The key to the safe, reliable and efficient functioning of lithium-ion battery packs is the battery management system (BMS). Known as the ‘brain’ of the battery, it is an essential component of all low and high voltage lithium-ion battery packs. An electronic supervisory system, the BMS manages the battery pack and is responsible for measuring cell voltages, ensuring balanced charge cycles, state identification, and controlling crucial safety systems. It is essentially responsible for safeguarding the batteries from damage.
In 2017, The Global Battery Management System market was valued at $2.92 Billion. It is now projected to reach $12.17 Billion by 2025. The market is growing at a CAGR of 19.6% from 2018 to 2025.
A number of countries who are investing in the future of renewable energy and pushing for aggressive adoption are helping increase the demand for better and smarter batteries. As the demand for electric vehicles increases globally, this significantly shifts the focus on to cutting-edge battery management systems.
Subscribe to our Newsletter
Join our editors every weekday evening as they steer you through the most significant news of the day, introduce you to fresh perspectives, and provide unexpected moments of joy
Today, Electric Vehicles (EV), homes & large solar/wind micro-grids are powered by lithium-ion batteries that batteries boast of the highest energy densities of any battery technology, have a relatively low self-discharge & require low maintenance. The most critical challenge that lithium-ion batteries face is that they have a limited life, which is affected by usage, charging patterns and the environment in which they operate, etc. Since batteries cost up to 40% of the actual electric vehicle cost, it is critical to optimize an EV’s battery life, improve performance and uptime.
Here are some of the technological innovations that are creating a huge impact in the BMS sector –
Smarter thermal Management
The Lithium-ion battery packs are usually designed for high energy density which means the cells need to be packaged tightly, close to the next cell, which makes the battery more temperature-sensitive and leads to a need of dedicated thermal management.
In the past, large battery packs did not necessarily require any special cooling as the physical size of the packs was sufficient and the relative flow of current was not large compared to the overall capacity of the pack. But as faster battery charging rates are demanded, special thermal management methods for the battery pack have become essential. Hence, BMS manufacturers are developing advanced cooling technologies, with an objective to improve battery life, while cutting down on charging time and cost.
IoT & Data Analytics
Leading EV battery manufacturers are offering customized and smart battery solutions that provide extensive system diagnostics such as accurate cell voltage, state of charge, temperature monitoring, cell balancing, real-time with the help of IoT and data analytics. This enables battery pack manufacturers, OEMs and electric mobility fleet operators to leverage smart hardware and data science to derive health insights, constantly monitor and improve the life and performance of the battery.
Machine Learning helps tap into the underlying potential and opportunity of battery life cycle management. The key to improving battery life lies in the data. Blending advanced electronics with IoT, data science and digital twin, Machine Learning uses the power of predictive intelligence to predict battery life, identify potential degradation/breakdown and their causes, fix delays/errors even before they arise. ML brings a layer of intelligence, after gathering and monitoring extensive data on battery life, performance, state of charge, stress from rapid acceleration and deceleration, temperature, number of charge cycles, etc. that are stored on the cloud.
With digital twin technology, it is possible to use real-time simulations and visualizations help deploy faster, data analytics to improve uptime, and machine learning to improve battery life. ML ingests and analyzes data sets from application and environment to identify key contributing factors for abnormal degradation of health and understand their magnitude of contribution so that businesses can take appropriate actions like configuration changes over the air, drive profile changes or environment changes.
Machine Learning makes sense of battery data, brings visibility into battery health and performance, derives valuable insights, and suggests actions that can significantly improve the battery life, reduce downtime and the overall ownership cost.