Predicting the end-of-life (EOL) of lithium-ion batteries across different manufacturers presents significant challenges due to variations in electrode materials, manufacturing processes, cell formats, and a lack of generally available data. Methods that construct features solely on voltage-capacity profile data typically fail to generalize across cell chemistries. This study introduces a methodology that combines traditional voltage-capacity features with Direct Current Internal Resistance (DCIR) measurements, enabling more accurate and generalizable EOL predictions. The use of early-cycle DCIR data captures critical degradation mechanisms related to internal resistance growth, enhancing model robustness. Models are shown to successfully predict the number of cycles to EOL for unseen manufacturers of varied electrode composition with a mean absolute error (MAE) of 150 cycles. This cross-manufacturer generalizability reduces the need for extensive new data collection and retraining, enabling manufacturers to optimize new battery designs using existing datasets. Additionally, a novel DCIR-compatible dataset is released as part of ongoing efforts to enrich the growing ecosystem of cycling data and accelerate battery materials development.

Key\-value separation is used in LSM\-tree to stored large value in separate log files to reduce write amplification, but requires garbage collection to garbage collect invalid values. Existing garbage collection techniques in LSM\-tree typically adopt static parameter based garbage collection to garbage collect obsolete values which struggles to achieve low write amplification and it's challenging to find proper parameter for garbage collection triggering. In this work we introduce DumpKV, which introduces learning based lifetime aware garbage collection with dynamic lifetime adjustment to do efficient garbage collection to achieve lower write amplification. DumpKV manages large values using trained lightweight model with features suitable for various application based on past write access information of keys to give lifetime prediction for each individual key to enable efficient garbage collection. To reduce interference to write throughput DumpKV conducts feature collection during L0\-L1 compaction leveraging the fact that LSM\-tree is small under KV separation. Experimental results show that DumpKV achieves lower write amplification by 38\%\-73\% compared to existing key\-value separation garbage collection LSM\-tree stores with small feature storage overhead.

Hyper-parameter optimization is the black art of tuning a learner's control parameters. In software analytics, a repeated result is that such tuning can result in dramatic performance improvements. Despite this, hyper-parameter optimization is often applied rarely or poorly in software analytics--perhaps due to the CPU cost of exploring all those parameter options can be prohibitive. We theorize that learners generalize better when the loss landscape is ``smooth''. This theory is useful since the influence on ``smoothness'' of different hyper-parameter choices can be tested very quickly (e.g. for a deep learner, after just one epoch). To test this theory, this paper implements and tests SMOOTHIE, a novel hyper-parameter optimizer that guides its optimizations via considerations of ``smothness''. The experiments of this paper test SMOOTHIE on numerous SE tasks including (a) GitHub issue lifetime prediction; (b) detecting false alarms in static code warnings; (c) defect prediction, and (d) a set of standard ML datasets. In all these experiments, SMOOTHIE out-performed state-of-the-art optimizers. Better yet, SMOOTHIE ran 300% faster than the prior state-of-the art. We hence conclude that this theory (that hyper-parameter optimization is best viewed as a ``smoothing'' function for the decision landscape), is both theoretically interesting and practically very useful. To support open science and other researchers working in this area, all our scripts and datasets are available on-line at https://github.com/yrahul3910/smoothness-hpo/.

Accurately predicting the lifetime of battery cells in early cycles holds tremendous value for battery research and development as well as numerous downstream applications. This task is rather challenging because diverse conditions, such as electrode materials, operating conditions, and working environments, collectively determine complex capacity-degradation behaviors. However, current prediction methods are developed and validated under limited aging conditions, resulting in questionable adaptability to varied aging conditions and an inability to fully benefit from historical data collected under different conditions. Here we introduce a universal deep learning approach that is capable of accommodating various aging conditions and facilitating effective learning under low-resource conditions by leveraging data from rich conditions. Our key finding is that incorporating inter-cell feature differences, rather than solely considering single-cell characteristics, significantly increases the accuracy of battery lifetime prediction and its cross-condition robustness. Accordingly, we develop a holistic learning framework accommodating both single-cell and inter-cell modeling. A comprehensive benchmark is built for evaluation, encompassing 401 battery cells utilizing 5 prevalent electrode materials across 168 cycling conditions. We demonstrate remarkable capabilities in learning across diverse aging conditions, exclusively achieving 10% prediction error using the first 100 cycles, and in facilitating low-resource learning, almost halving the error of single-cell modeling in many cases. More broadly, by breaking the learning boundaries among different aging conditions, our approach could significantly accelerate the development and optimization of lithium-ion batteries.

Accurate battery lifetime prediction is important for preventative maintenance, warranties, and improved cell design and manufacturing. However, manufacturing variability and usage-dependent degradation make life prediction challenging. Here, we investigate new features derived from capacity-voltage data in early life to predict the lifetime of cells cycled under widely varying charge rates, discharge rates, and depths of discharge. Features were extracted from regularly scheduled reference performance tests (i.e., low rate full cycles) during cycling. The early-life features capture a cell's state of health and the rate of change of component-level degradation modes, some of which correlate strongly with cell lifetime. Using a newly generated dataset from 225 nickel-manganese-cobalt/graphite Li-ion cells aged under a wide range of conditions, we demonstrate a lifetime prediction of in-distribution cells with 15.1% mean absolute percentage error using no more than the first 15% of data, for most cells. Further testing using a hierarchical Bayesian regression model shows improved performance on extrapolation, achieving 21.8% mean absolute percentage error for out-of-distribution cells. Our approach highlights the importance of using domain knowledge of lithium-ion battery degradation modes to inform feature engineering. Further, we provide the community with a new publicly available battery aging dataset with cells cycled beyond 80% of their rated capacity.

Predictions oflifetimes of dynamically allocated objects can be used to improve time and space efficiency of dynamic memory manage(cid:173) ment in computer programs. Barrett and Zorn [1993] used a simple lifetime predictor and demonstrated this improvement on a variety of computer programs. In this paper, we use decision trees to do lifetime prediction on the same programs and show significantly better prediction . Our method also has the advantage that during training we can use a large number of features and let the decision tree automatically choose the relevant subset. Dynamic memory allocation is used in many computer applications.

Predictions oflifetimes of dynamically allocated objects can be used to improve time and space efficiency of dynamic memory management in computer programs. Barrett and Zorn [1993] used a simple lifetime predictor and demonstrated this improvement on a variety of computer programs. In this paper, we use decision trees to do lifetime prediction on the same programs and show significantly better prediction. Our method also has the advantage that during training we can use a large number of features and let the decision tree automatically choose the relevant subset.

Predictions oflifetimes of dynamically allocated objects can be used to improve time and space efficiency of dynamic memory management in computer programs. Barrett and Zorn [1993] used a simple lifetime predictor and demonstrated this improvement on a variety of computer programs. In this paper, we use decision trees to do lifetime prediction on the same programs and show significantly better prediction. Our method also has the advantage that during training we can use a large number of features and let the decision tree automatically choose the relevant subset.

Predictions oflifetimes of dynamically allocated objects can be used to improve time and space efficiency of dynamic memory management incomputer programs. Barrett and Zorn [1993] used a simple lifetime predictor and demonstrated this improvement on a variety of computer programs. In this paper, we use decision trees to do lifetime prediction on the same programs and show significantly better prediction. Our method also has the advantage that during training we can use a large number of features and let the decision tree automatically choose the relevant subset.