Vehicle MPC Route Planning With Arrival Time Factors
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Solution Overview
Problem
Existing model predictive control (MPC) methods for vehicle trajectory planning do not accurately predict arrival times and fail to optimize routes for energy efficiency and time efficiency, especially considering factors like refueling stops, charging stops, and driver breaks.
Innovation Solution
The proposed solution involves an improved MPC system that calculates the complete route and trajectory of a vehicle, taking into account various arrival time factors such as break periods, refueling/charging times, traffic conditions, and weather. This system optimizes ground speed for optimal energy consumption and driving time, using a processor unit configured to execute an MPC algorithm with a longitudinal dynamic model of the vehicle's drive train.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If model predictive control calculates trajectory for a limited prediction horizon, then computing time is reduced, but arrival time prediction accuracy deteriorates
Solution Approach 1:
The patent divides the trajectory calculation into two segments: a complete route calculation considering all arrival time factors (break periods, refueling/charging stops, traffic conditions) for accurate arrival time prediction, and a sliding prediction horizon optimization for real-time control. This segmentation allows each part to serve its specific purpose without compromise.
Solution Approach 2:
The system performs preliminary calculation of the complete route and trajectory considering all arrival time factors before execution. This preliminary action provides accurate arrival time predictions that are then used to guide the real-time sliding prediction horizon optimization, ensuring both accuracy and computational efficiency.
2Use of energy by moving object
If model predictive control optimizes for energy consumption, then energy efficiency is improved, but driving time may increase
Solution Approach 1:
The patent changes the optimization parameters by incorporating multiple arrival time factors (break periods, refueling/charging stops, traffic conditions, weather) into the trajectory calculation. This allows the system to find optimal ground speed profiles that balance energy consumption and driving time under specific conditions, rather than optimizing for energy alone.
Solution Approach 2:
The system uses a sliding prediction horizon that dynamically adjusts the optimization based on current vehicle state, remaining distance, and predicted arrival time factors. This dynamic approach allows the controller to adapt between energy-efficient and time-efficient strategies depending on the situation.
3Measurement precision
If the system considers multiple arrival time factors (break periods, refueling/charging stops, traffic conditions), then route planning accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex route planning into two distinct phases: complete route calculation considering all arrival time factors for accuracy, and sliding prediction horizon optimization for real-time control. This segmentation manages complexity by handling different aspects separately.
Solution Approach 2:
The complete trajectory calculation acts as an intermediary that translates multiple arrival time factors into a structured ground speed profile. This intermediary representation simplifies the subsequent real-time control by providing a pre-processed optimization target that incorporates all relevant factors.
Data Source
AI summary
A processor unit (3) for model-based predictive control of a vehicle (1) taking into account an arrival time factor is configured to calculate a trajectory for the vehicle (1) based at least in part on at least one arrival time factor, with the trajectory including an entire route (20) to a specified destination (19) at which the vehicle (1) is to arrive, and with the at least one arrival time factor influencing an arrival time of the vehicle (1) at the specified destination (19). Additionally, the processor unit (3) is configured to optimize a section of the trajectory for the vehicle (1) for a sliding prediction horizon by executing a model-based predictive control (MPC) algorithm (13), where the MPC algorithm (13) includes a longitudinal dynamic model (14) of a drive train (7) of the vehicle (1) and a cost function (15) to be minimized.