Holistic Optimization of Energy Consumption of a Hybrid Powertrain with an “Equivalent Fuel Consumption Minimization Strategy” Algorithm

2021 ◽  
pp. 243-258
Author(s):  
Michael Zagun
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Hybrid Powertrain ◽  
Equivalent Fuel ◽  
Equivalent Fuel Consumption ◽  
Holistic Optimization

scholarly journals An Integrated Optimal Energy Management/Gear-Shifting Strategy for an Electric Continuously Variable Transmission Hybrid Powertrain Using Bacterial Foraging Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-15
Author(s):  
Syuan-Yi Chen ◽  
Yi-Hsuan Hung ◽  
Chien-Hsun Wu
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Vehicle Model ◽  
Hybrid Powertrain ◽  
Shifting Strategy ◽  
Equivalent Fuel ◽  
Variable Transmission ◽  
Optimal Energy Management ◽  
Bacterial Foraging Algorithm ◽  
Equivalent Fuel Consumption

This study developed an integrated energy management/gear-shifting strategy by using a bacterial foraging algorithm (BFA) in an engine/motor hybrid powertrain with electric continuously variable transmission. A control-oriented vehicle model was constructed on the Matlab/Simulink platform for further integration with developed control strategies. A baseline control strategy with four modes was developed for comparison with the proposed BFA. The BFA was used with five bacterial populations to search for the optimal gear ratio and power-split ratio for minimizing the cost: the equivalent fuel consumption. Three main procedures were followed: chemotaxis, reproduction, and elimination-dispersal. After the vehicle model was integrated with the vehicle control unit with the BFA, two driving patterns, the New European Driving Cycle and the Federal Test Procedure, were used to evaluate the energy consumption improvement and equivalent fuel consumption compared with the baseline. The results show that[18.35%,21.77%]and[8.76%,13.81%]were improved for the optimal energy management and integrated optimization at the first and second driving cycles, respectively. Real-time platform designs and vehicle integration for a dynamometer test will be investigated in the future.

scholarly journals Development of Hybrid Vehicle Energy Consumption Model for Transportation Applications—Part II: Traction Force-Speed Based Energy Consumption Modeling

2019 ◽  
Vol 10 (2) ◽  
pp. 22 ◽  
Author(s):  
Siriorn Pitanuwat ◽  
Hirofumi Aoki ◽  
Satoru IIzuka ◽  
Takayuki Morikawa
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Traction Force ◽  
Hybrid Vehicles ◽  
Specific Power ◽  
Mode Transition ◽  
Hybrid Powertrain ◽  
Modeling Methods ◽  
Consumption Model ◽  
Energy Consumption Modeling

In the transportation sector, the fuel consumption model is a fundamental tool for vehicles’ energy consumption and emission analysis. Over the past decades, vehicle-specific power (VSP) has been enormously adopted in a number of studies to estimate vehicles’ instantaneous driving power. Then, the relationship between the driving power and fuel consumption is established as a fuel consumption model based on statistical approaches. This study proposes a new methodology to improve the conventional energy consumption modeling methods for hybrid vehicles. The content is organized into a two-paper series. Part I captures the driving power equation development and the coefficient calibration for a specific vehicle model or fleet. Part II focuses on hybrid vehicles’ energy consumption modeling, and utilizes the equation obtained in Part I to estimate the driving power. Also, this paper has discovered that driving power is not the only primary factor that influences hybrid vehicles’ energy consumption. This study introduces a new approach by applying the fundamental of hybrid powertrain operation to reduce the errors and drawbacks of the conventional modeling methods. This study employs a new driving power estimation equation calibrated for the third generation Toyota Prius from Part I. Then, the Traction Force-Speed Based Fuel Consumption Model (TFS model) is proposed. The combination of these two processes provides a significant improvement in fuel consumption prediction error compared to the conventional VSP prediction method. The absolute maximum error was reduced from 57% to 23%, and more than 90% of the predictions fell inside the 95% confidential interval. These validation results were conducted based on real-world driving data. Furthermore, the results show that the proposed model captures the efficiency variation of the hybrid powertrain well due to the multi-operation mode transition throughout the variation of the driving conditions. This study also provides a supporting analysis indicating that the driving mode transition in hybrid vehicles significantly affects the energy consumption. Thus, it is necessary to consider these unique characteristics to the modeling process.

Equivalent fuel consumption optimal control of a series hybrid electric vehicle

2009 ◽  
Vol 223 (8) ◽  
pp. 1003-1018 ◽  
Author(s):  
J-P Gao ◽  
G-M G Zhu ◽  
E G Strangas ◽  
F-C Sun
Keyword(s):  
Optimal Control ◽  
Fuel Consumption ◽  
Control Strategy ◽  
Supervisory Control ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Optimal Control Strategy ◽  
Equivalent Fuel ◽  
Hybrid Electric ◽  
Equivalent Fuel Consumption

Improvements in hybrid electric vehicle fuel economy with reduced emissions strongly depend on their supervisory control strategy. In order to develop an efficient real-time supervisory control strategy for a series hybrid electric bus, the proposed equivalent fuel consumption optimal control strategy is compared with two popular strategies, thermostat and power follower, using backward simulations in ADVISOR. For given driving cycles, global optimal solutions were also obtained using dynamic programming to provide an optimization target for comparison purposes. Comparison simulations showed that the thermostat control strategy optimizes the operation of the internal combustion engine and the power follower control strategy minimizes the battery charging and discharging operations which, hence, reduces battery power loss and extends the battery life. The equivalent fuel consumption optimal control strategy proposed in this paper provides an overall system optimization between the internal combustion engine and battery efficiencies, leading to the best fuel economy.

scholarly journals Design of Real-Time Control Based on DP and ECMS for PHEVs

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Wang ◽  
Zhenjiang Cai ◽  
Shaofei Liu
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Mode Selection ◽  
Time Algorithm ◽  
Operating Conditions ◽  
Real Time Control ◽  
Time Control ◽  
Manual Adjustment ◽  
Equivalent Fuel ◽  
Equivalent Fuel Consumption

A real-time control is proposed for plug-in-hybrid electric vehicles (PHEVs) based on dynamic programming (DP) and equivalent fuel consumption minimization strategy (ECMS) in this study. Firstly, the resulting controls of mode selection and series mode are stored in tables through offline simulation of DP, and the parallel HEV mode uses ECMS-based real-time algorithm to reduce the application of maps and avoid manual adjustment of parameters. Secondly, the feedback energy management system (FMES) is built based on feedback from SoC, which takes into account the charge and discharge reaction (CDR) of the battery, and in order to make full use of the energy stored in the battery, the reference SoC is introduced. Finally, a comparative simulation on the proposed real-time controller is conducted against DP, the results show that the controller has a good performance, and the fuel consumption value of the real-time controller is close to the value using DP. The engine operating conditions are concentrated in the low fuel consumption area of the engine, and when the driving distance is known, the SoC can follow the reference SoC well to make full use of the energy stored in the battery.

scholarly journals Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Haitao Yan ◽  
Yongzhi Xu
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Control ◽  
Time Performance ◽  
Equivalent Fuel ◽  
Hybrid Electric ◽  
Equivalent Fuel Consumption

Energy control strategy is a key technology of hybrid electric vehicle, and its control effect directly affects the overall performance of the vehicle. The current control strategy has some shortcomings such as poor adaptability and poor real-time performance. Therefore, a transient energy control strategy based on terminal neural network is proposed. Firstly, based on the definition of instantaneous control strategy, the equivalent fuel consumption of power battery was calculated, and the objective function of the minimum instantaneous equivalent fuel consumption control strategy was established. Then, for solving the time-varying nonlinear equations used to control the torque output, a terminal recursive neural network calculation method using BARRIER functions is designed. The convergence characteristic is analyzed according to the activation function graph, and then the stability of the model is analyzed and the time efficiency of the error converging to zero is deduced. Using ADVISOR software, the hybrid power system model is simulated under two typical operating conditions. Simulation results show that the hybrid electric vehicle using the proposed instantaneous energy control strategy can not only ensure fuel economy but also shorten the control reaction time and effectively improve the real-time performance.

scholarly journals The Optimal Control of Fuel Consumption for a Heavy-Duty Motorcycle with Three Power Sources Using Hardware-in-the-Loop Simulation

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 22 ◽  
Author(s):  
Chien-Hsun Wu ◽  
Yong-Xiang Xu
Keyword(s):  
Fuel Consumption ◽  
Control Strategies ◽  
Lithium Ion ◽  
Digital Signal ◽  
Driving Cycle ◽  
Hardware In The Loop ◽  
Power Sources ◽  
Analog To Digital ◽  
Equivalent Fuel ◽  
Equivalent Fuel Consumption

This study presents a simulation platform for a hybrid electric motorcycle with an engine, a driving motor, and an integrated starter generator (ISG) as three power sources. This platform also consists of the driving cycle, driver, lithium-ion battery, continuously variable transmission (CVT), motorcycle dynamics, and energy management system models. Two Arduino DUE microcontrollers integrated with the required circuit to process analog-to-digital signal conversion for input and output are utilized to carry out a hardware-in-the-loop (HIL) simulation. A driving cycle called worldwide motorcycle test cycle (WMTC) is used for evaluating the performance characteristics and response relationship among subsystems. Control strategies called rule-based control (RBC) and equivalent consumption minimization strategy (ECMS) are simulated and compared with the purely engine-driven operation. The results show that the improvement percentages for equivalent fuel consumption and energy consumption for RBC and ECMS using the pure software simulation were 17.74%/18.50% and 42.77%/44.22% respectively, while those with HIL were 18.16%/18.82% and 42.73%/44.10%, respectively.

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