BESS Thermal Management via Forecast-Based Mode Sequencing
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Solution Overview
Problem
Battery energy storage systems (BESS) face challenges in maintaining optimal operating temperatures within limited ranges to prevent damage and battery fires, requiring significant energy and cost for thermal management, especially when external environmental conditions vary.
Innovation Solution
A method that identifies operating temperature limitations and forecasts external environmental conditions to select the most energy-efficient thermal management modes, using a thermal model to simulate and optimize temperature profiles across multiple time periods, ensuring the BESS operates within safe temperature ranges while minimizing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If thermal management systems operate continuously to maintain battery temperature within operating limitations, then battery safety and lifespan are protected, but energy consumption and operational costs increase significantly
Solution Approach 1:
The system performs preliminary actions by forecasting environmental conditions and battery temperature trends in advance, allowing thermal management modes to be selected proactively rather than reactively. This enables the system to prepare appropriate cooling or heating strategies before temperature violations occur, reducing the need for continuous high-energy thermal management operations while maintaining battery safety
Solution Approach 2:
The system dynamically adjusts thermal management strategies by evaluating multiple possible sequences of thermal management modes and selecting the optimal sequence based on forecasted conditions. This dynamic optimization allows the system to adapt thermal management intensity to actual needs, maintaining battery safety while minimizing energy consumption through intelligent, condition-based control
2Speed
If thermal management systems use aggressive cooling or heating modes to quickly return battery to operating range, then temperature control effectiveness improves, but energy consumption increases
Solution Approach 1:
The system applies partial action by selecting thermal management modes and sequences that provide sufficient temperature control effectiveness without always using the most aggressive (excessive) cooling or heating. The optimization evaluates multiple sequences to find the minimal effective intervention needed to keep battery temperature within operating limitations, reducing energy consumption while maintaining adequate temperature control speed
3Use of energy by moving object
If multiple thermal management modes are evaluated and optimized across multiple time periods, then energy efficiency improves, but system complexity increases
Solution Approach 1:
The system uses forecasting to create a copy or simulation of future environmental conditions and battery temperature responses. By evaluating thermal management sequences based on this forecasted model rather than real-time trial and error, the system achieves energy optimization without requiring complex real-time control infrastructure, managing complexity through predictive modeling
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach proactively manages BESS temperatures, reducing energy costs and extending battery life by selecting the most efficient thermal management sequences based on predicted environmental conditions and operational schedules, ensuring the system remains within safe temperature limits.
Implementation Method 1
During charging and discharging, battery cell temperatures tend to rise as a result of the resistive losses and can generate substantial amounts of heat. Thermal management systems operate to prevent the cell temperature from rising to any level that can impact the batteries negatively.
Implementation Method 2
Thermal management systems operate to prevent the cell temperature from rising to any level that can impact the batteries negatively.
Data Source
AI summary
The present disclosure provides systems and methods for managing a temperature of a battery energy storage system (“BESS”). A method may comprise identifying operating temperature limitations of the BESS; obtaining a forecast horizon comprising a forecast of external environmental conditions for a time period; identifying a charging/discharging schedule of the BESS; simulating operation of the BESS for the time period for each of a plurality of sequences of thermal management modes according to the charging/discharging schedule and the forecast horizon, the simulating generating an energy consumption and an operating temperature forecast of for each of the plurality of sequences of thermal management modes; selecting a sequence of thermal management modes of the plurality of sequences; and operating the equipment according to the selected sequence of thermal management modes.