Predictive Energy Management for Hybrid Vehicle Battery Optimization
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Solution Overview
Problem
Conventional hybrid vehicles inefficiently utilize energy storage components due to lack of predictive capability and optimal state of charge management, leading to sub-optimal energy recovery and reduced component lifespan, often requiring oversized components to prevent stress limits from being exceeded.
Innovation Solution
A system and method that utilizes a computer-controlled energy management system to acquire and store power usage data, create a database of historical energy usage, and optimize the state of charge for energy storage components based on expected power demands along specific routes, eliminating the need for terrain data and allowing for predictive energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the state of charge is maintained near the midpoint to react to charging and discharging events, then the system can respond to power demands, but the energy storage component is over-sized and cost increases
Solution Approach 1:
The system performs preliminary actions by predicting future power demands and adjusting the state of charge in advance. The processor forecasts power requirements based on historical data and vehicle operating conditions, then proactively charges or discharges the energy storage component before the actual demand occurs, eliminating the need for oversized components to handle unexpected power spikes.
Solution Approach 2:
The system implements feedback control by continuously monitoring actual power consumption, comparing it with predicted values, and adjusting the state of charge accordingly. This closed-loop control optimizes energy storage utilization by learning from past performance and adapting to changing operating patterns, allowing smaller components to meet demand reliably.
2Ease of operation
If the state of charge is maintained near the midpoint, then the system can react to charging events, but unnecessary applied stresses shorten component life
Solution Approach 1:
The system prepares the energy storage component in advance by adjusting its state of charge based on predicted future demands. This prevents sudden high-rate charging or discharging events that would apply stressful transients to the component, thereby extending lifespan while maintaining operational responsiveness.
Solution Approach 2:
The system dynamically changes operating parameters by adjusting the state of charge setpoint based on predicted power demands and component stress limits. By modifying these parameters proactively, the system avoids operating conditions that would exceed stress thresholds and reduce component reliability.
3Loss of energy
If regenerative capture operates at 100% power to maximize energy recovery, then energy efficiency improves, but excessive heat and temperature rise occur
Solution Approach 1:
The system performs preliminary thermal management by predicting future power demands and adjusting the state of charge before high-power regenerative events occur. This proactive approach allows the battery to be in an optimal charge state to absorb regenerative energy without exceeding thermal limits, preventing temperature rise while maximizing energy recovery.
Solution Approach 2:
Instead of always operating at 100% power, the system applies partial action by modulating the regenerative capture rate based on predicted demands and thermal constraints. This optimized approach recovers sufficient energy to meet future requirements while avoiding excessive heat generation that would occur with continuous maximum-power charging.
4Power
If hybrid assist is used to maximize power delivery, then acceleration performance improves, but the energy storage component reaches charge limits and halts operation
Solution Approach 1:
The system prepares the energy storage component in advance by adjusting its state of charge based on predicted power demands. This ensures that sufficient charge capacity is available before high-power acceleration events, allowing the hybrid assist system to operate continuously at maximum power without being halted by charge limits.
Solution Approach 2:
The system dynamically adjusts the state of charge parameter to optimize power delivery capability. By changing the charge level proactively based on predicted demands, the system maintains optimal operating conditions for high-power acceleration while ensuring continuous operation capability throughout the drive cycle.
Data Source
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AI summary
A system for optimizing energy storage component (24) usage in a vehicle comprising one of a hybrid vehicle and an electric vehicle, the vehicle (10) comprising a computer (26) programmed to identify if a vehicle position is associated with link data in a database (42) of historical power usage data, the link data comprising measured historical power usage data for a link data of vehicle travel. If the vehicle (10) position is associated with the link data, the computer (26) is programmed to obtain the link data of the link from the database (42), the link data absent terrain information from the database (42). The computer (26) is also programmed to determine an expected vehicle power usage of the vehicle (10) based on the obtained link data and optimize the energy storage component (24) usage based on the expected vehicle power usage and based on efficiency and life cycle costs of an energy storage component (24) of the vehicle (10) if the vehicle (10) position is associated with the link data.