What are the maintenance and fault prevention measures for industrial and commercial energy storage systems?

As a front-line practitioner in commercial and industrial energy storage, I understand deeply that scientific prevention and maintenance strategies are crucial for long-term system stability. Here’s my professional summary based on years of field experience:

1. Core Strategies for Fault Prevention and Maintenance
Battery System

In daily operations, I've found precise parameter control essential. Maintain battery temperature at 25±2℃, triggering alarms when deviating by ±15℃. Limit charge-discharge cycles to ≤1 per day, and initiate equalization charging when voltage deviation exceeds 30mV. Routine maintenance should include SOC calibration every 3 months, battery replacement every 5 years, and quarterly coolant tests (conductivity/pH value).

Table 1: Common Faults and Maintenance Cycles for Commercial and Industrial Energy Storage Equipment

BMS (Battery Management System)

Reliable operation hinges on communication redundancy and anti-interference design. Deploy dual Ethernet/GOOSE communication for 20ms self-healing, paired with shielded cables, filter capacitors, and TVS surge protection. Maintenance includes quarterly firmware upgrades, monthly protocol compatibility checks, and semi-annual sensor calibration (four-wire resistance measurement).

PCS (Power Conversion System)

I always prioritize high-reliability IGBT/SiC modules and optimize PWM parameters with enhanced protection functions. Regular upkeep should involve quarterly IGBT module inspections, semi-annual heat sink cleaning, and annual efficiency testing.

Temperature Control System

Vibration protection (optimized piping, 10x volume buffers, natural frequency adjustment) and high-seal corrosion-resistant materials are key. Maintenance includes quarterly seal inspections, semi-annual pressure testing, and annual coolant replacement.

EMS (Energy Management System)

Leverage machine learning for algorithm optimization and deploy SM4 encryption with access control. Regular tasks include quarterly model updates, monthly data integrity checks, and annual disaster recovery drills.

2. Best Practices for Maintenance System Construction
Preventive Maintenance Framework

Adopt a "detect-first" approach using tools like Huawei Smart PCS's health checks. Data analysis should drive maintenance prioritization to ensure proactive issue resolution.

Professional Competency Development

Maintenance teams need interdisciplinary knowledge (electrical, electronics, automation). Regular training on equipment principles, fault diagnosis, and emergency response is vital—for instance, mastering data analysis for CATL's BMS 4.0, which predicts battery anomalies 14 days in advance.

Spare Parts Management

Maintain dynamic inventories for critical components (BMS chips, IGBT modules, liquid-cooling seals). Strict classification, stock control, and usage records ensure rapid replacement, minimizing downtime for projects like Sungrow's 550kW/1145kWh system (90% cycle efficiency).

Environmental Adaptability Management
Customize protection measures according to local climates: implement moisture control during Guangdong's "humid return" season and apply anti-corrosion treatments in coastal areas. Regular environmental monitoring and protective inspections can extend equipment service life.

Data-Driven Maintenance Trend

Emulate Tencent's "Energy Brain" by building a battery health database with machine learning. This enables early fault warning and precise diagnosis, reducing response time from 2 hours to 15 minutes and cutting O&M costs by 40%.

3. Industry Trends and Practical Insights

Years of fieldwork have proven that in-depth fault analysis and targeted solutions for each subsystem form the foundation of a comprehensive maintenance system. As energy storage technology evolves, fault types and solutions continue to update.

Future advancements in AI, IoT, and big data will drive intelligent and precise fault diagnosis. New technologies like multi-modal grid-forming energy storage diagnosis, unsupervised reconstruction error methods, and data-driven SOC calibration will significantly enhance accuracy and efficiency. Higher system integration and intelligence will also streamline maintenance workflows.

It’s critical to adopt a systematic perspective—never isolate equipment or subsystems. Only through scientific strategies and professional teams can we ensure long-term stable operation, maximizing economic and social value for commercial and industrial energy storage in the global energy transition.