Expanding Battery Management Services to Embedded Systems

• Other - Battery
• Processors & Edge Intelligence - Embedded & Edge Computers

• Automotive
• Electronics

• Energy Storage Management
• Time Sensitive Networking

• Cloud Planning, Design & Implementation Services
• Hardware Design & Engineering Services

The client, a battery management solution provider, wanted to expand their market reach by making their solution compatible with embedded platforms. Their solution, which monitors individual cells within lithium-ion batteries, was not optimized for use on embedded platforms with microcontrollers. The solution could either be integrated as part of the Battery Management System (BMS) or operate independently on Docker or the cloud. Customers could retrieve data from the BMS for a specific time period, save it into a file, and incorporate it into the algorithm. The algorithm would then analyze the data and determine which cells work well and which do not. However, the client needed to analyze and prepare the solution for implementation on embedded systems.

The client is a battery management solution provider that helps customers ensure longer and healthier runtime of devices powered by lithium-ion batteries. Their solution allows diagnosing, forecasting, and preventing battery malfunctions with the help of proprietary early warning diagnostic algorithms and data analytics. The client's cutting-edge technology is used by enterprises that manufacture consumer electronics, aviation, e-mobility markets, and more. They have built an algorithm that monitors each individual cell within a lithium-ion battery, determining which cells do not perform at full capacity or pose the risk of catching on fire.

N-iX experts conducted a comprehensive analysis of the existing solution, identified challenges, and outlined ways of solving them. They rewrote the client’s battery management algorithm from scratch to prepare it for use with microcontrollers. The algorithm calculations were first optimized using Python, then rewritten from Python to C to make it compatible with the microcontrollers of the embedded systems. The team also implemented a separate Basic Algorithm solution that calculates basic battery capacity values. Additionally, a desktop application for running the algorithm was implemented, with work done to make it portable and transferable to Docker. The team also helped integrate hardware by customizing the algorithm based on the specific microcontrollers and compilers of their customers.

• The comprehensive analysis and subsequent rewriting of the client’s battery management algorithm made it compatible with embedded systems that use microcontrollers. The implementation of a desktop application that can launch the algorithm, and the preparation of the algorithm for migration to the cloud, extended the client's market reach. The solution's compatibility with embedded systems equipped with microcontrollers increased customer satisfaction by improving the user experience with faster and error-free battery management.