Battery Management System Dynamic Prediction Neural Network

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Solution Overview

Problem

Current battery management systems (BMS) lack the ability to accurately predict battery performance and optimize energy storage and discharge based on real-time data, leading to inefficiencies and potential battery degradation.

Innovation Solution

A method and system that utilize time series analysis and neural networks to process current, voltage, and temperature measurements from sensors, determining impedance and temperature parameters to predict future battery performance, enabling informed charging and discharging decisions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If current battery management systems use static rules for monitoring and control, then the system structure remains simple, but the ability to accurately predict battery performance and optimize energy storage is insufficient

Engineering Contradiction:

Improvebattery performance prediction accuracyVSAvoidsystem structure complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent transitions from static monitoring rules to dynamic prediction models that continuously adapt to changing battery conditions. The system uses time-varying parameters and real-time data processing to dynamically adjust charging/discharging decisions, improving prediction accuracy while managing complexity through structured algorithms

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent introduces intermediate processing layers including data preprocessing modules, feature extraction units, and prediction model intermediaries that bridge raw sensor data and control decisions. These intermediaries structure the complexity, making the system manageable while enabling accurate performance prediction through multi-stage processing

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If real-time data processing is implemented to optimize energy storage and discharge, then energy efficiency improves, but computational requirements and system complexity increase

Engineering Contradiction:

Improveenergy storage efficiencyVSAvoiddata processing complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent divides the data processing system into segmented functional modules: data acquisition layer, preprocessing layer, prediction layer, and control layer. Each module handles specific tasks independently, improving energy storage efficiency through specialized processing while managing overall system complexity through modular architecture

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements preliminary data preprocessing and feature extraction before main prediction operations. Historical data is pre-processed and stored in structured formats, and key features are extracted in advance, reducing computational burden during real-time operations and improving energy storage efficiency with manageable processing requirements

Inventive Principle:

Principle #10Preliminary action

3Duration of action of stationary object

If predictive algorithms are used to determine charging and discharging commands, then battery longevity is improved, but the complexity of measurement and analysis increases

Engineering Contradiction:

Improvebattery longevityVSAvoidperformance parameter analysis difficulty

Core Design Contradiction:

Duration of action of stationary objectVSDifficulty of detecting and measuring

Solution Approach 1:

The patent implements feedback mechanisms where prediction results are continuously compared with actual battery performance measurements. This feedback loop refines prediction accuracy over time and validates model assumptions, improving battery longevity through adaptive control while systematically managing measurement complexity through structured validation protocols

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces complex physical measurement and analysis mechanisms with computational prediction models. Instead of relying on intricate sensor arrays and physical analysis systems, the invention uses algorithmic prediction based on electrical measurements, simplifying the measurement system while improving battery longevity through intelligent control

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11368030B2Battery management system

Publication Date: 2022.06.21 EOS ENERGY TECHNOLOGY HOLDINGS LLC

AI summary

A method includes receiving current measurements from at least one current sensor configured to measure current of a battery system in communication with a power distribution network having a power plant. The method also includes receiving voltage measurements from at least one voltage sensor configured to measure voltage of the battery system and temperature measurements from at least one temperature sensor configured to measure temperature of the battery system. The method includes determining an impedance parameter of the battery system based on the received measurements, a temperature parameter of the battery system based on the received measurements, a predicted voltage parameter based on the impedance parameter, and a predicted temperature parameter based on the temperature parameter. The method includes commanding the battery system to charge power from the power plant or discharge power from the power plant based on the predicted voltage parameter and the predicted temperature parameter.