Electric Vehicle Driving Profile Control for Real-Time Energy Saving
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Solution Overview
Problem
Current methodologies for generating optimal driving profiles for electrically propelled vehicles, especially in urban contexts with varying operating conditions, face challenges in achieving real-time energy efficiency and timely arrival at predefined stops, requiring faster and more adaptable algorithms.
Innovation Solution
A method and control system that processes configuration and real-time data to calculate an optimal driving profile by dividing the remaining time into intervals, using equations of motion to determine hypothetical positions and speeds, and selecting the profile with the lowest energy consumption while adhering to vehicle and route constraints, allowing for real-time adjustments based on changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If on-line methodologies are used to generate optimal driving profiles in real-time, then the system can adapt to varying urban operating conditions and provide timely arrival, but the calculation complexity and processing time increase significantly
Solution Approach 1:
The patent segments the driving profile optimization into discrete time intervals (e.g., 1-second intervals) and divides the route into sequential segments between stops. This allows the complex real-time optimization problem to be broken down into manageable sub-problems that can be solved incrementally, reducing computational complexity while maintaining adaptability to changing conditions.
Solution Approach 2:
The system pre-calculates and stores optimal driving profiles for various route segments and operating conditions before actual operation. When the vehicle is in service, the control system quickly retrieves and adjusts these pre-computed profiles based on current conditions, avoiding the need for complex real-time calculations while maintaining adaptability.
2Use of energy by moving object
If complex algorithms are used to minimize energy consumption, then energy efficiency improves, but the calculation speed decreases making real-time implementation difficult
Solution Approach 1:
The optimization algorithm processes energy minimization in discrete time intervals and route segments rather than continuously over the entire journey. This segmented approach allows complex energy optimization calculations to be performed in small, manageable steps that can be completed within real-time constraints while still achieving significant energy savings.
Solution Approach 2:
The system focuses optimization efforts on critical segments of the journey where energy consumption has the greatest impact, such as acceleration phases and uphill sections, rather than attempting to optimize every aspect of driving equally. This partial action approach achieves substantial energy savings with reduced computational requirements.
3Reliability
If the system provides real-time driving suggestions to ensure timely arrival, then the arrival time reliability improves, but the system requires faster processing to keep up with driver response times
Solution Approach 1:
The control system pre-calculates optimal driving profiles and suggests acceleration commands in advance, before the driver needs to respond. By providing suggestions ahead of time and allowing anticipatory adjustments, the system ensures reliable arrival times without requiring instantaneous processing that would match the driver's reaction speed.
Solution Approach 2:
The system dynamically adjusts the timing and content of driving suggestions based on current vehicle state, route conditions, and predicted driver response. This dynamic adaptation allows the system to maintain arrival time reliability by providing suggestions at optimal moments rather than requiring uniformly fast processing for all situations.
Data Source
AI summary
An optimal driving profile is generated for vehicles provided with electric propulsion, by providing a sequence of consecutive time intervals until an arrival time and providing a plurality of accelerations that the vehicle can hypothetically take in each of the time intervals; by applying laws of uniformly accelerated motion for each of the accelerations and for each of the time intervals, hypothetical future points along the route are generated, starting from the current position and speed of the vehicle; for each of the time intervals, before analysing the subsequent time interval, it is checked whether the generated points meet all the predefined constraints; if the check has a negative outcome, the point being checked is eliminated from the plausible hypotheses; driving profiles are obtained, each defined by a respective sequence of remaining points; from among said driving profiles, the one requiring a lowest overall energy to be executed is selected.