Статті в журналах з теми "Torque split strategies"
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1
Zheng, Qingxing, Shaopeng Tian, and Qian Zhang. "Optimal Torque Split Strategy of Dual-Motor Electric Vehicle Using Adaptive Nonlinear Particle Swarm Optimization." Mathematical Problems in Engineering 2020 (May 21, 2020): 1–21. http://dx.doi.org/10.1155/2020/1204260.
Повний текст джерелаАнотація:
In order to exploit the potential of energy saving of dual-motor powertrain over single-motor powertrain, this paper proposes a time-efficient optimal torque split strategy for a front-and-rear-axle dual-motor electric powertrain. Firstly, a physical model of electric vehicle powertrain is established in Matlab/Simulink platform and further validated by real-vehicle experiments. Subsequently, a three-layer energy management strategy composed of demanded torque calculation layer, mode decision layer, and torque split layer is devised to enhance the total operating efficiency of two motors. Specifically, the optimal torque split strategy using adaptive nonlinear particle swarm optimization (ANLPSO) is embedded in the torque split layer. Finally, two conventional strategies (even distributed strategy and rule-based strategy) for dual-motor powertrain are considered for comparison to verify the efficacy of the proposed strategy. Tremendous results demonstrate that the dual-motor powertrain with this proposed optimal torque split strategy develops energy saving by 11.88% and 12.18% against single-motor powertrain in the NEDC and WLTP. Compared to two conventional torque split strategies, it is able to reduce the total motor loss by 12.17% and 8.1% in NEDC and 11.91% and 8.07% in WLTP, respectively, which indicates the prominent optimization performance and a great potential in realistic applications.
2
Hwang, Hsiu-Ying, Tian-Syung Lan, and Jia-Shiun Chen. "Control Strategy Development of Driveline Vibration Reduction for Power-Split Hybrid Vehicles." Applied Sciences 10, no. 5 (March 2, 2020): 1712. http://dx.doi.org/10.3390/app10051712.
Повний текст джерелаАнотація:
In order to achieve better performance of fuel consumption in hybrid vehicles, the internal combustion engine is controlled to operate under a better efficient zone and often turned off and on during driving. However, while starting or shifting the driving mode, the instantaneous large torque from the engine or electric motor may occur, which can easily lead to a high vibration of the elastomer on the driveline. This results in decreased comfort. A two-mode power-split hybrid system model with elastomers was established with MATLAB/Simulink. Vibration reduction control strategies, Pause Cancelation strategy (PC), and PID control were developed in this research. When the system detected a large instantaneous torque output on the internal combustion engine or driveline, the electric motor provided corresponding torque to adjust the torque transmitted to the shaft mitigating the vibration. To the research results, in the two-mode power-split hybrid system, PC was able to mitigate the vibration of the engine damper by about 60%. However, the mitigation effect of PID and PC-PID was better than PC, and the vibration was able to converge faster when the instantaneous large torque input was made. In the frequency response, the effect of the PID blocking vibration source came from the elastomer was about 75%, while PC-PID additionally reduced 8% by combining the characteristics of the two control methods.
3
Kim, Dong Hyun, Sung Ho Hwang, and Hyun Soo Kim. "Advanced Active Safety System Using Separated Front and Rear Motor Control for a 4WD Hybrid Electric Vehicle." Solid State Phenomena 120 (February 2007): 223–28. http://dx.doi.org/10.4028/www.scientific.net/ssp.120.223.
Повний текст джерелаАнотація:
Vehicle stability in 4 wheel drive(4WD) vehicles has been pursued by torque split based technology and brake based technology. The brake based methods are essentially brake maneuver strategies using the active control of the individual wheel brake. By comparison, the torque split based technologies realize stability by varying the traction torque split through powertrain to create an offset yaw moment. In the 4WD hybrid electric vehicle adopting separate front and rear motor, the vehicle stability enhancement algorithm using the rear motor control has some advantages such as faster response, braking energy recuperation, etc. However, since the left and right wheels are controlled by the same driving and regenerative torque from one motor, stability enhancement only by the front and rear motor control has a limitation in satisfying the required offset yaw moment. Therefore, to obtain the demanded offset yaw moment, a brake force distribution at each wheel is required. In this paper, a vehicle stability control logic using the front and rear motor and electrohydraulic brake(EHB) is proposed for a 4WD hybrid electric vehicle. A fuzzy control algorithm is suggested to compensate the error of the sideslip angle and the yaw rate by generating the direct yaw moment. Performance of the vehicle stability control algorithm is evaluated using ADAMS and MATLAB Simulink co-simulation.
4
Jin, Liqiang, Mingze Ling, and Jianhua Li. "Development of a new traction control system using ant colony optimization." Advances in Mechanical Engineering 10, no. 8 (August 2018): 168781401879215. http://dx.doi.org/10.1177/1687814018792152.
Повний текст джерелаАнотація:
An advanced traction control system can help limit wheel rotation and enhance vehicle stability. This article presents a new traction control system under complicated situations, including the low slippery road surface and split-µ road surface. First, a 15-degree-of-freedom nonlinear vehicle dynamics simulation model is established. Then, the driving wheel speed is regulated by adjusting the engine torque and the wheel brake pressure. The engine torque regulation is based on a proportional–integral–derivative plus ant colony optimization controller, and the wheel brake pressure regulation is based on a proportional–integral plus ant colony optimization controller. Finally, the proposed strategies are applied to simulation and road tests. Results indicate that the algorithm exhibits high control accuracy and robust performance. Compared with the traditional proportional–integral–derivative controller, the proposed strategies improve vehicle acceleration performance and stability.
5
Niculae, Andrei Laurentiu, Adnan Kadhim Rashid, and Radu Chiriac. "The effects of split direct injection on the operation of a tractor diesel engine fueled by biodiesel B20." E3S Web of Conferences 286 (2021): 01006. http://dx.doi.org/10.1051/e3sconf/202128601006.
Повний текст джерелаАнотація:
The use of biodiesel-diesel blends is a current solution to some important problems, such as the depletion of oil resources, global warming, and the pollutant emissions of smoke, carbon monoxide, and hydrocarbons of diesel engines. However, the use of this alternative fuel is characterized by a reduction in engine effective power and an increase in brake-specific fuel consumption and nitrogen oxide pollutant emissions. Using the AVL MCC zero-dimensional combustion model of the AVL BOOST simulation program, it was evaluated to what extent split injection strategies can improve the performance and fuel economy of a tractor diesel engine fuelled with biodiesel B20 at maximum brake torque condition considering noise and pollutant emissions limitation. Various pilot – main – post split injection strategies have been studied to establish the optimal injection characteristics in terms of performance and fuel economy. Subsequently, they have been adapted in terms of compliance with current emission standards. In this way, it has been emphasized that the split injection solution is a viable way to improve performance, economy, and pollutant emissions of a tractor diesel engine.
6
Wei, Haiqiao, Jie Yu, Aifang Shao, Lei Zhou, Jianxiong Hua, and Dengquan Feng. "Influence of injection strategies on knock resistance and combustion characteristics in a DISI engine." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 10 (October 8, 2018): 2637–49. http://dx.doi.org/10.1177/0954407018804118.
Повний текст джерелаАнотація:
The combustion of a direct injection spark ignition engine is significantly affected by the fuel injection strategy due to the impact this strategy has on the gas-mixture formation and the turbulence flow. However, comprehensive assessments on both knock and engine performances for different injection strategies are generally lacking. Therefore, the main objective of the present study is to provide an experimental evidence of how a single injection strategy and a split injection strategy compare in terms of both knock tendency and engine performances like thermal efficiency, torque and combustion stability. Starting from the optimization of a single injection strategy, a split injection strategy is then evaluated. Under the present operating conditions, an optimum secondary injection timing of 100 CAD BTDC is found to have significant improvements on both the knock resistance and the overall engine performances. It should be noted that the present results indicate that the relationship between double injection and anti-knock performance is not monotonous. In addition, the double injection shows superior potential in improving fuel economy and power performance in contrast with the single injection thanks to a more stable combustion when a late injection timing is applied.
7
Xu, Defeng, Jianwu Zhang, Bin Zhou, and Haisheng Yu. "Investigation of mode transition coordination for power-split hybrid vehicles using dynamic surface control." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 233, no. 3 (March 26, 2019): 696–713. http://dx.doi.org/10.1177/1464419319838931.
Повний текст джерелаАнотація:
For passenger cars propelled by the dedicated compound power-split hybrid powertrain, driveline oscillations-induced vehicle jerks are often excited during clutch-to-clutch shift operations while drive mode changes. To tackle this issue, a coordinated dynamic surface control is developed by integrating clutch slips and motor torque compensation strategies through trajectories tracking of both clutch slip speed and wheel speed. Uncertainties or disturbances are treated to be additional inputs of the system, and model nonlinearities are considered and implemented in discretized form through lookup tables. A complex simulation model including electro-hydraulic system is proposed and validated via experiments. The coordinated controller is validated by collaborative simulation. Numerical examples are made and simulation results verify that the controller is effective and robust enough against parameters uncertainties.
8
Gao, Aiyun, Xiaozhong Deng, Zhumu Fu, and Mingzhu Zhang. "OPTIMIZATION OF ICE START-STOP BASED ON MPC FOR AN HEV TO IMPROVE FUEL ECONOMY." Transactions of the Canadian Society for Mechanical Engineering 41, no. 3 (September 2017): 355–74. http://dx.doi.org/10.1139/tcsme-2017-1025.
Повний текст джерелаАнотація:
To improve hybrid electric vehicle (HEV) fuel efficiency further, the decision as to whether the internal combustion engine (ICE) should start or stop is important. This paper presents a novel optimization method of the ICE start-stop by using the model predictive control (MPC) based on equivalent consumption minimization strategy (ECMS). The optimization method and flow of the ICE start-stop are described in detail. Three torque-split control strategies are proposed for the comparison purpose. From the ICE operating points, the fuel consumption and the battery SOC, simulation results reveal that the transient MPC strategy with ICE start-stop has a huge potential for improving the overall fuel economy.
9
Geng, Stefan, Thomas Schulte, and Jürgen Maas. "Model-Based Analysis of Different Equivalent Consumption Minimization Strategies for a Plug-In Hybrid Electric Vehicle." Applied Sciences 12, no. 6 (March 11, 2022): 2905. http://dx.doi.org/10.3390/app12062905.
Повний текст джерелаАнотація:
Plug-in hybrid electric vehicles (PHEVs) are developed to reduce fuel consumption and the emission of carbon dioxide. Common powertrain configurations of PHEVs (i.e., the configuration of the combustion engine, electric motor, and transmission) can be operated either in series, parallel, or power split hybrid mode, whereas powertrain configurations with multimode transmissions enable switching between those modes during vehicle operation. Hence, depending on the current operation state of the vehicle, the most appropriate mode in terms efficiency can be selected. This, however, requires an operating strategy, which controls the mode selection as well as the torque distribution between the combustion engine and electric motor with the aim of optimal battery depletion and minimal fuel consumption. A well-known approach is the equivalent consumption minimization strategy (ECMS). It can be applied by using optimizations based on a prediction of the future driving behavior. Since the outcome of the ECMS depends on the quality of this prediction, it is crucial to know how accurate the predictions must be in order to obtain acceptable results. In this contribution, various prediction methods and real-time capable ECMS implementations are analyzed and compared in terms of the achievable fuel economy. The basis for the analysis is a holistic model of a state-of-the-art PHEV powertrain configuration, comprising the multimode transmission, corresponding powertrain components, and representative real-world driving data.
10
Huo, Da, and Peter Meckl. "Power Management of a Plug-in Hybrid Electric Vehicle Using Neural Networks with Comparison to Other Approaches." Energies 15, no. 15 (August 7, 2022): 5735. http://dx.doi.org/10.3390/en15155735.
Повний текст джерелаАнотація:
Many researchers spent much effort on the online power management strategies for plug-in hybrid vehicles (PHEVs) and hybrid electric vehicles (HEVs). Nowadays, artificial neural networks (ANNs), one of the machine learning techniques, have also been applied to this problem due to their good performance in learning non-linear and complicated multi-inputs multi-outputs (MIMO) dynamic systems. In this paper, an ANN is applied to the online power management for a plug-in hybrid electric vehicle (PHEV) by predicting the torque split between an internal combustion engine (ICE) and an electric motor (e-Motor) to optimize the greenhouse gas (GHG) emissions by using dynamic programming (DP) results as training data. Dynamic programming can achieve a global minimum solution while it is computationally intensive and requires prior knowledge of the entire drive cycle. As such, this method cannot be implemented in real-time. The DP-based ANN controller can get the benefit of using an ANN to fit the DP solution so that it can be implemented in real-time for an arbitrary drive cycle. We studied the hyper-parameters’ effects on the ANN model and different structures of ANN models are compared. The minimum training mean square error (MSE) models in each comparison set are selected for comparison with DP and equivalent consumption minimization strategy (ECMS). The total GHG emissions and state of charge (SOC) are the metrics used for the analysis and comparison. All the selected ANNs provide results that are comparable to the optimal DP solution, which indicates that ANNs are almost as good as the DP solution. It is found that the multiple hidden-layer ANN shows more efficiency in the training process than the single hidden-layer ANN. By comparing the results with ECMS, the ANN shows great potential in real-time application with the smallest deviation from the results of DP. In addition, our approach does not require any additional trip information, and its output (torque split) is more directly implementable on real vehicles.
11
Jacquelin, Frederic, Jungyun Bae, Bo Chen, Darrell Robinette, Pruthwiraj Santhosh, Troy Kraemer, and Bonnie Henderson. "Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution." Vehicles 4, no. 4 (September 22, 2022): 942–56. http://dx.doi.org/10.3390/vehicles4040051.
Повний текст джерелаАнотація:
The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process.
12
Lepot, I., M. Leborgne, R. Schnell, J. Yin, G. Delattre, F. Falissard, and J. Talbotec. "Aero-mechanical optimization of a contra-rotating open rotor and assessment of its aerodynamic and acoustic characteristics." Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 225, no. 7 (November 2011): 850–63. http://dx.doi.org/10.1177/0957650911413695.
Повний текст джерелаАнотація:
Within the frame of the ongoing 7th Framework European Project DREAM, this article presents a synthesis of Cenaero, DLR, and Onera's joint effort to demonstrate the aerodynamic and acoustic optimization potential of a contra-rotating open rotor. Within WP 3.2 led by Snecma, the objective was to maximize the propellers efficiency at top-of-climb conditions and to minimize the noise emission at take-off focusing on interaction noise while fulfilling the thrust and torque split specifications at both operating points. These objectives were successfully met by the development and exploitation of efficient multi-objective three-dimensional Reynolds-averaged Navier–Stokes (RANS)-based surrogate-assisted optimization strategies. In order to assess the aerodynamic and acoustic characteristics of both baseline and optimized geometries, coupled unsteady RANS (URANS) simulation and farfield prediction based on an integral Ffowcs Williams–Hawkings approach were then carried out. The results demonstrate that although the acoustic criterion driving the optimization process did not lead to an improvement of the noise characteristics over the whole directivity range, it may be regarded as a cost-effective way to incorporate noise-related aspects into the design intent.
13
Liang, Yu-Pei, Shuo-Han Chen, Yuan-Hao Chang, Yun-Fei Liu, Hsin-Wen Wei, and Wei-Kuan Shih. "A cache consolidation design of MLC STT-RAM for energy efficiency enhancement on cyber-physical systems." ACM SIGAPP Applied Computing Review 21, no. 1 (March 2021): 37–49. http://dx.doi.org/10.1145/3477133.3477136.
Повний текст джерелаАнотація:
Owing to the energy-constraint nature of cyber-physical systems (CPS), energy efficiency has become a primary design consideration for CPS. On CPS, owing to the high leakage power issue of SRAM, the major portion of its energy consumption comes from static random-access memory (SRAM)-based processors. Recently, with the emerging and rapidly evolving nonvolatile Spin-Transfer Torque RAM (STT-RAM), STT-RAM is expected to replace SRAM within processors for enhancing the energy efficiency with its near-zero leakage power features. The advances in Magnetic Tunneling Junction (MTJ) technology also realize the multi-level cell (MLC) STT-RAM to pack more cells with the same die area for achieving the memory density. However, the write disturbance issue of MLC STT-RAM prevents STT-RAM from properly resolving the energy efficiency of CPS. Although studies have been proposed to alleviate this issue, previous strategies could induce additional management overhead due to the use of counters or lead to frequent swap operations. Such an observation motivates us to propose an effective and simple strategy to combine direct and split cache mapping designs to enhance the energy efficiency of MLC STT-RAM. A series of experiments have been conducted on an open-source emulator with encouraging results.
14
Gao, Aiyun, Xiaozhong Deng, Mingzhu Zhang, and Zhumu Fu. "Design and Validation of Real-Time Optimal Control with ECMS to Minimize Energy Consumption for Parallel Hybrid Electric Vehicles." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/3095347.
Повний текст джерелаАнотація:
A real-time optimal control of parallel hybrid electric vehicles (PHEVs) with the equivalent consumption minimization strategy (ECMS) is presented in this paper, whose purpose is to achieve the total equivalent fuel consumption minimization and to maintain the battery state of charge (SOC) within its operation range at all times simultaneously. Vehicle and assembly models of PHEVs are established, which provide the foundation for the following calculations. The ECMS is described in detail, in which an instantaneous cost function including the fuel energy and the electrical energy is proposed, whose emphasis is the computation of the equivalent factor. The real-time optimal control strategy is designed through regarding the minimum of the total equivalent fuel consumption as the control objective and the torque split factor as the control variable. The validation of the control strategy proposed is demonstrated both in the MATLAB/Simulink/Advisor environment and under actual transportation conditions by comparing the fuel economy, the charge sustainability, and parts performance with other three control strategies under different driving cycles including standard, actual, and real-time road conditions. Through numerical simulations and real vehicle tests, the accuracy of the approach used for the evaluation of the equivalent factor is confirmed, and the potential of the proposed control strategy in terms of fuel economy and keeping the deviations ofSOCat a low level is illustrated.
15
Bidarvatan, Mehran, and Mahdi Shahbakhti. "Analysis and Control of Torque Split in Hybrid Electric Vehicles by Incorporating Powertrain Dynamics." Journal of Dynamic Systems, Measurement, and Control 140, no. 11 (June 18, 2018). http://dx.doi.org/10.1115/1.4040219.
Повний текст джерелаАнотація:
Hybrid electric vehicle (HEV) energy management strategies usually ignore the effects from dynamics of internal combustion engines (ICEs). They usually rely on steady-state maps to determine the required ICE torque and energy conversion efficiency. It is important to investigate how ignoring these dynamics influences energy consumption in HEVs. This shortcoming is addressed in this paper by studying effects of engine and clutch dynamics on a parallel HEV control strategy for torque split. To this end, a detailed HEV model including clutch and ICE dynamic models is utilized in this study. Transient and steady-state experiments are used to verify the fidelity of the dynamic ICE model. The HEV model is used as a testbed to implement the torque split control strategy. Based on the simulation results, the ICE and clutch dynamics in the HEV can degrade the control strategy performance during the vehicle transient periods of operation by around 8% in urban dynamometer driving schedule (UDDS) drive cycle. Conventional torque split control strategies in HEVs often overlook this fuel penalty. A new model predictive torque split control strategy is designed that incorporates effects of the studied powertrain dynamics. Results show that the new energy management control strategy can improve the HEV total energy consumption by more than 4% for UDDS drive cycle.
16
Li, Qing, Zhendong Zhang, Tong Zhang, and Han Guo. "Control optimization of a compound power-split hybrid power system for commercial vehicles." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, February 23, 2021, 095440702199363. http://dx.doi.org/10.1177/0954407021993639.
Повний текст джерелаАнотація:
In this paper, based on two planetary gear sets, a novel compound power-split hybrid power system for commercial vehicles is presented. Firstly, mathematical models of the system dynamic torque control and the system efficiency are established by using an equivalent lever diagram and analyzing the power flow respectively. Then the system operating modes which are divided into two pure electric modes and one hybrid mode and corresponding control strategies are analyzed by the combined lever diagrams. Finally, control strategies in different modes to looking for the optimal system efficiencies are designed and validated by the bench test. In order to prolong its service life, the battery is charged with small power in the hybrid mode. The design and test results indicate that the optimal system efficiency control strategies are reasonable and reliable. The maximum value of the optimal system efficiency can reach about 0.92 in the pure electric mode, and 0.39 in the hybrid mode. And the proportion of the engine operating points with the brake specific fuel consumption (BSFC) lower than 215 g/kWh is 81%. The co-simulation results show that the maximum system efficiency can achieve 14.9% fuel consumption per 100 km drop compared with the minimum system efficiency for an 80 km/h constant speed condition, which indicates that the optimal system efficiency control strategy can greatly improve the vehicle fuel economy. This study can offer a research direction for energy management of hybrid power systems.
17
Hwang, Hsiu-Ying. "Minimizing Seat Track Vibration That is Caused by the Automatic Start/Stop of an Engine in a Power-Split Hybrid Electric Vehicle." Journal of Vibration and Acoustics 135, no. 6 (June 19, 2013). http://dx.doi.org/10.1115/1.4023954.
Повний текст джерелаАнотація:
The use of hybrid electric vehicles is an effective means of reducing pollution and improving fuel economy. Certain vehicle control strategies commonly automatically shut down or restart the internal combustion engines of hybrid vehicles to improve their fuel consumption. Such an engine autostart/stop is not engaged or controlled by the driver. Drivers often do not expect or prepare for noticeable vibrations, noise, or an unsmooth transition when the engine is autostarted/stopped. Unsmooth engine autostart/stop transitions can cause driveline vibrations, making the ride uncomfortable and the customer dissatisfied with the vehicle. This research simulates the dynamic behaviors associated with the neutral starting and stopping of a power-split hybrid vehicle. The seat track vibration results of analysis and hardware tests of the baseline control strategy are correlated. Several antivibration control strategies are studied. The results reveal that pulse cancellation and the use of a damper bypass clutch can effectively reduce the fluctuation of the engine block reaction torque and the vibration of the seat track by more than 70% during the autostarting and stopping of the engine. The initial crank angle can have an effect on the seat track vibration as well.
18
Henry de Frahan, Marc T., Nicholas T. Wimer, Shashank Yellapantula, and Ray W. Grout. "Deep reinforcement learning for dynamic control of fuel injection timing in multi-pulse compression ignition engines." International Journal of Engine Research, May 24, 2021, 146808742110193. http://dx.doi.org/10.1177/14680874211019345.
Повний текст джерелаАнотація:
Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.