《仲恺农业工程学院学报》创刊于1988年，是由广东省教育厅主管，仲恺农业工程学院主办的自然科学学术期刊。
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《仲恺农业工程学院学报》主要刊登农学、园艺、园林、植物保护、生物技术、食品科学、资源环境、动物科学、动物医学、机电工程、农业经济、数学、物理、化学与化工、计算机、电子信息，以及其他相关学科及其交叉学科的基础理论和应用研究成果、实验新方法、新技术和中国国内外研究动态等。
《仲恺农业工程学院学报》实行开放的办刊政策，除主要刊登仲恺农业工程学院稿件外，也广泛接受外单位和社会各界学者的稿件，其中中山大学、华南理工大学、暨南大学、华南农业大学、华南师范大学、广东工业大学、南京林业大学、湖南师范大学、中国热带农业科学院、广东省农业科学院等高等院校及科研院所都有作者在《仲恺农业工程学院学报》发表文章。
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.
文章写好了文献综述应该不难啊，其实就相当于你论文的第二章。常用的套路可以分四段写，第一段引言，介绍这个命题研究的目的意义之类的，第二段写国外对这个现象有哪些研究，评述其中重要的几个人或几个角度，第三段写国内的研究，这两段一般都按时间由远及近，第四段是一个小结，评述一下前人的研究贡献和不足之处，提出建议。其实也不用一定按照上面的来写，不过要注意一点就是要突出你自己的评述和见解，不要只是简单罗列或引用。
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!
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在当今能源越来越缺少的社会上，能源与节能技术是不可缺少的。下面我给大家分享一些能源与节能技术论文，大家快来跟我一起欣赏吧。
新能源汽车节能技术的应用
摘 要 伴随着第二次工业革命发展，汽车行业如雨后春笋迅速的成长，但汽车在给人们出行带来方便的同时资源和环境也付出了巨大的代价。汽车的生产和使用需要大量的钢材和石油，而制成钢材的原材料和石油是不可再生资源，因此面对汽车的大量生产这些不可再生的能源将会逐步的枯竭。所以对新能源汽车的开发与节能技术的研究对减少使用不可再生资源有重大的意义。
关键词 新能源车;节能技术;应用
中图分类号TU5 文献标识码A 文章 编号 1674-6708(2013)95-0191-02
0引言
当今社会经济和科技在不断的快速发展的同时能源消耗太大造成能源不断的枯竭与环境污染严重等问题日益明显。如今全世界各个地方都在提倡节能、减排。绿色环保则成了当今社会上的主体。如今汽车行业已经成为世界上最大的能源消耗和污染行业之一。而要解决能源消耗与环境污染问题就应该先从汽车行业抓起，减少能源消耗和污染。
1汽车的节能技术
1.1混合动力技术
混合动力一般指由汽油、柴油与电能混合在仪器所形成的动力。这项技术的关键是混合动力系统。混合动力系统关系着整个车的性能。再经过多年混合动力研究的基础上，将原来的点火装置转变为由电动马达作为发动机的辅助动力来驱动汽车。由原来的离散结构向着一体化结构发展。也就是将发动机和电机与变速箱结合在一起。在启动的时候辅助发动机的电动马达可以产生很强的动力，也可以在汽车高速平稳的行驶时间段内减少发动机的出力减少油耗。而且混合动力技术能够对能量进行回收。在制动的时候，能够对热量进行转变吸收。
混合动力技术的分类可分为两类一是联结方式二是混合度;联结式分类是根据混合动力的驱动方式进行分的，其中联结式分类又分为了3种：1)串联式混合动力系统是由电能转化为动能从而驱动车轮转动，其中的电力是由内燃机带动发电机来引起的;2)并联式的混合动力系统区中的驱动系统内有两套，并且两套的驱动系统能够可以相互协调来共同完成驱动车辆，当然也可以使用单独的驱动系统来驱动车辆。这样的并联式混合驱动系统能够使车辆适用于更为复杂的路况;3)混联式混合驱动系统可以根据不同的路况来临时调节内部机器的输出功率的原因是因为它的内部中的内燃机和电机有自己的一套机械变速箱而且混合动力系统还可以分为以下4类：1)微混合动力系统该系统可以有效的防止电动机的不运转从而增加油耗和对环境的污染。对发动机的启动和停止进行控制;2)轻混合动力系统，而这种系统的主要代表是皮卡车;3)中混合动力系统动力系统采用的是高压机比低混合动力系统更加的灵敏;4)完全混合动力系统的混合度达到50%是未来逐步发展的方向。
1.2高效汽油机、柴油机技术
汽车节能的关键是内燃机的技术。在内燃机节能技术方面，应该从这几个方面讨论， 第一是汽油机直喷技术，稀薄和分层燃烧技术;第二是柴油机的高压喷射技术;第三是柴油机的多次喷射技术;第四是可变气门技术;第五是废气涡轮增压技术。
1.3高效载重汽车的发动机技术
目前我国载重汽车品种少，技术还很落后。发展高效的载重汽车，是在现代物欲横流的形势下，提高运输的效率，降低汽车用能源消耗的重要一步。因此，国家应重点支持这种高效载重汽车的开发和产业化发展。
1.4轿车、轻型车的柴油化技术
实现节能的重要途径是柴油化，随着汽车以很快的速度进入家庭，我们应该十分注重这项技术。不断的开发而且要有质量的保证。这样的话就减少了对能源的开发。实现了节能减排。
2新能源汽车节能技术的应用
2.1混合动力汽车
混合动力一般指由汽油、柴油与电能混合在仪器所形成的动力车型。这样能有效的改善燃油和功率输出低的车型。根据其不同，主要又可以分为汽油混合动力和柴油混合动力两种。他的优点是：1)采用混合动力后可以增加汽车内部机器功率的输出和减少耗油量。当大功率内燃机功率不足时，可由电池来补充，同时电池也可以得到充电，所以其行程和普通汽车是一样的;2)因为使用电池，可以方便地回收以便循环使用;3)在市中心人流量大的地方，完全用电池单独驱动，实现“零”排放;4)可以在现有的加油站加油，不必再投资建设新的加油站;5)用户可以让电池在延长寿命和降低成本的基础上保持良好的工作状态。
2.2纯电动汽车
纯电动汽车是直接采用电机作为驱动器，是全部以电力作为汽车的驱动力这种车的难点在于电力的储存技术。传统汽车消耗石油等不可再生能源造成能源消耗和环境污染，而电能可以从核能、水力和风力等可再生能源中获得且无污染。电动汽车还可以利用在其空余的时间进行充电，使发电设备日夜都能充分使用，大大提高它的行驶效率。由于这些优点，电动汽车的应用成为汽车工业的一个非常关心的问题。对于电动车而言由于建设成本高且基础设施不是一个独立的企业就能够完成的，需要各个企业联合起来与当地政府部门一起努力，才可能大规模的推广。这使得电动汽车的价格非常的高昂，但是与混合动力汽车相比较来说电动汽车的技术简单而且成熟且操作方便，且只要有足够的电力就能驱动汽车且充电方便。但是不足就是电动车所使用的蓄电池的蓄电能力不足存储的电量少，且构建电池的原材料成本高还没有形成一定的经济规模，所以购买价格高。
2.3燃料电池汽车
燃料电池汽车是以液化石油气(LPG)和压缩天然气为燃料，采用先进的电子控制技术和高性能的污染净化装置来减少污染。而且经过有机材料的化学反应产生的电流作为汽车的驱动力。
近年来燃料电池技术已经取得了重大的突破。燃料电池汽车，零排放，而且减少了机油泄漏带来的水污染和温室气体的排放等问题，还提高了燃油经济和发动机燃烧效率，运行平稳，没有噪声。
2.4氢动力汽车
氢动力汽车是真正实现零排放的，排放出来的是纯净水，没有任何污染。
但是氢燃料电池成本高，而且发展氢燃料的存储和运输按照技术条件很难实现，还有就是氢气的提取需要通过电解水，否则就不能从根本上降低二氧化碳排放。
这项技术虽然实施起来困难，但是随着新能源技术的不断发展，一定会得以解决实现。
3发展前景
随着国家政府部门的不断指引，各项政策的不断支持，新能源汽车的应用有很大的发展前景，新能源汽车节能技术的应用也会越来越广泛，并且应用在人们的日常生活中，给人们的生活带来很大的便利。通过不断的发展新的技术，以最低的成
本换取最大的经济效益，也将会引领新能源汽车走上一个更新，更广阔的台阶。
4结论
科学技术永远是第一生产力。汽车的迅猛发展，人们素养的不断提高，在大力提倡生态文明建设，打造美丽中国的时代背景下，人们对环境的要求会越来越高。
更加环保的的汽车会越来越受到人们的欢迎。而新能源的开发会越来越重要，那么新能源汽车节能技术的应用会越来越广阔，越来越受到人们的重视。
时代总是在不断的发展，科技也不断在进步。新能源汽车节能技术的应用将会备受重视。
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