 中国连锁. 2011(10)
[11] 闫欣洁. 浅析国内化妆品市场的'消费现状与趋势[J]. 经营管理者. 2011(09)
[12] 陈强. 国内外化妆品市场分析[J]. 日用化学品科学. 2011(01)
[13] 陆鹏，文华. 中国高端百货与高端化妆品对弈中的华丽转身[J]. 中国化妆品(行业). 2010(03)
[14] 陆文. 基于供应链管理的营销渠道研究[J]. 现代经济信息. 2010(02)
[15] Tony. 大卖场超市逼宫化妆品专营店[J]. 医学美学美容(财智). 2009(11)
[16] 刘伟，金远平. 基于J2EE的渠道信息管理系统的设计与实现[J]. 科技资讯. 2009(10)
[17] 本刊编辑部，张萍，郭俊. 2007,中国化妆品法规年--年度化妆品行业法规大事记[J]. 中国化妆品(行业). 2008(01)
[18] 龚振，陆巍，钟爱群. 基于渠道权力的营销渠道结构整合[J]. 商业时代. 2006(11)
[19] 范小军，陈洁，陆芝青. 营销渠道变革与模式选择研究理论述评[J]. 企业经济. 2006(03)
[20] 杨晶，江红红. Super Mario勇闯第一关：怎么管理经销商?[J]. 现代营销(学苑版). 2005(11)
[21] 朱桂平. 客户关系管理与分销渠道整合[J]. 商业时代. 2005(24)
[22] 赵晓飞. 营销渠道的选择及评价标准研究[J]. 市场研究. 2005(08)
[23] 张继明. 从药店到俱乐部--畅谈化妆品营销模式最新走向[J]. 日用化学品科学. 2005(05)
[24] 贺艳春，张志海. 营销渠道结构演变的理性分析[J]. 湖南工程学院学报(社会科学版). 2002(03)
[25] 冯赳善. 我国化妆品监督管理问题分析及对策研究[D]. 华东师范大学 2011
[26] 李南. 我国化妆品安全监管体制的现状与对策研究[D]. 广州中医药大学 2011
[27] 王佳蕾. 上海莱姿化妆品有限公司营销战略研究[D]. 厦门大学 2006
[28] 吴丹青. 泉州市化妆品安全问题及其治理研究[D]. 华侨大学 2014
[29] 董冰心. 特殊用途化妆品现状及监管研究[D]. 北京中医药大学 2011
[30] 袁铮. 化妆品营销渠道研究[D]. 四川大学 2003
[31] 苗丹. 化妆品购买行为偏好研究[D]. 渤海大学 2013
[1] 弗雷德·R·戴维着. 战略管理[M]. 北京. 经济科学出版社， 2006.
[2] 斯蒂芬. P. 罗宾斯着. 管理学. 中国人民出版社， 2002.
[3] 邓胜梁， 许绍李， 张庚森着. 市场营销管理： 理论与策略. 上海人民出版社， 1997: 270-271.
[4] Louise. Boone, David. Kurtz. 当代市场营销学. 机械工业出版社， 2005.
[5] 李睿. 我国市场营销渠道管理创新研究. 现代商业， 2009, (6)： 96-97.
[6] 段玉英. 市场营销调研探讨[J]. 前沿， 2005(9)： 97.
[7] 杨淑红. 浅析我国市场营销的现状及发展趋势. 科技资讯， 2007. 12.
[8] 王国栋. 营销理论的历史和未来, 江苏商论, 2005, (11): 60-61.
[9] 肖凤桢， 韦秀长. 4P 真的过时了吗. 经济师， 2003,(6)： 267.
[10] 杨涛， 葛松林. 企业营销渠道系统创新动因分析. 商业研究， 2000, (5)：91-93.
[11] 伯特·罗森布罗姆着. 营销渠道管理. 李乃和， 莫俊芳等译. 第 6 版， 机械工业出版社， 2003: 140-214.
[12] 菲利普 科特勒等着. 营销渠道管理. 李乃和， 奚俊芳等译. 第 11 版.华夏出版社， 2004.
[13] 臧良运. 关系营销的发展及其实施策略[J]. 商业时代， 2008, (9)： 20.
[14] 刘辉. 基于关系营销的销售策略研究[J]. 经济论坛， 2008, (8)： 15.
[1]李宝珠,王颖. 基于ANP的企业物流外包服务评价研究[J]. 中国农机化,2010,(2).
[2]彭本红,罗明,周叶. 物流外包中的最优契约分析[J]. 软科学,2007,(1).
[3]刘福华,陶杰,黄秀娟. 企业物流外包的风险与防范[J]. 物流科技,2005,(7).
[4]黄玉华. 基于资源基础理论的物流外包决策研究[D]. 兰州理工大学: 兰州理工大学,2009.
[5]黄赪. 金恒利公司物流外包服务水平提升策略研究[D]. 华南理工大学: 华南理工大学,2010.
[6]徐娟,刘志学. 基于实物期权的物流外包成本风险[J]. 系统工程,2007,(12).
[7]熊吉陵,雷霆. 中小企业物流外包的动因及策略简析[J]. 中国市场,2008,(2).
[8]李桂艳. 物流外包风险的防范策略[J]. 经济与管理,2008,(5).
[9]杨淼,邵鲁宁. 浅析物流外包[J]. 上海管理科学,2004,(3).
[10]涂筱兰. 生态坊化妆品有限公司物流外包研究[D]. 华中科技大学: 华中科技大学,2004.
[11]陈文粤. 成都可口可乐饮料有限公司物流外包研究[D]. 西南交通大学: 西南交通大学,2007.
[12]戴一兵. 广州地铁运营物资采购物流外包研究[D]. 华南理工大学: 华南理工大学,2009.
[13]宗涛. 外包关系对制造企业物流外包绩效的影响[D]. 西安理工大学: 西安理工大学,2009.
[14]田宠. 家具企业物流外包的策略研究[D]. 南京林业大学: 南京林业大学,2010.
[15]张洁. 基于WNN的企业物流外包风险预测研究[D]. 河北工程大学: 河北工程大学,2009.
[16]刘健. 基于委托代理理论的物流外包激励机制研究[D]. 清华大学: 清华大学,2009.
[17]姚卓顺,鲁雅萍. 基于企业物流外包的第三方物流选择[J]. 科技和产业,2010,(8).
[18]田宇. 从物流外包到物流联盟:契约机制体系与模型[J]. 国际贸易问题,2007,(2).
[19]罗勇,卿海锋. 物流外包和自营物流的比较分析——以新一佳超市有限公司为例[J]. 物流技术,2007,(5).
[20]赵卫华. 物流外包——烟草商业物流的方向[J]. 贵州工业大学学报(社会科学版),2008,(5).
[21]袁志锋. 企业物流外包与物流企业博弈探析[J]. 中国市场,2008,(10).
[22]洪怡恬,李晓青. 企业物流外包风险预警指标体系的构建及外包风险分析与评价[J]. 物流技术,2008,(9).
[23]顾睿. 生产企业物流外包中甄选最佳第三方物流供应商模型研究[D]. 武汉科技大学: 武汉科技大学,2008.
[24]曾叶. 物流外包及物流绩效评价研究[D]. 浙江工业大学: 浙江工业大学,2006.
[25]陈志. 制造业物流成本核算及物流外包决策研究[D]. 大连海事大学: 大连海事大学,2007.
[26]马鹏,刘斌,徐国强,李秋香. 企业物流业务外包的双向选择模型[J]. 华北水利水电学院学报,2006,(1).
[27]招莉莉. 供应链管理环境下的港口企业物流服务外包[D]. 中南大学: 中南大学,2009.
[28]记者 阮栩. 物流外包好看不好吃？[N]. 信息时报,2003-01-23(C04).
[29]程凯媛. 企业物流业务外包中存在的问题及解决方法[J]. 物流科技,2009,(2).
[30]田宇,阎琦. 物流外包关系中物流服务需求方信任的影响因素研究[J]. 国际贸易问题,2007,(5).
[31]胡从旭. 基于价值链的企业物流业务外包问题探讨[J]. 物流科技,2008,(11).
[32]刘联辉,王坚强. 企业物流外包风险分析及其防范[J]. 湖南工程学院学报(社会科学版),2005,(4).
[33]王淑云. 物流外包的效益及外包区域分析[J]. 公路交通科技,2004,(8).
[34]记者 鲁松实习生 时琪. 淮矿物流大市场“第三方物流外包”成功运作[N]. 淮南日报,2008-08-10(001).
[35]杨树果. 物流外包决策的模糊综合评价[J]. 黑龙江八一农垦大学学报,2010,(4).
[36]俞仲秋. 当代物流外包中企业战略关系矩阵的探索与研究[J]. 物流科技,2011,(4).
[37]俞仲秋. 当代物流外包中有效沟通系统模型研究[J]. 物流技术,2011,(3).
[38]杨涛,孙军伟. 物流外包风险管理研究现状述评[J]. 价值工程,2011,(13).
[39]虞上尚,刘丹. 基于承包商视角的物流外包风险分析与对策研究[J]. 物流技术,2011,(7).
[40]王宇楠. 基于企业生命周期的物流外包策略研究[J]. 辽宁工业大学学报(自然科学版),2011,(2).
[41]周立军. 企业物流外包风险分析与控制研究[J]. 物流技术,2010,(21).
[42]郑平,何雪君. 物流外包业务的风险矩阵模型[J]. 上海海事大学学报,2011,(1).
[43]李朝敏. 浙江省制造企业物流外包程度影响因素及对策研究[J]. 嘉兴学院学报,2011,(2).
[44]陈兰芳,吴刚. 基于委托-代理理论的逆向物流外包模式研究[J]. 数学的实践与认识,2010,(7).
[45]公彦德,李帮义. 三级CLSC物流外包与废品回收的临界条件整合研究[J]. 管理工程学报,2010,(2).
[46]周湘峰. 生产企业物流外包决策行为分析[J]. 华东经济管理,2010,(5).
[47]刘艳锐,孙福田,索瑞霞,孙玉凤. 基于效益最优的企业物流外包决策的量化研究[J]. 数学的实践与认识,2010,(10).
[48]怀劲梅,颜慧. 基于供应链环境的物流外包风险研究[J]. 物流工程与管理,2010,(6).
[49]余泳泽,马欣. 物流外包中专用性资产投资不足的治理模式研究[J]. 物流技术,2010,(12).
[50]包祖琦,杨斌. 非对称信息下企业的物流外包服务商数量选择模型[J]. 物流技术,2010,(12).
