Corresponding drivability control and energy control strategy in uninterrupted multi-speed mining trucks
Introduction
With growing air pollution and fossil fuel consumption, green energy vehicles [1], [2], [3], [4], especially hybrid electric vehicles (HEV) is identified as a better solution to solve the problems at present due to the limitation of battery technology and related techniques. For mining trucks, the electrification of the powertrain system seems to more imperative because the driving route and pattern are more determined and feature long ramps and low speed as shown in Fig. 1, which causes heavy fuel consumption and air pollution. Moreover, compared with typical passenger vehicles, the specific performance indexes for mining trucks are also more demanding [5]. Therefore, it is vitally important to design a hybrid transmission system with corresponding optimized control strategies for mining trucks.
Based on previous solutions [6], [7], [8], [9], [10], [11], [12], the combination of power-split devices and lay-shaft transmissions may be a preferable method to develop a multi-speed hybrid transmission configuration, improving the overall efficiency [13]. However, overmuch gear ratios can become problematic. For example, frequent gear shifts, unreliable and complicated control system, and expensive manufacturing costs are inevitable. Therefore, considering the special requirements of mining trucks, one planetary gear set and a 3-AMT are employed to develop a novel multi-input and multi-speed hybrid transmission system in this paper, which can achieve high overall efficiency by keeping the engine and motors always working in high efficiency area [14,15].
Although multi-gear ratios make it possible for mining trucks to always operate with high overall efficiency, the torque interruption of AMT often causes undesirable vehicle jerks and the loss of traction force during the shifting process, thus severely affects the driving comfort and safety. Given that, some scholars attempted to optimize system configurations to reduce shift jerk [16,17] together with the corresponding shifting control strategies [18]. Wang et al. proposed a position and force switching control scheme for gear engagement of AMT systems to improve gear-shift quality and reduce gear-shift shock [19]. Considering practical application, a dual-loop self-learning fuzzy control framework including outer loop and inner loop is designed to achieve the smooth and fast control of gear engagement [20]. In [21], LQR based feedforward controller are designed to improve the driving comfort. A finite-time LQR is adopted to optimize the torque and the relative speed variations during the torque phase and inertia phase [22].
Besides the drivability, fuel economy is another great challenge and an important index for mining trucks. Therefore, a proper energy management strategy (EMS), which can be generally classified in to rule-based control and optimized control [23,24], is critical [25]. The rule-based control is currently the most common one because of the easy implementations and robust control performance. However, it cannot guarantee the overall efficiency because it is not adaptive and heavily depends on the experience of the engineer [26]. Compared with the rule-based control strategy, the optimal strategies could obtain the optimal solutions with corresponding algorithm. However, many of them are not designed for embarked implementations because of the complex structures which require large computational resources and might interfere with the response time and the appropriate vehicle control commands [27]. Therefore, an online energy control strategy is developed here and DP, which could obtain the global optimal solution, is adopted and regarded as a benchmark to evaluate the overall efficiency improvements of the proposed HUMST configuration and the effectiveness of the proposed real-time energy control strategy.
For an optimal energy consumption performance, shift frequency, in other words, shift times during a given driving cycle is always neglected which may affect the driving comfort and fuel economy, as maintaining in high efficiency area often requires frequent gear shifts. Previous studies have proved that the optimal EMS which seeks maximum fuel economy can cause poor drivability [28]. As a result, a balance should be achieved between the shift frequency and the overall efficiency. In [29], a suboptimal solution is designed to reduce calculation burden caused by adding comfort criterion to DP and simulation results reveal the error is less than 2%. In [30], the controller based on the SP-SDP is designed to trade off the engine start-stop, shift frequency and fuel economy for hybrid electric cars. It demonstrated that energy consumption performance could be improved by 11% without compromising drivability. In [31], experimental results are given to verify the trade-off between fuel economy and drivability for a plug-in hybrid electric vehicle. However, they all ignore the impact of payload or slope. According to the particular performance requirements, an innovative hybrid uninterrupted multi-speed transmission with optimal shift control strategy and energy management strategy for mining trucks is proposed. More specially, the main contributions can be summarized as:
- •
An uninterrupted transmission integrates a planetary gear set and a parallel shaft transmission is proposed;
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Shift control strategy optimized by LQR is designed to compensate or alleviate the torque hole smoothly;
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A modified Gaussian distribution function is designed to reduce the shift frequency;
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An optimized online energy control strategy with MGA is proposed to achieve a tradeoff between the fuel economy performance and the shifting frequency.
The paper is organized in the following sequence. Section 2 introduces the transmission configuration and the dynamic mathematical model of the proposed HUMST. The proposed shifting control algorithm optimized by the quadratic performance index is explained in the third section. Section 4 demonstrates the potential fuel economy of HUMST evaluated by DP. Section 5 presents the proposed improved real-time energy control strategy with multi-objective genetic algorithm (MGA) to trade-off between the energy consumption performance and the drivability. Conclusions are demonstrated in the final section.
Section snippets
Transmission configuration
The hybrid transmission system has three power sources which are the engine, the generator (mg) and the traction motor (mc). The ring gear of the planetary gear is directly connected to the output shaft together with the three driven gears of the 3-AMT system. The engine is connected to the carrier of the power split device while the generator connected to the sun gear as shown in Fig. 2. The generator could help keep the engine always working in high-efficiency region by adjusting its speed
Shift process
The detailed shifting process control is demonstrated in Fig. 8 which consists of five stages: 1. Torque reduction, 2. Synchronizer disengagement, 3. Adjust the speed of the traction motor, 4. Engage the synchronizer, 5. Torque restoration.
- 1.
Torque reduction
After receiving the shifting signal, the traction motor which operates in torque mode will reduce the output torque to zero according to the proposed torque profile. Meanwhile, to avoid the torque interruption, the ring gear output torque
Comparison of fuel economy with DP
Dynamic programming (DP) is typically used to calculate the global optimization solution. However, the implementation of DP requires to know the whole process information in advance, which makes it hard to have real-time implementations. DP is applied to calculate the global optimal solution in this paper, which is regarded as a benchmark, to evaluate the efficiency improvements achieve by the proposed HUMST configuration and the corresponding real-time energy control strategy, respectively.
Objective function for fuel and electricity
A real-time control strategy (RTCS) without considering the drivability constraints is proposed in this section. The RTCS is implemented by optimizing the energy consumption at each moment, where electricity consumption is equivalent to the fuel consumption according to the energy conversion. The objection performance index can be demonstrated as follows.where is energy consumption of the battery at each moment.
The traditional ECMS cannot guarantee that the initial
Conclusion
A hybrid uninterrupted transmission system for mining trucks is developed with the corresponding optimized shift and control strategies. The multi-input transmission system improves the working efficiency of the power sources by adjusting the speeds and output torque through the cooperation of the power split device and the 3-AMT system, eliminates the torque hole and guarantees the drivability. Compared with conventional mining trucks, the energy consumption has been improved by 11.63%, which
Declaration of Competing Interest
None.
Acknowledgments
Thanks for the support from China Scholarship Council (No. 201706460069), the University of Technology Sydney and the University of Science and Technology Beijing.
References (37)
- et al.
A comparative study energy consumption and costs of battery electric vehicle transmissions
Appl. Energy
(2016) - et al.
Hybrid electric haulage trucks for open pit mining
IFAC Proc.
(2013) - et al.
Integrated design optimization of the transmission system and vehicle control for electric vehicles
IFAC-PapersOnLine
(2017) - et al.
A control-oriented simulation model of a power-split hybrid electric vehicle
Appl. Energy
(2013) - et al.
Power-on shifting in dual input clutchless power-shifting transmission for electric vehicles
Mech. Mach. Theory
(2018) - et al.
Gearshift and brake distribution control for regenerative braking in electric vehicles with dual clutch transmission
Mech. Mach. Theory
(2019) - et al.
Design, modelling and estimation of a novel modular multi-speed transmission system for electric vehicles
Mechatronics
(2017) - et al.
Gearshift control system development for direct-drive automated manual transmission based on a novel electromagnetic actuator
Mechatronics
(2014) - et al.
Modelling of an automated manual transmission system
Mechatronics
(2007) - et al.
A robust H∞ control-based hierarchical mode transition control system for plug-in hybrid electric vehicle
Mech. Syst. Signal Process
(2018)
Optimal control of the gear shifting process for shift smoothness in dual-clutch transmissions
Mech. Syst. Signal Process.
Investigation of a novel coaxial power-split hybrid powertrain for mining trucks
Energies
Energy management and shifting stability control for a novel dual input clutchless transmission system
Mech. Mach. Theory
A global optimal energy management system for hybrid electric off-road vehicles
SAE Int. J. Commer. Veh.
Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective
IEEE Trans. Veh. Technol.
Real-time application of Pontryagin's minimum principle to fuel cell hybrid buses based on driving characteristics of buses
Int. J. Precis. Eng. Manuf. Technol.
Hybrid electric vehicle propulsion system architectures of the e-CVT type
IEEE Trans. Power Electron.
Modeling and control of a power-split hybrid vehicle
IEEE Trans. Control Syst. Technol.
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