Demand for portable electronic and electrical devices has led to considerable growth in production of lithium-ion battery cells and the number of manufacturers thereof. However, due to lack of supplied information or independent verification, it is frequently difficult to compare cells based on available data. In this study, we conduct a comparative testing study on five types of 18650-format lithium-ion cells from three different commercial manufacturers, ranging from budget to high-performance cells. Key insights gathered in the comparison were that the tested budget cells frequently offer less than 20% of their rated capacity, that the budget cells degrade at a significantly higher proportional rate than other cells, and that certain high-performance cells exceed the size dimensions of the 18650-format by over 3%. Electrochemical impedance spectroscopy testing showed the budget cells to have internal impedances several times higher than other cells, leading to notably increased heat generation and a significantly reduced cell efficiency. Differential capacity analysis found this high internal resistance to notably impede lithium intercalation processes. The presented methodology is intended as a base framework for conducting subsequent comparative testing studies for Li-ion cells. A strong demand for portable electronic and electrical devices in recent years has led to a corresponding demand for high-performance batteries. Due to the large amount of energy that can be stored per unit of weight and volume, lithium-ion (Li-ion) batteries have become the battery of choice for many small devices such as mobile phones, laptop computers and power tools. Increasingly these cells are also being used for much larger applications including electric bicycles, electric cars and stationary energy storage in domestic or commercial settings.Li-ion battery cells first became commercially available in 1991. Today, there are many different cell manufacturers that offer a wide variety of battery cells to both corporate and personal customers. Unfortunately however, reliable information on the physical dimensions and electrical characteristics can be difficult to obtain for many cells. High-end suppliers commonly provide data sheets that contain technical product information including factors such as size, weight, capacity, voltage profile and cycle life, as well as some limited measurement data to support these numbers. Low-cost products are frequently supplied only with a nominal capacity rating, without any measurement data. Where available, the ratings are typically based on in-house testing without independent verification. As private or small commercial consumers rarely have both the specific equipment and know-how required to accurately test cells, there is a risk that manufacturers may inflate ratings in order to make their products appear more competitive. This adds further complexity to the already difficult task of choosing cells for commercial or personal applications. As a result, there is scope for academic researc...

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Abstract-The expected rise of electric vehicles will lead to significant additional demand on low voltage (LV) distribution systems. Uncontrolled charging could lead to problems such as thermal overload of transformers and lines, voltage deviation, harmonics, and phase unbalance. We propose two electric vehicle charging algorithms, one centralized and one distributed, and compare their performance in simulations that use real vehicle data, on a model based on a real LV network in northern Melbourne, Australia. Our experiments confirm that the locations of the vehicles in the network are an important factor in predicting adverse effects. Furthermore, our coordinated charging solutions allow penetrations of electric vehicles approximately 3-6 times higher than is possible using uncoordinated charging, in our network.

Affordability of battery energy storage critically depends on low capital cost and high lifespan. Estimating battery lifespan, and optimising battery management to increase it, is difficult given the associated complex, multi-factor ageing process. In this paper we present a battery life prediction methodology tailored towards operational optimisation of battery management. The methodology is able to consider a multitude of dynamically changing cycling parameters. For lithium-ion (Li-ion) cells, the methodology has been tailored to consider five operational factors: charging and discharging currents, minimum and maximum cycling limits, and operating temperature. These are captured within four independent models, which are tuned using experimental battery data. Incorporation of dynamically changing factors is done using rainflow counting and discretisation. The resulting methodology is designed for solving optimal battery operation problems.Implementation of the methodology is presented for two case studies: a smartphone battery, and a household with battery storage alongside solar generation. For a smartphone that charges daily, our analysis finds that the battery life can be more than doubled if the maximum charging limit is chosen strategically. And for the battery supporting domestic solar, it is found that the impact of large daily cycling outweighs that of small more frequent cycles. This suggests that stationary Li-ion batteries may be well suited to provide ancillary services as a secondary function.The developed methodology and demonstrated use cases represent a key step towards maximising the cost-benefit of Li-ion batteries for any given application.

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