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 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 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 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 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.

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