Automated Inspection and Predictive Maintenance for Battery Management Systems
Automated inspection and predictive maintenance for Battery Management Systems (BMSs) are the cornerstones of effective battery safety and lifespan maintenance. Automated inspection and predictive maintenance solutions continuously or periodically analyse the State of Health (SOH), State of Charge (SOC), temperature range, safety protocols, and other factors of the battery to provide an accurate analysis of battery performance and safety. This analysis enables businesses to mitigate premature battery failure and potentially hazardous conditions and can ultimately help extend battery life and improve operational efficiency and performance levels. By monitoring batteries for anomalies and providing accurate automated analysis, businesses can proactively respond to potential issues and maintenance requirements, avoid costly downtime, and save energy.
Important benefits of adopting automated inspection and predictive maintenance solutions include:
1.) Reduced unplanned downtime,
2.) Improved battery life,
3.) Better energy saving,
4.) Lower miscellaneous costs for spare parts and maintenance procedures,
5.) Reduced regulatory penalties, and
6.) Enhanced customer satisfaction.
Technologies for Automated Inspection and Predictive Maintenance for BMSs
Thermal imaging is a technology that utilises an infrared camera to detect changes in the ‘normal’ operating temperatures and thermal gradients of the various electrical components associated with the BMS system. These temperature anomalies can provide the user with an accurate prediction of potential failures before they occur, allowing for more cost-effective repairs and preventative maintenance. Thermal imaging can also be used to detect and resolve open circuits, faulty wiring, and other potential issues. Additionally, the thermal images generated provide an easy-to-understand visual representation of the battery and its components, which is a helpful aid for technicians and engineers as they diagnose and troubleshoot issues with the system.
Magnetic diagnostics is a technology that utilises a sophisticated array of permanent magnets to detect and rectify current imbalances within the system’s battery cells quickly and accurately, providing a cost-effective solution to any potential issues. Additionally, these magnetic diagnostic systems can be used to monitor the status of all battery cells over time, allowing for a more comprehensive predictive maintenance approach.
Accurate voltage measurements are essential for diagnosing potential issues with the system, as voltage changes can indicate a faulty battery or cell. By monitoring and measuring the voltage regularly, technicians and engineers can quickly detect any sudden or unexplained changes in the system, allowing for an efficient resolution. Voltage monitoring can also be used to detect problems such as excessive current draw or short circuits.
Wireless connectivity technology allows technicians, engineers, and service professionals to remotely access and diagnose the system, eliminating the need to physically inspect the equipment. This is a particularly useful tool when dealing with systems located in difficult-to-reach areas, as wireless connectivity makes it much easier to diagnose and troubleshoot issues without physically accessing the system. Additionally, by utilising wireless connectivity, service professionals can access real-time data from the system, allowing for more accurate predictive maintenance.
Automated Testing and Control:
Automated testing and control utilises sophisticated computing algorithms to regularly measure and analyse crucial data points such as battery voltage, temperature, and other vital metrics. This data can then be used to detect any potential signs of failure before they occur, allowing technicians to take preventative action and more accurately identify potential issues with the system. Additionally, automated testing and control can be used to reduce energy consumption by optimising system performance, allowing for greater energy efficiency and cost savings.
Bamomas is a Finnish start-up specialising in automated inspection and predictive maintenance for BMSs. They have developed an Artificial Intelligence (AI) powered smart monitoring platform that leverages advanced big data analytics to automatically identify abnormal battery operations, detect environmental threats, provide actionable maintenance alerts, and predict long-term failures. The application of the technology extends to any battery management system, encompassing electric vehicle batteries, satellite batteries, and electric grid/sea battery solutions. Their unique selling point is their daily ‘no-touch’ inspection utilising their automated protocols and machine learning algorithms. This ultimately allows for more proactive maintenance of battery systems and extends the life of the batteries while also reducing service costs. The solution provides real-time updates enabling predictive maintenance and includes customised sensors for increased accuracy and remote monitoring options that reduce the need for on-site personnel and invasive investigations.
Powin Energy offers a comprehensive and fully integrated automated inspection and predictive maintenance solution for BMSs. This solution leverages advanced algorithms and an integrated suite of digital tools to facilitate high-frequency monitoring and data analytics. The BMS monitors, records, and stores vital information such as cell temperatures, cell voltages, battery SOC, battery SOH, discharge cycles, and self-discharge rates. With this data, the system can automatically identify anomalies, track performance over time, and identify potential operational issues and risks.
Predictive Maintenance for Proactive Issue Detection
The predictive maintenance element of this solution then uses this data to predict any potential operational issues before they become a problem. The system triggers an alert whenever it identifies an anomaly, notifying the user of the developing issue. With this information, the user can take proactive, preventative measures to mitigate the risk of failure or costly repairs. This proactive approach helps reduce downtime, improve uptime, and extend the battery system’s life. As a result, users can accurately forecast and plan for battery maintenance activities, averting any unplanned outages or delays. The solution also features an intuitive user interface, which records all data and offers detailed analytics and graphical representations of the performance of the battery system. This enables users to make data-driven decisions for their BMS, ensuring ongoing performance and reliability.
The innovative solution offered by Twaice, founded in 2018 with research centres and offices in France and the US, for automated inspection and predictive maintenance for BMSs is their patented ‘Twaice Digital Twin’ technology. This technology uses AI, machine learning, and big data analytics to accurately identify battery health, performance, and other characteristics, along with their changes over time. It then predicts the maintenance and performance of the battery over time, enabling manufacturers and operators to stay ahead of any issues with an effective preventative maintenance strategy. Twaice Digital Twin also offers real-time monitoring and diagnosis capabilities, providing proactive insights to identify problems before they become significant.
Experts estimated the market size for predictive maintenance at US$4.2 billion in 2021, with a projected increase to US$15.9 billion by 2026, indicating a Compound Annual Growth Rate (CAGR) of 30.6%. This growth is driven by the recognition of the substantial benefits offered by non-destructive and non-intrusive predictive maintenance analytics and automated fault reporting, particularly in the context of the rising popularity of battery electric vehicles. The proliferation of battery electric vehicles contributes to this market growth as they begin to recognize the substantial benefits provided by non-destructive and non-intrusive predictive maintenance analytics and automated fault reporting.
Automated inspection and predictive maintenance for BMSs offer significant potential for cost savings and improved efficiency. Innovations in this field are focusing on using AI and machine learning to make predictive maintenance even more accurate and efficient. Additionally, new automated inspection devices that are capable of deep analysis of batteries without intrusive measurements are in development, which can provide extremely detailed information about battery health and performance. Thus, the overall outlook of this technology looks highly promising.