Refining automotive battery management systems
Image credit: COMSOL
For India’s transportation sector to meet its ambitious electrification goals, manufacturers must accelerate the development of essential components, such as battery management systems (BMS). Exicom optimises BMS performance by using multiphysics simulation to understand the thermal behaviour of different battery cell and pack designs.
India is a fast-growing market for electric vehicles (EVs), with one study predicting that over 30% of the vehicles sold in India will be electric by 2030. The battery packs that power EVs are one of the main drivers of the electric mobility revolution in India. In order to monitor and manage battery pack performance and safety, packs are usually equipped with a battery management system (BMS). A BMS is an electronic system that monitors a battery's voltage, temperature, coolant flow and health and predicts a number of other performance parameters, such as current variation and heat generation, helping to extract optimum performance from a battery pack.
The Role of Simulation in Developing Accurate BMS
Exicom Tele-Systems Pvt. Ltd. designs, develops and deploys energy solutions, including the latest Li-ion battery technologies. To date, it has deployed Li-ion battery solutions totalling more than 1.8 GWh — among the highest in the world by a single company. Exicom also offers charging solutions and BMS for electric two-wheelers and light electric vehicles, which are driving the growth of electric mobility in India. Exicom's innovative BMS solutions are prized for their performance and life.
At Exicom's R&D centre in Gurugram, India, the technology team led by Dr. Parmender Singh has developed a BMS that can be used to precisely monitor and manage Li-ion batteries in applications across a broad voltage range (up to 1000 V). This BMS is also chemistry agnostic; it can be used with Li-ion batteries of a range of chemistries such as lithium ferrophosphate, or lithium iron phosphate (LFP), lithium nickel manganese cobalt oxide (NMC) and lithium nickel cobalt aluminum oxide (NCA).
The precision of the BMS depends on the quality and accuracy of the inputs used for programming or calibrating the system. For example, the BMS includes a number of thermal sensors distributed across the battery pack. In order to accurately monitor a battery pack's temperature distribution and predict corresponding performance, it is imperative that the sensors be placed at the right locations. This requires a detailed understanding of the heat profile of each battery cell as well as how heat varies throughout the pack. This is where COMSOL Multiphysics® plays an integral part, by allowing for accurate computation and collation of the inputs, like heat profile information, that are required to develop a BMS with surgical precision.
Predicting and Preventing Potential Thermal Runaway
Dr. Singh's team at Exicom used COMSOL Multiphysics® to perform a number of analyses on the thermal behaviour of battery cells. They also used simulation to analyse potential external short circuits, which could cause thermal runaway — an uncontrolled self-heating process that can damage equipment or even cause fires. The Exicom team began by analysing the heat generated in cylindrical cells with different form factors and further extended this model to the pack level using the heat profile generated for the cells. "We were especially interested in improving the temperature gradient across the pack for air-cooled battery packs," said Dr. Singh.
The results for thermal modelling at the cell level for cylindrical cells during a 1C discharging are shown in Figure 1. The visualisation on the left in Figure 1 shows the temperature distribution, where the maximum temperature is observed in the middle of the cell. The visualisation on the right shows the contour distribution of temperature, where the maximum temperature is located in the active material of the cell.
Figure 1. The temperature distribution in a cylindrical cell at 1C discharge (left) and the contour distribution of temperature (right).
The simulation results, when validated with experimental findings, were observed to be within the error limits of ±5% at the standard charge–discharge profile. The model was then further extended for 2C discharge at 100% state of charge (SOC) according to Standard UL1642, which is defined for external short circuit testing.
Figure 2. The temperature profile in a cell after thermal runaway (left) and the electrochemical profile in a cell after thermal runaway (right).
The positive and negative terminals of the cell were shorted via an 80 ±20 mΩ resistance. The COMSOL® software’s lumped approach-based thermal model was validated against experimental data for charge–discharge profiles of the cell. They also developed:
- Cyclic and calendric capacity-fade models for cylindrical cells based on the optimisation features available in COMSOL®
- A high-fidelity pseudo two-dimensional (P2D) model for cylindrical cells using extracted electrochemical parameters
They found that the lumped approach enabled them to construct models using a minimal number of parameters — such as cell geometry, electrode thickness, thermal conductivity, heat capacity, drive cycle and open-circuit voltage (OCV)-SOC table — that are readily available from battery pack manufacturers.
Figure 3. Simulated and experimental data during external short-circuit testing.
Extracting these parameters experimentally is not only a time-consuming process but also prone to errors due to variable experimental conditions. For example, ambient temperature fluctuates, so extracting an accurate heat profile of a cell requires performing an extensive series of tests at different ambient temperatures. Using simulation, however, Dr. Singh and the team were able to perform these experiments with great ease. They were able to efficiently study charge and discharge profiles, thermal behaviour at different charge and discharge rates and thermal runaway (Figure 2) due to external or internal short circuits for different cell chemistries (Figure 3). They were also able to identify the hotspots in the battery pack and determine the cell grading based on capacity fade analysis with high accuracy. These results had direct applications in reducing the development cycle time of the BMS, as the hotspots indicated the best positions for deploying the thermal sensors within the BMS in order to function most efficiently. According to Dr. Singh, “COMSOL is an easy-to-learn and adaptable finite element tool for battery design and thermal modelling.”
Future Scope: Extending Battery Simulations to Predict Ageing
In addition to the thermal simulations, Dr. Singh has expanded the use of simulations to investigate another important phenomenon: battery ageing. During the lifetime of a battery, its state of health (SOH) progressively deteriorates due to irreversible physical and chemical changes, such as the growth of a solid electrolyte interphase (SEI) layer, which can lead to loss of porosity in a battery cell, which in turn can lead to an increase in polarisation and internal resistance. Magnetic field probing (MFP) is a noninvasive method for monitoring a battery's SOH. With the aim of demonstrating the potential of the MFP method, Dr. Singh developed a multiphysics model in COMSOL to evaluate the magnetic field response, battery polarisation and internal resistance of the Li-ion response (Figure 4). The team observed that variation in electrode porosity has a significant influence on the magnetic field response. Though this research is currently in its preliminary stages, the potential applications are far reaching. "We expect that further investigation into this phenomenon will allow for developing and deploying monitoring features for battery ageing as well as better protection mechanisms against it in the BMS itself," said Dr. Singh.
Figure 4. 3D-designed cell geometry (left). Variation of magnetic field response and polarisation behaviour during discharging at 0.12 and 0.36 anode porosity values (right).
The Exicom team is currently working on electrochemical P2D modelling for thermal and capacity-fading analysis at the cell level. It intends to further extend the model with additional thermal exothermic equations at the electrodes and SEI layer for better accuracy during thermal runaway. They also plan to use the lumped capacity fade model for cyclic and calendric predictive analysis. In the future, they also plan to implement a reduced order model for SOC and SOH and export the model to MATLAB® for code generation up to the ASIC level.
With the accelerating transition to electric mobility in India and worldwide, research on battery technology is expected to increase significantly in the coming years. Simulation offers a crucial head start to companies in the electric mobility space that want to provide more effective solutions and improve the time to market for their products.
- Sen, "30% vehicles in India will be electric by 2030: Study," The Times of India, 2022;
- Singh et al., "Li-Ion Battery Ageing Parameter: Porosity Behaviour Analysis Using Magnetic Field Probing," ECS Meet. Abstr.MA2021-02 294.
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