scholarly journals ANALYSIS OF EQUIVALENT FUEL SAVINGS OF ELECTRIC VEHICLES

2020 ◽  
Vol 20 ◽  
pp. 85-89
Author(s):  
A. Gavrilyk ◽  
M. Lemishko
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Fuel Cell Vehicle ◽  
Electric Cars ◽  
Advantages And Disadvantages ◽  
Equivalent Fuel ◽  
Equivalent Fuel Consumption

The development of electric vehicles in the near future is outlined, their general classification and problems of their use are given. The most common energy elements used to power electric traction electric motors are analyzed, their advantages and disadvantages are described. The analysis shows the most economical electric cars in 2018 and describes their traction and speed characteristics. The peculiarities of methodology for determining fuel economy for hybrid vehicles (PHEV - plugin hybrid electric vehicle) and for vehicles running on alternative fuel type (NGV-natural gas vehicle; FCV-fuel cell vehicle) are revealed and the possibility of its improvement is revealed. Methodological bases of estimation of fuel economy of electric vehicles are developed. This will allow potential buyers, owners or economists of the trucking companies to objectively estimate the equivalent fuel consumption and successfully choose one or the other brand of electric vehicle. An algorithm for determining the equivalent fuel economy of electric vehicles was developed and described taking into account the energy price policy for different countries of the world.It is concluded that lithiumion batteries have become the most widespread, as the feeding elements of electric vehicles. It is found that the equivalent fuel consumption is the most objective and informative, from the user's point of view, characterizing the use of electric vehicles compared to indicating the amount of energy (kWh) required to overcome 100 miles of travel. Using the proposed method, the equivalent fuel economy of these electric vehicles is calculated, the results are plotted against. It is established that for Ukraine, considering the cost of energy carriers, the use of electric vehicles is the most costeffective compared to other countries.

scholarly journals Support vector machine–based driving cycle recognition for dynamic equivalent fuel consumption minimization strategy with hybrid electric vehicle

2018 ◽  
Vol 10 (11) ◽  
pp. 168781401881102
Author(s):  
QIN Shi ◽  
Duoyang Qiu ◽  
Lin He ◽  
Bing Wu ◽  
Yiming Li
Keyword(s):  
Support Vector Machine ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Driving Cycle ◽  
Support Vector ◽  
Equivalent Fuel ◽  
Hybrid Electric ◽  
Equivalent Fuel Consumption

For a great influence on the fuel economy and exhaust, driving cycle recognition is becoming more and more widely used in hybrid electric vehicles. The purpose of this study is to develop a method to identify the type of driving cycle in real time with better accuracy and apply the driving cycle recognition to minimize the fuel consumption with dynamic equivalent fuel consumption minimization strategy. The support vector machine optimized by the particle swarm algorithm is created for building driving cycle recognition model. Furthermore,the influence of the two parameters of window width and window moving velocity on the accuracy is also analyzed in online application. A case study of driving cycle in a medium-sized city is introduced based on collecting four typical driving cycle data in real vehicle test. A series of characteristic parameters are defined and principal component analysis is used for data processing. Finally, the driving cycle recognition model is used for equivalent fuel consumption minimization strategy with a parallel hybrid electric vehicle. Simulation results show that the fuel economy can improve by 9.914% based on optimized support vector machine, and the fluctuations of battery state of charge are more stable so that system efficiency and batter life are substantially improved.

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.

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.

An adaptive equivalent consumption minimization strategy considering catalyst temperature for plug-in hybrid electric vehicle

2021 ◽  
pp. 095440702110434
Author(s):  
Tao Deng ◽  
Ke Zhao ◽  
Haoyuan Yu
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Test Procedure ◽  
Catalyst Temperature ◽  
Equivalent Factor ◽  
Simulation Results ◽  
Hybrid Electric ◽  
Equivalent Consumption Minimization Strategy

In the process of sufficiently considering fuel economy of plug-in hybrid electric vehicle (PHEV), the working time of engine will be reduced accordingly. The increased frequency that the three-way catalytic converter (TWCC) works in abnormal operating temperature will lead to the increasing of emissions. This paper proposes the equivalent consumption minimization strategy (ECMS) to ensure the catalyst temperature of PHEV can work in highly efficient areas, and the influence of catalyst temperature on fuel economy and emissions is considered. The simulation results show that the fixed equivalent factor of ECMS has great limitations for the underutilized battery power and the poor fuel economy. In order to further reduce fuel consumption and keep the emission unchanged, an equivalent factor map based on initial state of charge (SOC) and vehicle mileage is established by the genetic algorithm. Furthermore, an Adaptive changing equivalent factor is achieved by using the following strategy of SOC trajectory. Ultimately, adaptive equivalent consumption minimization strategy (A-ECMS) considering catalyst temperature is proposed. The simulation results show that compared with ordinary ECMS, HC, CO, and NOX are reduced by 14.6%, 20.3%, and 25.8%, respectively, which effectively reduces emissions. But the fuel consumption is increased by only 2.3%. To show that the proposed method can be used in actual driving conditions, it is tested on the World Light Vehicle Test Procedure (WLTC).

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