Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 105 (2017) 2753 – 2758
The 8th International Conference on Applied Energy – ICAE2016
Dynamic matching of Dual-Mode Electro-Mechanical Transmission (EMT) based on the optimal motor efficiency Teng WangaˈHui Liua,b,c,* ˈLijin Hana,b aSchool
of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081,China bNational Key Lab of Vehicular Transmission, Beijing 100081,China cCollaborate Innovation center of Electric Vehicle of Beijing ˈBeijing 100081,China
Abstract Energy and environment has been the problem which is the most concerned by all over the world. Hybrid electric vehicle (HEV), which has the virtue of low exhaustion and low consumption, is the optimal choice to solve these problems at present. As an innovative power-split transmission (PST) technology, electro-mechanical transmission (EMT) is widely applied in military and civilian vehicles. In this paper, the dynamic matching problem of a dualmode EMT is studied and a dynamic matching model is developed. A multi-objective optimization model is built to improve the motor energy utilization ratio through optimizing the design parameters. Sensitivity of the design parameters for dynamic matching performance are analyzed by design of experiments (DOE). Finally, the feasible region of the optimal parameters where the dynamic matching model get the optimal evaluation index is obtained. © 2017 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of ICAE Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy.
Keywords : hybrid electric vehicle; electro-mechanical transmission (EMT); dynamic matching; the motor energy utilization ratio; drive cycle simulation
1. Introduction In recent years, as a solution to economical use of energy problems, Hybrid electric vehicles (HEVs) which have higher energy efficiency than conventional vehicles are developed [1].Power-split transmission (PST) enables the engine to operate at its efficient regions, independent of the overall vehicle velocity range, and in fact, it provides an electrically variable transmission (EVT) [2-3]. Electro-mechanical transmission (EMT) determines the performance of a PST, is widely applied in heavy-load vehicles. In
* Corresponding author. Tel.: +86-10-68915986. E-mail address:
[email protected].
1876-6102 © 2017 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi:10.1016/j.egypro.2017.03.591
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this research, a dual-mode EMT model is composed of mechanical drive devices and electrical drive devices, as shown in Fig. 1. Battery Pack
MG A
Engine speed
MG B
mechanical connection electrical connection
Power distribution unit MG A
Engine
Front transmission
Z2
Electric Equipment
Z1
ωi
C1
MG B
Power coupling mechanism
Rear transmission
ωo Wheel
Fig.1 Dual-mode electro-mechanical transmission system
PG1
PG2
PG3 PG4
Fig.2 Configuration of the PCM
When the EMT is on working, engine power is split into two flows and joint in one by power coupling mechanism (PCM). One flow is transmitted to the following planetary gears sets, and the other is converted into electric energy by generator. Fig.2 shows the configuration of the PCM in this study, consisting of four planetary gears (PG1, PG2, PG3 and PG4), two motor-generators (MGs), one clutch (C1) and two brakes (Z1 and Z2). PG1 and PG2 are the power-split device of the dual-motor EMT while PG3 and PG4 is used as the reducer of the output shaft. Therefore, PG1 and PG2 affects the power-split of the entire system, whereas PG3 and PG4 only adjusts the speed and torque of the output shaft. The PCM can provide two modes (EVT1 and EVT2) by the working state conversions of the powertrain system components. MGs in the EMT can operate in both motoring and generating states corresponding to the different working modes. Dynamic characteristics of the dual-mode EMT exerts great effect on the system efficiency. Mechanical efficiency of the EMT changed very little during the whole drive cycle, while the efficiency of the MGs working points changed in large range. Therefore, improving the efficiency of the MGs working points can be very helpful to improve the system efficiency and it is necessary to carry out researches on parameter matching under dynamic conditions. The dynamic matching target is to ensure that the MGs get the optimal efficiency during the whole vehicles’ running process which dynamic conditions are in the majority. In this paper, according to evaluation index for HEV electric propulsion [4-5], an index for HEV dynamic matching is presented which is the motor energy utilization ratio. A dynamic matching model based on a specific driving cycle is developed. Based on the MATLAB software platforms, Simdriveline and Simpowersystem are used to build the model of the vehicle. Based on the Isight software composited with MATLAB, an optimization model is built for the multi-objective optimization with the optimal motor efficiency as objective and sensitivity of the design parameters for dynamic matching performance are analyzed by design of experiments (DOE). An example has been calculated and verified. The results show that the optimal design parameters can improve the energy utilization ratio of MG. Nomenclature Ted, Tload
dynamic torque and load torque of the engine, respectively
γ, Je
dynamic correction coefficient and equivalent rotational inertia of the engine, respectively
U, I
DC bus voltage and DC bus current, respectively
TMG, Tref, nMG
actual torque, target torque and actual speed of the MG, respectively
τ, s
time constant of the MG response and the Laplace variable, respectively
SOC0, ηb
initial state of SOC and charge-discharge efficiency of the battery pack, respectively
Quse, Qcap
used capacity and ampere-time capacity of the battery pack, respectively
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ωA, ωB, ωe, ωo angular speeds of MGA, MGB, engine and output shaft, respectively TA, TB, Te, To
torques of MGA, MGB, engine and output shaft, respectively
a, b
proportionality coefficient of speed and torque, respectively
CdAρ/2, v
aerodynamic drag resistance and vehicle speed, respectively
m,g, f
vehicle mass, gravity acceleration and road resistance coefficient, respectively
Pele
electricity power
ηmi-eff
utilization ratio of motor efficiency range
Nmi, Nm
the number of MG operating points in a specific interval and all of them, respectively
Em
motor energy utilization ratio
ηm-i
weighted coefficient of each motor efficiency range
ηm-max
maximum efficiency reference value of the same type of motor, ηm-max=1
k1, k2, k3, k4
characteristic parameters of PG1, PG2, PG3 and PG4
E A, E B
energy utilization ratios of MGA and MGB
2. Modeling of electro-mechanical transmission The PCM is connected with the internal combustion engine (ICE) through the front transmission device in the input part. The kinetic equations of engine can be obtained as follows:
Ted
Te J Te
dZe dt
, Je
dZe dt
Ted Tload
(1)
MGs can be used as motors to output driving power and generators to output electricity. The mechanical output characteristics of the MGs in this research can be performed by the speed-torqueefficiency characteristic. Under the generator mode, the electric power is obtained through the efficiency diagram of the mechanical power and generator power, so the output voltage and current are obtained from the following equation:
UI
KMGTMG nMG
(2)
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Considering the response time of the MGs control, a simple model is used to represent the inherent delay of the MGs in responding to the torque command. This delay can be modeled simply using a first order transfer function [6], as follows: Tref TMG = (3) W s 1 Simplify the battery pack and the power will be got from the internal resistance model. The state of charge (SOC) of the batteries with high precision by adopting the ampere-hour model is calculated as follows:
SOC
SOC0 Quse
Qcap , Quse Kb sgn( I ) ³ Idt
(4)
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The PCM is connected with the engine through the front transmission device in the input part and the drive axle through the planetary gear mechanism in the output part. Based on the transfer forms and construction under different gears, the relations of speed and torque are obtained as following:
ªZ A º «Z » ¬ B¼
ª a11 a12 º ªZe º ªTe º «a » « »,« » ¬ 21 a22 ¼ ¬Zo ¼ ¬To ¼
ª b11 b12 º ªTA º «b b » «T » ¬ 21 22 ¼ ¬ B ¼
(5)
According to the configuration of the PCM shown in Fig.2, the PCM model is established using Simdriveline with existing modeling module, as shown in Fig.3. 400 110 350
vehicle speed/kmph 0.12 road resistance coefficient the electricity power/kW 0.1
100
90 300 80 250 70 60 200 50 150 40 100 30 20 50 10 0 0
Fig.3 PCM model in Simulink
0.08 0.06 0.04
0.02 0
500
t/s
1000
0 1500
Fig.4 Driving cycle
One driving cycle [7] shown in Fig.4 is considered as the research object, which contains curves of vehicle speed, road resistance coefficient and the electricity power. Driving power demands include three parts which overcome the rolling resistance, air resistance and acceleration resistance, respectively. Therefore, total power demand can be calculated as follows: 1 dv (6) Pele P fmgv Cd AU v 3 mv 2 dt In order to evaluate the motor’s efficiency area in use rate quantitatively, motor energy utilization ratio is defined as follows:
Km i Kmi eff ,Kmi eff Km max 1
n
Em
100¦ i
N mi Nm
(7)
3. Optimization model Based on the Isight and MATLAB software platforms, an optimization model is built for the multiobjective optimization with the optimal motor efficiency as objective. Considering that PG4 is just used for climbing and the drive ratio of PG3 can be equivalent to the rear ratio, design parameters which influence the distribution of the MG operating points are defined as follows:
X
[k1 , k2 , i f , ir ]T
(8)
F(X), the optimization object function of the optimization model, is defined as follows:
F ( X ) max( EA ( X ), EB ( X ))
(9)
The influencing factors of dynamic matching performance were analyzed by design of experiments (DOE), using the optimal latin hypercube design. As a result, the main effect on the optimal dynamic matching performance was obtained, as shown in Figs.5-6. According to the DOE results, if and k2 have
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significant effect on the distribution of the motor operating points while ir and k1 have little effect on that. Therefore, if and k2 are used as optimal variables.
ir k1 k2 if
ir k1 k2 if
Fig.5 Main effect graph for EA
Fig.6 Main effect graph for EB
The dynamic matching optimization program flowchart is shown in Fig.7 using a fast elitist nondominated sorting genetic algorithm (NSGA-II). Isight sight software soft f ware NSGA-II NSGA-II Algorithm Algorithm optimization optimization module modu d le Data Data update u date up Matlab Matlab a calculation calculation module modu d le
Input Input design design parameters parameters
Calculate Calculate constraint constraint condition condition
Simulate Simulate the the dynamic dynamic matching matching model model
Calculate Calculate motor motor energy energy utilization utilization ratio ratio
Fig.7 Optimization program flowchart
4. Simulation and analysis The results of the multi-objective optimization of if and k2 with the optimal motor energy utilization ratio as objective are shown in Figs.8-9. EA 89 87 86 84 82 80 79 77 75 74 72 70 69 67 65 63
1.44 1.4 1.36
if
1.32 1.28 1.24 1.2 1.16 1.12 1.08 1.04 1
2.0 2.1 2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.1
3.2
3.3
3.4
EB
1.48
92 91 90 89 88 87 86 84 83 82 81 80 79 78 77 75
1.44 1.4 1.36 1.32
if
1.48
1.28 1.24 1.2 1.16 1.12 1.08 1.04 1
2.0 2.1 2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.1 3.2
3.3
3.4
k2
k2
Fig.8 The result of MGA energy utilization ratio
Fig.9 The result of MGB energy utilization ratio
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According to Figs.8-9, it is shown that both of if and k2 have significant effect on the MGs energy utilization ratio. The optimal matching area in Fig.9 (the red area) covers the optimal matching area in Fig.8, which means there is a common optimal matching area for MGA and MGB. As the value of if is 1.32~1.44 and k2 is 2.0~2.2, the optimal energy utilization ratio of MGA and MGB can be obtained at the same time. 6. Conclusion (1) An innovative dual-mode electro-mechanical transmission (EMT) which is suitable for heavy-load vehicles is introduced, and it enables the vehicles to work in dual modes of operation: EVT1 mode and EVT2 mode. (2) A dynamic matching model with the optimal motor efficiency as objective based on a specific driving cycle is built. Based on the Isight software composited with MATLAB, a multi-objective optimization model is developed. Sensitivity of the design parameters for dynamic matching performance are analyzed by design of experiments (DOE), revealing that the main factors influencing motor energy utilization ratio are the front transmission ratio if and the characteristic parameter k2. With the multiobjective optimization of if and k2, the common optimal matching area is obtained. Acknowledgements The authors are grateful for the support provided by the National Natural Science Foundation of China No.51305026. References [1] Paschero M, Mascioli F M F. A foward-facing hybrid vehicle simulation tool based on multi-physics lumped circuit approach[C]// IEEE, International Symposium on Industrial Electronics. 2014:1588-1593. [2] Behrooz Mashadi and Seyed A. M. Emadi, “Dual-Mode Power-Split Transmission for Hybrid Electric Vehicles,” IEEE Transactions on Vehicular Technology, vol.59, no.7, pp.3223-3232, September 2010. [3] Xiang C, Huang K, Ma Y, et al. Analysis of Characteristics for Mode Switch of Dual-Mode Electro-Mechanical Transmission (EMT)[C]// Vehicular Technology Conference. IEEE, 2014:145–146. [4] Gai H T, Zhang C N, Jiong L I. New Evaluation Index for HEV Electric Propulsion[J]. Micromotors Servo Technique, 2007. [5] Wang W. Evaluation Regime of Traction Motor for Hybrid Electric Vehicle[J]. Transactions of the Chinese Society for Agricultural Machinery, 2011, 42(8):20-25. [6] Fengchun Sun and Chengning Zhang, Technologies for the hybrid Electric drive system of armored vehicle, China: National defense industry press, 2008. [7] Liu H, Xiong S, Ma T, et al. Research on optimal power allocation control strategy for heavy-duty vehicle with Electromechanical Transmission[C]// Transportation Electrification Asia-Pacific. IEEE, 2014.
Biography Hui Liu is currently a professor in School of Mechanical Engineering, Beijing Institute of Technology and a researcher in National Key Laboratory of Vehicular Transmission. Her research is focused on the design method and theory of vehicle transmission and NVH technology of chassis interests.
