 Original Article
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CityBusRoute Demandbased Efficient Coupling Driving Control for Parallel Plugin Hybrid Electric Bus
Chinese Journal of Mechanical Engineering volume 31, Article number: 58 (2018)
Abstract
Recently, plugin hybrid electric bus has been one of the energyefficient 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 fuelsaving 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 plugin hybrid electric bus is presented. Combined with the typical feature of a citybusroute, the fuzzy logic inference is employed to quantify the driving intention, and then to determine the coupling driving mode and the gearshifting 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 hardinloop tests with the actual data of two typical driving conditions from the realworld citybusroute 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 plugin hybrid electric bus can be significantly improved by the proposed control scheme without loss of drivability.
Introduction
As representative of new energy vehicles, plugin hybrid electric vehicle is always a hot topic in the field of recent vehicle technology [1]. Especially in areas of urban public transport, the excellent performance of low energy consumption and low emissions makes the plugin hybrid electric bus (PHEB) become the primary solution [2, 3]. Recently, with the application of the idlestop technology [4], the allelectric range of PHEB might be extended by utilizing more pure electric driving [5]. 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 [6]. However, in most cases the electric energy stored in the PHEB might not cover the whole citybusroute, the optimal coordinated operation between the engine and the electric machine (EM) is very worthy of study [7]. Because of the configuration features, the coordinated control becomes very difficult especially for the singleshaft parallel hybrid powertrain with the automated mechanical transmission (AMT) [8,9,10]. To solve this problem, several solutions have been presented for realtime optimization of the steadystate energy flows utilizing dynamic programming presented by Li et al. [11] and Lin et al. [12], equivalent consumption minimization strategy presented by Yang et al. [13] and Geng et al. [14], or model predictive control (MPC) strategy presented by Yan et al. [15]. Nevertheless, the transient process, such as the complicated electromechanical coupling working mode and the multimodes transition, was not considered in these control strategies.
During a vehicle launch and accelerating process, PHEB might fulfill an electromechanical couplingdriving 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 [16,17,18]. 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 [16].
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 torquedemand control approach based on the MPC was proposed by He et al. [19], 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. [20], Minh et al. [21], and Metin et al. [22], 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 citybusroute, might greatly affect the robustness of the control system. To adaptively deal with the stochastic driving intention, a citybusroute demandbased coupling driving control approach is designed for the singleshaft parallel PHEB with AMT. Firstly, the timevarying driving intention is quantified with a fuzzy logic, and then the coupling driving mode and the AMT gearshifting 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 singleshaft parallel PHEB. The efficient coupling driving control approach is developed in Section 3. The results of simulation and hardinloop (HIL) test are given in Section 4. Finally, the conclusion and discussion are given in Section 5.
Model Descriptions
A typical singleshaft parallel hybrid powertrain is illustrated as Figure 1.
As shown in Figure 1, the EM is placed between the output of clutch and the input of AMT, and the clutch could implement the mode transition of this powertrain with engagement and disengagement operations. AMT could assist the vehicle to adapt to the demand from different conditions, and help the engine and the EM to work in their efficient zones as well. Furthermore, the parameters of the studied PHEB are shown in Table 1.
Energy Demand Analyses of City Bus Route
During the operation, PHEB with that powertrain could fulfil six basic working modes, including idling stop mode, pure electrical driving mode, engine driving mode, hybrid driving mode, engine active charging mode, and regenerative braking mode. The diagram of the PHEB energy demand corresponded to the working modes mentioned above is shown in Figure 2. As shown in Figure 2, between two bus stops, the expected engine operation in PHEB might contain two states, engineoff and engine highefficiency operation. The engineoff state occurs at the idling stop mode, pure electrical driving mode, and the regenerative braking mode. With the increasing driving demand torque the engine will be started and engaged into the driveline. Then the problem of fuel saving becomes the optimization problem of engine working points. It should be noted that the EM might be utilized to assist the engine running in its highefficiency area both in the hybrid driving mode and the engine active charging mode. Thus, those two modes are very important for improving the fuel economy of PHEB.
Diesel Engine Model
The simplified model of the diesel engine is employed for the torque control, which could be shown in Figure 3.
In Figure 3, ϕ represents the accelerator pedal position. J_{e} is the moment of inertia of the crankshaft; ω_{e} is the rotational speed of the crankshaft; φ(dω_{e}/dt) is the dynamic compensation factor; k_{e} and τ_{e} are the proportional coefficient and the time constant, respectively; T_{e} and T_{L} are the effective torque and the load torque of the engine, respectively; T_{es} is the static torque of the engine, which could be obtained from the engine map; ΔT_{e} represents a dynamic correction item of the engine torque, which could be described as follows:
Only considering the rotational dynamics of the crankshaft, the engine model could be written as follows:
EM Model
The controloriented dynamic model of the EM consists of two modes: the driving mode and the generating mode. In order to realize the transient torque tracking control, the axis models are employed, when the EM operates in the driving mode:
When the EM operates in the generating mode:
where i_{d} and i_{q} are the d and q axis stator currents, respectively; u_{d} and u_{q} are the d and q axis stator voltages, respectively; R_{s}, L, P and Φ represent the stator resistance, the stator inductance, the number of the pole pairs, and magnet’s flux linkage, respectively; J_{m} and B_{μ} are the moment of inertia of the EM output shaft and the damping coefficient, respectively; T_{e} is the engine torque when the PHEB runs in the active charging mode, and T_{m} is the EM torque, which can be described as the following equation:
CityBusRoute Demandbased Efficient Coupling Driving Control Strategy
In this section, a novel citybusroute demandbased coupling driving control strategy is presented. Firstly, the fuzzy logic controller quantifies the driver’s driving intention. Then the AMT gearshifting 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 singleshaft 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.
Fuzzy Logic Inference for Driving Intention
The driving intention is generated from the driver’s maneuvers during the PHEB accelerating process. However, it is difficult to describe the intention accurately by the mathematical expressions. Therefore, the fuzzy logic based on the test data and experience is adopted to implement the quantification of the driving intention. Diagram of the fuzzy logic controller is shown in Figure 4.
As shown in Figure 4, the input variables are vehicle speed v_{veh}, the relative accelerator pedal position ϕ_{rel}, and the absolute value of the change rate of accelerator pedal position dϕ/dt, the output variable is driving intention I_{d}. The relative accelerator pedal position could be obtained by the equation as follows:
where ϕ is actual accelerator pedal position, ϕ_{equ} is equilibrium accelerator pedal position reflecting the accelerator pedal position which maintains the vehicle driving on flat road with a uniform speed, and the value of ϕ_{equ} might be obtained through looking up the steadystate table with the inputs of the engine torque and the engine rotational speed, and the engine torque could be obtained by the vehicle longitudinal dynamics equation as follows:
where F_{f} and F_{w} are the rolling resistance and the aerodynamic drag, respectively. r, η_{T}, i_{g}, and i_{f} are wheel radius, transmission efficiency, AMT gear ratio, and differential ratio, respectively.
According to the test data of actual vehicle, the fuzzy logic rule base shown in Table 2 can be obtained. In Figure 5, the memberships of v_{veh} are L, M, and H, which represent the low speed, the middle speed, and the high speed, respectively. The memberships of f_{rel} are Nb, Ns, Z, Ps, and Pb, which are the negative big, the negative small, the zero, the positive small, and the positive big of the equilibrium accelerator pedal open, respectively. The memberships of df/dt are S, Mi, and Bi, which are the small, the middle, and the big of the change rate of accelerator pedal open, respectively. Moreover, the output variable I_{d} obtained by the defuzzification are quantified as St, D, K, A, and B, which represent the intention of stop, decelerating, keep, accelerating, urgent accelerating.
Balanced AMT Gearshifting Strategy and Driving Mode Determination Module
The doubleparameters balanced AMT gearshifting strategy (BGS), which balances the dynamic gearshifting (DGS) maneuver and the economic gearshifting (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 gearshifting maneuver might be determined that the gearshifting maneuver tends to DGS with urgent accelerating intention and conversely tends EGS with keep and normal accelerating intention. Therefore, the energyefficient operation of PHEB without drivability loss might be fulfilled by the proposed gearshifting strategy.
Combined with the quantified driving intention, the AMT gearshifting strategy and the mode selection during the coupling driving mode can be determined, the logic of which is shown in Figure 6. In Figure 6, I_{d}(t_{switch}) is the driving intention when the engine engages into the driveline at the time t_{switch}, and I_{th} represents the logic threshold of driving intention, and its value could be obtained by repeated tests.
According to the description of PHEB working modes, the coupling driving modes can ensure the efficient operation of PHEB. As shown in Figure 7, the engine working points are improved into its highefficiency area by EM in the coupling driving modes. However, during the simultaneous operation of engine and EM, the difference of dynamic characteristics between the engine and the EM causes the torque deviation of powertrain, which might significantly influence the drivability of PHEB. In this paper, the coordinated control scheme utilizes the EM to compensate the torque response deviation. Thus, the EM controller is very important that some uncertainties should be taken into account in the controller design. Then the design of the torque controllers for the engine and EM will be given in the next two parts.
PI torque Controller for Diesel Engine
In order to response the demand torque, a PI torque controller is designed. The expression can be written as:
where u_{eng} is the engine control input that represent the fuel injection quantity, T ^{r}_{e} is demand powertrain speed, k_{p}, k_{i} are proportional and integral gains, respectively.
Adaptive Robust Controllers for the EM Torque Tracking Control
To ensure the torque balance of the whole powertrain, the EM control system design might become the chief task. Then the objective of EM controller becomes that ensures the accurate trajectory tracking control with the existence of model uncertainties and external disturbances. First, the error models of EM in the driving mode and generating mode are necessary. Therefore, the error variables can be defined as follows:
where x and \({\bar{\varvec{x}}}\) are the error variables when the EM works in the driving mode and generating mode, respectively. ω ^{r}_{m} , i ^{r}_{d} , i ^{r}_{q} are the desired values of the EM speed, the daxis, and the qaxis stator currents when the EM operates in the driving mode, respectively. ω ^{*}_{m} , i ^{*}_{d} , i ^{*}_{q} are the desired values of the EM speed, the daxis, and the qaxis stator currents when the EM operates in the generating mode, respectively. Combining with the EM model described in Eqs. (3) and (4), the error equations that have considered the uncertainties can be defined when EM operates in the driving mode:
When EM operates in the generating mode:
where the uncertainty parameters θ_{1}, θ_{2}, τ_{1}, and τ_{2} are given as follows:
\(\tilde{\theta }_{i}\) and \(\bar{\tau }_{i}\)(i = 1, 2) are the error between the uncertain parameters and the adaptive estimated value, which would be described in later part. Moreover, w_{1}, w_{2} represent the load disturbances of EM control system. Then the performance vectors are defined as follows:
where ρ_{i} and \(\bar{\rho }_{i}\) (i = 1, 2, 3) are the weighting factors. T_{E} is an error term. Moreover, u_{1}, u_{2}, u_{3}, u_{4} are equivalent control inputs described as follows:
Then the control objective of EM controller becomes that the closedloop system is stable with L_{2}gain, that is, when w ≠ 0, the system from the disturbance inputs w_{i} (i = 1, 2) to the penalty outputs z and \(\bar{z}\) has finite L_{2}gain not larger than γ_{i} (i = 1, 2).
where T > 0 is any given scalar. γ_{i} (i = 1, 2) are the evaluating factors. Thus, regarding the system described in Eqs. (10) and (11), an adaptive robust controller for EM is designed when EM operates in the driving mode:
And the parameter adaptive update laws are chosen as
When EM operates in the generating mode:
And the parameter adaptive update laws are chosen as
where k_{i}(i = 1, 2, 3) and \(\bar{k}_{i} (i = 1,2,3)\) are adjustable parameters of the controller, \(Z_{1} = x_{3}  \alpha_{1} (x_{1} ,\hat{\theta }_{1} )\) and \(Z_{2} = \bar{x}_{3}  \alpha_{2} (\bar{x}_{1} ,\hat{\tau }_{1} )\), \(\alpha_{1} (x_{1} ,\hat{\theta }_{1} )\) and \(\alpha_{2} (\bar{x}_{1} ,\hat{\tau }_{1} )\) are virtual controllers which could be chosen as follows:
In addition, χ_{i}(i = 1, 2) and β_{i}(i = 1, 2) are adjustable parameters of adaptive laws.Taking the driving mode of EM for example, the adaptive robust controller described in Eqs. (17) and (18) might be designed by the closeloop system Lyapunov stability analysis. First, a positive definite Lyapunov function is defined as follows:
Then, its time derivative could be calculated as follows:
Remark: For the external disturbance w_{1}, the inequality transform might be used which can be written as follows:
Choosing the adaptive robust controller described in Eqs. (17) and (18), and combining Eq. (24), Eq. (23) might become as follows:
where the adjustable gains k_{i}(i = 1, 2, 3) should satisfy the conditions as follows:
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.
Validation Results and Analysis
The simulation and HIL tests are performed on a singleshaft parallel PHEB in MATLAB/Simulink. Basic parameters are listed in Table 1. Moreover, control parameters in Table 3 are selected among all parameters obtained from repeatedly debugging model with the proposed method. These selected parameters can show the best control performance.
To verify the effectiveness of the proposed control approach, a driving condition of citybusroute 613 in Chongqing, China, is selected as the simulation condition.
A road section of citybusroute is extracted from KongGangSquare station to YuBeiBroadcast station that the vehicle speed and the road grade information are shown in Figure 8. It should be noted that, the grade changes with the distance, so generally the gradedistance curve is used to reflect the grade information of a road section.
Driving Intention Quantification
To verify the intention recognition, the accelerator pedal position and the actual gear position curve were collected from the driving condition as shown in Figure 9.
As shown in Figure 9, two sections of routes are with fully different driver’s maneuvers. The first section from 0 s to 117 s shows the higher acceleration and the larger pedal open than that of the second section from 120 s to 300 s. Therefore, the quantified driving intention is shown as in Figure 10. The result shows that the intention could essentially reflect the actual maneuver. Moreover, the urgent accelerating intention appears in 4.5 s and the rest is the normal accelerating intention. Therefore, the threshold of I_{th} could be selected as 3.2. Because the focus in this paper is the process of PHEB driving mode, two vehicle launch process are extracted from the selected driving condition and the results are shown in Sections 4.2 and 4.3.
Results under Different Driving Intentions
A clear citybusroute without traffic jams is simulated in this part. The driver’s intention under this condition inclines to drive through this section of road fast, so according to the strategy determination described in Section 3.2, the simulation results are shown in Figure 11.
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).
A congested citybusroute is simulated to reflect the driver maneuvers with the intention I_{d}(t_{switch}) < I_{th}. The simulation results are shown in Figure 12. As shown in Figure 12(a), the vehicle speed of simulation is in accordance with the test data, and with the intention of slow driving, Figure 12(c) shows that the engine demand torque is elevated by the generating torque of EM in the driving condition of the low torque demand. In addition, due to the engine response characteristics, the PI controller with the control input of fuel injection cannot eliminate the torque deviation. However, the proposed coupling driving control approach utilizes the EM torque to compensate the above torque deviation, and the good tracking performance is ensured by the designed adaptive robust controller for EM, the effect curves of which is shown in Figure 12(d). Therefore, it can be concluded that the efficient operation of PHEB is ensured. It should be noted that Figure 11 and Figure 12 show two different driving conditions with relevant driving intentions. Under urgent driving condition, the test vehicle uses less time than that under slow driving condition, when it reaches the same speed. Thus, the timeline in Figure 11 shows less than that in Figure 12.
The working points of engine and EM in two different driving intentions are shown in Figure 13. As seen in Figure 13, the working areas of engine and EM are improved by the coupling driving mode, especially in engine active charging mode, the working points of engine are moved into its highefficiency area. To show the improvement of working points more clearly, the areas plot by the red dotted line are defined as the highefficiency areas of engine and EM. The results show that the working points of engine and EM are greatly improved by the proposed control strategy.
HIL Test Results
To verify the realtime capability of the proposed control method, the HIL test is carried out. The HIL test platform is shown in Figure 14.
As shown in Figure 14, the dSPACE real time test system is employed for running the vehicle model. Hybrid control unit (HCU) is the actual controller used in PHEB. The PHEB model built in MATLAB/Simulink are loaded into dSPACE through real time interface (RTI). The C codes of proposed control strategy is generated with the auto code function of real time workshop (RTW), meanwhile, the controller drive program is compiled with the main program in Tasking EDE tool. A PC is employed with control desk to monitor the test operation. The test is carried out in Chinese typical urban condition and its results are shown in Figure 15.
The outstanding vehicle speed tracking performance and the reasonable torque spilt effect can be shown in Figure 15, which proves the proposed control strategy is effective to the real time HIL test. To further show the advantage, a rulebased control strategy is adopted as the baseline strategy, which is commonly used in actual vehicle [19]. The FC and EC represent the fuel consumption and the electricity consumption, respectively. The average brake specific fuel consumption (BSFC) results reflect the improvement of working points. As shown in Table 4, the fuel consumption improvement is 10.4% under the given driving condition.
Conclusions

(1)
The driving intention is recognized by the designed fuzzy logic inference.

(2)
According to the quantified intention, the mode selection method in the coupling driving mode and AMT gearshifting strategy given in the strategy determination module by the preset threshold.

(3)
The adaptive robust controller is designed for EM to ensure the tracking effect with the uncertainties and disturbances.

(4)
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.

(5)
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.
References
 [1]
Yong Sun, ZiLin Ma, GongYou Tang, et al. Estimation method of stateofcharge for lithiumion battery used in hybrid electric vehicles based on variable structure extended kalman filter. Chinese Journal of Mechanical Engineering, 2016, 29(4): 717–726.
 [2]
Chao Yang, Liang Li, SiXiong You, et al. Cloud computingbased energy optimization control framework for plugin hybrid electric bus. Energy, 2017, 125: 11–26.
 [3[
Liang Li, SiXiong You, Chao Yang, et al. Drivingbehavioraware stochastic model predictive control for plugin hybrid electric buses. Applied Energy, 2016, 162: 868–879.
 [4]
Marcello Canova, Yann Guezennec, Steve Yurkovich. On the control of engine start/stop dynamics in a hybrid electric vehicle. ASME Transactions, Journal of Dynamic System, Measurement, and Control, 2009, 131(12), 061005: 1–12.
 [5]
Zheng Chen, Bing Xia, Chenwen You, et al. A novel energy management method for series plugin hybrid electric vehicles. Applied Energy, 2015, 145: 172–179.
 [6]
Chao Yang, Jian Song, Liang Li, et al. Economical launching and accelerating control strategy for a singleshaft parallel hybrid electric bus. Mechanical Systems and Signal Processing, 2016, 7677: 649–664.
 [7]
TianHeng Feng, Lin Yang, Qing Gu, et al. A supervisory control strategy for plugin hybrid electric vehicles based on energy demand prediction and route preview. IEEE Transactions on Vehicular Technology, 2015, 64(5): 1691–1770.
 [8]
HongCai Li, QingDong Yan, ChangLe Xiang, et al. Analysis method and principle of dualmode electromechanical variable transmission program. Chinese Journal of Mechanical Engineering, 2012, 25(3): 524–529.
 [9]
WeiDa Wang, LiJin Han, ChangLe Xiang, et al. Synthetical efficiencybased optimization for the power distribution of powersplit hybrid electric vehicles. Chinese Journal of Mechanical Engineering, 2014, 27(1): 58–68.
 [10]
Dongsuk Kum, Huei Peng, Norman Bucknor. Control of enginestarts for optimal drivability of parallel hybrid electric vehicles. ASME Transactions, Journal of Dynamic System, Measurement, and Control, 2014, 135(3), 021020: 1–10.
 [11]
Liang Li, Chao Yang, YaHui Zhang, et al. Correctional DPbased energy management strategy of plugin hybrid bus for citybusroute. IEEE Transactions on Vehicular Technology, 2015, 64(7): 2792–2803.
 [12]
ChanChiao Lin, Huei Peng, JessyW. Grizzle et al. Power management strategy for a parallel hybrid electric truck. IEEE Transactions on Control Systems Technology, 2003, 11(6): 839–849.
 [13]
Chao Yang, Siyu Du, Liang Li, et al. Adaptive realtime optimal energy management strategy based on equivalent factors optimization for plugin hybrid electric vehicle. Applied Energy, 2017, 203: 883–896.
 [14]
Bo Geng, JamesK. Mills, Dong Sun. Energy management control of microturbinepowered plugin hybrid electric vehicles using the telemetry equivalent consumption minimization strategy. IEEE Transactions on Vehicular Technology, 2011, 60(9): 4238–4248.
 [15]
FengJun Yan, JunMin Wang, KaiSheng Huang. Hybrid electric vehicle model predictive control torquesplit strategy incorporating engine transient characteristics. IEEE Transactions on Vehicular Technology, 2012, 61(6): 2458–2467.
 [16]
Li Chen, Gang Xi, Jing Sun. Torque coordination control during mode transition for a seriesparallel hybrid electric vehicle. IEEE Transactions on Vehicular Technology, 2012, 61(7): 2936–2949.
 [17]
K Koprubasi, E R Westervelt, G Rizzoni. Toward the systematic design of controllers for smooth hybrid electric vehicle mode changes. Proceedings of the 2007 American Control Conference, 2007: 2985–2990.
 [18]
Hyunsup Kim, Jihun Kim, Hyeongcheol Lee. Mode transition control using disturbance compensation for a parallel hybrid electric vehicle. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2012, 225: 125–150.
 [19]
Lin He, TieLong Shen, LiangYao Yu, et al. A modelpredictivecontrolbased torque demand control approach for parallel hybrid powertrains. IEEE Transactions on Vehicular Technology, 2013, 62(3): 1041–1052.
 [20]
Stefano Cordiner, Sergio Galeani, Francesco Mecocci, et al. Torque setpoint tracking for parallel hybrid electric vehicles using dynamic input allocation. IEEE Transactions on Control Systems Technology, 2014, 22(5): 2007–2015.
 [21]
V T Minh, A A Rashid. Modeling and model predictive control for hybrid electric vehicles. International Journal of Automotive Technology, 2012, 13(3): 477–485.
 [22]
Metin Gokasan, Seta Bogosyan, D J Goering. Sliding mode based powertrain control for efficiency improvement in series hybridelectric vehicles. IEEE Transactions on Power Electronics, 2006, 21(3): 779–790.
 [23]
Chao Yang, XiaoHong Jiao, Liang Li, et al. Electromechanical coupling driving control for singleshaft parallel hybrid powertrain. SCIENCE CHINA Technological Sciences, 2014, 57(3): 541–549.
 [24]
Chao Yang, XiaoHong Jiao, Liang Li, et al. Robust coordinated control for hybrid electric bus with singleshaft parallel hybrid powertrain. IET Control Theory and Application, 2015, 9(2): 270–282.
Authors’ Contributions
QPW and CY were in charge of the whole trial; CY wrote the manuscript; YHL and YBZ assisted with sampling and laboratory analyses. All authors have read and approved the final manuscript.
Authors’ Information
QinPu 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. Email: 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 energyefficient control strategy design for hybrid electric vehicles. Email: chaoyang1986@tsinghua.edu.cn.
YaHui 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 drivervehicle system. Email: liuyahui@tsinghua.edu.cn.
YuanBo 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. Email: yuanbzhang@163.com.
Competing Interests
The authors declare no competing financial interests.
Funding
Supported by National Natural Science Foundation of China (Grant No. 51605243), National Key Science and Technology Projects of China (Grant No. 2014ZX04002041), and 1class General Financial Grant from the China Postdoctoral Science Foundation (Grant No. 2016M590094).
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Wang, Q., Yang, C., Liu, Y. et al. CityBusRoute Demandbased Efficient Coupling Driving Control for Parallel Plugin Hybrid Electric Bus. Chin. J. Mech. Eng. 31, 58 (2018). https://doi.org/10.1186/s100330180257y
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Keywords
 Hybrid electric vehicle
 Singleshaft parallel electromechanical powertrain
 Coupling driving mode
 Adaptive robust control