EV Battery SOC Scheduling Based on Predicted Driving Patterns
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Solution Overview
Problem
Existing battery management systems for electric vehicles do not consider individual driving patterns or operational requirements, leading to inefficient battery usage and reduced battery life.
Innovation Solution
A system and method that utilizes a battery manager to estimate driving patterns, predict charging parameters based on these patterns, and schedule charging accordingly to optimize battery usage and extend battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the battery is charged to full capacity each time a recharge is performed, then the battery capacity is maximized for immediate use, but the useful life of the battery is reduced due to excessive charging that does not consider future driving patterns
Solution Approach 1:
The system performs preliminary analysis of future driving patterns and operational requirements before executing the charging action. The battery manager predicts future vehicle usage and adjusts the charging schedule accordingly, performing the intelligent decision-making action in advance to optimize both immediate capacity needs and long-term battery health
Solution Approach 2:
The charging strategy transitions from a static full-capacity approach to a dynamic, adaptive approach. The battery manager continuously monitors driving patterns, vehicle operational requirements, and environmental conditions to dynamically adjust charging parameters, enabling the system to optimize battery management in real-time based on changing conditions
2Ease of operation
If the battery management follows a standard state-of-charge schedule, then the charging process is simple and consistent, but it does not account for individual driving patterns or operational requirements reducing efficiency
Solution Approach 1:
The battery management system performs self-analysis and self-adjustment by automatically monitoring driving patterns, predicting future operational requirements, and independently optimizing charging schedules. The system serves itself by making intelligent decisions about when and how to charge without requiring manual user input or complex external coordination
Solution Approach 2:
The system implements a closed-loop feedback mechanism where the battery manager continuously monitors actual driving patterns, compares them against predicted patterns, and uses this feedback to refine future charging decisions. This feedback loop enables the system to adapt and improve battery management efficiency over time based on real-world vehicle usage
Data Source
AI summary
A battery manager includes a pattern estimator, a prediction engine, and a charging scheduler. The pattern estimator estimates a driving pattern of an electric vehicle. The prediction engine predicts a charging parameter based on at least one of the driving pattern or driver charging behavior. The charging scheduler outputs the charging parameter to control charging of a secondary battery of the electric vehicle. The pattern estimator may estimate the driving pattern based on information from at least one information source of the electric vehicle. The prediction engine predicts the charging parameter of the secondary battery in preparation of a future use of the electric vehicle.