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Mr. John Smith

Job title



The global trend to minimize emissions and fuel consumption is pushing towards new technologies in the fields of optimal powertrain control, electrification and road load reduction. The impact of technology improvements can be objectively evaluated by current test standards such as chassis dyno and Real Driving Emission (RDE) tests. However, current test standards do not quantify the impact of the driver behavior which has strong influence in the driving efficiency. Eco-driving algorithms aim to encourage and train the drivers to embrace cleaner driving behaviors in order to acknowledge this efficiency leverage option. The impact of these algorithms and good driving practices are difficult to quantify objectively as the impact depends on the interpretation of a human being that cannot be modelled as a vehicle component and the randomness of the driving environment. Some authors calculate the impact of the eco-driving advice by calculating the energy savings by following the target speed, but this approach does not verify that the target speed is feasible with a realistic surrounding environment. Quantification can also be performed through physical testing by driving two vehicles close to each other, one with eco-driving and one without. Whereas this method is robust and involves the surrounding traffic, the two vehicles have different drivers and encounter slightly different driving conditions, which adds randomness to the evaluation. This paper aims to propose an alternative virtual and objective validation method through co-simulation of the eco-routing algorithm, a high fidelity vehicle model to evaluate energy consumption and a traffic simulation environment. The traffic model provides the environment (surrounding vehicles, traffic lights, etc) and a driver model that analyzes the environment and decides when it is safe to follow the speed recommendation from the eco-driving algorithm. The traffic model will also generate the topographic and traffic predictive information of the route that needs to be feed to the predictive eco-driving optimization algorithms. With the traffic simulation, it is possible to guarantee that the same driving condition can be evaluated with the baseline and optimized algorithm in order to avoid the randomness. Virtual testing also permits to simulate a batch of cases to obtain higher numerical representativeness than physical testing. The co-simulation also permits to segment the impact by type of route, calculate the time delay caused by the eco-driving and the impact on the overall traffic. The scalability of this framework is promising in different levels. As the setup could be used to be evaluated in Driver-in-the-Loop or Driving Simulator. As well, at completely virtual level it would also permit to calculate the large-scale impact in the road net with different levels of eco-driving. The paper demonstrates that the new trend of co-simulation of vehicle models with traffic models leverages the possibility to include mobility ecosystem implications in the vehicle development process. This project received funding from the European Union’s (EU) Horizon 2020 Research and innovation program under grant agreement N 769935.

Mrs. Marina Roche Arroyos, Applus IDIADA, SPAIN Mr. Dídac Sabrià, Applus IDIADA, SPAIN Mr. Pablo Cano, Applus IDIADA, SPAIN Mr. Daniel Ruiz, Applus IDIADA, SPAIN Dr.-Ing. Marco Mammetti, Applus IDIADA, ITALY

Objective Impact Evaluation of Predictive Eco-Driving Optimization Algorithm in Traffic Simulation Environment

F2021-ACM-122 • Paper + Video • FISITA World Congress 2021 • ACM - Automated and Connected Mobility


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