Hybrid Vehicle Route-Based SOC Optimization
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Solution Overview
Problem
Hybrid vehicle control logic does not utilize location information and historical data effectively to maximize fuel efficiency, leading to inefficient energy use.
Innovation Solution
A system that includes a route identification module, memory for storing route history, and a processor to detect current routes, calculate confidence values, and adjust engine start/stop thresholds based on target state of charge (SOC) to optimize fuel efficiency by learning from route history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the hybrid control logic uses a fixed engine start/stop threshold, then the control system is simple and reliable, but fuel efficiency is not optimized on familiar routes
Solution Approach 1:
The system performs preliminary actions by storing historical route data and SOC information before the vehicle travels the same route again. When the vehicle re-traverses a familiar route, the controller has already prepared optimized start/stop thresholds based on previous trips, allowing it to proactively optimize fuel efficiency rather than reacting in real-time. This resolves the contradiction by adding complexity only when beneficial (on repeat routes) while maintaining simple fixed thresholds on new routes.
Solution Approach 2:
The engine start/stop threshold transitions from a fixed static value to a dynamic value that adapts based on route familiarity and historical SOC data. The controller adjusts the threshold dynamically: using fixed thresholds for simple/reliable operation on new routes, and switching to learned optimized thresholds on familiar routes to improve fuel efficiency. This dynamic adaptation resolves the contradiction between simplicity and optimization.
2Productivity
If the hybrid control logic does not use GPS location information, then the control logic remains simple, but fuel efficiency cannot be maximized by learning route patterns
Solution Approach 1:
The system uses GPS location data to perform preliminary actions by identifying when the vehicle is traveling on a familiar route. By comparing current GPS coordinates with stored route histories, the system proactively determines whether to apply learned SOC optimizations before the trip begins or early in the route traversal. This resolves the contradiction by adding GPS processing only when it enables fuel efficiency improvements on repeat routes.
3Loss of energy
If the hybrid control logic uses aggressive engine start/stop thresholds, then the engine runs more frequently recharging the battery, but energy overflow and force charge conditions cause waste
Solution Approach 1:
The system implements feedback by monitoring actual SOC levels during route traversal and comparing them against predicted SOC based on historical data. When the vehicle re-traverses a familiar route, the controller uses feedback from previous trips to adjust engine start/stop timing, preventing both energy overflow (when battery is already full) and force charge conditions (when battery needs charging but engine shouldn't start). This resolves the contradiction by using feedback to optimize the balance between recharging frequency and energy waste prevention.
Solution Approach 2:
The controller performs preliminary analysis of historical SOC data and route characteristics before the vehicle travels the route again. By predicting future SOC levels based on past trips, the system proactively adjusts engine start/stop thresholds to prevent energy overflow and force charge conditions before they occur. This resolves the contradiction by adding complexity only when it prevents energy waste on familiar routes.
Data Source
AI summary
A system and method for optimizing a fuel efficiency of a hybrid vehicle by learning from a route history. The system may be a hybrid vehicle including an engine, a motor-generator, a battery, a battery module, a GPS unit, a memory, and a processor. The method may include detecting a current route of the hybrid vehicle, calculating a confidence value corresponding to a probability that the current route has a match in the route history, detecting a battery state of charge (SOC), determining a target SOC based on the confidence value and the route history, and adjusting an engine start/stop threshold and/or an engine power request to achieve the target SOC. They system may learn and improve fuel efficiency over successive trips along the route.