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.

An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management Based on Driving Pattern Recognition

2006 ◽  
Author(s):  
Bo Gu ◽  
Giorgio Rizzoni
Keyword(s):  
Pattern Recognition ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Driving Pattern ◽  
Equivalence Factor ◽  
Equivalent Fuel ◽  
Driving Conditions ◽  
Equivalent Fuel Consumption

In this paper we present a novel adaptation method for the Adaptive Equivalent fuel Consumption Minimization Strategy (A-ECMS). The approach is based on Driving Pattern Recognition (DPR). The Equivalent (fuel) Consumption Minimization Strategy (ECMS) method provides real-time suboptimal energy management decisions by minimizing the "equivalent" fuel consumption of a hybrid-electric vehicle. The equivalent fuel consumption is a combination of the actual fuel consumption and electrical energy use, and an equivalence factor is used to convert electrical power used into an equivalent chemical fuel quantity. In this research, a driving pattern recognition method is used to obtain better estimation of the equivalence factor under different driving conditions. A time window of past driving conditions is analyzed periodically and recognized as one of the Representative Driving Patterns (RDPs). Periodically updating the control parameter according to the driving conditions yields more precise estimation of the equivalent fuel consumption cost, thus providing better fuel economy. Besides minimizing the instantaneous equivalent fuel consumption, the battery State of Charge (SOC) management is also maintained by using a PI controller to keep the SOC around a nominal value. The primary improvement of the proposed A-ECMS over other algorithms with similar objectives is that it does not require the knowledge of future driving cycles and has a low computational burden. Results obtained in this research show that the driving conditions can be successfully recognized and good performance can be achieved in various driving conditions while sustaining battery SOC within desired limits.

scholarly journals Evaluation of a Modified Equivalent Fuel-Consumption Minimization Strategy Considering Engine Start Frequency and Battery Parameters for a Plugin Hybrid Two-Wheeler

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3122
Author(s):  
Naga Kavitha Kommuri ◽  
Andrew McGordon ◽  
Antony Allen ◽  
Dinh Quang Truong
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategies ◽  
Optimum Operating ◽  
Energy Management Strategy ◽  
Rule Based ◽  
Equivalent Fuel ◽  
Equivalent Fuel Consumption ◽  
Two Wheeler

An appropriate energy management strategy is essential to enhance the performance of hybrid electric vehicles. A novel modified equivalent fuel-consumption minimization strategy (ECMS) is developed considering the engine operating point deviation from the optimum operating line. This paper focuses on an all-inclusive evaluation of this modified ECMS with other state-of-art energy management strategies concerning battery ageing, engine switching along with fuel economy and charge sustenance. The simulation-based results of a hybrid two-wheeler concept are analysed, which shows that the modified ECMS offers the highest benefit compared to rule-based controllers concerning fuel economy and reduction in engine switching events. However, the improvement in fuel economy using modified ECMS has significant negative potential effects on critical battery parameters influencing battery ageing. The results are analysed and found consistent for two different drive cycles and three different powertrain component configurations. The results show a significant reduction in fuel consumption of up to 21.18% and a reduction in engine switching events of up to 55% with modified ECMS when compared with rule-based strategies. However, there is a significant increase in battery temperature by 31% and battery throughput by 378%, which plays a major role in accelerating battery ageing. This paper emphasizes the need to consider battery-ageing parameters along with other control objectives for a robust assessment of energy management strategies. This study helps in laying down a foundation for future improvements in energy management development and it also aids in establishing a basis for comparing energy management controllers.

scholarly journals Minimum Energy Management Strategy of Equivalent Fuel Consumption of Hybrid Electric Vehicle Based on Improved Global Optimization Equivalent Factor

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2076 ◽  
Author(s):  
Xixue Liu ◽  
Datong Qin ◽  
Shaoqian Wang
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
High Efficiency ◽  
Energy Management Strategy ◽  
Target Value ◽  
Equivalent Fuel ◽  
Equivalent Factor ◽  
Equivalent Fuel Consumption ◽  
Engine Operating Point

A parallel hybrid electric vehicle (PHEV) is used to investigate the fuel economy effect of the equivalent fuel consumption minimization strategy (ECMS) with the equivalent factor as the core, where the equivalent factor is the conversion coefficient between fuel thermal energy and electric energy. In the conventional ECMS strategy, the battery cannot continue to discharge when the state of charge (SOC) is lower than the target value. At this time, the motor mainly works in the battery charging mode, making it difficult to adjust the engine operating point to the high-efficiency zone during the acceleration process. To address this problem, a relationship model of the battery SOC, vehicle acceleration a, and equivalent factor S was established. When the battery SOC is lower than the target value and the vehicle demand torque is high, which makes the engine operating point deviate from the high-efficiency zone, the time that the motor spends in the power generation mode during the driving process is reduced. This enables the motor to drive the vehicle at the appropriate time to reduce the engine output torque, and helps the engine operate in the high-efficiency zone. The correction function under US06 condition was optimized by genetic algorithm (GA). The best equivalent factor MAP was obtained with acceleration a and battery SOC as independent variables, and the improved global optimal equivalent factor of ECMS was established and simulated offline. Simulation results show that compared with conventional ECMS, the battery still has positive power output even when the SOC is less than the target value. The SOC is close to the target value after the cycle condition, and fuel economy improved by 1.88%; compared with the rule-based energy management control strategies, fuel economy improved by 10.17%. These results indicate the effectiveness of the proposed energy management strategy.

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.

Design of an Anti-Idling System for Police Vehicles

2010 ◽  
Author(s):  
Brian Fan ◽  
Amir Khajepour ◽  
Mehrdad Kazerani
Keyword(s):  
Power Management ◽  
Carbon Tax ◽  
Point Of View ◽  
Vehicle Model ◽  
Potential Cost ◽  
Equivalent Fuel ◽  
Equivalent Fuel Consumption ◽  
The Cost ◽  
Police Vehicles ◽  
The University

A configurable, modular, and flexible vehicle model utilizing scalable powertrain components has been developed at the University of Waterloo. The configurable vehicle model is modified to create an anti-idling system for police vehicles, where an additional battery is utilized to reduce the amount of engine idling time. The goal of the design study is to perform modeling and simulation of the anti-idling system from a financial cost point of view, in order to investigate the potential cost and fuel reduction over a conventional system. The cost function includes the total cost of the battery, the equivalent fuel consumption, and the carbon tax over a period of five years. It is concluded that the anti-idling system demonstrated significant fuel and cost reduction compared to one without. Furthermore, it is found that depending on the SOC threshold of the power management logic, the duration of time over which the engine is activated varied in a non-linear fashion. Future works include performing optimization of the power management logic and also investigates the effects of utilizing different battery types and sizes.

Sign in / Sign up

Export Citation Format

Share Document