Multi-Battery SoC Estimation Using Pack-Level Dynamic Prediction
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Solution Overview
Problem
Conventional methods for estimating state of charge (SoC) and capacity in multi-battery energy storage systems (ESS) with parallel-connected battery units are inaccurate due to non-homogeneous dynamic behaviors, leading to under- or over-utilization of ESS capacity and increased wear, as they often rely on average or weakest-link estimates.
Innovation Solution
A method that predicts time-evolved terminal voltage and current for each battery pack using a multi-battery system model, allowing for accurate computation of chargeable and dischargeable capacity, and subsequently the SoC, by considering the dynamics of each battery pack individually.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional average state estimates or weakest battery pack constraints are used, then the calculation complexity is reduced, but the measurement precision of SoC and capacity estimation deteriorates
Solution Approach 1:
The patent segments the multi-battery ESS into individual battery pack units, each with its own state estimation. Instead of treating the ESS as a homogeneous whole, the method divides it into discrete segments (battery packs) that can be individually monitored and evaluated for their unique dynamic behaviors, charge levels, and states.
Solution Approach 2:
The patent applies local quality by recognizing that different battery packs within the ESS have non-homogeneous dynamic behaviors due to variations in internal parameters, age, and charge levels. Each battery pack is assigned its own local state estimation rather than applying a single global average, allowing each unit to be evaluated according to its specific characteristics.
2Measurement precision
If individual battery pack dynamics are considered separately, then the SoC and capacity estimation accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent merges the individual battery pack state estimations into a unified ESS-level state estimation. By combining the segment-level information from each battery pack through a systematic aggregation process, the method achieves accurate overall ESS state estimation while maintaining the benefits of individual pack analysis.
Solution Approach 2:
The patent develops a universal multi-battery system model that can handle diverse battery pack characteristics (different types, ages, charge levels) through a single framework. This multi-functional model accommodates various battery configurations and dynamic behaviors without requiring separate specialized models for each scenario.
3Ease of operation
If conventional estimation methods are used, then the system operation is simpler, but the ESS capacity utilization is reduced due to under-utilization or over-utilization
Solution Approach 1:
The patent performs preliminary action by accurately estimating the state of each battery pack before making operational decisions. By pre-calculating individual pack states and capacities, the system can optimize charging/discharging strategies in advance, ensuring that the ESS operates at its true capacity limits without risking over-utilization or under-utilization.
Solution Approach 2:
The patent introduces dynamics by continuously updating the state estimation of each battery pack based on real-time measurements and their unique dynamic behaviors. This dynamic approach allows the system to adapt to changing conditions and accurately track the true capacity availability of the ESS, enabling optimal utilization while maintaining operational simplicity.
Data Source
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AI summary
The invention relates to a method (200) of computing a state of charge of an energy storage system, ESS, with multiple parallel battery packs, the method comprising: for each battery pack, predicting (210) a time-evolved terminal voltage and current based on a respective measured terminal voltage; based on the predicted time-evolved terminal voltages and currents, computing (212) a chargeable and/or dischargeable capacity of the ESS; and computing (214) the state of charge of the ESS based on the chargeable and/or dischargeable capacity of the ESS. The method may seek to provide an accurate computation of SoC for an ESS with multiple parallel battery packs such that the full capability of the ESS is utilized.