Periodic Reporting for period 2 - Predictive Battery Analytics (TWAICE predictive analytics and digital twin ecosystem to optimise and automate batteries second life and re-use)
Reporting period: 2023-10-01 to 2024-09-30
In 2020, the number of electric vehicles worldwide reached 10 million and it is expected that the demand for batteries from the mobility sector will reach 1,525 GWh annually by 2030. In this growing context, manufacturers are faced with the challenge that they must take back the used batteries; thus, becoming responsible for their after-life management. The current average usage of batteries in electric vehicles is about 10 years or 150.000 km and around 70-80% of used Li-ion batteries are still usable and in good health after the first use. However, since Automotive Original Equipment Manufacturers (OEMs) have no way of predicting capacity and remaining charge cycles, the residual value of batteries is very low, or even negative, as OEMs must pay industrial waste management companies to dispose the used batteries. With the expected growth in electric vehicles, soon, battery disposable will be an important expense for OEMs.
TWAICE provides a simple and automated method to re-pack and re-use batteries after first use and give resale value to today’s industrial waste, solving the major cost and safety problems posed by batteries. We have developed an analytic software which calculates the remaining capacity, state of health, and remaining charging cycles of the battery down to the individual modules, making it possible to predict the remaining lifetime of a used battery. Modules at the same health status and with the same expected remaining life-time are combined and re-packed into a new battery. The re-packed batteries are directed to the application better matching its remaining performance. By using our digital twin technology, battery health calculations and tests can be performed up to four times faster. During the battery’s 2nd life, the digital twin is constantly monitored. Based on the collected data, the usage of the batteries is constantly adapted to their state of health and remaining life-time. As calculations are made on a module-by-module basis, only modules that are no longer usable are disposed of. This eliminates the superfluous waste of resources by nearly 99 %.
TWAICE ecosystem for batteries lifetime management and second life upcycling will allow to at least double the effective use of a battery and reduce the environmental impact of the upcoming massive vehicles electrification, offering the first solution that can enable a circular economy model in the battery life management. This will allow waste batteries to have commercial resale value for second life application. OEMs will benefit from a solution that reduces the overall greenhouse gas emissions while providing an additional source of revenue.
TWAICE provides a simple and automated method to re-pack and re-use batteries after first use and give resale value to today’s industrial waste, solving the major cost and safety problems posed by batteries. We have developed an analytic software which calculates the remaining capacity, state of health, and remaining charging cycles of the battery down to the individual modules, making it possible to predict the remaining lifetime of a used battery. Modules at the same health status and with the same expected remaining life-time are combined and re-packed into a new battery. The re-packed batteries are directed to the application better matching its remaining performance. By using our digital twin technology, battery health calculations and tests can be performed up to four times faster. During the battery’s 2nd life, the digital twin is constantly monitored. Based on the collected data, the usage of the batteries is constantly adapted to their state of health and remaining life-time. As calculations are made on a module-by-module basis, only modules that are no longer usable are disposed of. This eliminates the superfluous waste of resources by nearly 99 %.
TWAICE ecosystem for batteries lifetime management and second life upcycling will allow to at least double the effective use of a battery and reduce the environmental impact of the upcoming massive vehicles electrification, offering the first solution that can enable a circular economy model in the battery life management. This will allow waste batteries to have commercial resale value for second life application. OEMs will benefit from a solution that reduces the overall greenhouse gas emissions while providing an additional source of revenue.
During the project, we have performed the following activities related to the product development:
- A pilot was run with OEMs and battery manufacturers gathering data from around 500 used batteries to qualify the evaluation and prediction tools. The results of the digital twin technology were validated and customers & end-users’ feedback was collected.
- Trials with bus fleet operators and automotive OEMs were conducted, collecting the battery data to calculate KPIs such as the State of Charge (SoC), State of Health capacity (SoHc), and State of Health resistance (SoHr).
- Collaboration with an insurance provider for performance guarantees was made, collecting data to qualify re-packed batteries to receive 5- to 10-years warranty and insurance coverage by insurance companies.
- Pilot installations were conducted with utility companies, utilizing the re-packed batteries as energy storage systems.
- An ecosystem based in our model approach for the circular economy model of Li-ion batteries was created, having brought OEMs as sources of used batteries, batteries manufacturers as potential re-packing partners and energy utility companies as buyers of re-packed batteries.
- Several patent applications, crucial to our technological advancements, have been filled.
- Significant certifications remarking our commitment to quality, security, and robust process management were achieved (including the provisional passing of ISO 27001 certification and the TÜV process certification).
Our main achievements reached during the project include:
- We developed cutting-edge methodologies using machine learning with a focus on supervised learning and regression techniques to estimate and predict the state of health (SoH) of lithium-ion batteries in battery energy storage systems (BESS) and to assess electric vehicle applications from the beginning of their life (BoL).
- We developed a SoH Prediction Model (for a single string and for multiple strings).
- We developed reliable predictions of a battery’s end of life (EoL) and assessed the right depth of discharge (DoD) to help dimension the battery correctly.
- We developed alternative validation approaches for SoH Estimation and Prediction.
- We improved our in-house predictive analytics algorithms.
- We created a tool for evaluating various BESS designs and operational strategies with a "single & multiple input" approach.
- We developed methods to conduct simulation studies to predict a 2nd life storage system’s performance and to perform a techno-economical assessment.
- We developed a method to predict repacked batteries’ performance (evaluating BESS Design performance and SoH predictions) allowing re-matching battery modules from used batteries.
- A pilot was run with OEMs and battery manufacturers gathering data from around 500 used batteries to qualify the evaluation and prediction tools. The results of the digital twin technology were validated and customers & end-users’ feedback was collected.
- Trials with bus fleet operators and automotive OEMs were conducted, collecting the battery data to calculate KPIs such as the State of Charge (SoC), State of Health capacity (SoHc), and State of Health resistance (SoHr).
- Collaboration with an insurance provider for performance guarantees was made, collecting data to qualify re-packed batteries to receive 5- to 10-years warranty and insurance coverage by insurance companies.
- Pilot installations were conducted with utility companies, utilizing the re-packed batteries as energy storage systems.
- An ecosystem based in our model approach for the circular economy model of Li-ion batteries was created, having brought OEMs as sources of used batteries, batteries manufacturers as potential re-packing partners and energy utility companies as buyers of re-packed batteries.
- Several patent applications, crucial to our technological advancements, have been filled.
- Significant certifications remarking our commitment to quality, security, and robust process management were achieved (including the provisional passing of ISO 27001 certification and the TÜV process certification).
Our main achievements reached during the project include:
- We developed cutting-edge methodologies using machine learning with a focus on supervised learning and regression techniques to estimate and predict the state of health (SoH) of lithium-ion batteries in battery energy storage systems (BESS) and to assess electric vehicle applications from the beginning of their life (BoL).
- We developed a SoH Prediction Model (for a single string and for multiple strings).
- We developed reliable predictions of a battery’s end of life (EoL) and assessed the right depth of discharge (DoD) to help dimension the battery correctly.
- We developed alternative validation approaches for SoH Estimation and Prediction.
- We improved our in-house predictive analytics algorithms.
- We created a tool for evaluating various BESS designs and operational strategies with a "single & multiple input" approach.
- We developed methods to conduct simulation studies to predict a 2nd life storage system’s performance and to perform a techno-economical assessment.
- We developed a method to predict repacked batteries’ performance (evaluating BESS Design performance and SoH predictions) allowing re-matching battery modules from used batteries.
TWAICE’s predictive battery analytics platform is the most important driver for a more sustainable energy for the mobility sector, promoting economic, environmental and social responsibility. The main impacts of our achievements are the following:
- We reduced the CO2 related emissions by prolonging usable first-life and enabling second-life usage.
- We help battery manufacturers to design their systems in the economic and ecological best way by providing accurate battery analytics and other metrics throughout the battery’s lifetime and different usage scenarios.
- We help to size the battery correctly; therefore, contributing to decrease the carbon footprint of battery productions through material savings.
- We provide the data and metrics necessaries for making optimal decisions for a second life for batteries promoting their remanufacturing, repurposing and reusing, enabling a sustainable circular economy model in the Li-ion battery ecosystem.
- We reduced the CO2 related emissions by prolonging usable first-life and enabling second-life usage.
- We help battery manufacturers to design their systems in the economic and ecological best way by providing accurate battery analytics and other metrics throughout the battery’s lifetime and different usage scenarios.
- We help to size the battery correctly; therefore, contributing to decrease the carbon footprint of battery productions through material savings.
- We provide the data and metrics necessaries for making optimal decisions for a second life for batteries promoting their remanufacturing, repurposing and reusing, enabling a sustainable circular economy model in the Li-ion battery ecosystem.