arXiv subjects

Gregory J. Offer

Publications and source records attributed to Gregory J. Offer.

6 recordsLinked to original sources

arXiv2025

Lithium-ion battery degradation: Introducing the concept of reservoirs to design for lifetime

Designing lithium-ion batteries for long service life remains a challenge, as most cells are optimized for beginning-of-life metrics such as energy density, often overlooking how design and operating conditions shape degradation. This work introduces a degradation-aware design framework built around finite, interacting reservoirs (lithium, porosity, and electrolyte) that are depleted over time by coupled degradation processes. We extend a physics-based Doyle-Fuller-Newman model to include validated mechanisms such as SEI growth, lithium plating, cracking, and solvent dry-out, and simulate how small design changes impact lifetime. Across more than 1,000 cycles, we find that increasing electrolyte volume by just 1% or porosity by 5% can extend service life by over 30% without significantly affecting cell energy density. However, lithium excess, while boosting initial capacity, can accelerate failure if not supported by sufficient structural or ionic buffers. Importantly, we show that interaction between reservoirs is crucial to optimal design: multi-reservoir tuning yields either synergistic benefits or compound failures, depending on operating conditions. We also quantify how C-rate and operating temperature influence degradation pathways, emphasizing the need for co-optimized design and usage profiles. By reframing degradation as a problem of managing finite internal reservoirs, this work offers a predictive and mechanistic foundation for designing lithium-ion batteries that balance energy, durability, and application-specific needs.

cond-mat.mtrl-sci↗

arXiv2025

Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity

Since the internal temperature is less accessible than surface temperature, there is an urgent need to develop accurate and real-time estimation algorithms for better thermal management and safety. This work presents a novel framework for resource-efficient and scalable development of accurate, robust, and adaptive internal temperature estimation algorithms by blending physics-based modeling with machine learning, in order to address the key challenges in data collection, model parameterization, and estimator design that traditionally hinder both approaches. In this framework, a physics-based model is leveraged to generate simulation data that includes different operating scenarios by sweeping the model parameters and input profiles. Such a cheap simulation dataset can be used to pre-train the machine learning algorithm to capture the underlying mapping relationship. To bridge the simulation-to-reality gap resulting from imperfect modeling, transfer learning with unsupervised domain adaptation is applied to fine-tune the pre-trained machine learning model, by using limited operational data (without internal temperature values) from target batteries. The proposed framework is validated under different operating conditions and across multiple cylindrical batteries with convective air cooling, achieving a root mean square error of 0.5 {\deg}C when relying solely on prior knowledge of battery thermal properties, and less than 0.1 {\deg}C when using thermal parameters close to the ground truth. Furthermore, the role of the simulation data quality in the proposed framework has been comprehensively investigated to identify promising ways of synthetic data generation to guarantee the performance of the machine learning model.

eess.SY↗

arXiv2024

Mapping safety transitions as batteries degrade: A model-based analysis towards full-lifespan battery safety management

Battery safety is important, yet safety limits are normally static and do not evolve as batteries degrade. Consequently, many battery systems are overengineered to meet increasingly stringent safety demands. In this work we show that failure behaviour evolves over time as batteries degrade, and discuss the challenges and opportunities to manage battery safety dynamically throughout its lifetime. We introduce the first framework for capturing how the likelihood and severity of battery failures change over time based upon the concepts of safety zones and their boundaries. Through the development of a comprehensive physics-based model that integrates multiple degradation and thermal runaway failure mechanisms, we then show how the safety zones and boundaries of a commercial 21700 battery change after varied use and how these changes may lead to false negatives with existing management strategies. Further analyses reveal that degradation mechanisms strongly affect safety characteristics, causing significant changes despite similar capacity fade, highlighting the limitations of using capacity fade alone to assess batteries' usability. By synthesising our results with literature, we map possible degradation-to-failure pathways and recommend future research needs to achieve full-lifespan battery safety management, with advanced diagnostic and modelling techniques to accurately define state-of-safety for real-world applications as key priorities.

physics.chem-ph↗

arXiv2024

Lithium-ion battery degradation: using degradation mode analysis to validate lifetime prediction modelling

Predicting lithium-ion battery lifetime is one of the greatest unsolved problems in battery research right now. Recent years have witnessed a surge in lifetime prediction papers using physics-based, empirical, or data-driven models, most of which have been validated against the remaining capacity (capacity fade) and sometimes resistance (power fade). However, there are many different combinations of degradation mechanisms in lithium-ion batteries that can result in the same patterns of capacity and power fade, making it impossible to find a unique validated solution. Experimentally, degradation mode analysis involving measuring the loss of lithium inventory, loss of active material at both electrodes, and electrode drift/slippage has emerged as a state-of-the-art requirement for cell degradation studies. In this paper we coupled five degradation mechanisms together for the first time. We also showed how three models with different levels of complexity can all fit the remaining capacity and resistance well, but only the model with five coupled degradation mechanisms could also fit the degradation modes at all temperatures. This work proves that validating only against capacity and power fade is no longer sufficient, and state-of-the-art experimental and modelling degradation studies should include degradation mode analysis for validation in the future.

physics.chem-ph↗

arXiv2023

Lithium-ion battery performance model including solvent segregation effects

A model of a lithium-ion battery containing a cosolvent electrolyte is developed and implemented within the open-source PyBaMM platform. Lithium-ion electrolytes are essential to battery operation and normally contain at least two solvents to satisfy performance requirements. The widely used Doyle-Fuller-Newman battery model assumes that the electrolyte comprises a salt dissolved in a single effective solvent, however. This single-solvent approximation has been disproved experimentally and may hinder accurate battery modelling. Here, we present a two-solvent model that resolves the transport of ethylene carbonate (EC) and lithium salt in a background linear carbonate. EC concentration polarization opposes that of Li+ during cycling, affecting local electrolyte properties and cell-level overpotentials. Concentration gradients of Li+ can be affected by cross-diffusion, whereby EC gradients enhance or impede salt flux. A rationally parametrized model that includes EC transport predicts 6% more power loss at 4.5C discharge and ~0.32% more capacity loss after a thousand 1C cycles than its single-solvent equivalent. This work provides a tool to model more transport behaviour in the electrolyte that may affect degradation and enables the transfer of microscopic knowledge about solvation structure-dependent performance to the macroscale.

physics.chem-ph↗

arXiv2022

Lithium-ion battery degradation: how to model it

Predicting lithium-ion battery degradation is worth billions to the global automotive, aviation and energy storage industries, to improve performance and safety and reduce warranty liabilities. However, very few published models of battery degradation explicitly consider the interactions between more than two degradation mechanisms, and none do so within a single electrode. In this paper, the first published attempt to directly couple more than two degradation mechanisms in the negative electrode is reported. The results are used to map different pathways through the complicated path dependent and non-linear degradation space. Four degradation mechanisms are coupled in PyBaMM, an open source modelling environment uniquely developed to allow new physics to be implemented and explored quickly and easily. Crucially it is possible to see 'inside' the model and observe the consequences of the different patterns of degradation, such as loss of lithium inventory and loss of active material. For the same cell, five different pathways that can result in end-of-life have already been found, depending on how the cell is used. Such information would enable a product designer to either extend life or predict life based upon the usage pattern. However, parameterization of the degradation models remains as a major challenge, and requires the attention of the international battery community.

physics.chem-ph↗