Optimization for speed regulation strategy of compound coupled hydro-mechanical transmission

2020 ◽  
pp. 095440702098056
Author(s):  
Jin Yu ◽  
Pengfei Shen ◽  
Zhao Wang ◽  
Yurun Song ◽  
Xiaohan Dong
Keyword(s):  
Fuel Consumption ◽  
Dynamic Programming Algorithm ◽  
Operating Conditions ◽  
Programming Algorithm ◽  
Speed Ratio ◽  
Mechanical Transmission ◽  
Speed Regulation ◽  
Ratio Change ◽  
Low Efficiency ◽  
Wheel Loaders

Heavy duty vehicles, especially special vehicles, including wheel loaders and sprinklers, generally work with drastic changes in load. With the usage of a conventional hydraulic mechanical transmission, they face with these problems such as low efficiency, high fuel consumption and so forth. Some scholars focus on the research to solve these issues. However, few of them take into optimal strategies the fluctuation of speed ratio change, which can also cause a lot of problems. In this study, a novel speed regulation is proposed which cannot only solve problems above but also overcome impact caused by speed ratio change. Initially, based on the former research of the Compound Coupled Hydro-mechanical Transmission (CCHMT), the basic characteristics of CCHMT are analyzed. Besides, to solve these problems, dynamic programming algorithm is utilized to formulate basic speed regulation strategy under specific operating condition. In order to reduce the problem caused by speed ratio change, a new optimization is applied. The results indicate that the proposed DP optimal speed regulation strategy has better performance on reducing fuel consumption by up to 1.16% and 6.66% in driving cycle JN1015 and in ECE R15 working condition individually, as well as smoothing the fluctuation of speed ratio by up to 12.65% and 19.01% in those two driving cycles respectively. The processes determining the speed regulation strategy can provide a new method to formulate the control strategies of CCHMT under different operating conditions particularlly under real-world conditions.

Performance Analysis of a Variable Speed-Ratio Metal V-Belt Drive

1988 ◽  
Vol 110 (4) ◽  
pp. 472-481 ◽  
Author(s):  
D. C. Sun
Keyword(s):  
Operating Conditions ◽  
Computational Scheme ◽  
Optimum Operating ◽  
Speed Ratio ◽  
Belt Drive ◽  
Variable Speed ◽  
Change Effect ◽  
Multiple Band ◽  
Ratio Change ◽  
Metal Block

A model of the metal V-belt drive (MBD), considering its detailed multiple-band and metal-block structure, and the ratio-change effect during its operation, is constructed and analyzed. A computational scheme is devised that adapts the analysis to the computation of the MBD’s performance for any specified drive-schedule. General performance characteristics of the MBD and an example illustrating its response to a given drive-schedule are presented. The use of the analysis and the computational scheme in the design of the MBD and in finding the optimum operating conditions is discussed.

Minimum Fuel Consumption and CO Emission and Optimum Speed of the Motorcycle With a CVT

2001 ◽  
Author(s):  
Yuan Mao Huang ◽  
Bi Shyang Hu
Keyword(s):  
Fuel Consumption ◽  
Simulated Annealing Algorithm ◽  
Gear Ratio ◽  
Design Parameters ◽  
Speed Ratio ◽  
Optimum Speed ◽  
Product Method ◽  
Ratio Change ◽  
Annealing Algorithm ◽  
Co Emission

Abstract The simulated annealing algorithm with the Bessel method for the curve fitting and the tensor product method for the surface fitting was used to transform the discrete experimental data into the form that the method of optimization can use these data directly. The rotational speeds of an engine starting the movement and corresponding the optimum speed of a motorcycle, minimum speed ratio of a CVT, optimum tooth numbers of gears and the gear ratio for the specific engine data were obtained. The rotational speeds of an engine corresponding the beginning and ending of the CVT speed ratio change, the minimum fuel consumption and the CO emission, the optimum design parameters can be determined. The results of the design parameters can be recommended for the CVT with the specific engine.

scholarly journals Profit Maximizing Control of a Microgrid with Renewable Generation and BESS Based on a Battery Cycle Life Model and Energy Price Forecasting

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2904 ◽  
Author(s):  
Wenhao Zhuo ◽  
Andrey V. Savkin
Keyword(s):  
Dynamic Programming ◽  
Wind Farm ◽  
Optimization Problem ◽  
Cost Model ◽  
Dynamic Programming Algorithm ◽  
Operating Conditions ◽  
Programming Algorithm ◽  
Renewable Generation ◽  
The Cost ◽  
Charge Discharge

In this paper, an optimal control strategy is presented for grid-connected microgrids with renewable generation and battery energy storage systems (BESSs). In order to optimize the energy cost, the proposed approach utilizes predicted data on renewable power, electricity price, and load demand within a future period, and determines the appropriate actions of BESSs to control the actual power dispatched to the utility grid. We formulate the optimization problem as a Markov decision process and solve it with a dynamic programming algorithm under the receding horizon approach. The main contribution in this paper is a novel cost model of batteries derived from their life cycle model, which correlates the charge/discharge actions of batteries with the cost of battery life loss. Most cost models of batteries are constructed based on identifying charge–discharge cycles of batteries on different operating conditions, and the cycle counting methods used are analytical, so cannot be expressed mathematically and used in an optimization problem. As a result, the cost model proposed in this paper is a recursive and additive function over control steps that will be compatible with dynamic programming and can be included in the objective function. We test the proposed approach with actual data from a wind farm and an energy market operator.

Minimum Fuel Consumption and CO Emission and Optimum Speed of the Motorcycle with a CVT

2003 ◽  
Vol 125 (4) ◽  
pp. 311-317 ◽  
Author(s):  
Yuan Mao Huang ◽  
Bi Shyang Hu
Keyword(s):  
Fuel Consumption ◽  
Rotational Speed ◽  
Simulated Annealing Algorithm ◽  
Design Parameters ◽  
Speed Ratio ◽  
Engine Torque ◽  
Design Variables ◽  
Variable Transmission ◽  
Ratio Change ◽  
Co Emission

The simulated annealing algorithm with the Bessel method for curve fitting and the tensor product method for surface fitting is used to transform engine discrete experimental data into a form that enables these data to be incorporated in the optimization process. Optimum curves of the engine torque versus the engine rotational speed and the engine rotational speed versus the motorcycle speed for the fuel consumption and the carbon monoxide (CO) emission are obtained for a motorcycle with a continuously variable transmission (CVT). The engine rotational speed at which a motorcycle begins to move for the specific engine data is obtained. From design parameters, engine rotational speeds corresponding to the maximum and minimum CVT speed ratio change, the minimum fuel consumption and CO emission, and optimum design variables can be determined.

scholarly journals Elements of Comprehensive Pipeline Optimization

1996 ◽  
Author(s):  
Michael Short ◽  
Steven H. Meller
Keyword(s):  
Real World ◽  
Dynamic Programming Algorithm ◽  
Computational Effort ◽  
Operating Conditions ◽  
Programming Algorithm ◽  
Cost Structures ◽  
Look Ahead ◽  
Full Analysis ◽  
Manageable Level ◽  
Utility Cost

It is well known that algorithms exist for reducing pipeline operating costs. These algorithms are exact for ideal pipelines and need to be modified to provide solutions for the real world. The issues include pipeline configurations, utility cost structures, and quantification of hydraulic safety. Successful modification requires understanding of the pipeline operating environment (on-line operations) and must be linked to pipeline operating conditions. Many of the optimization tools available to the pipeline industry today are based upon a dynamic programming algorithm attributed to Bellman. The costs of unit operations are balanced with the energy absorbed in heat due to frictional and other losses. This is carried out in such a way as to reduce the massive computational effort of an exhaustive solution search to a manageable level. For a pedagogical treatment of the problem, this is adequate. However, there are many significant factors which need to be added into and around this basic calculation. First, an algorithm with electrical cost factors only cannot evaluate penalties associated with poor hydraulics choices. Demand grouping, parallel pipelines, large amplitude pressure cycles, look ahead, and unit cycling also can and should be included in a full analysis. A modification to Bellman’s algorithm for non-linear pipeline configurations and electrical contracts will be developed and discussed in the context of a real-world petroleum pipeline operation.

Modeling of engine thermal dynamics and its application in energy management of HEVs considering engine warming-up

2021 ◽  
pp. 146808742110445
Author(s):  
Hongqing Chu ◽  
Haoyun Shi ◽  
Yuyao Jiang ◽  
Tielong Shen
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Management Strategy ◽  
Dynamic Programming Algorithm ◽  
Optimal Solution ◽  
Degree Of Approximation ◽  
Programming Algorithm ◽  
Energy Management Strategy ◽  
Thermal Dynamics ◽  
Behavior Simulation

The process of engine warming-up leads to additional fuel consumption. Energy management strategy considering engine warming-up is expected to further improve the energy economy of hybrid electric vehicles. This study provides a simple yet practical model for engine thermal dynamics. Then, the optimization problem of energy management considering engine warming-up is formulated on the basis of the control-oriented engine thermal dynamics. Thereafter, the optimal solution is derived by using the dynamic programming algorithm. Finally, the proposed engine thermal dynamics and energy management strategy are evaluated through simulation and experiments. Results show that the established engine thermal model effectively captures the main thermal behavior, simulation results reveal a high degree of approximation to experimental results for the engine temperature and fuel consumption, and the energy management strategy with engine temperature can further improve the energy efficiency.

Optimized Control Strategy Based on the Driving Cycle Type for a Hydraulic Hybrid Bus

2012 ◽  
Author(s):  
Ali Safaei ◽  
Vahid Esfahanian ◽  
Mohammad Reza Ha’iri-Yazdi ◽  
Mohsen Esfahanian ◽  
Masood Masih Tehrani ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Intelligent Control ◽  
Control Strategy ◽  
Dynamic Programming Algorithm ◽  
Driving Cycle ◽  
Programming Algorithm ◽  
Hybrid Powertrain ◽  
Hydraulic Hybrid ◽  
Hybrid Bus ◽  
Degree Of Similarity

Using hybrid powertrains is an attractive idea to reduce the fuel consumption in vehicles. Control strategy is the most challenging subject in designing of a hybrid powertrain. In this paper, an optimized control strategy based on the driving cycle type designed for a hydraulic hybrid bus has been presented. Because of considering the type of the driving cycle, the proposed control strategy can be named as an intelligent one. In this controller, at first, four standard driving cycles have been defined as the reference clusters. Then the optimized control strategy for each cluster has been derived using a dynamic programming algorithm. In addition, several multi-layered perceptron networks are modeled in order to use the output of each optimized control strategy. After that a clustering method with a feature selection algorithm has been implemented to assign degree of similarity to each cluster for the unknown driving cycle. Finally, a linear combination of four optimized control strategy outputs has been used for generating final output of the intelligent control strategy. In this combination, each output is weighted by the corresponding degree of similarity. Here, the hydraulic hybrid bus model is a feed forward one and has been simulated using a compound driving cycle. The compound driving cycle consists of six distinct 100s long portions of the Nuremburg driving cycle. The simulation results show that by using the intelligent control strategy, the fuel consumption of the hybrid bus has been reduced by almost 12% in comparison with the results of a rule-based control strategy.

scholarly journals Shift Strategy Optimization for Automatic Transmission of Heavy Trucks Based on Dynamic Programming Algorithm

2021 ◽  
Vol 11 (12) ◽  
pp. 5555
Author(s):  
Heng Zhang ◽  
Xinxin Zhao ◽  
Jue Yang ◽  
Weiwei Yang
Keyword(s):  
Dynamic Programming ◽  
Fuel Consumption ◽  
Automatic Transmission ◽  
Dynamic Programming Algorithm ◽  
Transmission Model ◽  
Programming Algorithm ◽  
Engine Fuel ◽  
Work Cycle ◽  
Shift Strategy ◽  
Two Parameter

The shift strategy of automatic transmission plays a vital role in the smoothness and economy of heavy-duty mining trucks. In this paper, an engine fuel consumption model, a 6 + 2 speed automatic transmission model, and a vehicle resistance model are built in MATLAB. Combined with the development of smart mining technology, the traditional two-parameter economic shift strategy is corrected based on vehicle load and road slope. The dynamic programming optimization algorithm is used to extract the best economical shift strategy under known working conditions to reduce the fuel consumption and the number of shifts. Finally, simulation experiments of the optimized shift strategy by dynamic programming in a typical mine work cycle are carried out. The simulation results show that the engine speed and output torque are maintained in a relatively stable and efficient working range by using the proposed shift strategy. Compared with the traditional two-parameter shift strategy, dynamic programming has advantages in reducing fuel consumption and shift numbers.

scholarly journals Online Control Strategy for Plug-In Hybrid Electric Vehicles Based on an Improved Global Optimization Algorithm

2020 ◽  
Vol 10 (23) ◽  
pp. 8352
Author(s):  
Shaoqian Wang ◽  
Datong Qin
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Dynamic Programming Algorithm ◽  
Online Control ◽  
Programming Algorithm ◽  
Fusion Algorithm ◽  
Optimal Sequence

Neural networks are widely used in the learning of offline global optimization rules to reduce the fuel consumption and real-time performance of hybrid electric vehicles. Considering that the torque and transmission ratio are direct control variables, online recognition by a neural network of these two parameters is insufficiently accurate. In the meanwhile, the dynamic program (DP) algorithm requires huge computing costs. Based on these problems, a fusion algorithm combining a dynamic programming algorithm and an approximate equivalent fuel consumption minimum control strategy (A-ECMS) is proposed in this paper. Taking the equivalent factor as the control variable, the global optimal sequence of the factor is obtained offline. The back propagation (BP) neural network is used to extract the sequence to form an online control strategy. The simulation results illustrate that, compared with the traditional dynamic programming algorithm, although the fuel consumption increases slightly, the computational cost of the fusion algorithm proposed in this paper is significantly reduced. Moreover, because the optimal sequence of the equivalent factors is within a particular range, the online control strategy based on DP-A-ECMS has a high robustness. Compared with an online control strategy based on the torque and transmission ratio, the fuel economy is improved by 2.46%.

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