Hybrid Vehicle Drive Mode Planning for Regenerative Energy Sections
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Solution Overview
Problem
Hybrid vehicles face challenges in allocating suitable driving modes along a route, particularly when regenerative energy collection is expected, as the battery state of charge may reach a lower limit, making it difficult to maintain EV mode usage.
Innovation Solution
A movement support apparatus and method that dynamically set up driving modes (EV and HV) for each section of a route based on energy consumption, regenerative energy availability, and distance to the destination, prioritizing HV mode when energy is low and regenerative energy is expected, and adjusting running loads to optimize battery usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If EV mode is set up for sections with regenerative energy collection to maximize energy recovery, then regenerative energy collection efficiency is improved, but battery state of charge may reach lower limit before reaching regenerative sections, making EV mode unavailable
Solution Approach 1:
The system performs preliminary calculation of battery state of charge changes from current location to each section, and preemptively adjusts driving mode allocations before reaching regenerative sections. By predicting future battery states and working backwards, the system ensures sufficient charge reaches regenerative sections to enable EV mode, while still maximizing overall energy recovery opportunities along the route.
Solution Approach 2:
The system dynamically adjusts driving mode allocations based on real-time battery state of charge predictions. Rather than static mode assignment, the controller continuously recalculates optimal EV/HV mode distribution considering predicted battery charge levels at each section, enabling flexible adaptation to ensure EV mode availability at regenerative sections while maximizing energy recovery.
2Use of energy by moving object
If EV mode is used in sections with low running load to conserve energy, then energy consumption is reduced, but battery may be depleted before sections requiring energy collection
Solution Approach 1:
The system calculates battery state of charge changes in advance for each section and uses these predictions to determine optimal EV mode allocation. By working backwards from destination requirements, the system ensures sufficient battery charge is maintained to reach regenerative sections, while still utilizing EV mode in appropriate low-load sections to minimize energy consumption.
Solution Approach 2:
The system changes the allocation parameters of driving modes based on predicted battery state of charge. By adjusting which sections are assigned EV versus HV mode according to calculated battery availability, the system optimizes energy consumption while ensuring battery reliability for reaching future regenerative energy collection opportunities.
3Productivity
If driving mode is allocated to balance total energy consumption along the entire route, then overall energy efficiency is improved, but local battery state of charge may reach lower limit before regenerative sections
Solution Approach 1:
The system performs preliminary calculation of battery state of charge changes for each section along the route and uses this information to adjust driving mode allocations. By predicting future battery states and working backwards from destination requirements, the system ensures sufficient charge reaches regenerative sections to enable EV mode, while still optimizing overall energy efficiency through strategic mode selection.
Solution Approach 2:
The system applies different driving mode allocations to different sections based on local conditions and predicted battery state of charge. Rather than uniform allocation, the controller optimizes EV/HV mode distribution section-by-section, ensuring local battery reliability at regenerative sections while maintaining overall energy efficiency through targeted EV mode usage in appropriate sections.
Data Source
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AI summary
An apparatus includes an unit that plans any driving mode of an EV mode in which the motor is used as the drive source and an HV mode in which at least the engine is used as the drive source for each section, into which a route from the current location to the destination is partitioned, when a load for running in each section is set. The setup unit plans the mode using an aspect in which the HV mode is preferentially planned for a section including at least one of a current section including the current location and a section after the current section when a remaining amount of the battery is lower than a threshold of the remaining amount of the battery, and regenerative energy obtained from running load information is higher than or equal to a value determining a restoration of the remaining amount of the battery.