Energy Storage System Performance Tracking via Simulation Model
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Solution Overview
Problem
Current electrical energy storage systems (ESS) lack reliable performance guarantees due to complex temporal evolution of key performance indicators (KPIs), leading to conservative predictions and suboptimal investment decisions, as suppliers often cannot accurately predict KPIs over the lifecycle, especially when usage history is complex.
Innovation Solution
A model-based approach that simulates the dynamics of ESS using time series data of operational parameters to predict and track performance indicators, allowing for more accurate and less conservative guarantees across various usage scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model-based approach with multiple input operational parameters is used to simulate ESS dynamics, then prediction accuracy of performance indicators is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-processing operational parameter data and pre-configuring the simulation model with appropriate parameters and relationships before actual performance prediction is needed. This allows the complex model to be ready and optimized in advance, reducing real-time computational burden while maintaining high prediction accuracy.
Solution Approach 2:
The simulation model acts as an intermediary between raw operational parameters and performance indicator predictions. It mediates the complex relationships between multiple input parameters and output predictions, allowing accurate predictions without directly exposing the complexity of the underlying model to users or downstream systems.
2Adaptability or versatility
If performance guarantees are provided for complex usage scenarios, then adaptability of the guarantee system is improved, but reliability of predictions deteriorates due to insufficient testing
Solution Approach 1:
The patent applies dynamics by creating a flexible simulation model that can dynamically adapt to different usage scenarios without requiring extensive re-testing. The model can simulate various operational conditions, charge/discharge patterns, and environmental factors, allowing reliable performance guarantees for complex and varying usage scenarios that static testing approaches cannot cover.
Solution Approach 2:
The patent uses parameter changes by allowing the simulation model to adjust operational parameters such as temperature, charge rates, discharge rates, and usage patterns to match different scenario requirements. This enables the system to provide reliable predictions for diverse usage scenarios by dynamically changing model parameters rather than requiring separate testing for each scenario.
3Ease of manufacture
If simple KPI evolution functions are used for guarantees, then ease of manufacture and implementation is improved, but measurement precision of performance tracking deteriorates
Solution Approach 1:
The patent applies segmentation by breaking down the complex performance tracking into separate modules: data collection from operational parameters, simulation model processing, and performance indicator calculation. This segmented approach maintains implementation simplicity through modular design while achieving high measurement precision through the detailed simulation model that captures complex KPI evolution patterns.
Data Source
AI summary
A method for predicting a performance of an electrical energy storage system includes receiving, as input, time series of data points pertaining to a plurality of operational parameters of the electrical energy storage system. The input time series is provided to a model that simulates dynamics of the electrical energy storage system. A plurality of performance indicators of the electrical energy storage system may be calculated based on the simulated dynamics of the electrical energy storage system.