Abstract
The eco-driving profiles are algorithms able to use additional information in order to create recommendations or limitation over the driver capabilities. They increase the autonomy of the vehicle but currently their usage is not related to the autonomy required by the driver. For this reason, in this paper, the eco-driving challenge is translated into two-layer optimal controller designed for pure electric vehicles. This controller is oriented to ensure that the energy available is enough to complete a demanded trip, adding speed limits to control the energy consumption rate. The mechanical and electrical models required are exposed and analyzed. The cost function is optimized to correspond to the needs of each trip according to driver behavior, vehicle, and traject information. The optimal controller proposed in this paper is a nonlinear model predictive controller (NMPC) associated with a nonlinear unidimensional optimization. The combination of both algorithms allows increasing around 50% the autonomy with a limitation of the 30% of the speed and acceleration capabilities. Also, the algorithm is able to ensure a final autonomy with a 1.25% of error in the presence of sensor and actuator noise.
Highlights
Guest Editor: Giorgio Sulligoi e eco-driving profiles are algorithms able to use additional information in order to create recommendations or limitation over the driver capabilities. ey increase the autonomy of the vehicle but currently their usage is not related to the autonomy required by the driver
According to the external current data about weather, road, traffic, and so forth, the acceleration profile is estimated based on the historical data set of acceleration profiles in the same conditions. e acceleration profile is translated to a power demand and it is sent to the state of charge (SoC) estimator. e SoC estimator uses the power demand profile and the battery dynamic model to determine if there is enough energy to complete the travel or not
An online approach of eco-driving speed profiles optimizer is presented; this optimizer is composed of an nonlinear model predictive controller (NMPC) and it proposes a dynamic speed limitation based on the distance required by the driver. is speed limitation proposed by the controller is able to save twice the amount of energy in comparison to closed-loop approaches present in the state of the art thanks to the fact that even when the dynamic system required by the NMPC is oriented to the fast compilation, and it takes into account the motor efficiency behavior
Summary
Guest Editor: Giorgio Sulligoi e eco-driving profiles are algorithms able to use additional information in order to create recommendations or limitation over the driver capabilities. ey increase the autonomy of the vehicle but currently their usage is not related to the autonomy required by the driver. Ey increase the autonomy of the vehicle but currently their usage is not related to the autonomy required by the driver. Guest Editor: Giorgio Sulligoi e eco-driving profiles are algorithms able to use additional information in order to create recommendations or limitation over the driver capabilities. For this reason, in this paper, the eco-driving challenge is translated into two-layer optimal controller designed for pure electric vehicles. There are some tools able to improve the capabilities of the driver to estimate the autonomy of the vehicle according to his current actions. E SoC estimator uses the power demand profile and the battery dynamic model to determine if there is enough energy to complete the travel or not According to the external current data about weather, road, traffic, and so forth, the acceleration profile is estimated based on the historical data set of acceleration profiles in the same conditions. e acceleration profile is translated to a power demand and it is sent to the SoC (state of charge) estimator. e SoC estimator uses the power demand profile and the battery dynamic model to determine if there is enough energy to complete the travel or not
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