Developed by NXP for use with the MathWorks MATLAB and Simulink platform, the toolbox simplifies the design, testing, and deployment of BMS algorithms on NXP processors, and helps streamline the path from concept to market-ready solutions for intelligent battery health management.
BMS is crucial for EVs as it ensures the optimal performance, durability, and safety of the battery packs that power these advanced vehicles, and the design process increasingly relies on modelling and simulation to fine-tune algorithms tailored to EVs' specific battery cell types and battery pack configuration.
Model-Based Design enables the efficient design of the BMS algorithms, providing a means to test them in simulation for different scenarios, such as driving habits, environmental conditions, and fault occurrences. MBDT for BMS makes it easier for engineers to transition directly from Simulink models to running and testing their BMS algorithms on an NXP processor. This capability simplifies the BMS development process and accelerates the prototyping and testing phases.
"We’re excited to collaborate with MathWorks to support automotive engineers in developing the next generation of BMS solutions," said NXP CTO Lars Reger. “Simplifying direct testing with MBDT on NXP processors offers a broad range of benefits, including faster design iterations that allow engineers to identify and fix issues upfront in the design process and reduce time to market.”
The MBDT for BMS solution bridges the gap between theoretical design and practical application. Engineers can directly implement their Simulink BMS models onto NXP processors without any manual coding, thereby preserving the integrity and efficiency of their original algorithms.
In addition, the MBDT BMS product features integrated Input/Output (IO) connectivity. This functionality allows engineers to perform dynamic, real-world testing on their BMS systems, providing immediate feedback from early hardware prototypes and insights into system performance under various conditions. This level of testing is critical for ensuring the reliability and safety of BMS solutions in real-world scenarios.
"By enabling engineers to go directly from creating BMS algorithms in Simulink to running them on an NXP processor, we're simplifying and accelerating the development process," said Jim Tung, MathWorks Fellow. “The growth of the EV market demands more efficient, reliable, and safer battery systems, and tools like MBDT that streamline and enhance the engineering process will be critical. Reducing development times, facilitating easier testing, and accelerating market entry will be differentiators in this competitive market.”
