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Measurements such as current and terminal voltage that are typically used to determine the battery’s state of charge (SOC) are augmented with measured force associated with electrode expansion as the lithium intercalates in its structure. The combination of the sensed behavior is shown to improve SOC estimation even for the lithium ion iron phosphate (LFP) chemistry, where the voltage–SOC relation is flat (low slope) making SOC estimation using measured voltage difficult. For the LFP cells, the measured force has a non-monotonic F–SOC relationship. This presents a challenge for estimation as multiple force values can correspond to the same SOC. The traditional linear quadratic estimator can be driven to an incorrect SOC value. To address these difficulties, a novel switching estimation gain is used based on determining the operating region that corresponds to the actual SOC. Moreover, a drift in the measured force associated with a shift of the cell SOC–expansion behavior over time is addressed with a bias estimator for the force signal. The performance of Voltage-based (V) and Voltage and Force-based (V&F) SOC estimation algorithms are then compared and evaluated against a desired ± 5 % absolute error bound of the SOC using a dynamic stress test current protocol that tests the proposed estimation scheme across wide range of SOC and current rates.

Conductive adhesives are found favorable in a wide range of applications including a lead-free solder in micro-chips, flexible and printable electronics and enhancing the performance of energy storage devices. Composite materials comprised of metallic fillers and a polymer matrix are of great interest to be implemented as hybrid conductive adhesives. Here we investigated a cost-effective conductive adhesive material consisting of silver-coated copper as micro-fillers using synchrotron-based three-dimensional (3D) X-ray nano-tomography. The key factors affecting the quality and performance of the material were quantitatively studied in 3D on the nanometer scale for the first time. A critical characteristic parameter, defined as a shape-factor, was determined to yield a high-quality silver coating, leading to satisfactory performance. A 'stack-and-screen' mechanism was proposed to elaborate such a phenomenon. The findings and the technique developed in this work will facilitate the future advancement of conductive adhesives to have a great impact in micro-electronics and other applications.

This article focuses on state‐of‐charge (SOC) estimation in a lithium‐ion battery, using measurements of terminal voltage and bulk force. A nonlinear observer designed using Lyapunov analysis relying on lower and upper bounds of the Jacobian of the nonlinear output function is utilized. Rigorous analysis shows that the proposed observer has feasible design solutions only in each piecewise monotonic region of the output functions and has no constant stabilizing observer gain when the entire SOC range is considered. The nonmonotonicity challenge is then addressed by designing a hybrid nonlinear observer that switches between several constant observer gains. The global stability of the switched system is guaranteed by ensuring overlap between regions and an adequate dwell time between switches. The performance of the observer is evaluated first through simulations using a high‐fidelity battery model and then through experiments. The performance of the nonlinear observer is compared with that of an extended Kalman Filter. Simulation results show that with no model uncertainty the nonlinear observer provides estimates with an RMS error of 1.1%, while the EKF performs better, providing an RMS error less than 1%. However, when model error is introduced into this nonmonotonic system, the EKF becomes unstable for even very small model errors in the output curves. The nonlinear observer, on the other hand, continues to perform very well, providing accurate estimates and never becoming unstable. The experimental results verify the observations from the simulation and the experimental EKF is found to become unstable due to model errors, while the hybrid nonlinear observer continues to work reliably.

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<div class="section abstract"><div class="htmlview paragraph">Drive cycles are a core piece of vehicle development testing methodology. The control and calibration of the vehicle is often tuned over drive cycles as they are the best representation of the real-world driving the vehicle will see during deployment. To obtain general performance numerous drive cycles must be generated to ensure final control and calibration avoids overfitting to the specifics of a single drive cycle. When real-world driving cycles are difficult to acquire methods can be used to create statistically similar synthetic drive cycles to avoid the overfitting problem. This subject has been well addressed within the passenger vehicle domain but must be expanded upon for utilization with tracked off-road vehicles. Development of hybrid tracked vehicles has increased this need further. This study shows that turning dynamics have significant influence on the vehicle power demand and on the power demand on each individual track. Hybrid tracked vehicle development must consider both power demands as they are a key factor when deciding location and sizing of electrified powertrain components. This study identifies four key parameters that must be included in drive cycle development for tracked vehicles and proposes a Markov chain model framework to generate synthetic drive cycles from limited reference data.</div></div>

<div class="section abstract"><div class="htmlview paragraph">As the U.S. Army moves to electrify portions of its vehicle fleet, it is worth considering the heavier combat vehicles. However, the high power demand of these vehicles coupled with the relatively low energy density of modern batteries result in electric vehicles with limited range and functionality. Hydrogen-based fuel cells are an alternative to batteries that can provide many of the same environmental and logistical benefits associated with electrification. This study models the energy consumption for two variants of the M2A4 Bradley Fighting Vehicle (BFV). The first variant is powered by a hydrogen-based Proton Exchange Membrane Fuel Cell; the second variant is powered through lithium-ion batteries. These models account for vehicle weight, accelerative forces, drag, road grade, tractive losses, and ancillary equipment and are compared against a conventional M2A4 BFV. The analysis also considers the weight and volume restrictions for the powertrain especially as they relate to the storage of hydrogen and batteries. In doing so, the range of the vehicle with each powertrain can be determined. Furthermore, the study looks at the logistical needs associated with such vehicles. In particular, it approximates the quantity of fuel, water, and solar panels required to produce enough electricity to recharge batteries or electrolyze water for hydrogen production. The analysis then evaluates the trade-offs between vehicle range and logistical footprint associated with the different powertrains. The study then concludes with a discussion on the technical challenges associated with each powertrain.</div></div>