This article is written by Gemini Deep Research.

BEV Battery Simulation Technology: Classification, Issues, and Applications

Introduction

The automotive industry is undergoing a revolution, with battery electric vehicles (BEVs) at the forefront of this transformation. As BEVs gain traction, the demand for high-performance, safe, and long-lasting batteries has surged. These batteries are some of the best current technologies for vehicle operation, offering significant advantages over traditional internal combustion engines1. However, developing such advanced battery technology presents numerous challenges, including achieving range parity with conventional vehicles. Range parity refers to the ability of BEVs to travel a comparable distance on a single charge as gasoline-powered vehicles with a full tank1.

Battery simulation technology has emerged as a critical tool in addressing these challenges. By enabling engineers to design, analyze, and optimize battery systems in a virtual environment, simulation significantly reduces development time and costs. This is achieved by minimizing the reliance on expensive and time-consuming physical prototypes, allowing for rapid iteration and exploration of different design options2. This report provides a comprehensive overview of BEV battery simulation technology, encompassing its classification, limitations, and applications in the automotive industry.

Classification of BEV Battery Simulation Technologies

BEV battery simulation technologies can be broadly classified into the following categories:

  • Electrochemical Simulation: This type of simulation delves into the chemical reactions and ion transport within the battery cell. It provides insights into the fundamental electrochemical processes that govern battery performance, such as charge and discharge behavior, capacity fade, and degradation mechanisms4. A key challenge in this area is accurately predicting the current and heat generation inside the battery, which depends on complex factors like electrolyte composition, ion flow, and electrode materials5. Building accurate models for CFD simulations also presents difficulties, especially when simulating battery modules/packs with a large number of cells and intricate cooling passages5.
  • Electrical Simulation: Electrical simulation models the electrical behavior of the battery, including its voltage, current, and internal resistance. This is crucial for analyzing the battery’s interaction with other electrical components in the vehicle, such as the motor, inverter, and charging system6. Electrical simulation helps achieve performance targets like increased driving range and faster charging times by enabling engineers to optimize the battery’s electrical characteristics and its integration with the vehicle’s powertrain6.
  • Thermal Simulation: Thermal simulation analyzes the heat generation and dissipation within the battery pack. It is essential for designing effective thermal management systems to maintain the battery within its optimal operating temperature range, ensuring safety and preventing performance degradation3. Accurately predicting surface temperatures of battery pack components is crucial for passenger safety and thermal management8.
  • Mechanical Simulation: Mechanical simulation examines the structural integrity of the battery pack under various mechanical loads, such as vibration, shock, and crush. This is vital for ensuring the battery’s safety in the event of a collision9.
  • Multiphysics Simulation: Multiphysics simulation combines two or more of the above simulation types to provide a more holistic understanding of the battery’s behavior. For example, a multiphysics simulation might couple electrochemical and thermal models to analyze the impact of temperature on battery performance10.
  • Data-Driven Simulation: Data-driven simulation leverages machine learning and artificial intelligence to predict battery behavior based on historical data. This approach can be particularly useful for predicting battery degradation and estimating remaining useful life11.
  • System-Level Simulation: This approach considers the battery’s interaction with the complete EV drivetrain, encompassing thermal modeling, battery cell development and integration into battery packs, and the battery management system (BMS)12. This type of simulation helps analyze the performance of the battery pack within the context of the entire vehicle system, including its impact on factors like energy efficiency and driving range12. Software tools like MapleSim are used for circuit-based battery modeling and analyzing battery pack performance within the EV drivetrain12.
  • Automated Test Systems: These systems play a crucial role in simulating battery behavior and testing battery management systems. Examples include:
    • Battery Cyclers: Simulate charge and discharge cycles to assess battery performance and lifespan13.
    • Power HIL Systems: Hardware-in-the-loop systems that emulate the electrical behavior of the battery and its interaction with other powertrain components13.
    • Bidirectional DC Power Supplies: Provide precise control over voltage and current to simulate various charging and discharging scenarios13.

Limitations and Challenges of BEV Battery Simulation Technologies

While BEV battery simulation technologies offer significant advantages, they also have limitations and challenges:

Electrochemical Simulation

  • Requires detailed knowledge of battery chemistry and complex mathematical models5.
  • Can be computationally expensive, especially for high-fidelity models14.
  • May not accurately capture all the complexities of real-world battery behavior15.

Electrical Simulation

  • May oversimplify the battery’s behavior by neglecting complex electrochemical processes15.
  • Requires accurate characterization of the battery’s electrical parameters16.
  • May not accurately predict battery behavior under extreme operating conditions17.

Thermal Simulation

  • Requires accurate modeling of heat generation and transfer within the battery pack18.
  • Can be challenging to capture the dynamic thermal behavior of the battery during fast charging and discharging19.
  • May not accurately predict thermal runaway events, which are a major safety concern3. This highlights the importance of simulation in ensuring battery safety.

Mechanical Simulation

  • Requires accurate material properties and complex finite element models17.
  • Can be challenging to simulate the behavior of battery components under extreme mechanical loads20.
  • May not accurately predict battery failure mechanisms21.

Multiphysics Simulation

  • Can be computationally expensive and require specialized software tools20.
  • Requires careful consideration of the coupling between different physical phenomena22.
  • May not accurately capture all the interactions between different physics23.

Data-Driven Simulation

  • Requires large amounts of high-quality data for training and validation24.
  • May not be generalizable to different battery chemistries or operating conditions25.
  • Can be challenging to interpret the results and understand the underlying physical mechanisms26.

Thermal Management in BEV Battery Simulation

Thermal management is a critical aspect of BEV battery design and operation. Maintaining the battery within its optimal temperature range is crucial for ensuring safety, performance, and longevity. Simulation plays a vital role in analyzing and optimizing thermal behavior, helping engineers design effective cooling and heating systems for battery packs.

Thermal simulation techniques are used to model heat generation and transfer within the battery pack, considering factors such as cell chemistry, pack geometry, and cooling strategies. These simulations help identify potential hotspots and optimize the design of cooling components like cold plates and heat sinks.

Companies Using BEV Battery Simulation

Many companies in the automotive industry are using BEV battery simulation to accelerate the development of advanced battery technology. Some notable examples include:

  • Tesla: Employs electrochemical, thermal, and mechanical simulation for battery design and optimization. For instance, they utilize thermal simulation to design efficient cooling systems for their high-performance battery packs, ensuring optimal temperature regulation during operation27.
  • BMW: Utilizes multiphysics simulation for battery pack design and thermal management. This approach allows them to analyze the complex interactions between electrical, thermal, and mechanical factors, leading to optimized battery pack designs that meet stringent performance and safety requirements28.
  • Volkswagen: Leverages quantum computing for battery material simulation and optimization. This cutting-edge approach enables them to explore new materials and chemistries at the atomic level, potentially leading to breakthroughs in battery performance and energy density29.
  • Toyota: Employs electrochemical and thermal simulation for next-generation battery development. This includes the development of solid-state batteries, which are expected to offer significant improvements in energy density and safety compared to conventional lithium-ion batteries30.
  • Ford: Utilizes quantum computing for battery chemistry simulation and material discovery. This approach allows them to explore a wider range of potential battery materials and chemistries, accelerating the development of next-generation battery technologies31.
  • General Motors: Employs multiphysics simulation for battery pack design and thermal management. This approach helps them optimize battery pack designs for various vehicle platforms and applications, ensuring optimal performance and safety across a range of operating conditions32.
  • Hyundai: Utilizes data-driven simulation for battery degradation prediction and health monitoring. This approach allows them to predict battery lifespan and optimize battery management strategies, enhancing the overall reliability and longevity of their electric vehicles33.
  • Kia: Employs electrochemical and thermal simulation for battery pack design and optimization. This approach helps them design battery packs that meet the specific requirements of their electric vehicles, balancing performance, range, and cost considerations34.
  • Nissan: Utilizes multiphysics simulation for all-solid-state battery development. This approach allows them to analyze the complex behavior of solid-state batteries, addressing challenges related to material properties, interfaces, and thermal management35.
  • Renault: Employs electrochemical and thermal simulation for battery pack design and cost reduction. This approach helps them optimize battery pack designs for affordability, while maintaining performance and safety standards36.
  • Stellantis: Employs multiphysics simulation for battery pack design and integration with other vehicle systems. This approach allows them to analyze the battery’s performance within the context of the entire vehicle, optimizing its interaction with other components like the motor and power electronics37.
  • Mercedes-Benz: Employs multiphysics simulation for battery pack design and thermal management. This approach helps them design high-performance battery packs that meet the demanding requirements of their luxury electric vehicles, ensuring optimal performance and efficiency38.
  • BYD: Employs thermal and mechanical simulation for battery safety testing and validation. This includes rigorous testing like nail penetration tests to assess the battery’s response to extreme conditions and ensure its safety in the event of damage39.
  • CATL: Employs multiphysics simulation for battery pack design and performance optimization. This approach allows them to design battery packs that meet the diverse needs of their customers, balancing performance, cost, and safety considerations40.
  • LG Energy Solution: Employs electrochemical and thermal simulation for battery cell and pack development. This approach helps them design high-quality battery cells and packs that meet the stringent requirements of their automotive partners, ensuring optimal performance and reliability41.
  • Samsung SDI: Employs multiphysics simulation for battery pack design and safety enhancement. This approach allows them to design battery packs with enhanced safety features, minimizing the risk of thermal runaway and other hazards42.
  • SK Innovation: Employs electrochemical and thermal simulation for battery cell and pack development. This approach helps them design high-performance battery cells and packs that offer fast charging capabilities and long lifespans43.
  • Panasonic: Employs multiphysics simulation for battery pack design and thermal management. This approach allows them to design battery packs that are optimized for various vehicle applications, ensuring optimal performance and efficiency44.

Applications of BEV Battery Simulation

In addition to the examples above, BEV battery simulation is used in various applications, including:

  • Battery Interconnect Design: Companies like ARaymond utilize simulation to design and validate battery interconnects for high power capacity and safety. This ensures that the interconnects can handle the high currents and voltages present in modern EVs, while minimizing the risk of electrical failures45.
  • Battery Crush Test Simulation: Companies like Altair use simulation to predict mechanical damage and thermal runaway in battery crush tests. This helps ensure compliance with safety regulations and optimize battery pack designs for crashworthiness46.

The future of BEV battery simulation technology is poised for significant advancements driven by several key trends:

TrendDescriptionPotential Impact
Increased use of AI and MLArtificial intelligence and machine learning will play an increasingly important role in battery simulation, enabling more accurate predictions of battery behavior and accelerating the development of new battery technologies47.Accelerated development of new battery materials and chemistries, improved battery management systems, and enhanced prediction of battery lifespan and performance.
Development of more sophisticated multiphysics modelsMultiphysics simulation will become more prevalent, allowing engineers to capture the complex interactions between different physical phenomena within the battery48.More accurate prediction of battery behavior under various operating conditions, improved design of thermal management systems, and enhanced understanding of battery degradation mechanisms.
Integration of simulation with digital twin technologyDigital twin technology will be used to create virtual representations of battery systems, enabling real-time monitoring and optimization of battery performance49.Real-time monitoring of battery health, optimized charging and discharging strategies, and predictive maintenance capabilities.
Focus on sustainability and lifecycle analysisSimulation will be used to assess the environmental impact of battery production and disposal, promoting the development of more sustainable battery technologies50.Development of more environmentally friendly battery chemistries, optimized battery recycling processes, and reduced carbon footprint of battery production.

Conclusion

BEV battery simulation technology is an indispensable tool for developing advanced battery systems that meet the evolving needs of the automotive industry. By empowering engineers to analyze and optimize battery performance, safety, and longevity, simulation accelerates the development process and reduces reliance on costly physical prototypes. As the technology continues to evolve, it will play an even more critical role in driving the transition to electric mobility and achieving a sustainable transportation future.

引用文献

1. Electric vehicle battery - Wikipedia, 1月 11, 2025にアクセス、 https://en.wikipedia.org/wiki/Electric_vehicle_battery
2. Electric Vehicle Battery Test Systems | High Voltage Battery Tester - Dyno One, 1月 11, 2025にアクセス、 https://www.dyno-one.com/products/electric-vehicle-battery-test-systems/
3. Battery Pack Thermal Management: Can Simulations Prevent Performance Issues?, 1月 11, 2025にアクセス、 https://mechartes.com/resources/battery-pack-thermal-management-can-simulations-prevent-performance-issues/
4. BEST Battery and Electrochemistry Simulation Tool - Fraunhofer ITWM, 1月 11, 2025にアクセス、 https://www.itwm.fraunhofer.de/en/departments/sms/products-services/best-battery-electrochemistry-simulation-tool.html
5. Top Challenges in EV Battery Simulation | ESPL - CAE Services, 1月 11, 2025にアクセス、 https://eqmsol.com/ev-battery-simulation.php
6. How Battery Emulation Makes Electric Cars and Medical Devices Safer | Keysight Blogs, 1月 11, 2025にアクセス、 https://www.keysight.com/blogs/en/tech/educ/2023/battery-emulation
7. Battery Thermal Management System - MATLAB & Simulink - MathWorks, 1月 11, 2025にアクセス、 https://www.mathworks.com/discovery/battery-thermal-management-system.html
8. Battery Thermal Management: Simulation Based Design - Enteknograte, 1月 11, 2025にアクセス、 https://enteknograte.com/thermal-analysis-cfd-fea/transient-vehicle-thermal-management/battery-thermal-management-simulation-design/
9. EV design – battery simulation - x-engineer.org, 1月 11, 2025にアクセス、 https://x-engineer.org/ev-design-battery-simulation/
10. Multiphysics Simulation of Battery Cells and Packs for Electric Vehicles - COMSOL, 1月 11, 2025にアクセス、 https://www.comsol.com/paper/multiphysics-simulation-of-battery-cells-and-packs-for-electric-vehicles-136062
11. A Review on Battery Model-Based and Data-Driven Methods for Battery Management Systems - MDPI, 1月 11, 2025にアクセス、 https://www.mdpi.com/1996-1073/16/23/7807
12. Software tools for system-level EV battery modeling - EV …, 1月 11, 2025にアクセス、 https://www.evengineeringonline.com/what-software-tools-are-available-for-ev-battery-modeling-at-the-system-level/
13. Battery Simulator Software | EV < Chroma, 1月 11, 2025にアクセス、 https://www.chromausa.com/product/battery-simulator/
14. Advances and challenges in thermal runaway modeling of lithium-ion batteries - PMC, 1月 11, 2025にアクセス、 https://pmc.ncbi.nlm.nih.gov/articles/PMC11089405/
15. Global Advancements and Current Challenges of Electric Vehicle Batteries and Their Prospects: A Comprehensive Review - MDPI, 1月 11, 2025にアクセス、 https://www.mdpi.com/2071-1050/14/24/16684
16. Testing Challenges for Electric Vehicle Powertrains - Rexgear, 1月 11, 2025にアクセス、 https://rexgear.com/pages/testing-challenges-for-electric-vehicle-powertrains
17. EV Powertrain Testing Challenges and Solutions - NI - National Instruments, 1月 11, 2025にアクセス、 https://www.ni.com/en/solutions/transportation/electric-vehicle-test/electric-drive-test/ev-powertrain-testing-challenges-solutions.html
18. Overcoming Thermal Management Challenges In The Electric Vehicle Industry: Comprehensive Guide - Trumonytechs, 1月 11, 2025にアクセス、 https://www.trumonytechs.com/overcoming-thermal-management-challenges-in-the-electric-vehicle-industry/
19. Challenges of thermal management in BEVs - Magna International, 1月 11, 2025にアクセス、 https://www.magna.com/stories/inside-automotive/2023/breaking-a-sweat-while-fast-charging-challenges-of-thermal-management-in-bevs
20. Meeting the Challenge of Electric Vehicle Battery Cell Innovation - 3DS Blog, 1月 11, 2025にアクセス、 https://blog.3ds.com/brands/simulia/meeting-challenge-electric-vehicle-battery-cell-innovation
21. Why EV Battery Design Is So Difficult - Semiconductor Engineering, 1月 11, 2025にアクセス、 https://semiengineering.com/why-ev-battery-design-is-so-difficult/
22. Multi-Scale Modeling of Battery Physics | Transportation and Mobility Research - NREL, 1月 11, 2025にアクセス、 https://www.nrel.gov/transportation/multi-scale-battery-physics-modeling.html
23. Multiphysics Simulations: Challenges and Opportunities - Argonne Scientific Publications, 1月 11, 2025にアクセス、 https://publications.anl.gov/anlpubs/2012/11/72183.pdf
24. Data-driven model improves accuracy in predicting EV battery degradation - Microsoft, 1月 11, 2025にアクセス、 https://www.microsoft.com/en-us/research/blog/data-driven-model-improves-accuracy-in-predicting-ev-battery-degradation/
25. Data-driven prediction of battery failure for electric vehicles - PMC - PubMed Central, 1月 11, 2025にアクセス、 https://pmc.ncbi.nlm.nih.gov/articles/PMC9010759/
26. Advantages and disadvantages of model-based, data-driven, and coulomb-counter methods. - ResearchGate, 1月 11, 2025にアクセス、 https://www.researchgate.net/figure/Advantages-and-disadvantages-of-model-based-data-driven-and-coulomb-counter-methods_tbl1_353476715
27. Which Batteries Is Tesla Using in Each Model Today? - Greenlit Content, 1月 11, 2025にアクセス、 https://www.greenlitcontent.com/companies/which-batteries-is-tesla-using
28. From material selection to prototype construction: The battery cell technology of the future is taking shape at the BMW Group Battery Cell Competence Centre, 1月 11, 2025にアクセス、 https://www.press.bmwgroup.com/global/article/detail/T0302622EN/from-material-selection-to-prototype-construction:-the-battery-cell-technology-of-the-future-is-taking-shape-at-the-bmw-group-battery-cell-competence-centre?language=en
29. VW to use quantum computing to develop tailored batteries for its EVs - Motor Authority, 1月 11, 2025にアクセス、 https://www.motorauthority.com/news/1117173_vw-to-use-quantum-computing-to-develop-tailored-batteries-for-its-evs
30. Electrified Technologies - Batteries, Fundamental technologies to innovate BEV | Corporate | Global Newsroom | Toyota Motor Corporation Official Global Website, 1月 11, 2025にアクセス、 https://global.toyota/en/newsroom/corporate/39330500.html
31. Ford used a quantum computer to explore EV battery materials - Popular Science, 1月 11, 2025にアクセス、 https://www.popsci.com/technology/ford-quantum-ev-battery/
32. GM proposes making prismatic EV batteries differently - Green Car Reports, 1月 11, 2025にアクセス、 https://www.greencarreports.com/news/1145388_gm-proposes-making-prismatic-ev-batteries-differently
33. Electric Car Battery Technology | Hyundai News, 1月 11, 2025にアクセス、 https://www.hyundai.com/au/en/news/fleet/unlocking-the-potential-of-electric-cars-how-battery-breakthroughs-are-shaking-up-the-sector
34. 2019 Kia Niro - Battery Design, 1月 11, 2025にアクセス、 https://www.batterydesign.net/kia-niro/
35. All-solid-state batteries | Innovation | Nissan Motor Corporation Global Website, 1月 11, 2025にアクセス、 https://www.nissan-global.com/EN/INNOVATION/TECHNOLOGY/ARCHIVE/ASSB/
36. Ampere leads a groundbreaking battery strategy for Renault Group with LFP technology and Cell-to-Pack solutions, 1月 11, 2025にアクセス、 https://media.renaultgroup.com/ampere-leads-a-groundbreaking-battery-strategy-for-renault-group-with-lfp-technology-and-cell-to-pack-solutions/
37. Electrification - Stellantis.com, 1月 11, 2025にアクセス、 https://www.stellantis.com/en/technology/electrification
38. Mercedes MMA Battery, 1月 11, 2025にアクセス、 https://www.batterydesign.net/mercedes-mma-battery/
39. BYD’s New Blade Battery Set to Redefine EV Safety Standards, 1月 11, 2025にアクセス、 https://en.byd.com/news/byds-new-blade-battery-set-to-redefine-ev-safety-standards/
40. High Energy Density Technology - CATL, 1月 11, 2025にアクセス、 https://www.catl.com/en/research/technology/
41. Advanced Automotive Battery ｜ LG Energy Solution - LG에너지솔루션, 1月 11, 2025にアクセス、 https://www.lgensol.com/en/business-automotive-battery
42. Simulation Helps Samsung Deliver Safer, More Efficient Lithium-Ion Battery Packs, 1月 11, 2025にアクセス、 https://www.techbriefs.com/component/content/article/27702-simulation-helps-samsung-deliver-safer-more-efficient-lithium-ion-battery-packs
43.

\[CES 2023\]

Into the 1nnovation! ① SK batteries that boost outstanding performance and safety – SK Innovation Newsroom SKinno News, 1月 11, 2025にアクセス、 https://skinnonews.com/global/archives/12306
44. Battery Modeling for Emulators in Vehicle Test Cell - MDPI, 1月 11, 2025にアクセス、 https://www.mdpi.com/2313-0105/10/6/199
45. Challenges in EV Battery Manufacturing Enhanced Battery Pack Design Using Cloud-Native Simulation - YouTube, 1月 11, 2025にアクセス、 https://www.youtube.com/watch?v=QtTbJKz0pnU
46. USING MULTIPHYSICS TO PREDICT AND PREVENT EV BATTERY FIRE - Altair, 1月 11, 2025にアクセス、 https://altair.com/docs/default-source/resource-library/altair_whitepaper_using-multiphysics-to-predict-and-prevent-ev-battery-fire_letter_06182020.pdf?sfvrsn=8fc7bd9e_3
47. Fully charged: how AI-powered battery testing can support the EV boom, 1月 11, 2025にアクセス、 https://www.weforum.org/stories/2024/12/ev-battery-ai/
48. OPmobility at CES 2025 - Embracing the future of mobility thanks to Artificial Intelligence & High technology, 1月 11, 2025にアクセス、 https://www.opmobility.com/wp-content/uploads/2025/01/opmobility-presskit-at-the-ces-2025-embracing-the-future-of-mobility-thanks-to-artificial-iIntelligence-and-high-technology-20250107.pdf
49. What to Expect From EV Batteries in 2024 - Engineering.com, 1月 11, 2025にアクセス、 https://www.engineering.com/what-to-expect-from-ev-batteries-in-2024/
50. The Future of EV Batteries - GreenCars, 1月 11, 2025にアクセス、 https://www.greencars.com/greencars-101/the-future-of-ev-batteries