Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Battery energy storage systems have gained increasing interest for serving grid support in various application tasks. In particular, systems based on lithium-ion batteries have evolved rapidly with a wide range of cell technologies and system architectures available on the market. On the application side, different tasks for storage deployment demand distinct properties of the storage system. This review aims to serve as a guideline for best choice of battery technology, system design and operation for lithium-ion based storage systems to match a specific system application. Starting with an overview to lithium-ion battery technologies and their characteristics with respect to performance and aging, the storage system design is analyzed in detail based on an evaluation of real-world projects. Typical storage system applications are grouped and classified with respect to the challenges posed to the battery system. Publicly available modeling tools for technical and economic analysis are presented. A brief analysis of optimization approaches aims to point out challenges and potential solution techniques for system sizing, positioning and dispatch operation. For all areas reviewed herein, expected improvements and possible future developments are highlighted. In order to extract the full potential of stationary battery storage systems and to enable increased profitability of systems, future research should aim to a holistic system level approach combining not only performance tuning on a battery cell level and careful analysis of the application requirements, but also consider a proper selection of storage sub-components as well as an optimized system operation strategy.

https://www.mdpi.com/1996-1073/10/12/2107/pdf

Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces.

/pdf/closed-loop-optimization-of-fast-charging-protocols-for-3ao21j48es.pdf

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter

We present a wide range of testing results on an excellent moderate-energy-density lithium-ion pouch cell chemistry to serve as benchmarks for academics and companies developing advanced lithium-ion and other "beyond lithium-ion" cell chemistries to (hopefully) exceed. These results are far superior to those that have been used by researchers modelling cell failure mechanisms and as such, these results are more representative of modern Li-ion cells and should be adopted by modellers. Up to three years of testing has been completed for some of the tests. Tests include long-term charge-discharge cycling at 20, 40 and 55°C, long-term storage at 20, 40 and 55°C, and high precision coulometry at 40°C. Several different electrolytes are considered in this LiNi0.5Mn0.3Co0.2O2/graphite chemistry, including those that can promote fast charging. The reasons for cell performance degradation and impedance growth are examined using several methods. We conclude that cells of this type should be able to power an electric vehicle for over 1.6 million kilometers (1 million miles) and last at least two decades in grid energy storage. The authors acknowledge that other cell format-dependent loss, if any, (e.g. cylindrical vs. pouch) may not be captured in these experiments.

https://iopscience.iop.org/article/10.1149/2.0981913jes/pdf

A Wide Range of Testing Results on an Excellent Lithium-Ion Cell Chemistry to be used as Benchmarks for New Battery Technologies

https://robots.iopscience.iop.org/article/10.1149/2.0411609jes/pdf

Calendar Aging of Lithium-Ion Batteries I. Impact of the Graphite Anode on Capacity Fade

Residential photovoltaic (PV) battery systems increase households’ electricity self-consumption using rooftop PV systems and thus reduce the electricity bill. High investment costs of battery systems, however, prevent positive financial returns for most present residential battery installations in Germany. Tesla Motors, Inc. (Palo Alto, CA, USA) announced a novel battery system—the Powerwall—for only about 25% of the current German average market price. According to Tesla’s CEO Elon Musk, Germany is one of the key markets for their product. He has, however, not given numbers to support his statement. In this paper, we analyze the economic benefit of the Powerwall for end-users with respect to various influencing parameters: electricity price, aging characteristics of the batteries, topology of battery system coupling, subsidy schemes, and retrofitting of existing PV systems. Simulations show that three key-factors strongly influence economics: the price gap between electricity price and remuneration rate, the battery system’s investment cost, and the usable battery capacity. We reveal under which conditions a positive return on invest can be achieved and outline that the Powerwall could be a worthwhile investment in multiple, but not all, scenarios investigated. Resulting trends are generally transferrable to other home storage products.

https://www.mdpi.com/2313-0105/2/2/14/pdf

Ralph Ch. Karl

Papers

Calendar Aging of Lithium-Ion Batteries I. Impact of the Graphite Anode on Capacity Fade

Economics of Residential Photovoltaic Battery Systems in Germany: The Case of Tesla’s Powerwall

Lithium-ion Battery Cost Analysis in PV-household Application

Betriebsabhängige Kostenberechnung von Energiespeichern

Battery lifetime prediction for different technologies based on stress characterization and aging experiments