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HYBex3 Concept Vehicle
Predictive Functions in the HYBex3 Concept Vehicle
The hybridization of powertrains is an important step toward efficient and clean mobility. In particular, the possibility of shifting the operation of the combustion engine to ranges with a higher efficiency level and representing purely electric driving modes is one of the main advantages of hybrid drives. This shifting of the load point can be further optimized on the basis of route data that includes the expected vehicle speed as well as the road gradient, and is considered to be the state of the art with regard to modern hybrid drives.
Combined with the development of predictive and automated driving functions, further potentials can be tapped. The key factor for an actual reduction of the energy requirement under real driving conditions is a precise forecast of the future development of a traffic situation. This forecast can be based on a multitude of potential sources, such as sensor data, high-resolution maps, and vehicle communication, whereby all the data is fused into a comprehensive environmental model.
Based on the information from this model, the longitudinal guidance of the vehicle and the powertrain control can be optimized. In cooperation with the Institute for Combustion Engines of RWTH University Aachen, Germany, FEV has developed a function structure that is capable of using a multitude of potential data sources. This creates a solution space for predictive speed profile optimization. This speed profile can then be used in order to optimize the operation of torque distribution between the hybrid components.
The function structure was integrated in a hybrid prototype vehicle constructed jointly with DENSO. A robust, real-time model predictive control algorithm is used in order to optimize the longitudinal guidance of the vehicle.
The HYBex3 concept vehicle
The HYBex3 (”HYBrid power exchange 3 modes“) vehicle was developed in order to determine the impact of a cost-effective DHT transmission concept on the driveability of the vehicle and test it under real conditions. It was developed jointly with DENSO AUTOMOTIVE Germany. The base vehicle is a MINI Cooper with a turbocharged 100 kW three-cylinder combustion engine. The serial transmission was replaced with the hybrid transmission to be examined, which was specially developed for the application case. The powertrain topology is equivalent to a mixed hybrid equipped with two electric engines (EE) in a P2/P3 layout. The P2 machine is located between the electrohydraulically powered clutch and the two-stage spur gear component. The synchronization elements are also actuated electrohydraulically. The P3 machine is positioned at the transmission output and therefore has a fixed transmission ratio to the wheel.
Various operating modes can be represented with this DHT transmission. For purely electric driving, the combustion engine is stopped and the clutch is opened. Electric engine P2 can therefore be operated in both transmission stages. In addition to a high starting torque in the first gear, this enables a maximum vehicle speed of 140 km/h in the second gear.
In hybrid operation, serial or parallel driving is possible. In parallel operation, one of the two gear sets is engaged. In serial operation mode, the transmission is shifted to neutral. The combustion engine is then exclusively connected to electric engine P2 while electric engine P3 operates the wheels. All gear changes are synchronized entirely electrically, so that the friction clutch can remain closed even in hybrid operation. The serial operation in the low speed range and the parallel operation at higher speeds enable a significant increase of the system efficiency level.
The operating strategy provides for the combustion engine being operated at a very low dynamic and the implementation of fast load changes by the electric path. The transmission ratios enable a significant reduction of the rotational speed of the combustion engine, without compromising the overall dynamic of the powertrain. The operating strategy was optimized with a Design of Experiments. For this purpose, the parameters of the stop-start strategy of the combustion engine were optimized simultaneously with the parameters of the battery charging strategy. For the final parameterization, a compromise between the layouts for different driving cycles was selected.
The distribution of the torques of the two electric engines, both in parallel operation and in fully electric driving, is determined by an online optimization patented by FEV. The search algorithm varies the torque distribution until the energetically optimal case is found. In doing so, both the battery limits and the power limits of the electric engines are taken into account for the current situation.
The function structure developed for predictive longitudinal dynamic control is designed in such a way that a multitude of data sources, optimization routines, and powertrain structures can be represented in said function structure.
The first step is an aggregation and fusion of the available data into an environmental model, followed by a prediction of the traffic situation. This enables an optimization of the speed profile. On the basis of that, an acceleration control of the vehicle is carried out. The planned speed profile can also be used in order to adjust the charging status strategy. If the desired charging performance is determined, the torque distribution between the powertrain components is carried out on the basis of said performance and the wheel torque requirement.
The precise forecast of the current traffic situation requires the aggregation of all available data. This includes, for instance, RADAR sensors, LIDAR sensors, or optical cameras that traffic participants can identify with the help of image recognition techniques. Usually, these sensors indicate the type (passenger car, truck, pedestrian, etc.), the relative positions and, potentially, the relative speed of the detected objects.Further information can be obtained from the on-board navigation systems, which indicate speed limits, road gradients and curvatures as well as, potentially, intersection data for the most probable path of the vehicle via an “electronic horizon”. If the navigation system is connected to the internet, data on average speeds along the planned route and traffic jams can be provided.
Additional data can be obtained through the future connection of vehicles using 5G or ETSI ITS G5. This Vehicle-to-everything (V2X) communication should include, among other things, the positions, direction, and speeds of other vehicles, as well as the layout of intersections and the status of traffic light systems. The vehicle communication can therefore provide data that goes beyond the horizon detectable via on-board sensors.
Since the same object can therefore be detected multiple times by various data sources, the data aggregation must also include a functionality for data fusion. This is especially advantageous for hardware setups with different types of sensors, e.g. a RADAR sensor and camera sensor. The RADAR sensor can precisely define the distance to and the relative position of a vehicle driving ahead, but cannot determine the lateral position of the vehicle in relation to the road markings. In contrast, the camera sensor can only provide estimates regarding the relative speed and the distance, but can precisely determine whether the detected object is in the same lane as the vehicle under consideration. After the fusion of several data sources, an aggregated object list is created, which only contains valid and relevant data for all detected objects, and generates a corresponding environmental model.
Before an optimization of the vehicle trajectory can be carried out, there must be a forecast of the development of the current situation. This forecast is based on the relevant objects that the environmental model provides. The first step is the determination of the speed limit along the prediction horizon. Based on that and the current condition of detected vehicles driving ahead, the speed and position trajectory of these vehicles is forecast.
On the basis of this, a solution space is spread out in which the downstream optimization algorithm can operate. The function structure developed by FEV and the Institute for Combustion Engines enables the implementation of different algorithms to this end. Depending on the requirement, simple, rule-based approaches, as well as model predictive control or discrete dynamic programming methods can be represented.
Application in the vehicle
To test the function structure, a real time-compatible model predictive control (MPC) was implemented in the rapid prototyping control unit of the HYBex3 concept vehicle and various test scenarios were carried out. In a first demonstration, the functionality and real-time compatibility of these scenarios for a predictive adjustment of the HYBex3 concept vehicle was proven. With an efficient implementation of the MPC using the qpOASES tool, an optimization of the speed curve for a horizon of 10 s can be carried out within less than 100 µs.
In the future, the modular design of the function structure can be used to expand the forecast horizon of the vehicle – for instance, with traffic lights ahead – or to represent predictive, automated driving functions such as Predictive Cruise Control (PCC).