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Energy Storage Active Safety Comprehensive Monitoring System

Energy Storage Active Safety Comprehensive Monitoring System

Product overview

Energy Storage Active Safety Comprehensive Monitoring System helps achieve life cycle management over the energy storage equipment through cloud computing, Big Data mining, digital twin, and AI. It leverages technical means such as risk source positioning, active fault prevention, fault degradation deduction, and troubleshooting decision support to quickly and accurately isolate equipment with risks, remove equipment safety inducements and risk sources, and achieve pre-event active operation and maintenance to energy storage equipment fault.

Product parameters

Super early and super safe

Build active safety alarm system, roll out up to 42 alarm strategies, and recognize the whole through observation of the part;

AI algorithm assists in early/super early abnormal situation recognition and sub-health diagnosis, seeing the beginning to know the end;

Micro short circuit identification accuracy >93%, thermal runaway alarm timing advance>15min, super safety protection.

“3A” PHM health management

Build “All-round”, “All-process” and “All-coverage” PHM health management system, in order to examine thoroughly the energy storage station’s health and energy efficiency status.

 High-precision sensing

Quickly identify and accurately locate the system’s abnormal situations, and greatly fill existing BMS alarm system’s deficiency;

Precisely correct battery SOX estimation error, and empower battery performance for in-depth optimization.

Technical Indexes

Key technical indexes for energy storage system active safety alarm

Micro short circuit identification accuracy ≥93%, alarm timing advance ≥7 days.

Thermal runaway very early alarm timing advance ≥15min, accuracy ≥95%, positioning precision <0.2m.

Abnormal battery failure testing accuracy ≥90%.

Thermal management system abnormal alarm accuracy ≥95%.

Technical parameters for energy storage station PHM health management

24 cell-level, equipment-level, and station-level diagnosis indexes.

Associated with 42 AI alarm models, overall diagnosis accuracy ≥95%, and under-reporting rate<10%.

Technical parameters for battery status estimation

Battery SOH and SOC estimating error <5%.

Application scenarios

■ Household energy storage: families and small factories and enterprises.

■ Industrial & commercial energy storage: commercial buildings and industrial parks.

■ Source-side energy storage: renewable energy with energy storage, thermal power with energy storage, and independent energy storage.