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.
[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).
你要哪个？我给你。
[1]刘东红,. 浅谈英语教学中学生创新思维能力的培养[J]. 阿坝师范高等专科学校学报,2006,(S1).
[2]廖燕慧. 创新与英语教学——英语课堂教学改革试探[J]. 龙岩师专学报,2001,(S1).
[3]赵玉君. 浅谈突破传统模式的英语教学[J]. 石油教育,2003,(3).
[4]陈丽霞. 英语教学如何培养学生的语感[J]. 泰安教育学院学报岱宗学刊,2005,(1).
[5]党兰玲. 英语教学应重视英汉思维模式的差异[J]. 郑州航空工业管理学院学报(社会科学版),2004,(4).
[6]李春梅. 英语教学中学生创造力的培养[J]. 邯郸职业技术学院学报,2001,(4).
[7]红英. 如何在英语教学中提高学生的整体素质[J]. 内蒙古电大学刊,2001,(4).
[8]李冲. 英语教学中的智力因素[J]. 武警工程学院学报,2001,(6).
[9]赵大中. 在英语教学中培养学生的创新意识和创新思维[J]. 连云港化工高等专科学校学报,2001,(S1).
[10]王灵霞. 浅议英语教学中创新思维的培养[J]. 沧州师范专科学校学报,2004,(3).
[11]徐彩连. 试论在英语教学中如何培养学生的创新思维能力[J]. 大众科技,2005,(2).
[12]魏纯雅. 英语教学中实施语篇教学应注意的问题[J]. 南平师专学报,2004,(3).
[13]谢丽娟. 英语教学中语感的培养[J]. 湖南工程学院学报(社会科学版),2005,(1).
[14]陆影. 英语思维与英语教学[J]. 台州学院学报,2001,(4).
[15]王爱玲. 英语教学应重视英汉思维模式的差异[J]. 河南财政税务高等专科学校学报,2003,(5).
[16]金波. 在英语教学中培养创新性思维能力[J]. 昭乌达蒙族师专学报,2004,(4).
[17]石玉华. 浅谈英语听力教学[J]. 呼伦贝尔学院学报,2002,(Z1).
[18]张雯. 谈如何在英语教学中培养学生的创造力[J]. 济宁师范专科学校学报,2002,(6).
[19]周涛汛. 浅议英语教学中的启发式思维[J]. 中国科技信息,2005,(17).
[20]雷耘. 谈英语教学中跨文化交际能力的培养[J]. 山东师范大学外国语学院学报(基础英语教育),2003,(1).
[21]刘永红. 英语教学与学生的智力开发[J]. 广东教育学院学报,1999,(2).
[22]颜超. 英语教学中启发学生发散性思维的方法[J]. 黎明职业大学学报,2003,(1).
[23]庞绍波,. 在英语教学中培养学生的创新思维能力[J]. 现代教育科学,2007,(10).
[24]吴雪松. 谈英语教学与文化意识[J]. 常州信息职业技术学院学报,2005,(1).
[25]金丽青. 浅谈英语教学中创造性思维的培养[J]. 新乡教育学院学报,2004,(3).
[26]郭欣宇. 英语教学与学生创造性思维的培养[J]. 黑龙江教育学院学报,2005,(4).
[27]邓炳杰. 立体思维与介词[J]. 沈阳师范学院学报(社会科学版),1997,(4).
[28]王吉琼,邓晓华. 创新思维能力在英语教学中的培养[J]. 井冈山师范学院学报,2002,(S2).
[29]李梅. 试论英语教学中的创新性教育[J]. 邯郸职业技术学院学报,2004,(2).
[30]陈乃赞,赵春燕,. 在英语教学中如何培养学生的创新思维能力初探[J]. 科学大众,2008,(6).
[31]马建春. 英语教学中的发散思维训练[J]. 宿州教育学院学报,2002,(1).
[32]郑梅. 对英语教学中创造性思维能力的思考[J]. 伊犁教育学院学报,2001,(1).
[33]邓秀华,陈贤淑. 英语教学中学生问题意识的研究[J]. 内江师范学院学报,2004,(5).
[34]龚隽文. 浅谈大学英语教学中的思维能力培养[J]. 鹭江职业大学学报,2002,(4).
[35]黎振海,乔军钗,郭斌. 英语教学中如何进行创新教育[J]. 教育实践与研究,2001,(6).
[36]杨仙菊,. 如何在英语教学中实施创新教育[J]. 科教文汇(上半月),2006,(7).
[37]杨为明,曹喜梅. 试论中西方思维文化差异及其语言体现形式[J]. 中原工学院学报,2002,(S1).
[38]郑广俊. 浅谈在英语教学中创新意识与思维能力的培养[J]. 钦州师范高等专科学校学报,2001,(1).
[39]张莉,. 英语教学中创造性思维的培养[J]. 克山师专学报,2002,(2).
[40]李小军,. 中学英语教学中创新能力的培养[J]. 科技信息(学术研究),2008,(9).
[41]王蕾,. 精读课堂教学中如何培养学生的创造性思维[J]. 科技资讯,2007,(1).
[42]李红. 在英语教学中培养学生的创造性思维[J]. 淮阴工学院学报,2003,(2).
[43]王荣芝,. 浅议创新思维能力在英语教学中的培养[J]. 时代文学(双月版),2007,(4).
[44]郭立琴. 浅谈文化、思维差异与英语教学[J]. 山西财政税务专科学校学报,2003,(1).
[45]郭萍利,. 如何在英语教学中培养学生的创新思维能力[J]. 渭南师范学院学报,2005,(S2).
[46]张慧琴,李晋,. 大学英语教学与学生创造性思维的培养[J]. 中北大学学报(社会科学版),2006,(1).
[47]郭小红. 试论英语教学中创造性思维的培养[J]. 平原大学学报,2004,(2).
[48]梁红,李津花. 论学生用英语思维时母语向英语的迁移现象[J]. 广西商业高等专科学校学报,1998,(3).
[49]崔崧,. 在英语教学中培养学生的创新思维能力[J]. 成都教育学院学报,2006,(5).
[50]葛传红,. 英语教学中启发思维运用与探索[J]. 科学大众,2007,(10).
985工程”学校是我国政府的一项高等教育建设工程。目的是为建设若干所世界一流大学和一批国际知名的高水平研究型大学。985大学学报的含金量也是很高的，那么有哪些容易发表的985大学学报呢?这里为大家推荐的几本：山东大学学报(哲学社会科学版)、吉林大学社会科学学报、西北工业大学学报、武汉大学学报((理学版))、湖南大学学报(自然科学版)。投稿985大学学报可咨询网站在线客服。

《山东大学学报(哲学社会科学版)》(双月刊)创刊于1985年，是教育部主管,山东大学主办的哲学社会科学学术理论期刊,主要发表校内外人文社会科学领域最新的学术理论研究成果。

《吉林大学社会科学学报》创刊于1955年7月，是由国家教育部主管、吉林大学主办的综合性学术理论刊物，创刊于1955年，双月刊，由吉林大学社会科学学报编辑部出版。

《西北工业大学学报》(双月刊)创刊于1957年，由西北工业大学主办。本刊为综合性工业技术刊物。主要发表该校科研成果，包括航空航天、热能工程、电子工程论文。

《武汉大学学报(理学版)》(双月刊)创刊于1930年，是由国家教育部主管、武汉大学主办的自然科学综合性学术期刊。

《湖南大学学报(自然科学版)》(月刊)创刊于1956年，是由教育部主管、湖南大学主办的综合性学术理论刊物，面向国内外公开出版发行。
“985工程”学报期刊投稿一般难度较大，作者可亲自投稿，也可以联系咨询本平台在线客服提供相应的论文发表指导。
湖南工学院是二本。
湖南工学院坐落于湖南省衡阳市，是2007年经教育部批准由湖南建材高等专科学校和湖南大学衡阳分校合并升格的省属公办普通本科院校，2010年3月湖南工业科技职工大学整体并入，是全国实施“卓越工程师教育培养计划”最年轻的本科院校,是湖南省硕士学位授予立项建设单位。
学校设有18个二级教学院（部）、18个党政管理机构、3个党群组织、9个直属单位，45个本科专业。其中，国家一流本科专业建设点和特色专业4个，教育部“卓越计划”试点专业3个，省特色专业和重点资助建设专业4个，湖南省一流专业建设点和综合改革试点专业14个，通过教育部高等教育教学认证中心的工程教育认证专业1个。
师资队伍：
学校现有全日制在校学生19466人。有教职工1320人，其中正高职称112人（教授100人），副高职称312人（副教授206人），博、硕士908人。有享受国务院政府特殊津贴专家6人、湖南省新世纪“121人才工程”第一、二、三层次人选6人。
以上内容参考：湖南工学院官网——学校简介
