Development of an Embedded Battery Management System for Enhanced Monitoring and Diagnostics

The client’s current battery management solution effectively monitors lithium-ion batteries using proprietary algorithms and data analytics. However, it is not optimized for deployment on embedded systems with microcontrollers, limiting its market reach and operational efficiency. The client seeks to adapt and optimize their existing solution for embedded platforms to enable faster, error-free performance, and broader application in devices such as automotive systems, smartphones, and IoT devices.

A medium to large enterprise specializing in battery technology solutions for various sectors, aiming to extend their existing diagnostic algorithms to embedded platforms for wider market adoption.

• Redesign and optimize existing battery diagnostic algorithms to run efficiently on embedded microcontrollers with limited resources.
• Ensure compatibility of the battery management solution across diverse embedded hardware platforms without reliance on extensive external libraries or frameworks.
• Develop a portable, lightweight desktop application and prepare solutions for cloud migration to facilitate deployment and scalability.
• Expand the client’s market reach by enabling deployment in embedded systems used in automotive, consumer electronics, and IoT devices.
• Improve diagnostic accuracy and operational speed to enhance user experience and customer satisfaction.

• Algorithm analysis and rewriting to ensure compatibility with embedded systems, including code optimization for memory and processing constraints
• Conversion of floating-point calculations to fixed-point arithmetic to maintain precision and performance
• Implementation of a core algorithm for calculating battery state of health, risk levels, and capacity metrics
• Development of a portable desktop application to run the battery diagnostics on various platforms
• Setup of deployment workflows for migrating the solution to cloud-based environments, such as Docker containers
• Customization support for integrating the algorithm with different microcontrollers and toolchains used by clients

• Microcontroller hardware platforms specific to client deployment environments
• Data input interfaces for retrieving battery and operational data
• Cloud services via Docker for remote monitoring and data analytics
• Device firmware for seamless integration with existing embedded systems

• Real-time processing with latency under 100 milliseconds
• Minimal memory footprint, optimized to run within 256 KB RAM
• High reliability and fault tolerance for safety-critical applications
• Scalability to support multiple battery systems concurrently
• Secure data handling and communication adhering to industry standards

Successfully developing an embedded-compatible battery management system is expected to significantly expand the client’s market presence by enabling deployment across automotive and consumer electronic devices. Anticipated outcomes include increased customer satisfaction through faster, error-free diagnostics, broader adoption in IoT and embedded devices, and an estimated increase in market reach leading to higher revenue streams and industry competitiveness.