Vehicle Battery Pack Uncertainty Control for SoP and SoE Limits
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Solution Overview
Problem
Existing energy storage systems in vehicles face inefficiencies in estimating state-of-power (SoP) and state-of-energy (SoE), leading to conservative or aggressive estimates that can result in wasted power capacity, high power loss, rapid temperature increase, premature termination of charging/discharging, accelerated degradation, and even fire hazards.
Innovation Solution
A method that acquires battery pack performance data for SoP and SoE, including uncertainties, and processes it through an estimation model to determine the maximum expected uncertainty of the overall SoP and SoE of the energy storage system, allowing proactive management of battery pack connections/disconnections to optimize charging/discharging loads and operational windows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conservative SoP/SoE estimation is used, then system safety is improved, but power capacity utilization deteriorates
Solution Approach 1:
The system continuously monitors individual battery pack SoP and SoE estimates along with their uncertainties, feeds this information back to the control unit, and dynamically adjusts the overall system power capacity utilization accordingly. This closed-loop feedback mechanism allows the system to operate closer to actual limits while maintaining safety margins.
Solution Approach 2:
The invention changes the parameter representation from fixed conservative estimates to dynamic estimates with associated uncertainties. By modeling SoP and SoE as parameters with uncertainty ranges rather than fixed values, the system can adaptively determine power capacity utilization based on actual confidence levels in the estimates.
2Power
If aggressive SoP/SoE estimation is used, then power capacity utilization is improved, but harmful effects worsen
Solution Approach 1:
The system performs preliminary estimation of individual battery pack SoP and SoE with uncertainty quantification before determining overall system power capacity. By proactively assessing the confidence levels in battery state estimates beforehand, the control unit can make informed decisions about power delivery that prevent harmful effects before they occur.
Solution Approach 2:
Continuous monitoring of battery pack states and their uncertainties provides real-time feedback that allows the system to adjust power delivery to prevent harmful effects. When uncertainty in individual pack estimates increases, the system automatically reduces power utilization to avoid potential harm.
3Device complexity
If individual battery pack uncertainties are not considered, then system complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The invention segments the overall battery system into individual battery packs for separate SoP and SoE estimation. Each pack's state is estimated independently with its own uncertainty, allowing the system to maintain detailed precision while managing complexity through modular processing rather than attempting to model the entire system as a single unit.
Solution Approach 2:
Individual battery pack estimates and their uncertainties are merged to determine the overall system SoP and SoE. The control unit combines information from multiple segmented estimates, considering their respective uncertainties, to produce a comprehensive system-level assessment that maintains precision without excessive complexity.
Data Source
AI summary
The present invention relates to a method for improving the availability of an energy storage system in a vehicle. The energy storage system comprises a plurality of battery packs, and the method comprises:acquiring battery pack performance data comprising at least one of the state-of-power, SoP, and the state-of-energy, SoE, for each one of the battery packs, wherein the acquired SoP and/or SoE for each battery pack comprises an estimated value and associated uncertainty;processing the battery pack performance data with an estimation model adapted to relate the sum of the uncertainties to a maximum expected uncertainty of an overall SoP and/or SoE of the energy storage system; andsetting the maximum allowed energy storage system charging/discharging load and/or energy storage system operational windows in response to the overall SoP and/or SoE of the energy storage system and corresponding maximum expected uncertainty.