Vehicle Battery Charge Planning for Regenerative Route Segments
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Solution Overview
Problem
Existing vehicle control technologies for hybrid vehicles face challenges in efficiently managing charge and discharge schedules, particularly when drivers have non-average speed patterns or infrequently traveled roads, leading to inaccurate energy estimation and potential inefficiencies in regenerative power collection and battery assistance.
Innovation Solution
A vehicle control device and method that acquires road information to identify control target segments with predicted capacity changes, adjusts vehicle traveling load based on segment attributes, and corrects vehicle speed estimates using vehicle speed and road attribute data to optimize charge and discharge planning, ensuring efficient energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If charge/discharge schedule is set based on average vehicle speed and standard route data, then energy management can be simplified, but accuracy of energy estimation deteriorates when drivers have non-average speed patterns or travel infrequently visited roads
Solution Approach 1:
The system changes the parameter basis for charge/discharge scheduling from fixed average values to dynamically adjusted values based on actual driving behavior. By monitoring actual vehicle speed deviations from average speed and adjusting the vehicle traveling load parameter accordingly, the system adapts to individual driver patterns while maintaining operational simplicity.
Solution Approach 2:
The system implements feedback by continuously monitoring actual vehicle speed, comparing it with average speed data, and using this information to adjust the vehicle traveling load parameter. This feedback loop enables the system to learn and adapt to individual driver patterns over time, improving energy estimation accuracy without complicating the user interface.
2Measurement precision
If vehicle traveling load is calculated using actual vehicle speed data, then energy estimation accuracy improves, but the system becomes more sensitive to driving property variations
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing average vehicle speed data for different route segments before actual travel. This advance preparation allows the system to quickly compare actual speed with historical data during operation, improving real-time energy estimation accuracy while filtering out extreme outliers through statistical averaging.
Solution Approach 2:
The system adjusts the vehicle traveling load parameter based on the degree of deviation between actual and average vehicle speed. By dynamically modifying this parameter rather than using fixed values, the system adapts to different driving patterns while maintaining stability through gradual parameter adjustment rather than abrupt changes.
3Productivity
If charge/discharge planning is optimized for specific driving patterns, then energy efficiency improves for those patterns, but performance deteriorates for drivers with different speed characteristics
Solution Approach 1:
The system implements dynamic adaptation by continuously adjusting the vehicle traveling load parameter based on real-time speed monitoring and historical data comparison. This dynamic approach allows the system to optimize energy efficiency for the current driver's pattern while maintaining adaptability to future pattern changes, unlike static optimization for fixed driving patterns.
Solution Approach 2:
The system performs self-service by automatically learning and adapting to individual driver patterns through continuous monitoring and parameter adjustment. No manual intervention or reconfiguration is needed when drivers change patterns; the system autonomously adapts by comparing actual speed data with historical averages and adjusting the vehicle traveling load parameter accordingly.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach minimizes the influence of driving properties on energy management, allowing for accurate regenerative power collection and battery assistance, even with non-average speed drivers, by adjusting charge and discharge strategies based on real-time data and road conditions.
Implementation Method 1
an electric motor connected to a driving wheel, able to be driven by supply of electric power from the electrical storage device, and configured to supply regenerative electric power generated upon a regenerative operation to the electrical storage device
Data Source
AI summary
A vehicle control device includes power source, electrical storage device, electric motor connected to driving wheel, able to be driven by supply of electric power from electrical storage device and supplys regenerative electric power generated upon regenerative operation to electrical storage device, a road information acquisition part acquires road information related to scheduled traveling route of the vehicle, a control target segment extraction part extracts control target segment, in which change of remaining capacity of the electrical storage device equal to or greater than predetermined value is predicted, on scheduled traveling route, and a charge/discharge planning part plans charge/discharge of the electrical storage device based on vehicle traveling load in road from the vehicle to the control target segment, and the charge/discharge planning part determines the vehicle traveling load of the segment located before the control target segment according to attribute of the control target segment.