Abstract
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.
Highlights
One of the most Hybrid Electric Vehicle (HEV) advantages is the possibility to optimize the use of energy storage during the trip using the Energy Management System (EMS)
The results are promising since the average of the absolute values of percentage errors between the experimental driving cycles and forecast ones is 3.89% for fuel consumption, increasing to 6.80% for energy
The results highlight that the method can predict the considered quantities with an acceptable error (−2.36% relative percentage error for energy, −4.11% relative percentage error for fuel) in long drive city trips
Summary
One of the most Hybrid Electric Vehicle (HEV) advantages is the possibility to optimize the use of energy storage during the trip using the Energy Management System (EMS). Statistic and Cluster Analysis based Recognition: this category collects the techniques that use the historical and current vehicle’s speed profile parameters to predict future conditions. 3. Global Positioning System (GPS) and Intelligent Transportation Systems (ITS) based prediction: this category collects the techniques that use the historical and current vehicle’s parameter, GPS, and ITS data to forecast the DC. 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. Considering all the research, it is clear that the EMSs can forecast the vehicle’s energy expenditure and fuel consumption only if many data are available It means the use of sensor-equipped cars and cities, which is not always easy to achieve. The GM algorithm and WLTC have worldwide nature so the study suits all cities without modifying or adding infrastructure
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