Hybrid Vehicle Route Segmentation for SOC-Based Mode Control
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Solution Overview
Problem
Existing vehicle control technologies are limited in their application and accuracy due to high computational demands and time consumption in predicting battery state of charge (SOC) and fuel consumption, particularly on roads with significant slope fluctuations, and are not optimized for efficient hybrid electric vehicle (HEV) mode control.
Innovation Solution
A vehicle control device that predicts speed changes based on route information, divides the route into partial segments, and uses sensor data to optimize HEV and electric vehicle (EV) modes for minimizing fuel consumption by calculating SOC and transit times, employing dynamic programming and neural networks for efficient power management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If map information and sensor data are used in parallel to predict SOC and fuel consumption, then prediction accuracy is improved, but computational burden and time consumption increase
Solution Approach 1:
The route is divided into multiple partial routes based on speed change predictions, allowing SOC and fuel consumption to be calculated separately for each segment. This segmentation reduces the overall computational burden by breaking down a complex full-route calculation into smaller, more manageable partial route calculations.
Solution Approach 2:
The system predicts speed changes and divides the route into partial routes in advance before performing detailed SOC and fuel consumption calculations. This preliminary action allows the system to prepare calculation segments beforehand, reducing real-time computational burden while maintaining prediction accuracy.
2Loss of energy
If the route is divided into partial routes for detailed SOC calculation, then fuel consumption optimization is improved, but processing time increases
Solution Approach 1:
The route is segmented into partial routes based on predicted speed changes, enabling targeted SOC and fuel consumption calculations for each segment. This allows the system to optimize fuel consumption through detailed partial route analysis while managing processing time by working with smaller segments rather than the entire route at once.
Solution Approach 2:
The system performs calculations for partial routes rather than the complete route, applying partial action to reduce processing time while still achieving sufficient fuel consumption optimization for the current driving context.
3Measurement precision
If real-time sensor data is used to predict acceleration and fuel consumption, then control accuracy is improved, but computational resources are consumed
Solution Approach 1:
Real-time sensor data processing is applied selectively to partial routes rather than the entire route. This segmentation allows the system to maintain high control accuracy using real-time acceleration predictions while reducing overall computational resource consumption by limiting real-time processing to relevant route segments.
Solution Approach 2:
The system applies real-time sensor data processing and detailed calculations locally to the current partial route where the vehicle is located, rather than uniformly across the entire route. This local quality approach maintains control accuracy where needed while conserving computational resources in other areas.
Data Source
AI summary
A vehicle control device and a method thereof are provided. The vehicle control device includes a processor, a sensor, a battery, and a memory. The processor: predicts a change in speed of a vehicle based on a route of the vehicle, using map information received from an external server, while driving the vehicle; divides the route of the vehicle into a plurality of partial routes, using the change in speed of the vehicle; obtains state of charge (SOC) information of the battery in each partial route of the plurality of partial routes, wherein the SOC information changes based on the change in speed of the vehicle; and obtains ratio information between a transit time when the vehicle passes through each partial route and a driving time of the vehicle based on a hybrid electric vehicle (HEV) mode in each partial route, using the SOC information.