Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms

Fang, Lincun; Qin, Shiyin; Xu, Gang; Li, Tianli; Zhu, Kemin

doi:10.3390/en4030532

Cited by 61 publications

(41 citation statements)

References 22 publications

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“…It was found that the promotion of PHEVs and EVs could help cut per-kilometer petroleum use to a great extent; however, it makes the CO 2 emission mitigation becomes even more difficult to achieve. The evaluation of HEVs emissions were also studied in [17].…”

Section: Introductionmentioning

confidence: 99%

Energy and Environmental Implications of Hybrid and Electric Vehicles in China

et al. 2013

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Abstract:The promotion of hybrid and electric vehicles (EVs) has been proposed as one promising solution for reducing transport energy consumption and mitigating vehicular emissions in China. In this study, the energy and environmental impacts of hybrid and EVs during 2010-2020 were evaluated through an energy conversion analysis and a life cycle assessment (LCA), and the per-kilometer energy consumptions of gasoline, coal, natural gas (NG), oil, biomass, garbage and electricity for EVs and HEVs were estimated. Results show that the EVs and HEVs can reduce the energy consumption of vehicles by national average ratios of 17%-19% and 30%-33%, respectively. The study also calculated the detailed emission factors of SO 2 , NO X , VOC, CO, NH 3 , PM 10 , PM 2.5 , OC, EC, CO 2 , N 2 O, CH 4 , Pb and Hg. It is indicated that the HEVs can bring significant reductions of NO X , VOC and CO emissions and lesser decreases of SO 2 and CO 2 for a single vehicle. The EVs could decrease many of the VOC, NH 3 , CO and CO 2 emissions, but increase the SO 2 , NO X and particles by 10.8-13.0, 2.7-2.9 and 3.6-11.5 times, respectively. In addition, the electricity sources had significant influence on energy consumption (EC) and emissions. A high proportion of coal-fired energy resulted in large ECs and emission factors. The total energy consumption and pollutants emission changes in 2015 and 2020 were also calculated. Based on the energy use and emission analysis of HEVs and EVs, it is suggested that EVs should be promoted in the regions with higher proportions of OPEN ACCESSEnergies 2013, 6 2664 hydropower, natural gas-fired power and clean energy power, while HEVs can be widely adopted in the regions with high coal-fired power ratios. This is to achieve a higher energy consumption reduction and pollutant emission mitigation. Moreover, the results can also provide scientific support for the total amount control of regional air pollutants in China.

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Section: Introductionmentioning

confidence: 99%

Energy and Environmental Implications of Hybrid and Electric Vehicles in China

et al. 2013

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“…The methods based on Calculus of Variations obtain a local optimization result, for example, Pontryagin's minimum principle. Other examples of optimization can be found in [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information

Jiménez

Cabrera-Montiel

2014

Energies

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Nowadays, reducing the energy and fuel consumption of road vehicles is a key issue. Different strategies have been proposed. One of them is to promote Eco-driving behaviour among drivers. Most Eco-driving tips take into account only the road stretch where the vehicle is located. However, larger improvements could be achieved if information from subsequent stretches is used. The main objective of this work is to develop a system to warn the driver in real time of the optimal speed that should be maintained on every road segment in order to optimize the energy used and the fuel consumed while observing a time schedule. The system takes into account the road vertical profile, the fixed and variable speed limits and the traffic information retrieved using V2V and V2I communications. The system has been tested on real road sections with satisfactory results in fuel savings.

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“…This is not only related to the design parameters of the power system, but also the control parameters of the energy management strategy (EMS). To improve HEV performance in terms of fuel economy and ensure excellent driving performance, the simultaneous optimization for the main parameters of powertrain components and control system is necessary [1]. Recently, numerous works have been proposed to find the best solution.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition

et al. 2017

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Abstract:The driving pattern has an important influence on the parameter optimization of the energy management strategy (EMS) for hybrid electric vehicles (HEVs). A new algorithm using simulated annealing particle swarm optimization (SA-PSO) is proposed for parameter optimization of both the power system and control strategy of HEVs based on multiple driving cycles in order to realize the minimum fuel consumption without impairing the dynamic performance. Furthermore, taking the unknown of the actual driving cycle into consideration, an optimization method of the dynamic EMS based on driving pattern recognition is proposed in this paper. The simulation verifications for the optimized EMS based on multiple driving cycles and driving pattern recognition are carried out using Matlab/Simulink platform. The results show that compared with the original EMS, the former strategy reduces the fuel consumption by 4.36% and the latter one reduces the fuel consumption by 11.68%. A road test on the prototype vehicle is conducted and the effectiveness of the proposed EMS is validated by the test data.

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Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms

Cited by 61 publications

References 22 publications

Energy and Environmental Implications of Hybrid and Electric Vehicles in China

Energy and Environmental Implications of Hybrid and Electric Vehicles in China

System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information

A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition

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