Online learning of energy consumption for navigation of electric vehicles

Åkerblom, Niklas; Chen, Yuxin; Chehreghani, Morteza Haghir

doi:10.1016/j.artint.2023.103879

Cited by 4 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies often treat it as a constant value, which may introduce a certain degree of error in prediction outcomes. Therefore, this study incorporates the unit distance energy consumption model from References [36,37] into the traffic flow cellular automaton model to accurately calculate the real-time energy consumption of EVs during their operation, as depicted in (12). e r (v r ) = (0.5ρΘϖv 2 r air resistance + µM ev g cos θ rolling resistance +M ev g sin θ) gravity /3600ς + P acc 3.6v r (12) where e r represents the energy consumption per unit distance of an EV traveling on road r (kWh); v r is the average speed of the EV on road r (m/s); ρ denotes the air density, assumed to be 1.2 kg/m 3 ; Θ is the drag coefficient, with a value of 0.29; ϖ is the frontal area of the vehicle facing the wind, taken to be 2.27 m 2 ; µ is the friction coefficient, with a value of 0.012; M ev is the weight of the EV, assumed to be 2000 kg; g is gravitational acceleration, taken as 9.81 m/s 2 ; θ is the road grade, assumed to be 0 • ; ς is the efficiency parameter, with a value of 0.9; P acc is the energy consumption of auxiliary systems, assumed to be 2 kW.…”

Section: Unit Distance Energy Consumption Modelmentioning

confidence: 99%

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

Bian,

Ren,

Guo

et al. 2024

Energies

View full text Add to dashboard Cite

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

show abstract

Section: Unit Distance Energy Consumption Modelmentioning

confidence: 99%

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

Bian,

Ren,

Guo

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…A vast array of applications can benefit from large-scale traffic simulation-based testing: evaluating missions or energy consumption of a vehicle in a simulated environment [15]. Similarly, energy-based navigation of vehicles [16] or GLOSA algorithms [17] require large-scale traffic scenarios to test comprehensively. Considering such scenarios, accurate driving behavior is required near the tested vehicle while farther away, knowing the congestion state of each road link is sufficient.…”

Section: Introductionmentioning

confidence: 99%

EGO-Centric, Multi-Scale Co-Simulation to Tackle Large Urban Traffic Scenarios

2023

View full text Add to dashboard Cite

Simulating automotive functions that rely on interaction with other vehicles (e.g., perceptionbased control or algorithms relying on inter-vehicular communication) created a demand for traffic simulation in the automotive field as well. Large-scale traffic simulation can be used to generate long, synthetic drive-cycles for EGO vehicles with realistic traffic. An EGO vehicle is defined as the vehicle the scenario revolves around, presumably running a control algorithm to be tested. On the other hand, simulating an entire district or city with thousands of vehicles present is superfluous and comes with a heavy computational burden while only the vicinity of the EGO vehicle is relevant. On the other hand, major traffic patterns can that could still influence the nearby traffic (e.g., traffic disruptions farther away) but can be simulated with lesser accuracy. Thus, simulation accuracy far from the EGO vehicle can be traded for simulation speed. This paper achieves this trade-off by co-simulating SUMO in microscopic and mesoscopic modes using Libsumo API. Microsimulated traffic is continuously spawned in an EGO-centered sub-network based on traffic states in the mesoscopic simulation. Simulation results in large urban scenarios suggest that the behavior of the EGO vehicle in terms of velocity distribution, headway distribution, and lane changes accurately matches pure microsimulation while simulation speed increased by 3 − 10 times. This result assumes linear time complexity control algorithms with respect to the vehicle number and a single EGO vehicle. Reducing the number of microsimulated vehicles with co-simulation yields even larger simulation speed gains for more computationally complex algorithms. The aggregate (macroscopic) traffic parameters match for both the micro-, meso-, and co-simulated cases. Thus coupling the two simulators does not distort the mesoscopic simulation.

show abstract