scholarly journals A Modified ABC-SQP-Based Combined Approach for the Optimization of a Parallel Hybrid Electric Vehicle

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4529
Author(s):  
S. N. Shivappriya ◽  
S. Karthikeyan ◽  
S. Prabu ◽  
R. Pérez de Pérez de Prado ◽  
B. D. Parameshachari
Keyword(s):  
Boundary Conditions ◽  
Fuel Consumption ◽  
Hybrid Electric Vehicle ◽  
Test Procedure ◽  
Heuristic Approach ◽  
Emissions Control ◽  
Driving Cycles ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Fuel Consumption And Emissions

In this paper, an improved fuel consumption and emissions control strategy based on a mathematical and heuristic approach is presented to optimize Parallel Hybrid Electric Vehicles (HEVs). The well-known Sequential Quadratic Programming mathematical method (SQP-Hessian approach) presents some limitations to achieve fuel consumption and emissions control optimization, as it is not able to find the global minimum, and it generally shows efficient results in local exploitation searches. The usage of a combined Modified Artificial Bee Colony algorithm (MABC) with the SQP approach is proposed in this work to obtain better optimal solutions and overcome these limitations. The optimization is performed with boundary conditions, considering that the optimized vehicle performance has to satisfy Partnership for a New Generation of Vehicles (PNGV) constraints. The weighting factor of the vehicle’s performance parameters in the objective function is varied, and optimization is carried out for two different driving cycles, namely Federal Test Procedure (FTP) and Economic commission Europe—Extra Urban Driving Cycle (ECE-EUDC), using the MABC and MABC with SQP approaches. The MABC with SQP approach shows better performance in terms of fuel consumption and emissions than the pure heuristic approach for the considered vehicle with similar boundary conditions. Moreover, it does not present significant penalties for final battery charging and it offers an optimized size of the key vehicle’s components for different driving cycles.

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).

scholarly journals Research on energy management strategy considering battery life for plug-in hybrid electric vehicle

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879776 ◽  
Author(s):  
Jianjun Hu ◽  
Zhihua Hu ◽  
Xiyuan Niu ◽  
Qin Bai
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Battery Life ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Comprehensive Test ◽  
Hybrid Electric

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.

Research on Modeling and Control Strategy of Parallel Hybrid Electric Back-Loading Compression Sanitation Vehicle

2013 ◽  
Vol 420 ◽  
pp. 355-362
Author(s):  
Rong Yang ◽  
Di Ming Lou ◽  
Pi Qiang Tan ◽  
Zhi Yuan Hu
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Stable Operation ◽  
Parallel Hybrid Electric Vehicle ◽  
Fuel Consumption Reduction ◽  
Consumption Reduction ◽  
Parallel Hybrid ◽  
Hybrid Electric

Establish simulation models of the conventional and parallel hybrid electric back-loading compression sanitation vehicle by AVL CRUISE and MATLAB/Simulink software. Study on control strategy of parallel hybrid electric vehicle based on the work characteristics of back-loading compression sanitation. Results show that: about 24.5% fuel consumption reduction in hybrid modeling compared to the conventional sanitation vehicle under heavy commercial vehicle standard test cycle (C-WTVC, Adapted World Transient Vehicle Cycle), and battery SOC was little changed at 50%. About 32% fuel consumption reduction in hybrid compared to the conventional vehicle under the actual road testing spectrum, and SOC increased about 21.6% relative to the initial state. It controls the engine to work in more stable operation region and reduces engine idle time, but increases engine start-stop times. It also could provide some references for specific engine development of parallel hybrid electric vehicle

scholarly journals Multiobjective optimisation of a series hybrid electric vehicle using DIRECT algorithm

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Abdelmoula Rihab ◽  
◽  
Ben Hadj Naourez ◽  
Chaieb Mohamed ◽  
Neji Rafik ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Vehicle Performance ◽  
Performance Requirements ◽  
Toxic Emissions ◽  
Series Hybrid Electric Vehicle ◽  
Hybrid Electric ◽  
Fuel Consumption And Emissions

With the economic development, transportation in the city becomes more crowded. Furthermore, fuel consumption is causing a serious problem of pollution in the urban environment. Hybrid electric vehicles are considered as a good solution compared to conventional internal combustion engine vehicles. In order to solve those problems, the components parameters of a series hybrid electric vehicle are selected and tested with the ADvanced VehIcle SimulatOR (ADVISOR) simulation tool, which is a software-based on Matlab_simulink. Then, an optimisation was done to minimise simultaneous fuel consumption and emissions (HC, CO, and NOx) of the vehicle engine. In addition, the driving performance requirements are also examined during the urban dynamometer driving schedule (UDDS) to fix their optimal control parameters. Finally, the results show that those steps help reduce fuel consumption and emissions while guaranteeing vehicle performance. Hence, the series hybrid electric vehicle greatly improves fuel economy and reduces toxic emissions.

scholarly journals Driving Cycles Based on Fuel Consumption

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3064 ◽  
Author(s):  
José Huertas ◽  
Michael Giraldo ◽  
Luis Quirama ◽  
Jenny Díaz
Keyword(s):  
Fuel Consumption ◽  
Test Procedure ◽  
Region Of Interest ◽  
Driving Cycle ◽  
Normal Operation ◽  
Characteristic Parameters ◽  
Driving Cycles ◽  
Driving Pattern ◽  
Relative Differences ◽  
Fuel Consumption And Emissions

Type-approval driving cycles currently available, such as the Federal Test Procedure (FTP) and the Worldwide harmonized Light vehicles Test Cycle (WLTC), cannot be used to estimate real fuel consumption nor emissions from vehicles in a region of interest because they do not describe its local driving pattern. We defined a driving cycle (DC) as the time series of speeds that when reproduced by a vehicle, the resulting fuel consumption and emissions are similar to the average fuel consumption and emissions of all vehicles of the same technology driven in that region. We also declared that the driving pattern can be described by a set of characteristic parameters (CPs) such as mean speed, positive kinetic energy and percentage of idling time. Then, we proposed a method to construct those local DC that use fuel consumption as criterion. We hypothesized that by using this criterion, the resulting DC describes, implicitly, the driving pattern in that region. Aiming to demonstrate this hypothesis, we monitored the location, speed, altitude, and fuel consumption of a fleet of 15 vehicles of similar technology, during 8 months of normal operation, in four regions with diverse topography, traveling on roads with diverse level of service. In every region, we considered 1000 instances of samples made of m trips, where m varied from 4 to 40. We found that the CPs of the local driving cycle constructed using the fuel-based method exhibit small relative differences (<15%) with respect to the CPs that describe the driving patterns in that region. This result demonstrates the hypothesis that using the fuel based method the resulting local DC exhibits CPs similar to the CPs that describe the driving pattern of the region under study.

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