Digital Twin Model Development and Validation

Description of the subject of the contract a. Brief description of the subject of the contract: The subject of the contract is the development and delivery of an offline Digital Twin model for electric bus battery systems based on laboratory and field operational data. The solution shall enable advanced battery diagnostics, lifetime prediction, charging optimization, and vehicle range simulation. The Digital Twin shall be delivered together with source code, technical documentation, validation reports, and integration interfaces enabling future expansion and implementation within the Operator's battery management ecosystem. The Digital Twin solution shall be developed exclusively for a single battery system type based on Lithium Iron Phosphate cell technology used in electric bus applications.

The scope of the project does not include the development or validation of models for other battery system architectures. b. Purpose of the contract: The purpose of the contract is to develop an advanced Digital Twin solution capable of accurately representing the behavior, degradation mechanisms, and operational performance of lithium-ion battery systems used in electric buses. The developed solution shall support predictive maintenance, optimize charging strategies, improve battery utilization, and enable reliable prediction of battery lifetime and vehicle range under various operating conditions. The Digital Twin shall provide tools for battery State of Charge (SoC) and State of Health (SoH) estimation, charging optimization, battery lifetime prediction, and range simulation. The solution shall improve decision-making regarding battery operation and maintenance while reducing operating costs and extending battery service life. c. Detailed description of the subject of the contract: The Contractor shall develop, validate, and deliver a Digital Twin model for electric bus battery systems using operational and laboratory data provided by the Contracting Authority. The scope of work shall include the development of the following modules: 1. State of Charge (SoC) and State of Health (SoH) estimation module The Contractor shall develop algorithms capable of estimating: • Battery State of Charge (SoC); • Battery State of Health (SoH); • Remaining usable battery capacity. The target prediction accuracy shall be not less than 95%, subject to verification based on the available data quality and agreed validation methodology. Accuracy shall be assessed according to a validation methodology jointly agreed between the Contractor and the Ordering Party based on available datasets. 2. Charging optimization module The Contractor shall develop a charging optimization module capable of: • recommending optimal charging strategies and charging profiles for electric buses; • evaluating the impact of fast charging on battery degradation; • assessing the influence of various charging regimes on battery aging and expected lifetime; • supporting operational decisions aimed at maximizing battery service life. 3. Battery lifetime prediction module The Contractor shall develop predictive models capable of: • forecasting battery End of Life (EOL), defined as reaching 80% State of Health; • predicting capacity fade as a function of operating conditions and time; • predicting internal resistance growth where feasible based on available data; • estimating remaining useful life (RUL) of battery systems. The prediction accuracy of this module shall exceed 90% and shall be demonstrated through a validation process documented in the final project report. Accuracy shall be assessed according to a validation methodology jointly agreed between the Contractor and the Ordering Party based on available datasets. 4. Range simulation module The Contractor shall develop simulation tools capable of: • estimating vehicle driving range on a single battery charge; • estimating remaining cumulative vehicle mileage until battery End of Life; • evaluating the impact of battery degradation on vehicle range; • performing analyses under different operating, environmental, and charging scenarios. Data processing and model development The Digital Twin shall be developed using: • cell-level test data provided by battery suppliers; • laboratory test data from battery cells and battery packs; • battery pack design and architecture information; • field operational data collected from a minimum of 15 electric buses, including current, voltage, temperature, charging events, route profiles, and other relevant operational parameters. The Digital Twin model shall be developed and validated for one selected battery system utilizing Lithium Iron Phosphate cell technology. All modelling activities, algorithm development, validation procedures, and prediction accuracy assessments shall be performed exclusively for this battery system configuration based on the data provided by the Contracting Authority. Deliverables The Contractor shall deliver: • Complete Digital Twin model in offline form; • Full source code (Python and/or Simulink or equivalent); • System architecture documentation; • User manual; • API enabling future integration and expansion of the solution; • Validation methodology and validation report, especially confirming over 90% of battery lifetime prediction accuracy; • Final technical report summarizing model development, assumptions, limitations, and achieved performance indicators. Project Completion The project shall be completed no later than 28 October 2026. The delivered solution shall allow future incorporation of additional operational data and further model refinement without requiring redevelopment of the entire system. d. CPV codes for the contract: 73100000-3 – Research and experimental development services 72242000-3 – Design-modelling services e. Term of the contract: • planned starting date: 24.08.2026 r. Within the first week following the contract commencement, the Ordering Party will provide the Contractor with all projec...