Vehicle Battery Module Life Estimation for Predictive Replacement
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Solution Overview
Problem
Existing battery technologies struggle to accurately assess battery health and performance, leading to inefficient energy usage and increased fuel consumption due to failed start/stop events in vehicles, and consumers lack understanding of when to replace batteries, resulting in reduced battery efficiency and increased emissions.
Innovation Solution
A system and method for determining battery life estimation and replacement scheduling based on enterprise acceptability values, utilizing sensors, processors, and remote servers to calculate battery state markers and maintenance schedules, integrating with vehicle ECUs to monitor battery health and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If battery modules are used in vehicles to provide power, then energy storage capability is improved, but battery life estimation accuracy deteriorates due to lack of precise monitoring
Solution Approach 1:
The system performs preliminary actions by continuously collecting battery data (temperature, voltage, current, state of charge) and calculating state of health metrics before actual battery failure occurs. This proactive monitoring enables accurate life estimation and predictive maintenance scheduling, resolving the contradiction between energy storage capability and estimation accuracy.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring battery parameters, comparing actual performance against expected degradation patterns, and adjusting life estimation accordingly. The remote server receives battery data, calculates updated state of health metrics, and provides feedback on remaining useful life, enabling accurate prediction despite the battery's energy storage function.
2Ease of operation
If consumers lack understanding of battery replacement timing, then ease of operation is improved, but fuel consumption increases due to failed start/stop events
Solution Approach 1:
The battery management system performs self-service by automatically monitoring its own health status, calculating state of charge and state of health metrics, and determining optimal replacement timing without requiring consumer expertise. The system generates maintenance schedules and sends notifications to users, maintaining operational simplicity while preventing energy loss from failed start/stop events.
Solution Approach 2:
The remote server acts as an intermediary between the battery system and the consumer. It processes complex battery data, performs life estimation calculations, and translates technical metrics into simple replacement recommendations. This intermediary maintains ease of operation for consumers while ensuring timely replacement to prevent fuel consumption from failed start/stop events.
3Reliability
If battery performance monitoring is enhanced, then reliability is improved, but device complexity increases due to additional sensors and processing
Solution Approach 1:
The system achieves multi-functionality by using a single remote server to perform multiple tasks: collecting battery data, calculating state of charge, determining state of health, estimating remaining useful life, and generating maintenance schedules. This universal approach improves reliability through comprehensive monitoring while avoiding the complexity of distributed processing across multiple components.
Solution Approach 2:
The system extracts complex processing functions from the vehicle's onboard systems and relocates them to a remote server. By taking out the heavy computational tasks (life estimation, degradation analysis, maintenance scheduling) and performing them remotely, the system improves monitoring reliability without significantly increasing the complexity of the in-vehicle device.
Data Source
AI summary
A system and method for determination of and communicating replacement determinations and maintenance scheduling for battery modules is disclosed. The method includes calculating a life estimation for the battery module based on received information and collected data, the battery module may be in communication with a server remote from the battery module, with the server having a value for enterprise acceptability for the battery module, the server may comprise a communication module to receive the information from the battery module, a processor; and memory operatively connected to the processor, with the processor executing the instructions for calculating a life estimation for the battery module based on the received information and the collected data, further the system may provide a for a predictive maintenance program.