ESS End-of-Life Forecasting for Battery Replacement Timing
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Solution Overview
Problem
Current methods for determining the end of life (EoL) of electric Energy Storage Systems (ESS) and electric vehicles are inefficient, leading to premature battery cell replacement and suboptimal utilization of ESS capacity.
Innovation Solution
A method and device that determine actions based on forecasted EoL parameters for electric ESS and electric vehicles by comparing ESS and vehicle parameters, allowing for optimization of ESS utilization, such as second-life applications, and adjusting parameters like temperature, power output, and charging to extend ESS life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If battery cells are under dimensioned to fit current vehicle needs, then vehicle performance is optimized, but battery cells must be replaced before vehicle end-of-life
Solution Approach 1:
The system performs preliminary forecasting of EoL parameters for both the vehicle and battery cells before the actual EoL is reached. By comparing forecasted EoL vehicle parameters with forecasted EoL battery parameters in advance, the system can plan and execute battery replacement or second-life applications at optimal times, preventing premature replacement while ensuring vehicle performance needs are met.
2Duration of action of stationary object
If battery cells are over dimensioned to last the whole vehicle lifetime, then battery replacement is avoided, but ESS capacity is wasted
Solution Approach 1:
The system determines optimal battery sizing by forecasting EoL parameters and comparing them with vehicle requirements. This allows using exactly the necessary battery capacity (partial action) rather than over-dimensioning, while still ensuring the battery lasts the vehicle lifetime through optimized utilization strategies and timely replacement or second-life applications.
Solution Approach 2:
The system changes key parameters including forecasted EoL capacity, forecasted EoL power, and forecasted EoL energy throughput to determine optimal battery sizing and utilization strategies. By dynamically adjusting these parameters based on forecasts, the system optimizes ESS capacity utilization without wasting resources.
3Duration of action of stationary object
If battery cells are optimized for maximum life, then replacement cost is reduced, but ESS utilization during life is suboptimal
Solution Approach 1:
The system continuously monitors actual ESS parameters (capacity, power, energy throughput) against forecasted EoL parameters and provides feedback for optimization. This feedback loop enables real-time adjustment of utilization strategies to maximize both ESS life and productivity, preventing suboptimal utilization while extending battery life through informed decision-making.
Data Source
AI summary
The invention relates to a method performed by a device for determining an action to be taken based on forecasted EoL parameters for an electric ESS and for an at least partly electric vehicle in which the electric ESS is comprised. The device obtains at least one ESS parameter impacted by utilization of the at least one electric vehicle and obtains at least one vehicle parameter impacted by utilization of the at least one electric vehicle. The device determines a forecasted EoL vehicle parameter based on the at least one vehicle parameter and a forecasted EoL ESS parameter based on the at least one ESS parameter. The device compares the forecasted EoL ESS parameter and the forecasted EoL vehicle parameter and determines the action to be taken based on a result of the comparing.