The expansion of lithium-ion batteries from consumer electronics to larger-scale transport and energy storage applications has made understanding the many mechanisms responsible for battery degradation increasingly important. The literature in...

Fast charging of batteries is currently limited, particularly at low temperatures, due to difficulties in understanding lithium plating. Accurate, online quantification of lithium plating increases safety, enables charging at speeds closer to the electrochemical limit and accelerates charge profile development. This work uses different cell cooling strategies to expose how voltage plateaus arising from cell self-heating and concentration gradients during fast charging can falsely indicate plating, contrary to prevalent current assumptions. A solution is provided using Differential Voltage (DV) analysis, which confirms that lithium stripping is observable. However, scanning electron microscopy and energy-dispersive X-ray analysis are used to demonstrate the inability of the plateau technique to detect plating under certain conditions. The work highlights error in conventional plating quantification that leads to the dangerous underestimation of plated amounts. A novel method of using voltage plateau end-point gradients is proposed to extend the sensitivity of the technique, enabling measurement of lower levels of lithium stripping and plating. The results are especially relevant to automotive OEMs and engineers wishing to expand their online and offline tools for fast charging algorithm development, charge management and state-of-health diagnostics.

Lithium-ion battery development is conventionally driven by energy and power density targets, yet the performance of a lithium-ion battery pack is often restricted by its heat rejection capabilities. It is therefore common to observe elevated cell temperatures and large internal thermal gradients which, given that impedance is a function of temperature, induce large current inhomogeneities and accelerate cell-level degradation. Battery thermal performance must be better quantified to resolve this limitation, but anisotropic thermal conductivity and uneven internal heat generation rates render conventional heat rejection measures, such as the Biot number, unsuitable. The Cell Cooling Coefficient (CCC) is introduced as a new metric which quantifies the rate of heat rejection. The CCC (units W.K −1) is constant for a given cell and thermal management method and is therefore ideal for comparing the thermal performance of different cell designs and form factors. By enhancing knowledge of pack-wide heat rejection, uptake of the CCC will also reduce the risk of thermal runaway. The CCC is presented as an essential tool to inform the cell down-selection process in the initial design phases, based solely on their thermal bottlenecks. This simple methodology has the potential to revolutionise the lithium-ion battery industry.

There is no universal and quantifiable standard to compare a given cell model’s capability to reject heat. The consequence of this is suboptimal cell designs because cell manufacturers do not have a metric to optimise. The Cell Cooling Coefficient for pouch cell tab cooling (CCC tabs ) defines a cell’s capability to reject heat from its tabs. However, surface cooling remains the thermal management approach of choice for automotive and other high-power applications. This study introduces a surface Cell Cooling Coefficient, CCC surf which is shown to be a fundamental property of a lithium-ion cell. CCC surf is found to be considerably larger than CCC tabs , and this is a trend anticipated for every pouch cell currently commercially available. However, surface cooling induces layer-to-layer nonuniformity which is strongly linked to reduced cell performance and reduced cell lifetime. Thus, the Cell Cooling Coefficient enables quantitative comparison of each cooling method. Further, a method is presented for using the Cell Cooling Coefficients to inform the optimal design of a battery pack thermal management system. In this manner, implementation of the Cell Cooling Coefficient can transform the industry, by minimising the requirement for computationally expensive modelling or time consuming experiments in the early stages of battery-pack design.

Lithium-ion batteries get hot, and it is hard to keep them cool. Industry has paid too little attention to this problem for the past decade. The focus has been elsewhere: on cutting costs and on boosting the amount of energy a single cell in a battery can store (energy density). This strategy has, for example, increased the longevity and capabilities of mobile phones. Future applications, such as electric vehicles and smart grids, need thousands of cells in a battery pack. These are prone to overheating.Manufacturers of large, high-energy battery packs must design complicated systems to manage heat. The battery pack in electric-vehicle maker Tesla's Model 3 car, for example, holds more energy than 6,000 iPhone 11 handsets. Coolant fluid is pumped through a network of channels to carry heat away from the individual cells. But these cumbersome additions make the battery pack heavy and drain its energy 1 . Developers are wasting time and money on these inefficient designs. Heat-removal strategies must be improved to make battery packs both light and powerful.Why this lack of attention? One reason is that there is no standard way of judging the thermal performance of battery packs. Manufacturers of single cells compete by chasing ever greater energy density. Their product-specification sheets do not cover how easy it is to remove heat from a cell. Designers of battery packs A new measure for the rate of heat removal from battery packs gives manufacturers a simple way to compare products.

The main barrier to fast charging of Li-ion batteries at low temperatures is the risk of short-circuiting due to lithium plating. In-situ detection of Li plating is highly sought after in order to develop fast charging strategies that avoid plating. It is widely believed that Li plating after a single fast charge can be detected and quantified by using a minimum in the differential voltage (DV) signal during the subsequent discharge, which indicates how much lithium has been stripped. In this work, a pseudo-2D physics-based model is used to investigate the effect on Li plating and stripping of concentration-dependent diffusion coefficients in the active electrode materials. A new modelling protocol is also proposed, in order to distinguish the effects of fast charging, slow charging and Li plating/stripping. The model predicts that the DV minimum associated with Li stripping is in fact a shifted and more abrupt version of a minimum caused by the stage II-stage III transition in the graphite negative electrode. Therefore, the minimum cannot be used to quantify stripping. Using concentration-dependent diffusion coefficients yields qualitatively different results to previous work. This knowledge casts doubt on the utility of DV analysis for detecting Li plating.

Lithium-ion battery research has historically been driven by power and energy density targets. However, the performance of a lithium-ion cell is strongly influenced by its heat generation and rejection capabilities which have received less attention. The development of adequate thermal metrics able to capture the anisotropic thermal conductivity and uneven internal heat generation rates characteristic of lithium-ion cells is therefore paramount. The Cell Cooling Coefficient (CCC), in W.K-1, has been introduced as a suitable metric to quantify the rate of heat rejection of a given cell and thermal management method. However, there is no standardised methodology defining how to measure the heat generation capabilities of a cell. In this study, we applied the CCC empirical methodology to evaluate the rates of irreversible heat generation at various operation conditions, providing maps which give a complete insight into cell thermal performance. The maps derived show how the most important operational variables (frequency, C-rate, SOC and temperature) influence the cell thermal performance. These maps can be used along with the CCC by pack engineers to optimise the design of thermal management systems and to down select cells according to their thermal performance.

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