Probabilistic analysis course in electromobility
About our
Probabilistic Analysis in Electromobility Course
Explore the application of advanced statistical and probabilistic methods in the design and optimization of electromobility systems. It focuses on modeling uncertainty in critical components such as batteries, electric motors, and control systems, using Monte Carlo simulation, sensitivity analysis, and robust optimization tools. The course addresses the impact of variability on the performance, reliability, and safety of electric vehicles, integrating concepts of system reliability, uncertainty management, and design for robustness. The aim is to improve decision-making in the development of electric vehicles and the planning of charging infrastructure.
This course provides practical knowledge in the use of specialized software for probabilistic analysis, preparing participants for roles such as design engineers, systems analysts, reliability experts, and simulation specialists. The course focuses on applying the methodologies to real-world case studies and complying with relevant industry safety regulations.
Probabilistic analysis course in electromobility
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 2
550 $
Competencies and results
What you will learn
1. Probabilistic Analysis in Electromobility Systems: Design and Optimization
- Understand the fundamentals of probability theory and its application to evaluating the reliability and performance of electromobility systems.
- Identify and analyze the key random variables that influence the design and operation of electromobility systems, including component parameters, environmental conditions, and usage patterns.
- Apply probabilistic modeling techniques to simulate the behavior of electromobility systems, considering the inherent uncertainty in the data and operating conditions.
- Evaluate the impact of variability and uncertainty on the performance, efficiency, and lifespan of electromobility systems.
- Use probabilistic optimization methods to design electromobility systems that are robust, reliable, and efficient in the presence of uncertainty.
- Develop design and operating strategies that minimize risks and maximize the performance of electromobility systems under different operating scenarios and conditions.
- Apply specialized software tools for probabilistic analysis and optimization to the design and evaluation of electromobility systems.
- Analyze and understand the application of this knowledge to different electromobility components and subsystems, such as batteries, electric motors, control systems, and energy management systems.
- Interpret and communicate the results of probabilistic analyses clearly and effectively to make informed decisions about the design, operation, and maintenance of electromobility systems.
2. Optimization and Design of Rotors in Electromobility: Modeling and Performance
- Master the design and analysis of rotors for electric vehicles, focusing on aerodynamics and efficiency.
- Understand the principles of electromobility and their application in the design of rotary propulsion systems.
- Apply specialized software for modeling and simulating rotor behavior under various operating conditions.
- Evaluate rotor performance, considering factors such as power, torque, speed, and energy efficiency.
- Implement optimization techniques to improve rotor design, maximizing efficiency and durability.
- Analyze aspects of rotor structure, including material selection, strength, and stiffness.
- Study rotor manufacturing methods, considering different technologies and their implications for performance.
- Develop skills in rotor design for specific electromobility applications, such as land vehicles, air and sea applications.
- Learn to assess and mitigate the effects of vibrations and resonances in rotors.
- Become familiar with the relevant regulations and standards in the design and manufacture of rotors for electromobility.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. Probabilistic Analysis in Electromobility: Modeling and Optimization of Systems
Here is the requested content:
4. Probabilistic Analysis in Electromobility: Modeling and Optimization of Systems
You will understand the fundamentals of probabilistic analysis applied to electromobility systems.
You will learn to model uncertainty in key parameters of electrical and mechanical systems.
You will develop skills to optimize the design and operation of electromobility systems considering variability and risk.
You will explore tools and techniques for simulation and probabilistic analysis of different components and subsystems.
You will analyze the reliability and availability of electromobility systems based on random variables.
You will apply robust optimization methods for the design of fault-tolerant and adverse-condition systems.
You will evaluate the impact of uncertainty on the performance, safety, and lifespan of electromobility systems.
5. Probabilistic Analysis in Electromobility: Risk Assessment and Performance Optimization
5. Probabilistic Analysis in Electromobility: Risk Assessment and Performance Optimization
Understand the fundamentals of probabilistic analysis applied to electromobility systems.
Identify and assess the risks associated with the design, manufacture, and operation of electric vehicles.
Apply probabilistic modeling techniques to simulate the behavior of electromobility components and systems under uncertainty.
Analyze the variability in key parameters, such as battery life, motor efficiency, and charging system reliability.
Use statistical methods to quantify the probability of failures and assess design robustness.
Optimize the performance and efficiency of electromobility systems considering uncertainty.
Develop strategies for risk management and data-driven decision-making.
Explore the use of specialized software for probabilistic analysis in Electromobility.
Apply the acquired knowledge to relevant practical cases and case studies.
Evaluate the impact of uncertainty on production and maintenance planning.
6. Probabilistic Analysis in Electromobility: Failure Assessment and Reliability Optimization
- Understand the fundamentals of probabilistic analysis applied to electromobility systems.
- Identify and evaluate critical failure modes in electric vehicle components and systems.
- Apply probabilistic modeling techniques to quantify uncertainty in reliability assessment.
- Analyze the influence of factors such as temperature, vibration, and stress on the probability of failure.
- Use simulation tools to predict the service life of electromobility components and systems.
- Develop strategies to optimize the reliability and safety of electric vehicles.
- Evaluate the impact of different designs and materials on the reliability of electromobility systems.
- Apply risk analysis methods to identify and mitigate potential hazards associated with failures.
- Analyze failure data and use it to improve the design and maintenance of electric vehicles.
- Understand the regulations and standards related to reliability and safety in electromobility.
Who this program is for:
Probabilistic analysis course in electromobility
- Engineers with degrees in Aerospace Engineering, Mechanical Engineering, Industrial Engineering, Automation Engineering, or related fields.
- Professionals with experience in the electromobility industry, including OEM aircraft (rotorcraft/eVTOL), MRO (Maintenance, Repair, and Operations), specialized consulting, and technology centers focused on electromobility.
- Experts in areas such as Flight Testing, aircraft certification, avionics, control systems, and flight dynamics, who wish to deepen their knowledge and skills in applied probabilistic analysis to electromobility.
Aviation regulators/authorities and professionals involved in the development and regulation of UAM/eVTOL (Urban Air Mobility/eVTOL) who need to acquire specific skills in compliance and risk analysis.
Recommended Requirements: A solid foundation in areas such as aerodynamics, control systems, and structures is recommended. A B2+ or C1 level of proficiency in Spanish or English is required. Bridging tracks are offered for those with lower levels to facilitate course comprehension.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
2.1 Rotor Modeling and Simulation in Electromobility Systems
2.2 Aerodynamic Design and Material Selection
2.3 Rotor Optimization Methods for Energy Efficiency
2.4 Performance Analysis and Experimental Validation
2.5 Integration of Rotors with Electric Propulsion Systems
2.6 Impact of Rotors on Stability and Control
2.7 Noise and Vibration Reduction in Rotors
2.8 Optimization of Rotor Lifespan and Reliability
2.9 Failure Analysis and Failure Modes in Rotors
2.10 Case Studies: Rotor Applications in Electromobility
3.1 Fundamentals of Probabilistic Analysis in Electromobility
3.2 Uncertainty Modeling in Electrical Systems
3.3 Monte Carlo Simulation and Sensitivity Analysis
3.4 Reliability Assessment of Components and Systems
3.5 Risk Analysis and Failure Mitigation
3.6 Design and Performance Optimization under Uncertainty
3.7 Continuous Improvement Techniques and Machine Learning
3.8 Case Studies: Practical Applications and Examples
3.9 Validation and Verification of Probabilistic Models
3.10 Tools and Software for Probabilistic Analysis
4.1 Introduction to Probabilistic Analysis in Electromobility Systems
4.2 Modeling of Electrical Components and Systems
4.3 Probability-Based Optimization Techniques
4.4 Analysis of System Reliability and Durability
4.5 Risk Assessment and Decision Making
4.6 Implementation of Optimization Strategies
4.7 Simulation and Validation of Results
4.8 Practical Applications in Design and Operation
4.9 Tools and Software for Probabilistic Analysis
4.10 Case Studies and Success Stories
5.1 Introduction to Probabilistic Analysis and Risk Assessment
5.2 Identification and Modeling of Risks in Electromobility
5.3 Performance Optimization Techniques under Uncertainty
5.4 Sensitivity Analysis and Monte Carlo Simulation
5.5 Evaluation of Reliability and Availability
5.6 Risk Mitigation Strategies
5.7 Cost-Benefit Analysis and Decision Making
5.8 Applications in Design, Operation, and Maintenance
5.9 Tools and Software for Probabilistic Analysis
5.10 Case Studies and Best Practices
6.1 Fundamentals of Failure Analysis in Electromobility
6.2 Modeling Failure Modes and Their Effects
6.3 Optimization Techniques to Improve Reliability
6.4 Root Cause Analysis and Corrective Actions
6.5 Design for Reliability and Maintainability
6.6 Safety and Performance Assessment
6.7 Failure Data Analysis and Trends
6.8 Implementation of Continuous Improvement Programs
6.9 Tools and Software for Failure Analysis
6.10 Case Studies and Practical Examples
7.1 Introduction to Prediction and Reliability in Systems
7.2 Probabilistic Modeling Techniques and Data Analysis
7.3 Predicting the Service Life and Performance of Systems
7.4 Risk Assessment and Sensitivity Analysis
7.5 Optimization to Improve Reliability and Availability
7.6 Implementation of Predictive Maintenance Strategies
7.7 Failure and Root Cause Analysis
7.8 Validation and Verification of predictive models
7.9 Tools and software for prediction and reliability
7.10 Case studies and practical examples
8.1 Introduction to reliability modeling, simulation, and optimization
8.2 Probabilistic modeling and simulation techniques
8.3 Analysis of system reliability and availability
8.4 Design for reliability and maintainability
8.5 Optimization of design and operation
8.6 Failure analysis and risk assessment
8.7 Implementation of maintenance strategies
8.8 Validation and verification of models and simulations
8.9 Tools and software for modeling and simulation
8.10 Case studies and practical applications
2.2 Introduction to Probabilistic Analysis in Electromobility
2.2 Fundamentals of Electromobility System Design
2.3 Probabilistic Modeling of Electrical Components
2.4 Optimization of Electrical Circuits and Systems
2.5 Design and Optimization of Batteries and Charging Systems
2.6 Sensitivity Analysis and Risk Assessment
2.7 Tools and Software for Probabilistic Analysis
2.8 Case Studies: Design and Optimization of Specific Systems
2.9 Design for Reliability and Maintainability
2.20 Future Trends in Electromobility Design
2.2 Fundamentals of Rotor Design for Electromobility
2.2 Aerodynamic Modeling of Rotors
2.3 Finite Element Analysis (FEA) of Rotors
2.4 Rotor Design Optimization: Methods and Strategies
2.5 Rotor Materials and Manufacturing
2.6 Rotor Performance and Efficiency Analysis
2.7 Simulation of Computational Flow Analysis (CFD) in Rotors
2.8 Rotor Design for Different Applications (Drones, eVTOL)
2.9 Evaluation of Rotor Lifespan and Reliability
2.20 Future Trends in Rotor Design
3.2 Introduction to Probabilistic Evaluation in Electromobility
3.2 Reliability Analysis Techniques
3.3 Identification and Evaluation of Failure Modes
3.4 Fault Tree and Event Tree Analysis
3.5 Monte Carlo Methods in Electromobility
3.6 Evaluation of Availability and Maintainability
3.7 System Optimization Based on Probabilistic Evaluation
3.8 Implementation of Continuous Improvement Strategies
3.9 Case Studies: Analysis and Improvement in Specific Systems
3.20 Challenges and Opportunities in Probabilistic Evaluation
4.2 Introduction to Systems Modeling in Electromobility
4.2 Modeling of Electrical and Electronic Components
4.3 Modeling of Control Systems and Battery Management
4.4 System Optimization: Algorithms and Techniques
4.5 Simulation of Electromobility Systems
4.6 Sensitivity and Robustness Analysis in Design
4.7 Design for Fault Tolerance
4.8 Model Integration and Simulation
4.9 Case Studies: Modeling and Optimization of Complex Systems
4.20 Future Trends in Modeling and Optimization
5.2 Introduction to Risk Analysis in Electromobility
5.2 Risk Identification and Assessment
5.3 Risk Mitigation Techniques
5.4 Cost-Benefit Analysis in Risk Management
5.5 Performance Optimization in Risk Environments
5.6 Design of Resilient Systems
5.7 Scenario Analysis and Risk Simulation
5.8 Integration of Risk Analysis into the Design
5.9 Case Studies: Performance Evaluation and Optimization
5.20 Future Trends in Risk Management
6.2 Introduction to Failure Analysis in Electromobility
6.2 Failure Mode Identification and Analysis
6.3 Root Cause Analysis (RCA)
6.4 Design for Reliability (DfR)
6.5 Maintenance Strategies and Asset Management
6.6 Reliability Optimization through Design
6.7 Failure Data Analysis and Testing
6.8 Implementation of Condition Monitoring Systems
6.9 Case Studies: Reliability Optimization
6.20 Future Trends in Failure Analysis
7.2 Introduction to Prediction in Electromobility
7.2 Failure Prediction Techniques
7.3 Data Analysis and Trends
7.4 System and Component Reliability
7.5 Predictive Modeling in Electromobility
7.6 Maintenance Predictive
7.7 Condition Monitoring and Diagnostics
7.8 Implementation of Reliability Strategies
7.9 Case Studies: System Prediction and Reliability
7.20 Advances in Prediction and Reliability
8.2 Introduction to Modeling, Simulation, and Optimization
8.2 Modeling of Electromobility Systems
8.3 Simulation of Electrical and Mechanical Systems
8.4 Reliability Optimization in Design
8.5 Sensitivity and Robustness Analysis
8.6 Fault-Tolerant System Design
8.7 Monte Carlo Simulation
8.8 Application of Tools and Software
8.9 Case Studies: Modeling, Simulation, and Optimization
8.20 Future Trends in Modeling and Simulation
3.3 Risk Assessment in Electric Propulsion: Probabilistic Analysis
3.2 Modeling and Simulation of Failures in Electromobility Systems
3.3 Performance Optimization: A Probabilistic Approach
3.4 Reliability Analysis: Probabilistic Techniques
3.5 Failure Assessment: Probabilistic Methodologies
3.6 Failure Prediction and Reliability in Electromobility
3.7 Reliability Simulation and Optimization: Electromobility Systems
3.8 Design for Reliability: Probabilistic Analysis
3.9 Risk Management: Application in Electromobility
3.30 Cost-Benefit Analysis: Integrating the Probabilistic Perspective
4.4 Systems Modeling in Electromobility: Introduction to Key Concepts
4.2 Probabilistic Parameter Analysis in Electrical System Design
4.3 Monte Carlo Simulation Applied to Electromobility Systems
4.4 Probability-Based Optimization for Component Design
4.5 Risk Assessment and Sensitivity Analysis in Design
4.6 Reliability Modeling of Electromobility Systems
4.7 Application of Probabilistic Optimization Techniques
4.8 Integration of Probabilistic Models into the Design Process
4.9 Case Studies: Practical Applications of Probabilistic Analysis
4.40 Future Perspectives and Trends in Probabilistic Analysis in Electromobility
5.5 Modeling of Electromobility Systems: Components and Subsystems
5.5 Probability Theory and Applied Statistics
5.3 Uncertainty Analysis: Sources and Quantification
5.4 Robust Design: Optimization Under Uncertainty
5.5 Monte Carlo Simulation: Application in Electromobility Systems
5.6 Design of Electric Propulsion Systems: Probabilistic Optimization
5.7 Design of Energy Storage Systems: Probabilistic Optimization
5.8 Design of Control Systems: Probabilistic Optimization
5.9 Sensitivity Analysis and Scenario Analysis
5.50 Case Studies: Optimization of Electromobility Systems
5.5 Rotor Design: Fundamentals and Principles
5.5 Aerodynamic Modeling of Rotors: CFD and BEM
5.3 Structural Modeling of Rotors: FEM
5.4 Rotor Optimization: Algorithms and Techniques
5.5 Rotor Performance Analysis: Efficiency and Noise
5.6 Rotor Optimization in High-Uncertainty Environments
5.7 Rotor Design for Different Applications Electromobility
5.8 Case studies: Rotor optimization for electric vehicles
5.9 Experimental validation: Rotor testing
5.50 Future trends in rotor design
3.5 Introduction to probabilistic evaluation: Key concepts
3.5 Probability distributions: Selection and fitting
3.3 Sensitivity analysis: Identifying critical variables
3.4 Monte Carlo simulation: Application in evaluation
3.5 Performance evaluation: Probabilistic metrics
3.6 Reliability evaluation: Failure mode analysis
3.7 Safety evaluation: Risk analysis
3.8 Data analysis: Tools and techniques
3.9 Systems improvement: Strategies based on probabilistic evaluation
3.50 Case studies: Probabilistic evaluation in electromobility
4.5 Systems modeling: Systems approach
4.5 Modeling of components and subsystems: Tools and techniques
4.3 Probabilistic modeling: Incorporating uncertainty
4.4 Systems optimization: Objectives and constraints
4.5 Algorithms of Optimization: Selection and Application
4.6 Systems Simulation: Tools and Methods
4.7 Results Analysis: Interpretation and Visualization
4.8 Design of Experiments: Optimization Techniques
4.9 Case Studies: Modeling and Optimization of Electrical Systems
4.50 Systems Integration: Challenges and Solutions
5.5 Risk Identification and Assessment: Methodologies
5.5 Risk Analysis: Tools and Techniques
5.3 Risk Quantification: Probability and Impact
5.4 Performance Optimization: Strategies and Techniques
5.5 Robust Design: Risk Mitigation
5.6 Sensitivity Analysis: Identification of Critical Factors
5.7 Monte Carlo Simulation: Risk Scenario Analysis
5.8 Risk Management: Planning and Control
5.9 Case Studies: Performance Optimization Under Risk
5.50 Regulatory and Normative Considerations
6.5 Failure Analysis: Methodologies and Tools
6.5 Failure Modes: Identification and Classification
6.3 Root Cause Analysis: Techniques and Methods
6.4 Reliability Assessment: Metrics and Models
6.5 Design for Reliability: Strategies and Techniques
6.6 Availability and Maintainability Analysis
6.7 Probabilistic Failure Analysis: Modeling and Simulation
6.8 Reliability Optimization: Techniques and Algorithms
6.9 Case Studies: Failure Analysis in Electromobility
6.50 Implementing Reliability Improvements
7.5 Introduction to Prediction: Concepts and Techniques
7.5 Predictive Modeling: Model Selection
7.3 Data Analysis: Techniques and Tools
7.4 Life Estimation: Models and Methods
7.5 Failure Prediction: Techniques and Tools
7.6 System Reliability: Assessment and Improvement
7.7 Simulation: Scenarios and Analysis
7.8 Validation: Methods and Metrics
7.9 Case Studies: Prediction in Electromobility
7.50 Future Trends in Prediction and Reliability
8.5 Reliability Modeling: Concepts and Models
8.5 Reliability Simulation: Methods and Tools
8.3 Reliability Optimization: Techniques and Algorithms
8.4 Analysis Data analysis: techniques and tools
8.5 Uncertainty assessment
8.6 Sensitivity and robustness analysis
8.7 Case studies: simulation and optimization
8.8 Implementing reliability optimization
8.9 Integrating reliability into the product lifecycle
8.50 Future trends in simulation and optimization
6.6 Failures in Electromobility Systems: Identification and Analysis
6.2 Probabilistic Modeling of Failures in Critical Components
6.3 Design for Reliability (DfR) in Electromobility
6.4 Failure Mode and Effects Analysis (FMEA)
6.5 Reliability Optimization Using Probabilistic Techniques
6.6 Stress Testing and Failure Simulation
6.7 Evaluation of Useful Life and Performance under Failures
6.8 Failure Mitigation and Redundancy Strategies
6.9 Reliability-Centered Maintenance (RCM)
6.60 Case Studies: Practical Application in Real Systems
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
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F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).