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

Jiménez, Felipe; Cabrera-Montiel, Wilmar

doi:10.3390/en7063576

Cited by 22 publications

(16 citation statements)

References 49 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are some advances in this regard, e.g., Jimenez et al [67] have developed an objective function that optimizes the energy used on the trip, while observing the time schedule. However, the different spatial impact of transport emissions should also be taken into account, in order to efficiently achieve not only greener, but also cleaner cities.…”

Section: Discussionmentioning

confidence: 99%

Managing Traffic Flows for Cleaner Cities: The Role of Green Navigation Systems

2017

View full text Add to dashboard Cite

Cities worldwide suffer from serious air pollution problems and are main contributors to climate change. Green Navigation systems have a great potential to reduce fuel consumption and exhaust emissions from traffic. This research evaluates the impacts of different percentages of green drivers on traffic, CO 2 , and NO x over the entire Madrid Region. A macroscopic traffic model was combined with an enhanced macroscopic emissions model and a GIS (Geographic Information Systems) to simulate emissions on the basis of average vehicle speeds and traffic intensity at the link level. NO x emissions are evaluated, taking into account not only the exhaust emissions produced by transport activity, but also the amount of the population exposed to these air pollutants. Results show up to 10.4% CO 2 and 13.8% NO x reductions in congested traffic conditions for a 90% penetration of green drivers; however, the population's exposure to NO x increases up to 20.2%. Moreover, while traffic volumes decrease by 13.5% for the entire region, they increase by up to 16.4% downtown. Travel times also increase by 28.7%. Since green drivers tend to choose shorter routes through downtown areas, eco-routing systems are an effective tool for fighting climate change, but are ineffective to reduce air pollution in dense urban areas.

show abstract

Section: Discussionmentioning

confidence: 99%

Managing Traffic Flows for Cleaner Cities: The Role of Green Navigation Systems

2017

View full text Add to dashboard Cite

show abstract

“…Driving behavior can change average speed (i.e., speeding or conservative driving) and the aggressiveness of acceleration. A third factor of driving behavior is the capability to anticipate behavior of other vehicles or traffic lights to avoid slowing down and accelerating again, which in combination with intelligent traffic systems (ITS) has proven to reduce fuel consumption in internal combustion engine (ICE) vehicles [30] and energy consumption in EVs [13]. Traffic density can influence the driving behavior by imposing a de facto maximum speed or a higher frequency of stops and accelerations.…”

Section: Introduction and State-of-the-artmentioning

confidence: 99%

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

et al. 2017

View full text Add to dashboard Cite

Limited driving range remains one of the barriers for widespread adoption of electric vehicles (EVs). To address the problem of range anxiety, this paper presents an energy consumption prediction method for EVs, designed for energy-efficient routing. This data-driven methodology combines real-world measured driving data with geographical and weather data to predict the consumption over any given road in a road network. The driving data are linked to the road network using geographic information system software that allows to separate trips into segments with similar road characteristics. The energy consumption over road segments is estimated using a multiple linear regression (MLR) model that links the energy consumption with microscopic driving parameters (such as speed and acceleration) and external parameters (such as temperature). A neural network (NN) is used to predict the unknown microscopic driving parameters over a segment prior to departure, given the road segment characteristics and weather conditions. The complete proposed model predicts the energy consumption with a mean absolute error (MAE) of 12-14% of the average trip consumption, of which 7-9% is caused by the energy consumption estimation of the MLR model. This method allows for prediction of energy consumption over any route in the road network prior to departure, and enables cost-optimization algorithms to calculate energy efficient routes. The data-driven approach has the advantage that the model can easily be updated over time with changing conditions.

show abstract

“…To validate the optimality of the RL technique, the Dyna algorithm, SDP [24], and DP [30] were controlled on the experimental driving schedule shown in Figure 10; Figure 13 presents the simulation results. The SOC terminal values were close to the initial values because of the final constraint in the cost function.…”

Section: Comparative Analysis Of the Results Of Dyna Algorithm Sdp mentioning

confidence: 99%

Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle

Zou

Liu

et al. 2015

Energies

View full text Add to dashboard Cite

This paper presents a reinforcement learning (RL)-based energy management strategy for a hybrid electric tracked vehicle. A control-oriented model of the powertrain and vehicle dynamics is first established. According to the sample information of the experimental driving schedule, statistical characteristics at various velocities are determined by extracting the transition probability matrix of the power request. Two RL-based algorithms, namely Q-learning and Dyna algorithms, are applied to generate optimal control solutions. The two algorithms are simulated on the same driving schedule, and the simulation results are compared to clarify the merits and demerits of these algorithms. Although the Q-learning algorithm is faster (3 h) than the Dyna algorithm (7 h), its fuel consumption is 1.7% higher than that of the Dyna algorithm. Furthermore, the Dyna algorithm registers approximately the same fuel consumption as the dynamic programming-based global optimal solution. The computational cost of the Dyna algorithm is substantially lower than that of the stochastic dynamic programming.

show abstract

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

Cited by 22 publications

References 49 publications

Managing Traffic Flows for Cleaner Cities: The Role of Green Navigation Systems

Managing Traffic Flows for Cleaner Cities: The Role of Green Navigation Systems

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle

Contact Info

Product

Resources

About