배터리 시뮬레이션 소프트웨어 시장 : 기회, 성장 촉진요인, 산업 동향 분석, 예측(2025-2034년)

The Global Battery Simulation Software Market was valued at USD 1.03 billion in 2024 and is estimated to grow at a CAGR of 11.4% to reach USD 3 billion by 2034.

This growth reflects a broader push toward smarter, cost-effective, and energy-efficient battery systems in response to surging demand for electric vehicles and grid-scale energy storage. Simulation software offers a powerful toolset to model battery behavior, streamline design, and optimize performance while minimizing costly physical prototyping. Automakers and energy solution providers are increasingly leveraging simulation to enhance battery safety, extend range, and align with evolving energy storage regulations. With renewable energy sources being added to national grids, there's a need for dependable storage that supports load balancing, reduces peak pressure, and stabilizes supply. Battery simulation platforms are emerging as essential to meeting these goals, especially as grid operators and utility providers scale up smart energy infrastructure. The transition to digital engineering has been accelerated by disruptions like the COVID-19 pandemic, where limited access to labs and travel restrictions drove enterprises toward remote design and virtual testing. Companies now rely on hybrid cloud environments, digital twin systems, and validated virtual models to advance battery technology development and shorten innovation cycles.

The lithium-ion battery segment held 53% share in 2024 and is projected to maintain a CAGR of 11% through 2034. Lithium-ion batteries remain the most prominent choice for electric vehicles, grid energy systems, and mobile electronics due to their high energy density, long cycle life, and efficient performance characteristics. Simulation software enables developers to improve lithium-ion battery design through predictive modeling of thermal behavior, electrochemical reactions, and charge-discharge cycles. These tools also play a vital role in improving battery longevity and system reliability. As electric mobility and clean energy sectors continue to scale, simulation provides a necessary foundation for innovation, ensuring these batteries meet increasingly rigorous performance and safety benchmarks.

The electrochemical simulation segment captured 39% share in 2024 and is anticipated to grow at a CAGR of 11% from 2025 to 2034. This segment stands out due to its capacity to simulate battery chemistry and internal processes at the molecular level. It allows manufacturers to evaluate ion dynamics, charging behavior, and reaction mechanisms before physical trials, making development faster and more cost-effective. Electrochemical modeling is essential for refining battery architecture, optimizing electrode materials, and tailoring electrolyte composition. This simulation type supports deeper insights into performance under variable operating conditions, which is crucial for applications where safety and durability are mission-critical, including electric vehicles and aerospace systems.

United States Battery Simulation Software Industry held an 85% share in 2024, generating USD 324.9 million. The country's battery simulation sector benefits from its mature tech ecosystem, access to advanced computing infrastructure, and a strong presence of cloud service providers offering scalable environments for simulation workloads. The demand for multi-physics, high-fidelity simulation models is growing, particularly among EV manufacturers, aerospace companies, and clean energy startups. The US also leads in R&D investment and digital engineering transformation, enabling companies to reduce physical prototyping costs and shorten time-to-market through cloud-enabled modeling platforms.

Notable players in the Global Battery Simulation Software Industry include Dassault, ESI, Siemens, COMSOL, AVL List, MathWorks, Autodesk, Ansys, and Altair Engineering. To solidify their market position, companies in the battery simulation software sector are prioritizing innovation, collaboration, and cloud integration. Firms are advancing simulation accuracy by investing in AI-enhanced modeling tools that adapt to real-world battery usage conditions. Many players are forming partnerships with OEMs, battery developers, and academic institutions to develop proprietary algorithms and co-develop industry-specific applications. There's a strong focus on offering hybrid deployment options-cloud-based and on-premises-catering to varying IP sensitivity levels. Leading providers are also improving user interfaces, reducing simulation runtimes, and supporting multi-physics environments to attract more enterprise users.

Table of Contents

Chapter 1 Methodology

• 1.1 Market scope and definition
• 1.2 Research design
  • 1.2.1 Research approach
  • 1.2.2 Data collection methods
• 1.3 Data mining sources
  • 1.3.1 Global
  • 1.3.2 Regional/Country
• 1.4 Base estimates and calculations
  • 1.4.1 Base year calculation
  • 1.4.2 Key trends for market estimation
• 1.5 Primary research and validation
  • 1.5.1 Primary sources
• 1.6 Forecast model
• 1.7 Research assumptions and limitations

Chapter 2 Executive Summary

• 2.1 Industry 360⁰ synopsis, 2021 - 2034
• 2.2 Key market trends
  • 2.2.1 Regional
  • 2.2.2 Battery Type
  • 2.2.3 Simulation
  • 2.2.4 Application
  • 2.2.5 Enterprises
  • 2.2.6 Deployment mode
  • 2.2.7 End Use
• 2.3 TAM Analysis, 2025-2034
• 2.4 CXO perspectives: Strategic imperatives
  • 2.4.1 Executive decision points
  • 2.4.2 Critical success factors
• 2.5 Future outlook and strategic recommendations

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
  • 3.1.1 Supplier landscape
  • 3.1.2 Profit margin analysis
  • 3.1.3 Cost structure
  • 3.1.4 Value addition at each stage
  • 3.1.5 Factor affecting the value chain
  • 3.1.6 Disruptions
• 3.2 Industry impact forces
  • 3.2.1 Growth drivers
    • 3.2.1.1 Rising adoption of electric vehicles (EVs)
    • 3.2.1.2 Increasing investment in renewable energy storage
    • 3.2.1.3 Technological advancements in battery chemistry
    • 3.2.1.4 Integration of AI and cloud computing in simulation
  • 3.2.2 Industry pitfalls and challenges
    • 3.2.2.1 High initial investment and software complexity
    • 3.2.2.2 Data availability and model accuracy challenges
  • 3.2.3 Market opportunities
    • 3.2.3.1 Expansion in emerging markets
    • 3.2.3.2 Collaboration with battery manufacturers and OEMs
    • 3.2.3.3 Integration with digital twin and IoT technologies
    • 3.2.3.4 Customization for next-generation batteries
• 3.3 Regulatory landscape
  • 3.3.1 North America
  • 3.3.2 Europe
  • 3.3.3 Asia Pacific
  • 3.3.4 Latin America
  • 3.3.5 Middle East & Africa
• 3.4 Porter's analysis
• 3.5 PESTEL analysis
• 3.6 Technology and Innovation landscape
  • 3.6.1 Current technological trends
  • 3.6.2 Emerging technologies
• 3.7 Patent analysis
• 3.8 Pricing trends and economic analysis
• 3.9 Use cases
  • 3.9.1 Cell-level design and optimization
  • 3.9.2 Module and pack-level integration
  • 3.9.3 System-level performance and integration
  • 3.9.4 Lifecycle and degradation analysis
• 3.10 Best-case scenario
• 3.11 Investment landscape and funding analysis
  • 3.11.1 Global battery industry investment trends
  • 3.11.2 Simulation software investment and R&D spending
  • 3.11.3 Regional investment patterns and government support
  • 3.11.4 Technology transfer and commercialization
• 3.12 Cost-benefit analysis
  • 3.12.1 Software implementation cost structure
  • 3.12.2 Operational benefits and value creation
  • 3.12.3 Strategic benefits and competitive advantage
  • 3.12.4 ROI analysis and payback assessment
• 3.13 Sustainability and environmental impact analysis
  • 3.13.1 Lifecycle assessment and environmental modeling
  • 3.13.2 Sustainable design and optimization
  • 3.13.3 Environmental compliance and reporting
  • 3.13.4 Green technology and innovation
• 3.14 Future technology roadmap and innovation timeline
  • 3.14.1 Simulation technology evolution (2024-2034)
  • 3.14.2 Battery technology integration and adaptation
  • 3.14.3 Technology convergence and platform evolution
  • 3.14.4 Market evolution and disruption scenarios
• 3.15 Quality assurance and validation framework
  • 3.15.1 Model validation and verification
  • 3.15.2 Software quality assurance
  • 3.15.3 Regulatory compliance and documentation
  • 3.15.4 Continuous improvement and innovation
• 3.16 Technology integration and workflow optimization
  • 3.16.1 CAD and design tool integration
  • 3.16.2 PLM and data management integration
  • 3.16.3 Manufacturing and testing integration
  • 3.16.4 Digital twin and IOT integration

Chapter 4 Competitive Landscape, 2024

• 4.1 Introduction
• 4.2 Company market share analysis
  • 4.2.1 North America
  • 4.2.2 Europe
  • 4.2.3 Asia Pacific
  • 4.2.4 LATAM
  • 4.2.5 MEA
• 4.3 Competitive analysis of major market players
• 4.4 Competitive positioning matrix
• 4.5 Strategic outlook matrix
• 4.6 Key developments
  • 4.6.1 Mergers & acquisitions
  • 4.6.2 Partnerships & collaborations
  • 4.6.3 New Product Launches
  • 4.6.4 Expansion Plans and funding

Chapter 5 Market Estimates & Forecast, By Battery type, 2021 - 2034 ($Bn)

• 5.1 Key trends
• 5.2 Lithium-Ion
• 5.3 Lead-Acid
• 5.4 Solid-State
• 5.5 Others

Chapter 6 Market Estimates & Forecast, By Simulation, 2021 - 2034 ($Bn)

• 6.1 Key trends
• 6.2 Electrochemical simulation
• 6.3 Thermal simulation
• 6.4 Structural & mechanical simulation
• 6.5 Electrical & circuit simulation
• 6.6 Others

Chapter 7 Market Estimates & Forecast, By Deployment Mode, 2021 - 2034 ($Bn)

• 7.1 Key trends
• 7.2 On-Premise
• 7.3 Cloud
• 7.4 Hybrid

Chapter 8 Market Estimates & Forecast, By Application, 2021 - 2034 ($Bn)

• 8.1 Key trends
• 8.2 Automotive & transportation
• 8.3 Consumer electronics
• 8.4 Energy storage systems
• 8.5 Industrial equipment

Chapter 9 Market Estimates & Forecast, By Enterprises, 2021 - 2034 ($Bn)

• 9.1 Key trends
• 9.2 SME
• 9.3 Large Enterprises

Chapter 10 Market Estimates & Forecast, By End Use, 2021 - 2034 ($Bn)

• 10.1 Key trends
• 10.2 OEM
• 10.3 Battery manufacturers
• 10.4 Research & development organizations
• 10.5 Universities & academic institutions

Chapter 11 Market Estimates & Forecast, By Region, 2021 - 2034 ($Bn)

• 11.1 Key trends
• 11.2 North America
  • 11.2.1 US
  • 11.2.2 Canada
• 11.3 Europe
  • 11.3.1 Germany
  • 11.3.2 UK
  • 11.3.3 France
  • 11.3.4 Italy
  • 11.3.5 Spain
  • 11.3.6 Russia
• 11.4 Asia Pacific
  • 11.4.1 China
  • 11.4.2 India
  • 11.4.3 Japan
  • 11.4.4 Australia
  • 11.4.5 South Korea
  • 11.4.6 Philippines
  • 11.4.7 Indonesia
• 11.5 Latin America
  • 11.5.1 Brazil
  • 11.5.2 Mexico
  • 11.5.3 Argentina
• 11.6 MEA
  • 11.6.1 South Africa
  • 11.6.2 Saudi Arabia
  • 11.6.3 UAE

Chapter 12 Company Profiles

• 12.1 Global Players
  • 12.1.1 Ansys
  • 12.1.2 Siemens
  • 12.1.3 Altair Engineering
  • 12.1.4 MathWorks
  • 12.1.5 Dassault Systemes
  • 12.1.6 AVL List GmbH
  • 12.1.7 ESI Group
  • 12.1.8 Ricardo
  • 12.1.9 Intertek Group
  • 12.1.10 Hexagon
  • 12.1.11 Synopsys
  • 12.1.12 COMSOL
  • 12.1.13 dSPACE
  • 12.1.14 Gamma Technologies
• 12.2 Regional Players
  • 12.2.1 OpenCFD
  • 12.2.2 TWAICE Technologies GmbH
  • 12.2.3 Batemo
  • 12.2.4 Maplesoft
  • 12.2.5 ThermoAnalytics
  • 12.2.6 Shenzhen Finite Element Technology
  • 12.2.7 Suzhou Yilaikede Technology
  • 12.2.8 Mid-Atlantic Power Specialists
  • 12.2.9 UK Battery Industrialization Centre
• 12.3 Emerging Players
  • 12.3.1 Battery Design LLC
  • 12.3.2 BATEMO GmbH
  • 12.3.3 Keysight Technologies
  • 12.3.4 Gamma Technologies
  • 12.3.5 AVL List GmbH
  • 12.3.6 Cadmus Group
  • 12.3.7 Electrochemical Engine Simulation