HEV Battery Power Control via Predictive Energy Usage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Hybrid electric vehicle (HEV) powertrain optimization is challenging due to sub-optimal battery power management, which results in sub-optimal fuel economy and inefficient engine operation, as existing methods fail to accurately balance battery state of charge (SOC) and capacity limits with driver demand.
Innovation Solution
A system and method that uses predictive information, such as map data, traffic information, and driving history, to estimate battery energy usage during a future time window, allowing the controller to adjust battery power to balance SOC and operate the engine at system-efficient power, ensuring maximum efficiency and avoiding battery capacity limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the engine is commanded to operate at a power corresponding to maximum system efficiency, then system efficiency is improved, but battery state of charge balance deteriorates due to capacity limits and constraints
Solution Approach 1:
The controller predicts future battery energy usage and charging/boosting opportunities in advance, then adjusts current battery power requests accordingly. This preliminary action allows the system to proactively manage battery SOC balance while maintaining engine operation at maximum efficiency points, resolving the contradiction between efficiency optimization and battery constraint management.
2Ease of operation
If static optimization methods are used for battery power request determination, then control simplicity is maintained, but fuel economy deteriorates due to sub-optimal battery power requests
Solution Approach 1:
The system implements a feedback mechanism where the controller continuously monitors current battery SOC, compares it with predicted future energy usage patterns, and adjusts battery power requests dynamically. This feedback loop enables optimal fuel economy by making informed battery power decisions based on actual system state and predicted conditions, while maintaining computational efficiency through structured prediction algorithms.
3Device complexity
If heuristic methods are used for battery power request determination, then computational complexity is reduced, but powertrain efficiency deteriorates resulting in sub-optimal fuel economy
Solution Approach 1:
The system transforms the battery power request determination from a static heuristic approach to a dynamic parameter-based optimization. By incorporating predicted future battery energy usage as a key parameter, the controller can calculate optimal battery power requests that maximize powertrain efficiency. The structured parameter changes maintain computational tractability while significantly improving fuel economy outcomes.
Data Source
AI summary
A system and method for controlling battery power in a hybrid vehicle for a given driver demand that balances battery state of charge and battery capacity limits while operating the engine at a system efficient engine power. Predictive information may be used to predict battery energy usage during a future time window that indicates a charging opportunity (excess power will be absorbed by the battery) or a boosting opportunity (battery power will be discharged). Based on this information and the current state of charge of the battery, an associated battery power for a given driver demand is determined.