The challenges stated by many researchers are:
There are a number of obstacles that must be overcome for battery digital twins to be developed and used successfully. These difficulties include, among others:
Data Availability and Quality: Accurate digital twin construction calls on a wealth of high-quality data about the operation and behaviour of the battery. Finding this information can be difficult, particularly in real-time applications where it is necessary to maintain dependable and constant monitoring of multiple battery metrics, including voltage, temperature, and current. Accurate digital twin modelling depends on the availability and dependability of data sources.
Accuracy and model complexity: Battery systems are complicated and nonlinear, with sophisticated electrochemical reactions taking place at various sizes. Accurate model development that accounts for these intricacies is difficult. In order to accurately anticipate a battery's state of charge (SoC), state of health (SoH), and other performance metrics, battery digital twins should be able to replicate the behaviour of the battery under various operating conditions. It might be difficult to balance model complexity and computational effectiveness.
Validation and Calibration: Another key problem is validating and calibrating digital twins in order to verify their accuracy. Ageing, usage patterns, and environmental conditions are just a few of the variables that affect how a battery behaves in real life. The digital twin must be thoroughly validated and calibrated against real-world data in order to incorporate these aspects and guarantee that it truly replicates the physical battery. It takes a lot of time and resources to complete this iterative procedure.
Scalability and Adaptability: Digital twins for batteries should be scalable and adaptable to various battery chemistries, designs, and configurations. Due to the variations in electrochemical behaviour and performance traits, designing digital twins that can handle many battery kinds and sizes is difficult. Digital twins must also be able to adapt to changing battery circumstances, including ageing, degradation, and various operational profiles.
Cybersecurity and Data Privacy: Batteries digital twins are subject to cybersecurity risks since they depend on the gathering and transmission of data. To fend against potential attacks and unwanted access to private data, it is essential to guarantee the security and privacy of battery data. To protect the digital twin and related data, it is crucial to implement strong cybersecurity safeguards and encryption techniques.
Integration and Interoperability: Digital twins frequently belong to bigger systems or Internet of Things ecosystems, necessitating smooth connections with other platforms and components. Interoperability between various hardware and software systems, data formats, and communication protocols can be difficult to achieve. It is essential to make sure that battery digital twins can interact and communicate with other parts efficiently, providing comprehensive monitoring and control of battery systems.
Battery makers, data scientists, subject matter experts, and software developers must work together to address these issues. By overcoming these obstacles, battery digital twins will be deployed and used successfully, leading to greater predictive maintenance, improved battery performance, and optimal energy storage systems.
Challenges in terms of
Battery Digital Twin Modelling
Real-Time State Estimation
Dynamic Charging Control
Dynamic Thermal Management
Dynamic Equalisation Control