Battery Management Systems: Design by Modelling
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Media Partners. Slight variances in cell make-up during manufacture can accelerate degradation of a cell that may have a slightly higher rate of SEI growth than others in the stack. This will cause the SEI growth rate to increase faster in the more deeply discharged cell. As seen in Figure 4, this will increase the likelihood of that cell failing either through complete loss of charge capacity or, worse, overheating.
This is why it is critically important for the BMS to monitor the state of health of each cell and control the current into and out of them in order to balance the charge across all cells. While the MapleSim physics models are fast, they are not as scalable as the equivalent-circuit model. Therefore, it was decided to use the EC models, with some customized elements from the physics models, for this specific purpose. The model follows this structure with each cell being a shared, fully parameterized subsystem, using the customized equivalent-circuit model described earlier.
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Additionally, each cell can be switched to open circuit using logical parameters. The stack model is made of 18 cell subsystems connected either in parallel or series, depending on the requirement. Input signals are provided for charge balancing from the BMS. Additionally, any deviation in performance due to manufacturing variations needed to be included in order to test the charge-balancing capability of the BMS.
Instead of testing every cell, a smaller batch was tested, from which the average cell response could be determined as well as the statistical distribution of the variants.
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These were used to implement the manufacturing variability into the cell stack. The average cell response was determined using the parameter-estimation tool supplied with the MapleSim Battery Library. This response was then validated against response data from other cells to ensure close estimation of the resulting model.
SoH behavior was implemented as a look-up table based on experimental results. This was applied to all cells and then compared with the real test results.
Electric Vehicle Battery and BMS Masterclass - Sep 12222
In Figure 8, we see that the maximum variance of the voltage from the experimental data is 14 mV, while from the simulation it is 13 mV: acceptable for the purpose of this project. The simplest solver was used and the performance bench showed that the average execution time was approximately 20 times faster than real-time, occupying 5. This shows that the battery model can be easily scaled up, if required. A schematic of the final BMS test system developed for the client is shown in Figure 9.
The engineer can go back to the model at any time to make any necessary changes to the model configuration, and then generate the model for use on the real-time platform. The MapleSim Connector for NI VeriStand Software automates the model integration process, allowing the engineer to produce the real-time model quickly and reliably.
Summary Test automation and simulation is critical in system-level testing. Issues such as time and cost of failure analysis, constant development pressure, costs of repeated tests and lengthy set-up times can all be addressed with improved test systems. They allow the engineer to avoid the risks of damage to the batteries, and subsequent costs, while testing and optimizing the BMS design in a close-to-reality loading environment.
Techniques for battery modeling have advanced significantly over the years to the point where physics-based cell models that would have taken hours to solve in a FE or CFD tool can now be implemented in a system model in order to predict how they would behave under loading from complex multi-domain systems mechanical, electromechanical, fluids, thermal.
The use of virtual battery technology in the design of test systems can facilitate the development of better products, reduce project risks and get products to market faster. Lithium Battery Power. Mailing List. Request Information. Josh Johnson. Related Posts. Tweets by BatteryPowerMag.