EV Battery Module Selection for Degradation-Aware Power Delivery
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Solution Overview
Problem
Electric vehicle batteries degrade over time due to charge cycle fading and calendar ageing, leading to reduced operational health and efficiency, and potentially requiring premature replacement.
Innovation Solution
A system comprising a processor and memory that includes a battery monitoring component to track the operational conditions of battery modules, a vehicle operation component to monitor vehicle use, and a battery degradation component to determine degradation and inform the operator of adverse operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If the battery is used continuously for vehicle operation, then the vehicle can maintain mobility and functionality, but the battery experiences degradation from charge cycle fading and calendar ageing
Solution Approach 1:
The system performs preliminary actions by continuously monitoring battery parameters (voltage, temperature, current) and predicting degradation trends before significant damage occurs. The battery management system analyzes state-of-charge patterns, charging rates, and operating conditions to forecast remaining useful life and alert operators to potential issues before they manifest as failures.
Solution Approach 2:
The system implements feedback mechanisms by continuously measuring battery parameters, comparing them against degradation models, and adjusting charging strategies or providing warnings to operators. The system uses feedback from temperature sensors, voltage monitors, and current measurements to dynamically modify charging rates and provide real-time information about battery health status and expected lifespan.
2Power
If the battery operates at higher power levels to meet vehicle performance demands, then the vehicle can achieve required speed and acceleration, but the battery degrades faster due to increased stress from charging and discharging operations
Solution Approach 1:
The system dynamically adjusts charging and discharging rates based on real-time battery conditions, temperature, and predicted degradation risks. Rather than using fixed power limits, the battery management system continuously modifies operational parameters to optimize the balance between power delivery and battery preservation, allowing higher power when conditions permit and reducing stress when degradation risk is elevated.
Solution Approach 2:
The system changes operational parameters such as charging voltage, current, and temperature thresholds based on battery state and degradation predictions. By dynamically adjusting these parameters, the system can accommodate high power demands when battery health is good while implementing protective parameter changes when degradation accelerates, thereby extending overall operational life.
3Duration of action of moving object
If the battery is charged frequently to maintain adequate range, then the vehicle can maintain sufficient operating range, but the battery experiences accelerated degradation from repeated charging operations
Solution Approach 1:
The system performs preliminary analysis of charging patterns, predicting which charging events will cause the most degradation based on current battery state, temperature, and charging rate. By identifying high-risk charging scenarios in advance, the system can alert operators to potential issues and adjust charging strategies before damage occurs.
Solution Approach 2:
The system uses feedback from charging session data, temperature measurements, and voltage monitoring to continuously refine degradation predictions and adjust future charging recommendations. The battery management system learns from past charging patterns and their impact on battery health, providing increasingly accurate guidance on optimal charging timing and rates to balance range requirements with battery preservation.
Data Source
AI summary
Various embodiments and approaches are described to minimize degradation of respective modules combined to form a battery pack onboard an electric vehicle (EV). Systems and components are presented to determine a respective operational state of the modules, and based thereon, a first subset of modules can be selected to provision power to various EV components while a second subset of modules can be deselected. Module selection can be based upon a threshold operating condition. A visual representation of the modules and their respective operational state can be presented, in conjunction with one or more alarms and recommended corrective operations. Artificial intelligence methods can be utilized to determine an operational state of a module(s). A module can be scheduled for replacement. Limiting degradation to a first module can minimize degradation of a second module. The various components can be stored in a memory and executed by a processor.