Gear Shift Research Articles

Mixed‐Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Electric Vehicle Energy Management

ABSTRACTMany optimal control problems require the simultaneous output of discrete and continuous control variables. These problems are typically formulated as mixed‐integer optimal control (MIOC) problems, which are challenging to solve due to the complexity of the solution space. Numerical methods such as branch‐and‐bound are computationally expensive and undesirable for real‐time control. This article proposes a novel hybrid‐action reinforcement learning (HARL) algorithm, twin delayed deep deterministic actor‐Q (TD3AQ), for MIOC problems. TD3AQ leverages actor‐critic and Q‐learning methods to manage discrete and continuous action spaces simultaneously. The proposed algorithm is evaluated on a plug‐in hybrid electric vehicle (PHEV) energy management problem, where real‐time control of the discrete variables, clutch engagement/disengagement and gear shift, and continuous variable, engine torque, is essential to maximize fuel economy while satisfying driving constraints. Simulation results show that TD3AQ achieves near‐optimal control, with only a 4.69% difference from dynamic programming (DP), and outperforms baseline reinforcement learning algorithms for hybrid action spaces. The sub‐millisecond execution time indicates potential applicability in other time‐critical scenarios, such as autonomous driving or robotic control.

Adaptive small-scale fisheries in the eastern Cantabrian coast through reliance on essential species

Abstract In recent decades, small-scale fisheries (SSF) activity along the Basque coast (eastern Cantabrian) has declined, which has led remaining vessels to undergo notable shifts in their targeted species and therefore the fishing gears used, aimed at enhancing efficiency. Within that context, this study combines logbooks and sales notes spanning from 1995 to 2022 to assess inter-annual and seasonal variations in fishing activity and the main target species across different fishing gears, namely ‘fleet segments’. Results reveal that the spring Atlantic mackerel (Scomber scombrus) and summer albacore (Thunnus alalunga) seasons, with an intensified harvesting under favourable conditions during the past decade, affected the activity of all other segments throughout the year. In the face of climate change affecting harvested species, a scenario where mackerel and/or albacore seasons are disturbed would lead the SSF to predominantly depend on European hake, mainly caught by declining longlines and set nets, as well as on complementary species. Assessing essential species targeted by each SSF segment relies on is crucial for stakeholders as it helps manage interactions between fleets targeting the same species (e.g. SSF vs. industrial and recreational fisheries), and understand gear shifts by vessels targeting specific species in certain seasons.

Modeling and simulative analysis of shift control for at powertrain on the car

The problem with this power transmission system is related to increased fuel consumption, and small performance. The above issues are decisive for the management of the AT transmission system. In other words, determining when to shift gears is one of the factors that determine the advantages of this system. In this paper, the authors focus on building a model consisting of the engine, hydraulic torque converter, gearbox, final drive and differential, drive wheels, and vehicle body dynamic system and propose a gear shift control algorithm for AT based on the throttle opening level (%) and the longitudinal velocity of the car. Finally, the effectiveness and advantage of the proposed control schemes are indicated by illustrative results through MATLAB/SIMULINK. This approach is the basis for research on improving performance and optimizing fuel for automotive powertrains.

Real-Time Co-optimization of Gear Shifting and Engine Torque for Predictive Cruise Control of Heavy-Duty Trucks

Fuel consumption is one of the main concerns for heavy-duty trucks. Predictive cruise control (PCC) provides an intriguing opportunity to reduce fuel consumption by using the upcoming road information. In this study, a real-time implementable PCC, which simultaneously optimizes engine torque and gear shifting, is proposed for heavy-duty trucks. To minimize fuel consumption, the problem of the PCC is formulated as a nonlinear model predictive control (MPC), in which the upcoming road elevation information is used. Finding the solution of the nonlinear MPC is time consuming; thus, a real-time implementable solver is developed based on Pontryagin’s maximum principle and indirect shooting method. Dynamic programming (DP) algorithm, as a global optimization algorithm, is used as a performance benchmark for the proposed solver. Simulation, hardware-in-the-loop and real-truck experiments are conducted to verify the performance of the proposed controller. The results demonstrate that the MPC-based solution performs nearly as well as the DP-based solution, with less than 1% deviation for testing roads. Moreover, the proposed co-optimization controller is implementable in a real-truck, and the proposed MPC-based PCC algorithm achieves a fuel-saving rate of 7.9% without compromising the truck’s travel time.

Experimental Investigation and Control of Driveline Torsional Vibrations during Clutch-to-Clutch Shifts of Electrified Vehicles

An electrified vehicle equipped with a stepped-ratio transmission and clutch(es) requires precise control of the clutch actuator(s) and power sources to achieve optimal gear shift performance, which is characterized by smooth and swift gear shifts. Owing to the absence of the smoothing effect of torque converters, dual-clutch transmission (DCT) powertrains are prone to inducing abrupt shift shocks—particularly during rapid clutch-to-clutch shifts. Balancing the smoothness and speed of shifts is a significant challenge and was the key focus of this study. Multiple experiments and model-based analyses were conducted to investigate the tradeoff between smoothness and shift time during the clutch-to-clutch shifts of a parallel-type hybrid electric vehicle with a dry DCT. Additionally, the adverse effects of inaccurate power-source control on shift quality were experimentally investigated. The results revealed the primary physical factors in terms of control causing torsional driveline oscillations in clutch-to-clutch shifts. According to these observations, a detailed quantitative guide including how to generate reference trajectories for shift control is proposed, with the aim of reducing the driveline torsional vibrations without compromising the shift time. The effectiveness of the proposed control strategy was demonstrated through real-time experiments on an electrified powertrain with a DCT using a dedicated test bench. This study provides valuable insights for optimizing the shift performance of electrified vehicles—particularly for managing torsional vibrations during clutch-to-clutch shifts.

Analysis of technical solutions for a high-power drive of a pumping unit intended for hydraulic fracturing of reservoir bridges in oil and gas isolated cavities

In order to simplify the design and increase the reliability of a high-power drive, the possibilities of using various high-speed two-shaft double-flow mechanical gear shift transmissions in a pumping unit designed for hydraulic fracturing of reservoir bridges in oil and gas isolated cavities are considered. Keywords:power drive, gear shift transmission, friction device, planetary gearbox, hydraulic pump, power flow au@vstu.ru

Optimal Gear Shift Control of Two-Speed Dual-Clutch Transmission in Electric Vehicle for Smoothness and Friction Loss Reduction

Optimal Gear Shift Control of Two-Speed Dual-Clutch Transmission in Electric Vehicle for Smoothness and Friction Loss Reduction

Computationally efficient gear shift strategy for hybrid electric vehicles

Computationally efficient gear shift strategy for hybrid electric vehicles

Energy Management for Hybrid Electric Vehicles Using Safe Hybrid-Action Reinforcement Learning

Reinforcement learning has shown success in solving complex control problems, yet safety remains paramount in engineering applications like energy management systems (EMS), particularly in hybrid electric vehicles (HEVs). An effective EMS is crucial for coordinating power flow while ensuring safety, such as maintaining the battery state of charge within safe limits, which presents a challenging task. Traditional reinforcement learning struggles with safety constraints, and the penalty method often leads to suboptimal performance. This study introduces Lagrangian-based parameterized soft actor–critic (PASACLag), a novel safe hybrid-action reinforcement learning algorithm for HEV energy management. PASACLag utilizes a unique composite action representation to handle continuous actions (e.g., engine torque) and discrete actions (e.g., gear shift and clutch engagement) concurrently. It integrates a Lagrangian method to separately address control objectives and constraints, simplifying the reward function and enhancing safety. We evaluate PASACLag’s performance using the World Harmonized Vehicle Cycle (901 s), with a generalization analysis of four different cycles. The results indicate that PASACLag achieves a less than 10% increase in fuel consumption compared to dynamic programming. Moreover, PASACLag surpasses PASAC, an unsafe counterpart using penalty methods, in fuel economy and constraint satisfaction metrics during generalization. These findings highlight PASACLag’s effectiveness in acquiring complex EMS for control within a hybrid action space while prioritizing safety.

Parameter Study for Establishing a Synchronizer Control Strategy in Tractor Dual-Clutch Transmission

Research on dual-clutch transmissions in agricultural tractors is on the rise. Dual-clutch transmissions provide a smooth driving experience and easy operation compared to manual transmissions, as they eliminate power interruptions. Accordingly, there is ongoing research to improve the conventional synchronizer pre-selection method. The study introduces a new control strategy to enhance shifting quality by focusing on shift force and timing. Specifically, the goal is to optimize the impact of pre-selection-induced shock and delayed shift time, minimizing discomfort for the driver. According to the experimental results, it was determined that factors influencing the impact of preselect engagement on gear shift shock were absent, while factors capable of affecting shift time were identified in the form of shift force. However, it was concluded that optimizing shift force and shift timing based on the number of shifts can enhance the quality of gear shifting. Even during preselect disengagement, the factor influencing gear shift shock was identified as the shift timing, with no discernible factor affecting shift time. Nevertheless, it was judged that optimizing shift force and shift timing based on the number of shifts can lead to an improvement in gear shifting quality. In conclusion, the control strategy determined through the parameter study is expected to improve the shifting quality of tractors equipped with dual-clutch transmission. These findings have the potential to support efficient and convenient tractor operation in the agricultural sector.

Gear Shifting and Vehicle Speed Optimization for Eco-Driving on Curved Roads

Gear Shifting and Vehicle Speed Optimization for Eco-Driving on Curved Roads

Adaptive ECMS With Gear Shift Control by Grey Wolf Optimization Algorithm and Neural Network for Plug-In Hybrid Electric Buses

The Plug-in hybrid electric bus (PHEB) is an important means of public transportation. For PHEB, this paper proposes an energy management strategy that considers both gear shift control and power splitting. For gear shift control, a large number of gear-switching data under different working conditions are calculated by using the dynamic programming algorithm. The neural network is trained by these data in the offline part so that it can provide the appropriate gear-switching signal in time in the online model. For power-split control, this paper mainly selects an improved equivalent fuel consumption minimization strategy (ECMS) and uses the grey wolf optimization algorithm to iteratively solve the optimal equivalent factor. The proposed control strategy has not only been verified under various operating conditions but also verified by relevant experiments on the HIL platform. The results show that the proposed strategy has better fuel economy than ECMS and the rule-based strategy under the provided mixed operating conditions. Compared with the dynamic programming algorithm, the fuel consumption is only increased by 3.23%, but the problem of the curse of dimensionality is avoided.

Energy Condition of Gear Shifting under Load in Commercial Vehicles

Energy Condition of Gear Shifting under Load in Commercial Vehicles

Multi-time scale thermal tribological dynamics coupling model of the helicopter intermediate reducer under severe loss of lubrication

There is no lubrication in the contact area, which leads to a significant influence of the thermal characteristics on the friction dynamics and life of the helicopter intermediate reducer because the helicopter experiences severe loss of lubrication. In this study, for evaluating the non-linear friction dynamics and transient thermal network models of the intermediate gearbox, a multi-time scale coupling method was proposed to establish a proportional allocation method between the oil film region and the rough peak contact region of the tooth surface by considering the effect of temperature and to study the adsorption film under the condition of severe lubrication loss. The coupled thermal and kinetic characteristics of the severe lubrication loss condition were obtained by calculating the friction coefficients in the lubricated and dry-friction regions. The results showed that the friction coefficient of the bevel gear tooth surface fluctuated between 0.1 and 0.6, and the degree of the gear axis trajectory shift was more evident owing to the increase in the friction force excitation caused by the increase in the friction coefficient.

Deep learning assisted three triboelectric driving operation sensors for driver training and behavior monitoring

Improving the driving skills of drivers, particularly during the training stage, is crucial in reducing the likelihood of road traffic accidents. In this work, a driver training assistance system (DTAS) is developed for driver training and behavior monitoring. The DTAS integrates three triboelectric driving operation sensors, including gear shift sensor, steering angle sensor, and pedal sensors. Through the ingenious structural design of contact-separation and freestanding-triboelectric-layer mode, these triboelectric sensors have the characteristics of simple structure, easy manufacture and installation, and self-powered, which avoids the complex wiring problem in the limited space of the vehicle. The basic electrical performance test of triboelectric sensors and driving simulation experiment show that the developed DTAS can monitor the driver behavior and provide the feedback on each driver’s operation process in real-time. Combined with deep learning (DL) technology, the DTAS can identify whether the driving operation of drivers in specific training scenarios is correct or not, with an accuracy rate of 97.5%. This work is aimed at assisting the novice or learner drivers in driving training, which helps to improve their driving skills and form good driving habits. The proposed scheme can provide new ideas for the innovative exploration of driving training modes without coaching.

Development of a Real-Time Tractor Model for Gear Shift Performance Verification

Verification of the system is essential during the development of a tractor; however, there are cost and time limitations when verification is performed on an actual tractor. To solve this problem, we developed a tractor model for real-time simulation to verify the gear shift performance of the tractor and evaluate the control algorithm. This study examined and modeled a dual-clutch transmission (DCT)-type 105 kW class tractor. The proportional control valve, synchronizer, and clutch were modeled to accurately implement the shift behavior, and the developed individual model was verified based on actual individual product test data. The 45 s driving simulation was conducted to confirm whether real-time simulation of the entire developed tractor model was possible and whether it simulated the behavior of the target tractor well. The driving simulation results confirmed that the driving speed of the tractor model matched the engine speed, transmission gear ratio, and tractor specifications, and the gear shift performance of the tractor model according to the number of gears was confirmed. The simulated model thus satisfies the characteristics of the target tractor and can be used to verify the gear shift performance, indicating that the model can verify the performance of the control algorithm in real time.

Robust Position Control for an Electrical Automatic Transmission under Gear-Shifting Link Friction

The automotive industry is evolving, with software becoming a vital part of vehicles. Conventional automakers are shifting to software-centric entities, embracing over-the-air (OTA) updates and service-centric models. To move software-driven vehicles, the vehicle must also be electrified. Several automobile manufacturers are electrifying vehicle parts, and recently, a gear shift selector for automatic transmissions was adapted from mechanical to electronic. However, as conventional mechanical systems are modified to electrical systems, problems such as shift delay and accuracy emerge. This study addresses these problems that emerge in the electronic system type of automatic transmission, including a gear shift selector developed to electrify automobiles. Accordingly, we first analyze the structure of automatic transmission systems, then define the operation sequence. Next, a novel position control algorithm based on a disturbance observer is proposed to reduce shift delay and increase accuracy. The proposed algorithm operates harmoniously with the vehicle control unit (VCU). To verify the proposed algorithm, a hardware-in-the-loop simulation (HILS) was developed to experiment with vehicle shifting using a commercial electronic gear shift selector. Moreover, the proposed control algorithm for gear shifting in an automatic transmission was analyzed using experimental results obtained by assuming a specific driving situation in the HILS.

Gear shift process control of integrated motor-transmission system considering actuator fault

Gear shift process control of integrated motor-transmission system considering actuator fault

Analysis of control systems for mechatronic transmission gear shift mechanisms

Introduction (problem statement and relevance). Within the study, review and analysis of possible variants for gear shift control systems architecture for vehicles with mechatronic transmissions are made to detect the respective advantages and disadvantages for each scheme and to determine the most optimum solution for application within the mentioned wheeled vehicles in terms of energy consumption and performance.The purpose of the study is to determine the most optimum control system for mechatronic transmission gear shift mechanisms of electric vehicles.Methodology and research methods. This paper is based on methods of reviewing and analyzing gear shift mechanism control systems for the transmission configurations in question.Scientific novelty and results. The results of the conducted analysis showed that the most suitable variant for the gear shift process control would be the systems based on electric actuators. These control systems will allow providing the required performance when shifting gears as well as lowering total energy costs.Practical significance. The obtained conclusions can be used when designing gear shift control systems within mechatronic transmissions for vehicles propelled by electrochemical power sources.

Mathematical modelling of gear upshift and downshift schedule for two-wheeler amt transmission

Gear shifting strategy is the key for the successful working of an automated manual transmission (AMT) as it affects the driving experience, fuel economy and dynamic performance of the vehicle. Depending upon the driver’s requirement, AMT can be designed for maximum acceleration performance and fuel efficiency. This project involves generation of gear shift schedule for both the maximum acceleration and fuel economy performance. To generate the gear shift schedule, a mathematical model is developed in MATLAB environment which takes input parameters as: vehicle speed, acceleration and throttle position. For generation of gear upshift schedule for maximum acceleration and fuel economy equation relating acceleration with tractive forces and vehicle’s fuel consumption equation at different throttle position is used respectively. Fuel consumption equation relates to brake specific fuel consumption (BSFC) along with vehicle structure. In order to obtain BSFC data, a GT-SUITE model of motorcycle is developed which incorporates data like gear ratio, power, torque and motorcycle weight. Finally, a buffer zone is applied to generated upshift schedule for both maximum acceleration and fuel economy case to get the downshift schedule. The generated gear shift schedule can be utilized for the development of the retrofit AMT kit on manual transmission motorcycle.