Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles

He, Hongwen; Guo, Jun; Zhou, Nana; Sun, Chao; Peng, Jiankun

doi:10.3390/en10111796

Cited by 20 publications

(9 citation statements)

References 24 publications

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“…However, these general driving cycles do not take into account all the statistical uncertainties of the studied bus route. In other works, driving cycles are developed from real-world data, as reported in [7] presents a freeway driving cycle developed based on the traffic information in California. In the present work, instead, the energy consumption is found by simulating the driving cycle of an electric bus on two different sections of the considered line.…”

Section: Introductionmentioning

confidence: 99%

Electrification of a Bus Line in Savona Considering Depot and Opportunity Charging

Longo¹,

Leone²,

Lorenz³

et al. 2021

Adv. sci. technol. eng. syst. j.

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A transition towards electrification of the public transport sector is ongoing in many cities around the world, as a response to global warming and pollution. However, the question is whether the current state of technology is already sufficient to replace the conventional buses with electric ones and if the existing charging facilities are appropriate to ensure the smooth operation of the buses. Therefore, this work aims to verify the technical feasibility of the electrification of an existing urban line. The purpose is achieved by evaluating a case study on a public transport bus line in the city of Savona, Italy. The average energy consumption of an electric bus operating in the considered line path is estimated in order to investigate the possible locations and sizes of the charging systems to install. The results show that the correct service operation of the electric buses can be achieved by installing one opportunity charger of at least 300 kW in one of the terminals or by installing three 43 kW charging ports in the depot.

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Section: Introductionmentioning

confidence: 99%

Electrification of a Bus Line in Savona Considering Depot and Opportunity Charging

Longo¹,

Leone²,

Lorenz³

et al. 2021

Adv. sci. technol. eng. syst. j.

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show abstract

“…Refs. [60], [61] propose an economic driving principle which fully considers real-time traffic information to assist determination of the reference SOC trajectory.…”

Section: Introductionmentioning

confidence: 99%

Trip-Oriented Model Predictive Energy Management Strategy for Plug-in Hybrid Electric Vehicles

et al. 2019

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Energy management strategies play a critical role in performance optimization of plug-in hybrid electric vehicles (PHEVs). In order to attain effective energy distribution of PHEVs, a predictive energy management strategy is proposed in this study based on real-time traffic information. First, an exponentially varied model for the velocity prediction is established, of which the tunable decay coefficient is regulated by the supported vector machine (SVM). In this manner, the prediction precision is improved. Then, by properly simplifying the powertrain model, the state of charge (SOC) reference trajectory is generated based on the fast dynamic programming (DP) with fast calculation speed and consideration of the traffic information. Moreover, the typical DP algorithm is leveraged to solve the nonlinear rolling optimization problem for minimizing the operating cost in a receding horizon. Simulation results demonstrate that the proposed algorithm can reach 92.83% operating savings, compared with that of the traditional DP; and save 6.18% cost compared with the MPC algorithm only with reference of the trip duration. INDEX TERMS Plug-in hybrid electric vehicles (PHEVs), energy management, model predictive control (MPC), traffic information, state of charge (SOC) reference.

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“…The speed profile depends on the traffic light distribution, the average speed of the vehicle's flow-rate, and the historical traffic state data. He et al [21] use a dataset similar to Qiuming et al [20] assigning a driving cycle in a freeway road section. The main difference is the possibility to modify the speed profile according to the car's GPS information.…”

mentioning

confidence: 99%

“…The main difference is the possibility to modify the speed profile according to the car's GPS information. Zhang et al [22] use data similar to He et al [21], adding the near field vehicles' GPS information. A NN makes up the DC and uses it to predict each vehicle's energy expenditure for a ten-second length time window.…”

mentioning

confidence: 99%

An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles

et al. 2021

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The most efficient energy management strategies for hybrid vehicles are the “Optimization-Based Strategies”. These strategies require a preliminary knowledge of the driving cycle, which is not easy to predict. This paper aims to combine Worldwide Harmonized Light-Duty Vehicles Test Cycle (WLTC) low section short trips with real traffic levels for vehicle energy and fuel consumption prediction. Future research can focus on implementing a new strategy for Hybrid Electric Vehicle (HEV) energy optimization, taking into account WLTC and Google Maps traffic levels. First of all, eight characteristic parameters are extracted from real speed profiles, driven in urban road sections in the city of Messina at different traffic conditions, and WLTC short trips as well. The minimum distance algorithm is used to compare the parameters and assign the three traffic levels (heavy, average, and low traffic level) to the WLTC short trips. In this way, for each route assigned from Google maps, vehicle’s energy and fuel consumption are estimated using WLTC short trips remodulated with distances and traffic levels. Moreover, a vehicle numerical model was implemented and used to test the accuracy of fuel consumption and energy prediction for the proposed methodology. The results are promising since the average of the percentage errors’ absolute value between the experimental driving cycles and forecast ones is 3.89% for fuel consumption, increasing to 6.80% for energy.

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Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles

Cited by 20 publications

References 24 publications

Electrification of a Bus Line in Savona Considering Depot and Opportunity Charging

Electrification of a Bus Line in Savona Considering Depot and Opportunity Charging

Trip-Oriented Model Predictive Energy Management Strategy for Plug-in Hybrid Electric Vehicles

An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles

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