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Objective To characterize the neurologic phenotypes associated with COL4A1/2 mutations and to seek genotype-phenotype correlation. Methods We analyzed clinical, EEG, and neuroimaging data of 44 new and 55 previously reported patients with COL4A1/COL4A2 mutations. Results Childhood-onset focal seizures, frequently complicated by status epilepticus and resistance to antiepileptic drugs, was the most common phenotype. EEG typically showed focal epileptiform discharges in the context of other abnormalities, including generalized sharp waves or slowing. In 46.4% of new patients with focal seizures, porencephalic cysts on brain MRI colocalized with the area of the focal epileptiform discharges. In patients with porencephalic cysts, brain MRI frequently also showed extensive white matter abnormalities, consistent with the finding of diffuse cerebral disturbance on EEG. Notably, we also identified a subgroup of patients with epilepsy as their main clinical feature, in which brain MRI showed nonspecific findings, in particular periventricular leukoencephalopathy and ventricular asymmetry. Analysis of 15 pedigrees suggested a worsening of the severity of clinical phenotype in succeeding generations, particularly when maternally inherited. Mutations associated with epilepsy were spread across COL4A1 and a clear genotype-phenotype correlation did not emerge. Conclusion COL4A1/COL4A2 mutations typically cause a severe neurologic condition and a broader spectrum of milder phenotypes, in which epilepsy is the predominant feature. Early identification of patients carrying COL4A1/COL4A2 mutations may have important clinical consequences, while for research efforts, omission from large-scale epilepsy sequencing studies of individuals with abnormalities on brain MRI may generate misleading estimates of the genetic contribution to the epilepsies overall.

Improving the efficiency and utilization of battery systems can increase the viability and cost-effectiveness of existing technologies for electric vehicles (EVs). Developing smarter battery management systems and advanced sensing technologies can circumvent problems arising due to capacity fade and safety concerns. This paper describes how efficient simulation techniques and improved algorithms can alleviate some of these problems to help electrify the transportation industry by improving the range of variables that are predictable and controllable in a battery in real-time within an electric vehicle. The use of battery models in a battery management system (BMS) is reviewed. The effect of different simulation techniques on computational cost and accuracy are also compared, and the validity of implementation in a microcontroller environment for model predictive control (MPC) is addressed. Using mathematical techniques to add more physics without losing efficiency is also discussed. Behavioral predictions can be made using mathematical models without the need to directly observe the states using expensive and time consuming physical experiments. Such predictions allow for more intelligent design of new systems, which is generally limited by the mathematical techniques used and the computational resources available. An improved modeling and simulation approach can achieve the following goals when applied to engineering systems: r More accurate predictions by using more meaningful models r Faster simulation with fewer computational resources r Optimization of design parameters r Better control, allowing aggressive performance while maintaining safetyHere we focus on the application of such principles to the use of physics-based battery models in battery management systems in electric vehicles.In recent years, battery electric vehicles (BEV) have increased in popularity to reduce the dependence on fossil fuels. Lithium-ion batteries are a popular choice as an energy storage medium for high demand applications due to their large energy density but are not utilized to their full capacity in BEV applications; operating a Li-ion battery too aggressively can lead to reduced cycle life and unpredictable thermal runaway reactions. These challenges reduce the functional capacity of the battery available for propulsion.The consumer expects the vehicle's performance and capabilities to remain uniform regardless of the state of charge or age of the battery, as they have become accustomed to internal combustion engines. When the battery is nearly depleted, it is difficult or impossible to satisfy high power demand, which is aggravated as the battery ages. To avoid these difficulties, the BMS shuts off the battery with a large amount of energy unused, so that Li-ion batteries for EVs are greatly overdesigned and carry extra weight and volume, reducing efficiency and increasing cost.1 Research is underway to better understand the internal limitations of Li-ion batteries including SEI layer growth, side * Electrochemical Society ...

The graphite anode in lithium-ion batteries is vulnerable to capacity fade due to several mechanisms. Advancement in understanding of such capacity fade mechanisms has paved the way for selecting design parameters that consider these effects. This paper shows the effect of porosity, thickness, and tortuosity of the anode on capacity fade mechanisms. Three main capacity fade mechanisms are analyzed in this paper: (1) solid electrolyte interface (SEI) side reaction, (2) lithium plating side reactions and (3) mechanical degradation due to intercalation induced stresses. Moreover, for a given thickness and porosity of anode, the effect of porosity variation on capacity fade mechanisms is also presented. Lithium-ion chemistries are attractive for many applications due to high cell voltage, high volumetric and gravimetric energy density (100 Wh/kg), high power density (300 W/kg), good temperature range, low memory effect, and relatively long battery life.1-3 Capacity fade, underutilization, and thermal runaway are the main issues that need to be addressed in order to use a lithium-ion battery efficiently and safely over a long life.Research on various fronts is underway to address the issues mentioned above. While finding better materials and improving their properties is one approach, the use of system level strategies to reach better efficiency in existing and emerging systems is another approach. The true potential of battery materials cannot be realized due to system level efficiencies, especially where transport effects become dominant (e.g. higher rates of charging/discharging at normal temperature or low temperatures operations).One of the many problems that can be addressed by continuum level modeling approaches is finding the optimum thicknesses and porosities of anode and cathode materials while keeping various processes and objectives in mind. These objectives may be discharge capacities at higher rates, charging time, mechanical degradation due to intercalation induced stresses, loss of active lithium due to parasitic side reaction (SEI layer and lithium plating), safety etc. While one would like to maximize energy density by packing the solid phase material compactly with larger thickness; rate capacity, safety and capacity fade may cause such an approach to be impractical.How should one choose the porosity and length of anode and cathode is an interesting research problem. Design optimization (porosity and thickness) for lithium-ion battery can be traced back to the work done by Prof. Newman using the reaction zone model 4 and with the pseudo two dimensional model. 5 Work on determining the optimal porosity distribution by considering the ohmic drop has been done by Ramadesigan et al. 6 Effect of low temperature and porosity on the performance of lithium-ion batteries is also studied by Ji et al. 7 While these works are based on maximizing the energy/power density of lithium-ion batteries by choosing optimal design parameters, no work has been done in quantifying the effect of design parameters on capaci...

Two of the main factors influencing the performance of Li-ion battery (LIB) electrodes are the kinetic losses due to the charge transfer resistance of the active material ( R ct ) and the ionic transport resistance in the electrolyte phase within the electrode pores ( R ion ). Seeking to increase the energy density of LIBs, ever higher active material loadings are applied, resulting in thicker electrodes for which R ion becomes dominant. As electrochemical impedance spectroscopy is commonly used to quantify R ct of electrodes, understanding the impact of R ion on the impedance response of thick electrodes is crucial. By use of a simplified transmission line model (TLM), we simulate the impedance response of electrodes as a function of electrode loading. This will be compared to the impedance of graphite anodes (obtained using a micro-reference electrode), demonstrating that their impedance response varies from purely kinetically limited at 0.6 mAh cm−2 to purely transport limited at 7.5 mAh cm−2. We then introduce a simple method with which R ct and R ion can be determined from the electrode impedance, even under transport limited conditions. Finally, we show how the initially homogenous ionic current distribution across porous electrodes under kinetically limited conditions becomes severely inhomogeneous under transport limited conditions.

This paper illustrates the application of dynamic optimization in obtaining the optimal current profile for charging a lithium-ion battery by restricting the intercalation-induced stresses to a pre-determined limit estimated using a pseudo 2-dimensional (P2D) model. This paper focuses on the problem of maximizing the charge stored in a given time while restricting capacity fade due to intercalation-induced stresses. Conventional charging profiles for lithium-ion batteries (e.g., constant current followed by constant voltage or CC-CV) are not derived by considering capacity fade mechanisms, which are not only inefficient in terms of life-time usage of the batteries but are also slower by not taking into account the changing dynamics of the system. Lithium-ion chemistries are attractive for many applications due to high cell voltage, high volumetric and gravimetric energy density (100 Wh/kg), high power density (300 W/kg), good temperature range, low memory effect, and relatively long battery life.1-3 Capacity fade, underutilization, and thermal runaway are the main issues that need to be addressed in order to use a lithium-ion battery efficiently and safely over a long life.In order to address the aforementioned issues and increase battery utilization, smarter battery management systems are required which can exploit the dynamics of a battery to derive better operational strategies. Recognizing the potential of reducing the weight and volume of these batteries by 20-25% for vehicular applications, the Department of Energy has recently initiated a $30M program through ARPA-E named Advanced Management and Protection of Energy Storage Devices (AMPED). 4 The use of physically meaningful models in deriving these strategies has received attention. Methekar et al. 5 looked at the problem of energy maximization for a set time with constraints on voltage using Control Vector Parametrization (CVP). Klein et al. 6 considered the minimum-time charging problem while including constraints on temperature rise and side reactions. Rahimian et al. 7 calculated the optimal charging current as a function of cycle number for a lithium-ion battery experiencing capacity fade using a single-particle model (SPM). Hoke et al.9 used a lithium-ion battery life-time model to reduce battery degradation in a variable electricity cost environment using the SPM. Previous efforts included the derivation of optimal charging profiles considering various phenomena that account for capacity fade separately (plating over-potential at the anode, 13 side reaction during charging, 6 thermal degradation, 10 intercalation-induced stress using SPM 11 etc.). Fracture of solid electrode particles due to intercalation induced stresses is one of the dominant capacity fade mechanics which affect the battery capacity in two ways: 12 (1) It leads to loss of solid phase due to isolation from the electronically conducting matrix of electrode. (2) It also increases the surface area, which lead to SEI * Electrochemical Society Student Member.* * Electrochemical Soci...

The cost and safety related issues of lithium-ion batteries require intelligent charging profiles that can efficiently utilize the battery. This paper illustrates the application of dynamic optimization in obtaining the optimal current profile for charging a lithium-ion battery using a single-particle model while incorporating intercalation-induced stress generation. In this paper, we focus on the problem of maximizing the charge stored in a given time while restricting the development of stresses inside the particle. Conventional charging profiles for lithium-ion batteries (e.g., constant current followed by constant voltage) were not derived by considering capacity fade mechanisms. These charging profiles are not only inefficient in terms of lifetime usage of the batteries but are also slower since they do not exploit the changing dynamics of the system. Dynamic optimization based approaches have been used to derive optimal charging and discharging profiles with different objective functions. The progress made in understanding the capacity fade mechanisms has paved the way for inclusion of that knowledge in deriving optimal controls. While past efforts included thermal constraints, this paper for the first time presents strategies for optimally charging batteries by guaranteeing minimal mechanical damage to the electrode particles during intercalation. In addition, an executable form of the code has been developed and provided. This code can be used to identify optimal charging profiles for any material and design parameters.

The tortuosity of a porous electrode is one of the critical parameters that governs the effective transport properties such as effective conductivity and effective diffusivity of ions in the electrolyte phase. While there exist several methods to determine the through-plane tortuosity for lithium-ion battery electrodes, methods to determine in-plane tortuosity are scarcely explored. In this paper, we present a method that restricts ion transport to only the in-plane direction, which facilitates the determination of transport properties in the in-plane direction. The here proposed cell design is analyzed using a blocking condition transmission line model to measure the in-plane tortuosity, followed by validation experiments to establish its validity and robustness. The measurement of the in-plane tortuosity may act as an indispensable tool for the design, characterization, and modeling of 3-dimensional battery architectures (e.g., laser structured electrodes) and help to estimate the distribution of various properties in the through-plane direction such as porosity, tortuosity, binder content, etc.