Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 105 (2017) 2885 – 2890
The 8th International Conference on Applied Energy – ICAE2016
Road Roughness Identification and Shift Control Study for a Heavy-Duty Powertrain Pan Liua, Bolan Liua*, Tianpu Donga, Chuang Zhanga Beijing Institute of technology, 5 South Zhongguancun Street, Beijing 100081, China
Abstract
The road condition of heavy-duty vehicles is generally complex, which includes rugged and bumpy terrain etc. The undesired shifting phenomenon occurs easily. Thus, the fuel consumption and pollution increase and the vehicles’ dynamic driving performance decline significantly. In this paper, an intelligent shift control method based on the identification of road roughness level is developed. The road roughness level is reflected by engine speed change rate signal. Then, the shift control model is built to modify the shift points. The method is validated by the simulation and test data. The results prove that the methods are validity and feasibility. © 2017 The Authors. 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:Heavy-duty Vehicle; Engine Speed Rate; Threshold; Road roughness; Shift Control
1. Introduction Nowadays, there are more and more concerns in man-machine engineering and energy saving as well as environmental protection. Civil vehicles’ driving modes can be changed according to the driver’s selection. However, for heavy-duty vehicle, it can be difficult. With its uniqueness and complex working environment, the precise mathematical model is difficult to be developed as the system has strong nonlinear. The conventional shift control method which is based on steady match seems to have poor ability to adapt to the changeable driving environment. Phenomenon like shift hunting and unexpected shifting is prone to happen based on the steady state theory. Domestic and foreign scholars try to use neural network [1,2,3], fuzzy control [4,5], fuzzy neural network control [6,7] and fuzzy genetic algorithms [8] to control vehicle shifting. In the paper [9], vehicle
* Corresponding author. Tel.: +86-139-1065-6794; fax: +0-000-000-0000 . E-mail address:
[email protected].
1876-6102 © 2017 The Authors. 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.2017.03.645
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operation state and environment have been taken into consideration, and shift points are adjusted according to the change of vehicle operation state and work environment. A technique based on the use of fuzzy logic estimating running resistance represented by the road gradient has been incorporated in a new shift schedule control method that eliminates shift hunting [10]. Hitachi company Takaba [11,12] presents a method to estimate the slope ramp by the gearbox output torque and successfully solves the shift hunting problem. Jeng-Hsiang Lin [13] proposes a spectral approach taking the influence of vehicle speed and road roughness into consideration to evaluate the changes in dynamic vehicle load. Researchers in Tongji University [14] put forward the engine speed control algorithm during shifting process to improve automobile dynamic property. For the energy conservation, there are many researches about the fuel consumption and CO2 emission. Nagoya University proposes a vehicle dynamics based CO2 emission model and an eco-routing approach by using large-scale GPS and CAN bus data and finds the most ecofriendly path in terms of minimum emissions constrained by a travel time budget [15,16]. The paper consists of the following sections: Section 2 introduces the crossing threshold method and the shift control model. Section 3 gives the basic data of the vehicle powertrain and the results discussion. The conclusions and future work are drawn in Section 4. . 2. Methodology 2.1. The crossing threshold method The characteristics of the rough road are mainly the change of road slope and the coefficient of the deformation resistance of the ground. Method of crossing threshold is put forward to identify road roughness level through analyzing the characteristics of engine speed change rate signal in time domain. Fluctuating period and amplitude of the engine speed change rate is the main features. Crossing threshold method is to set fixed thresholds and count the number of times that the absolute values of engine speed change rate exceed the defined thresholds. The thresholds are corresponding to the road roughness level. The count number will accumulate for one time once the absolute values of engine speed change rate exceed its corresponding threshold. In this paper, every 10-second is taken as an identification period. By the final accumulated number in one period, the corresponding level of road roughness is obtained. According to the analysis of the engine speed signals, road roughness level is divided into flat road, commonly rough road, relatively rough road and special rough road which are defined as level 1, 2, 3, 4 respectively. In order to identify the road roughness level more accurately, the decrement value of each roughness level is defined through calibration method. Real-time algorithm flow chart of the crossing threshold method is shown in Fig 1. In order to verify the validity of the method, the asphalt and gravel pavement road are applied. The recognition results are shown in Figs 2 and 3 respectively under the asphalt pavement and bumpy gravel pavement. When the vehicle drives on the asphalt pavement, the roughness level is low. However, when the vehicle drives on gravel pavement road, the roughness level is higher. The engine speed change rate varies greatly on the two different roads. The results show that method is feasible and effective.
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1 2 …
Self-decrement parameter di d1 d2 …
Self-decrement d
1 Counting number of crossing the threshold ai
2 …
a1 a2 …
Self-decrement parameter pi p1 p2 …
Accumulated value p
Counter N=N-d Counter N=N+p
1 2 … Counter number N [ ] [ ] … Road uneveness level R d1 d2 …
Road unevenness level R
Fig 1. Flow chart of the crossing threshold method
Fig 2. Asphalt pavement road
Fig 3. Gravel pavement road
2.2. Shift control model When the heavy-duty vehicles drive on the rough road, dynamic shift schedule should be applied to satisfy the power demand to adapt to the level of road roughness. According to the traditional two parameters shift schedule which takes the throttle opening and vehicle speed as shifting judgment parameters, shift hunting is likely to happen when the vehicle speed fluctuates. In this paper, shift points are modified when the heavy-duty vehicles drive on the bumpy gravel pavement road. The shift points modification equation can be expressed as:
vR
v0 wR
(1)
where vR is the vehicle modified gear-shifting speed on rough pavement; v0 is the original vehicle gearshifting speed; w is the modification coefficient; R is the level of road roughness. 3. Basic data and results discussion In this work, the powertrain system mainly includes the engine, hydraulic torque converter, gearbox and other transmission mechanism. The basic data of other parts of the powertrain is given in Table 1. According to the vehicle dynamics model, the simulation model of the real vehicle is built.The vehicle road test is carried out on a test ground including heavy-duty vehicles’ typical driving cycle conditions.
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The test ground is a gravel pavement road with variable slope which is about 6 km long. In the vehicle test, the road data is acquired by the posture sensors. It can be seen in Fig 4. First, the traditional shift schedule is adopted. The information of gear, vehicle speed and engine speed are shown in Fig 5. It can be seen that the vehicle speed varies frequently, and the shift hunting phenomenon occurs. Second, the shift points modification based on engine speed change rate is developed. As mentioned above, the road roughness recognition using crossing threshold method is applied. The recognition results are shown in Fig 6. Under the simulation environment of Matlab/Simulink, the simulation experiment is carried out to validate the effectiveness of the crossing threshold method. Simulation results are shown in Fig 7 adopting the modified shift schedule. It can be seen that the crossing threshold method can accurately identify the road roughness level, and the shift schedule can be modified accordingly to eliminate the shift hunting phenomenon. Third, to acquire the fuel consumption amount before and after the shift modification, five times of the experiment are carried out. The fuel consumption is shown in Fig 8. The change of fuel consumption amount is obtained as shown in Fig 9. It can be seen that the fuel consumption is reduced by about 9.6% after shift modification. Table 1 Specifications of the vehicle powertrain Items Vehicle Weight Engine Type Engine capacity Calibration power / calibration speed Maximum torque / speed Maximum speed Calibration cycle fuel consumption rate Transmission type Transmission speed ratio Reverse gear ratio Side ratio
Fig 4. Gradient of the test ground
Basic data 16.8 tons Deutz BF6M1015CP In line, V6 cylinder, Exhaust Gas Turbocharged Diesel Engine 11.9 L 330[kW]/2100[r/min] 1830[Nm]/1300[r/min] 2300[r/min] 246[g/ (Kw / h)] T300 Gear1: 3.28 Gear2: 2.05 Gear3:1.21 Gea4:0.76 -3.10 5.03
Fig 5. Vehicle test results
Pan Liu et al. / Energy Procedia 105 (2017) 2885 – 2890
Fig 6. Recognition results applying the crossing threshold method
Fig 7. Results of the powertrain applying the modified shift schedule
Fig 8. Cycle fuel consumption before and after the shift modification
Fig 9. Fuel reduction percentage after the shift modification
4. Conclusions and future work In this paper, a novel approach based on the engine speed change rate to recognize the driving environment and to change the shift points accordingly is proposed. The engine speed signal is chosen as the identification signal. Crossing threshold algorithm in time-domain is applied to analyze and recognize the road roughness level. The shift points are modified according to the identified road roughness level in real-time. The following conclusions are obtained: x The engine speed change rate signal is suitable to be chosen as the factor to recognize road roughness level. It can reflect the current road condition accurately. x The shift schedule is modified in real-time according to the identified road roughness level results. The undesired shift hunting phenomenon can be eliminated based on the identified results. x The fuel consumption is reduced by about 9.6% after applying the shift schedule adapting to the complex road condition. x Equipment like sensors needed to acquire the road information is unneeded, cost will be reduced. The method is mainly for the heavy-duty vehicles especially for the off-road vehicles running under complicated environment. The study is developed mainly based on simulation for heavy-duty vehicles and it should be applied and verified on light vehicles intelligent control in the future work.
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