One of the primary challenges in battery thermal management is ensuring temperatures are below maximum operating limits. Higher temperatures can lead to reduced efficiency, accelerated aging, and potential safety hazards. Engineers must have knowledge of the heat generated by a battery to adequately design cooling systems.
Understanding and predicting the thermal behavior of battery modules requires integrating the heat rejection of a battery with the electrical-mechanical properties of the battery cell. By linking the electrical properties of a battery cell, better battery heat rejection rates can be made available for cooling system design.
Simulating battery thermal behavior can be enhanced via physical test data of battery cells. One type of battery testing is called Hybrid Pulse Power Characterization (HPPC). This testing can enable calculation of the battery internal resistance. Below is an example of one pulse from an example HPPC data set. The internal resistance of a battery cell is proportional to the voltage drop divided by the current. Twin Builder generates resistance values from an entire HPPC data set which can include multiple temperature and State of Charge (SOC) levels. This resistance is used along with the circuit current and voltage to predict cell heat loss power.
Ansys Twin Builder software provides tools for simulating and analyzing thermal behavior of battery cells and modules. With its Battery Wizard capabilities, Twin Builder allows engineers to model complex thermal interactions and evaluate different battery discharge behavior. Twin Builder is able to utilize HPPC data to quickly generate heat rejection values.
By using Twin Builder, engineers can conduct parametric studies to explore various design configurations. This enables the identification of solutions that ensure uniform temperature distribution and efficient heat dissipation, ultimately improving battery performance and safety.
To address thermal management challenges, engineers can evaluate multiple inputs using Ansys software. These inputs can include different cell capacity, C-Rate, and HPPC data. Twin Builder's simulation capabilities allow for evaluation of these different inputs.
Setting up battery simulations with Ansys Twin Builder in this discussion involves several steps. These steps include thought map, product map, and Twin Builder case set up.
Thought Map: A thought map of the battery cell is generated to organize and represent ideas, concepts, or information in a structured way. The thought map below shows the objective of the simulation study and questions asked to address the objective. Each question is followed by a theory, action, and prediction to address each question. Results would also be added to the bottom of each branch as they are generated.
Product Maps: A product map of the battery cell in circuit is generated to list and categorize product features. A product map indicates factors that correspond to theories/actions in the thought map.
The map below shows an example battery HPPC data file and a Twin Builder circuit. Text items in red are variable or constant factors.
The map below shows an example battery HPPC data set and manipulated voltage pulses for the study. Text items in red are variable factors.
Twin Builder Simulation: Twin Builder models are generated per the studies produced by the thought map. In this case a 7-factor, 2-level, fractional factorial DOE is employed which results in 8 unique Twin Builder treatments. The images below show the sequence of steps for populating inputs for the battery model. The first image is of the Cell Configuration Tool within the Battery Wizard, and the second is of the resulting cell in a circuit.
The current source uses a trapezoidal profile with an amplitude of 10 Amps for a duration of 10 seconds after an initial delay of 20 seconds.
The simulation calculations are executed to generate the results, focusing on battery cell heat loss, voltage and current. Treatments data of heat loss are analyzed to answer the theory questions and confirm or contradict predictions.
Graphical Analysis: The chart below displays the transient battery cell power loss results for the treatments. The chart indicates that voltage depth is the most significant factor. When the voltage drop in the HPPC data is larger, the battery resistance is higher, resulting in higher power loss. Other input factors cause smaller variation in heat loss.
The charts below also each display that the HPPC voltage depth is the most significant factor on cell power loss. Circuit input temperature, HPPC current, and Twin Builder battery capacity are mildly significant. Voltage shift and time stretch have negligible influence.
Voltage Drop Depth: Higher voltage drop depth in a HPPC pulse results in higher internal resistance and, therefore, higher heat loss.
Circuit Temperature: Circuit temperature influences resistance mildly because the voltage drops for pulses at 25C are larger than those at 45C. Larger voltage drops result in higher resistance and higher heat loss.
HPPC Data Current: Higher current specified in HPPC file results in smaller resistance, and, therefore, smaller heat loss.
Battery Wizard Cell Capacity: Cell capacity had a minor influence on resistance on resistance and, therefore, minor influence on heat loss.
HPPC SOC: HPPC SOC had minor influence on resistance on resistance and, therefore, minor influence on heat loss.
Voltage Shift: Voltage shift has negligible influence on resistance and, therefore, negligible influence on heat loss.
Voltage Time Stretch: Time stretch has negligible influence on pulse voltage drop and, therefore, negligible influence on heat loss.
The Twin Builder simulations each took less than 2 seconds to solve. The engineer can quickly determine the thermal heat loss for a battery cell from the HPPC data.
Setup Details: The following video steps through highlights of the set up.
ANSYS offers advanced capabilities for simulating battery cells and modules which offer numerous benefits, including enhanced design optimization, improved safety, and cost savings. By accurately predicting cell or module performance, manufacturers can design batteries that meet specific requirements more efficiently.
Applications of these simulations extend across various industries, such as electric vehicles, consumer electronics, and renewable energy storage solutions. They help in developing batteries with higher energy densities, longer lifespans, and better thermal management, which are critical for the advancement of these technologies.
Ansys Twin Builder enables the evaluation of multiple design/input factors such as capacity, SOC, temperature, and discharge pulse voltage. A thermal engineer can evaluate multiple design options to understand the thermal behavior as well as some of the electrical behavior of the cell. Beyond Twin Builder, ANSYS provides tools such as LS-Dyna, DesignXplorer, OptiSLang, and Fluent for further design parametrization and evaluation.
Ozen Engineering Inc. leverages its extensive consulting expertise in CFD, FEA, optics, photonics, and electromagnetic simulations to achieve exceptional results across various engineering projects, addressing complex challenges like battery module thermal behavior for normal and abnormal operation.
We offer support, mentoring, and consulting services to enhance the performance and reliability of your battery system. Trust our proven track record to accelerate projects, optimize performance, and deliver high-quality, cost-effective results for both new and existing systems. For more information, please visit https://ozeninc.com.
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