A simulation-based case study for powertrain efficiency improvement by automated driving functions

Abstract

An increasing level of driving automation and a successive electrification of modern powertrains enable a higher degree of freedom to improve vehicle fuel efficiency and reduce pollutant emissions. Currently, both domains themselves, driving automation as well as powertrain electrification, face the challenge of a rising development complexity with extensive use of virtual testing environments. However, state-of-the-art virtual testing environments typically strictly focus on just one domain and neglect the other. This paper shows the results of a simulation-based case study considering both domains simultaneously. The influence of energy saving automated functionalities on a conventional, a hybrid, and a pure electric powertrain is investigated for a carefully selected inner-city driving scenario. The vehicle simulation models for the different powertrain configurations are calibrated using test bench results and vehicle measurements. A model predictive acceleration controller is developed for realizing the speed optimization function. By considering traffic conditions such as traffic light schedules and a preceding vehicle as the boundary conditions, unnecessary accelerations and decelerations are avoided to reduce the energy demand. The case study is realized by applying this function to the three powertrains variants. As a final result, a clear difference in energy demand is observed: the hybrid powertrain benefits the most in terms of energy demand reduction in the given use case. The results clearly underscore that in future vehicle development programs, the powertrain and the real-world driving functionalities have to be optimized simultaneously to minimize the energy demand during everyday vehicle operation.

Energy saving potentials of modern powertrains utilizing predictive driving algorithms in different traffic scenarios

“…In our previous research on this topic, 24 we were able to show that applying moderate cost to acceleration and deceleration already lead to a reduction in energy demand, independent of the powertrain configuration. In order to further minimize the fuel consumption, a parameter study was conducted, which showed that each powertrain need a different calibration of the cost function weights to yield optimal results.…”

“…21,22 Our previous research on this topic showed that with an MPC-based approach, the achievable reduction in energy and fuel consumption is directly affected by the electrification of the powertrain. 23,24 Another essential influencing factor is the traffic condition the vehicle is operated in. The traffic mix is reported to have an impact of −15% to +15% on the real-world CO 2 emissions of passenger cars when operated manually.…”

Predictive kinetic energy management for an add‐on driver assistance eco‐driving of heavy vehicles

“…In [26], an energy‐optimal reference velocity planning scheme is presented that incorporates the future state of traffic lights. In [27], a clear benefit in energy consumption (>20%) is noted for all powertrain variants: a conventional internal combustion (IC) engine vehicle, a hybrid electric vehicle (HEV), and a battery electric vehicle (BEV), with the HEV having the most improvement (27.7%) when adopting the proposed MPC that considers traffic light schedules, motions of the preceding vehicle, as well as energy demand, and optimises the ego vehicle's speed for an inner‐city driving scenario.…”

Eco-Driving Optimization Based on Variable Grid Dynamic Programming and Vehicle Connectivity in a Real-World Scenario

“…As formalized in [32], eco-driving can be regarded as an optimal control problem where the drive commands are chosen to minimize the energy consumption for a given trip, and, among the set of methods provided by the optimal control theory, some solution techniques that are commonly employed are Model Predictive Control (MPC) and Dynamic Programming (DP). MPC can be implemented as either an optimization problem considering the nonlinearities in the powertrain efficiency characteristics [33] or a computationally less expensive linear optimization problem that only considers the vehicle kinematics [34]. DP [35], as proposed by [29] to optimize the velocity profiles for achieving automated ecodriving, can provide an optimal result even for highly nonlinear problems, such as ecodriving.…”