Adaptive Fleet Charging Control for Plug-in Vehicle Battery Longevity
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Solution Overview
Problem
High-voltage batteries in plug-in electric vehicles degrade faster due to high state of charge maintenance, high charging currents, and temperatures, leading to reduced battery life, especially when vehicles are driven short distances and fully charged frequently, causing range anxiety and inefficient use.
Innovation Solution
An adaptive system and method that uses a controller with an algorithm to monitor and learn driving habits and charging behavior, optimizing battery charging to maintain an optimal state of charge, rotating vehicles for even use, and controlling charging operations to extend battery life and improve fleet efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If battery packs are maintained at high state of charge level to provide sufficient driving range, then driving range is improved, but battery degradation accelerates and battery life is reduced
Solution Approach 1:
The system dynamically adjusts the state of charge (SOC) setpoint based on real-time conditions including driving patterns, battery age, temperature, and charging rates. The SOC setpoint transitions from static to dynamic, allowing the battery management system to optimize between range and longevity by adjusting charge levels adaptively rather than maintaining a fixed high SOC level
Solution Approach 2:
The system changes key battery parameters including SOC setpoint, charging current limits, and temperature thresholds based on battery state and usage patterns. By dynamically modifying these parameters, the system prevents harmful conditions while ensuring sufficient driving range, thereby extending battery life without sacrificing operational capability
2Reliability
If fleet vehicles are fully charged at every charging event to ensure availability, then vehicle availability is improved, but unnecessary high-level charging accelerates battery degradation
Solution Approach 1:
The system implements feedback loops that continuously monitor battery state, driving patterns, and charging history. Based on this feedback, the battery management system learns optimal charging strategies for each vehicle, adjusting charge levels to match actual usage patterns rather than universally applying full charging, thus preserving battery life while maintaining availability
Solution Approach 2:
The system performs preliminary analysis of driving patterns and forecasts future energy needs to determine optimal charging levels in advance. By predicting when full charging is actually necessary versus when partial charging suffices, the system prevents unnecessary high-level charging that would accelerate degradation while ensuring vehicles are charged adequately for anticipated usage
3Speed
If high charging currents are used to reduce charging time, then charging speed is improved, but battery degradation is accelerated
Solution Approach 1:
The system dynamically adjusts charging current based on real-time battery conditions including state of charge, temperature, and battery age. Charging current transitions from static high-current charging to dynamic adjustment, reducing current when battery vulnerability increases while maintaining higher current when conditions permit, thereby optimizing the balance between charging speed and battery preservation
Data Source
AI summary
A system and method for optimizing a plug-in vehicle fleet having a plurality of battery packs across the vehicle fleet. Each vehicle includes a battery pack. The system includes sensors for measuring battery performance data and includes an open-circuit voltage, charging current, and/or temperature of the battery pack, a GPS receiver, a user interface, and a controller. The controller executes a method to monitor degradation of each battery pack using a variety of current and historical data points. The controller then controls a charging operation of each battery pack in a fleet via a charging control signal sent to a particular vehicle in the fleet. The controller also then determines the new location for each particular vehicle in a fleet.