For the battery industry, quick determination of the ageing behaviour of lithium-ion batteries is important both for the evaluation of existing designs as well as for R&D on future technologies. However, the target battery lifetime is 8–10 years, which implies low ageing rates that lead to an unacceptably long ageing test duration under real operation conditions. Therefore, ageing characterisation tests need to be accelerated to obtain ageing patterns in a period ranging from a few weeks to a few months. Known strategies, such as increasing the severity of stress factors, for example, temperature, current, and taking measurements with particularly high precision, need care in application to achieve meaningful results. We observe that this challenge does not receive enough attention in typical ageing studies. Therefore, this review introduces the definition and challenge of accelerated ageing along existing methods to accelerate the characterisation of battery ageing and lifetime modelling. We systematically discuss approaches along the existing literature. In this context, several test conditions and feasible acceleration strategies are highlighted, and the underlying modelling and statistical perspective is provided. This makes the review valuable for all who set up ageing tests, interpret ageing data, or rely on ageing data to predict battery lifetime.

A battery's open circuit voltage (OCV) curve can be seen as its electrochemical signature. Its shape and age-related shift provide information on aging processes and material composition on both electrodes. However, most OCV analyses have to be conducted in laboratories or specified field tests to ensure suitable data quality. Here, we present a method that reconstructs the OCV curve continuously over the lifetime of a battery using the operational data of home storage field measurements over eight years. We show that low-dynamic operational phases, such as the overnight household supply with electricity, are suitable for recreating quasi OCV curves. We apply incremental capacity analysis and differential voltage analysis and show that known features of interest from laboratory measurements can be tracked to determine degradation modes in field operation. The dominant degradation mode observed for the home storage systems under evaluation is the loss of lithium inventory, while the loss of active material might be present in some cases. We apply the method to lithium nickel manganese cobalt oxide (NMC), a blend of lithium manganese oxide (LMO) and NMC, and lithium iron phosphate (LFP) batteries. Field capacity tests validate the method.

It is widely recognized that temperature has a significant influence on the cycle lifetime of lithium-ion batteries (LIBs). Although there are several studies in the literature exploring the effect of elevated ambient temperature on the cyclic aging behavior of LIBs, statistically robust conclusions regarding the capacity-temperature relation remain challenging due to the limited sample sizes used in the available experiments. In this work, we perform cyclic aging tests on 48 NCA/Gr-SiOx cells at six temperature levels, ranging from 25 degrees C to 55 degrees C. First, we classify the tested cells into two groups with the help of a normal mixture model based on their initially extracted capacity. Then, a temperature dependent regression model is presented and fitted to the capacity and resistance results after 600 and 1200 partial cycles. Our investigation shows, that cycling within an ambient temperature range of 35 degrees C to 40 degrees C strikes a balance between achieving the highest mean capacity and minimizing cell-to-cell variance. Furthermore, the presented classification and regression models can be applied to enhance and manage the overall reliability of LIB packs.

This work introduces a comprehensive modeling framework designed to simulate the electrical, thermal, and aging behavior of battery cells connected in various parallel and series configurations. By utilizing Monte Carlo simulation techniques, the framework is used to investigate the inherent variability in cell attributes, including initial capacity, aging rate, and application profiles. Besides the estimation of expected battery life, this simulation environment enables the detailed investigation of failure distributions across different cell configurations and intensities of parameter variations. Results obtained from these simulations can be used, as an example, in the context of the automotive industry, where the insights of simulation in understanding the inherent variability of the aging process are particularly vital. As electric vehicles become more prevalent, understanding the performance and longevity of battery packs under various conditions is essential for effective design and management strategies, optimizing vehicle range, safety, and cost-effectiveness also on a fleet-level. Moreover, the ability to investigate failure distributions provides invaluable information for improving battery reliability and safety, key factors in the consumer acceptance of electric vehicles. Ultimately, the simulation environment provides a powerful tool for designing and optimizing efficient and durable battery technologies, with a focus on failure distribution analysis.

The anode overhang has been proven to be a non-negligible influencing factor in the ageing trajectory of lithium-ion batteries. It acts through the transfer of active lithium between the anode overhang and the active anode by changing reversibly the cell balancing. In this work, the anode overhang is proven to influence the open-circuit voltage. Through a high-precision measurement, a persistent rise of the open-circuit voltage was observed, which we have demonstrated to originate in the anode overhang effect. The dimensions and structure of the anode overhang were verified by a post-mortem analysis of the cell and matched with the voltage behaviour. Derived from this finding, an existing electrical–thermal ageing model was extended to allow the simulation of the interaction between the anode overhang and the capacity. With a Bayesian optimisation approach, the extended model allowed to drastically improve model parametrisation when ageing test data include an increase in capacity. The resulting model was verified with the simulation of two ageing profiles, each including varying ageing conditions and phases of capacity recovery. The model allows ageing predictions with a deviation below 4% of the remaining capacity after more than 750 days of ageing. The model is publicly available as part of an open-source project.

A comprehensive electric vehicle model is developed to characterize the behavior of the Smart e.d. (2013) while driving, charging and providing vehicle-to-grid services. To facilitate vehicle-to-grid strategy development, the EV model is completed with the measurement of the on-board charger efficiency and the charging control behavior upon external set-point request via IEC 61851-1. The battery model is an electro-thermal model with a dual polarization equivalent circuit electrical model coupled with a lumped thermal model with active liquid cooling. The aging trend of the EV’s 50 Ah large format pouch cell with NMC chemistry is evaluated via accelerated aging tests in the laboratory. Performance of the model is validated using laboratory pack tests, charging and driving field data. The RMSE of the cell voltage was between 18.49 mV and 67.17 mV per cell for the validation profiles. Cells stored at 100% SOC and 40 °C reached end-of-life (80% of initial capacity) after 431–589 days. The end-of-life for a cell cycled with 80% DOD around an SOC of 50% is reached after 3634 equivalent full cycles which equates to a driving distance of over 420,000 km. The full parameter set of the model is provided to serve as a resource for vehicle-to-grid strategy development.

The composition of the liquid electrolyte is a key factor in lifetime performance of lithium-ion batteries. The selection and quantification of additives to the electrolyte is an active field of research. This study focuses on finding the optimal additive combination of fluoroethylene carbonate (FEC) and vinylene carbonate (VC) for NMC622-Graphite cells. The central goal of this work is to accelerate the experimental search in a large search area by using a Bayesian-optimization algorithm to guide the search. Different measurements are used as target variable such as open-circuit voltage gradient and coulombic efficiency. Consequentially, the capability of these measurements for accelerated lifetime prediction compared to conventional ageing tests by cycling is investigated. The search gathered and confirmed additive combinations with excellent performance after four iterations with a total of 15 additive combinations analyzed. The results of this study give insights into the interaction of VC and FEC with regard to ageing.