- Original Article
- Open Access
City-Bus-Route Demand-based Efficient Coupling Driving Control for Parallel Plug-in Hybrid Electric Bus
© The Author(s) 2018
Received: 14 December 2016
Accepted: 22 June 2018
Published: 5 July 2018
Recently, plug-in hybrid electric bus has been one of the energy-efficient solutions for urban transportation. However, the current vehicle efficiency is far from optimum, because the unpredicted external driving conditions are difficult to be obtained in advance. How to further explore its fuel-saving potential under the complicated city bus driving cycles through an efficient control strategy is still a hot research issue in both academic and engineering area. To realize an efficient coupling driving operation of the hybrid powertrain, a novel coupling driving control strategy for plug-in hybrid electric bus is presented. Combined with the typical feature of a city-bus-route, the fuzzy logic inference is employed to quantify the driving intention, and then to determine the coupling driving mode and the gear-shifting strategy. Considering the response deviation problem in the execution layer, an adaptive robust controller for electric machine is designed to respond to the transient torque demand, and instantaneously compensate the response delay and the engine torque fluctuation. The simulations and hard-in-loop tests with the actual data of two typical driving conditions from the real-world city-bus-route are carried out, and the results demonstrate that the proposed method could guarantee the hybrid powertrain to track the actual torque demand with 10.4% fuel economy improvement. The optimal fuel economy can be obtained through the optimal combination of working modes. The fuel economy of plug-in hybrid electric bus can be significantly improved by the proposed control scheme without loss of drivability.
As representative of new energy vehicles, plug-in hybrid electric vehicle is always a hot topic in the field of recent vehicle technology . Especially in areas of urban public transport, the excellent performance of low energy consumption and low emissions makes the plug-in hybrid electric bus (PHEB) become the primary solution [2, 3]. Recently, with the application of the idle-stop technology , the all-electric range of PHEB might be extended by utilizing more pure electric driving . Considering the traffic congestion in the big city of China, the vehicle launch and accelerating condition might frequently appear in the driving cycles of the city bus . However, in most cases the electric energy stored in the PHEB might not cover the whole city-bus-route, the optimal coordinated operation between the engine and the electric machine (EM) is very worthy of study . Because of the configuration features, the coordinated control becomes very difficult especially for the single-shaft parallel hybrid powertrain with the automated mechanical transmission (AMT) [8–10]. To solve this problem, several solutions have been presented for real-time optimization of the steady-state energy flows utilizing dynamic programming presented by Li et al.  and Lin et al. , equivalent consumption minimization strategy presented by Yang et al.  and Geng et al. , or model predictive control (MPC) strategy presented by Yan et al. . Nevertheless, the transient process, such as the complicated electromechanical coupling working mode and the multi-modes transition, was not considered in these control strategies.
During a vehicle launch and accelerating process, PHEB might fulfill an electromechanical coupling-driving mode after a pure electric driving mode to ensure the operation efficiency until the engine torque satisfies the demand torque on a high efficient zone [16–18]. Therefore, the coupling driving mode, which refers to the hybrid driving mode or the engine active charging mode, is crucial for the vehicle launch and accelerating process of PHEB .
Considering the difference between dynamic characteristics of the engine and the EM, it is necessary to further study the coordinated control method for an efficient solution of the hybrid powertrain. Therefore, a novel torque-demand control approach based on the MPC was proposed by He et al. , to implement the torque control of parallel hybrid powertrain. In addition, for a parallel hybrid powertrain, the coordinated control method using dynamic input allocation, MPC, and sliding mode control method presented by Cordiner et al. , Minh et al. , and Metin et al. , respectively. Using the fast response behavior of the EM, an electromechanical coupling driving control scheme was proposed by Yang et al. [23, 24] to achieve good torque tracking performance.
The coordinated control strategies can ensure the torque tracking performance during a coupling driving process. However, the instantaneous variation of the traffic flows, road conditions, and the passenger loads in a city-bus-route, might greatly affect the robustness of the control system. To adaptively deal with the stochastic driving intention, a city-bus-route demand-based coupling driving control approach is designed for the single-shaft parallel PHEB with AMT. Firstly, the time-varying driving intention is quantified with a fuzzy logic, and then the coupling driving mode and the AMT gear-shifting strategy are determined with a strategy determination module. Secondly, considering the dynamic characteristics of the EM, the adaptive robust controller is designed for the EM to respond to the transient torque demand. Meanwhile, the response deviation and the transient fluctuation of the engine torque are compensated with the fast response behavior of the EM.
The rest of the paper is organized as follows: Section 2 gives the models of the single-shaft parallel PHEB. The efficient coupling driving control approach is developed in Section 3. The results of simulation and hard-in-loop (HIL) test are given in Section 4. Finally, the conclusion and discussion are given in Section 5.
2 Model Descriptions
Parameters of HEB powertrain
YC6J200-42, nominal power: 147 kW
Permanent magnet, max torque: 750 Nm, nominal power: 94 kW, peak power: 121 kW
Lithium titanate battery, capacity: 60 Ah,
nominal voltage: 346 V
AMT, gear ratios: 7.05, 4.13, 2.52, 1.59, 1, 0.78. Efficiency of gearbox is assumed 95%
Final drive ratio
2.1 Energy Demand Analyses of City Bus Route
2.2 Diesel Engine Model
2.3 EM Model
3 City-Bus-Route Demand-based Efficient Coupling Driving Control Strategy
In this section, a novel city-bus-route demand-based coupling driving control strategy is presented. Firstly, the fuzzy logic controller quantifies the driver’s driving intention. Then the AMT gear-shifting strategy and the coupling driving mode are determined with the quantified driving intention in the strategy determination module. Secondly, the designed PI controller for engine and adaptive robust controllers for EM implement the torque tracking control in the coupling driving mode. Moreover, considering the properties of the single-shaft parallel hybrid powertrain, the response error of engine torque is compensated by the accurate EM torque control to guarantee the torque tracking performance of powertrain.
3.1 Fuzzy Logic Inference for Driving Intention
Fuzzy logic rule base
Relative pedal open ϕrel
Change rate of accelerator pedal position dϕ/dt
3.2 Balanced AMT Gear-shifting Strategy and Driving Mode Determination Module
The double-parameters balanced AMT gear-shifting strategy (BGS), which balances the dynamic gear-shifting (DGS) maneuver and the economic gear-shifting (EGS) maneuver, is employed. This strategy tends to dynamic or economic depending on the driving intention, which could be quantified by the designed fuzzy inference. Taking the effects of engine operating points for example, the full DGS ensures that engine might work on the external characteristic line, and the full EGS reflects that engine would work on the optimal operating line with the highest efficiency. According to the quantified driving intention, the AMT gear-shifting maneuver might be determined that the gear-shifting maneuver tends to DGS with urgent accelerating intention and conversely tends EGS with keep and normal accelerating intention. Therefore, the energy-efficient operation of PHEB without drivability loss might be fulfilled by the proposed gear-shifting strategy.
3.3 PI torque Controller for Diesel Engine
3.4 Adaptive Robust Controllers for the EM Torque Tracking Control
Therefore, combing with the LaSalle invariant set principle, it can be concluded that the designed controller can achieve the mentioned above control objectives. Because the adaptive controller described in Eqs. (19) and (20) for the generating mode of EM is deduced that is similar with that of the driving mode of EM, so the proof procedure will be omitted here.
4 Validation Results and Analysis
To verify the effectiveness of the proposed control approach, a driving condition of city-bus-route 613 in Chongqing, China, is selected as the simulation condition.
4.1 Driving Intention Quantification
4.2 Results under Different Driving Intentions
As shown in Figure 11(a), the results of vehicle speed obtained from the simulation are close to the test data, and the deviation reflects the error between simulation model and actual vehicle. With the increasing vehicle speed, the AMT will execute the gear shifting operation in accordance with BGS strategy. The engine torque tracking performance can be ensured, which is shown in Figure 11(c). The designed adaptive robust controller can respond to the demand EM torque quickly and accurately, as shown in Figure 11(d).
4.3 HIL Test Results
Comparison results of two control strategy
The driving intention is recognized by the designed fuzzy logic inference.
According to the quantified intention, the mode selection method in the coupling driving mode and AMT gear-shifting strategy given in the strategy determination module by the pre-set threshold.
The adaptive robust controller is designed for EM to ensure the tracking effect with the uncertainties and disturbances.
The proposed control approach could guarantee the torque tracking performance, and the fuel economy can be improved 10.4% through adjusting the engine working points under different driving intentions.
The real time capability of the control approach has been validated by HIL tests. The proposed control method has the potential to apply in the actual vehicle.
Q-PW and CY were in charge of the whole trial; CY wrote the manuscript; Y-HL and Y-BZ assisted with sampling and laboratory analyses. All authors have read and approved the final manuscript.
Qin-Pu Wang, born in 1964, is the General Manager Assistant at Zhongtong Bus Hold Co., Ltd., China. He received his bachelor degree from Xi’an Highway Institute, China, in 1984. His research interests include design and manufacture of hybrid electric vehicle. E-mail: QinPu_Wang@163.com.
Chao Yang, born in 1986, is currently a postdoctoral fellow at State Key Laboratory of Automotive Safety and Energy, Tsinghua University, China. He received his PhD degree on Control Science and Engineering from Yanshan University, China, in 2016. His research interests include energy-efficient control strategy design for hybrid electric vehicles. E-mail: email@example.com.
Ya-Hui Liu, born in 1980, is currently an associate professor at State Key Laboratory of Automotive Safety and Energy, Tsinghua University, China. He received his bachelor degree from Jilin University, China, in 2003 and PhD degree from Beihang University, China, in 2009, respectively. His research interests include vehicle system dynamics, steering system and driver-vehicle system. E-mail: firstname.lastname@example.org.
Yuan-Bo Zhang, born in 1990, is currently working at State Key Laboratory of Automotive Safety and Energy, Tsinghua University, China. His research interests include regenerative braking control strategy for electrified vehicles. E-mail: email@example.com.
The authors declare no competing financial interests.
Supported by National Natural Science Foundation of China (Grant No. 51605243), National Key Science and Technology Projects of China (Grant No. 2014ZX04002041), and 1-class General Financial Grant from the China Postdoctoral Science Foundation (Grant No. 2016M590094).
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