A review of concepts and contributions in lithium metal anode development

Using lithium (Li) directly as metal anode for a higher energy density battery is one of the most attractive battery researches in the past decade. To address its intrinsic issues including uncontrolled growth of Li dendrites and unstable solid electrolyte interphase (SEI), which are believed as the main origins of safety issues and short lifetime, proposing the groundbreaking concepts and contributing valuable improvements in the development of Li metal anodes (LMAs) have always been the mandate of all battery scientists. This review presents a historical framework of various concepts and contributions in enabling LMAs to be applied practically. We begin with an overview of these important concepts and breakthroughs in different aspects to advance LMAs. Moreover, assisted by the big data sources from Web of Science, the major contributions from institutions, journals, corresponding authors, and highly cited papers are discussed and summarized. Finally, future trends and challenges are concluded for designing an ideal LMA. We hope that such as a comprehensive evolutionary story of LMAs can motivate more researchers to pave the way for high-energy lithium metal batteries (LMBs) in the future.     

A new cost-minimizing power-allocating strategy for the hybrid electric bus with fuel cell/battery health-aware control

The two primary challenges preventing the commercialization of fuel cell hybrid electric vehicles (FCHEV) are their high cost and limited lifespan. Improper use usage can could also hasten the breakdown of proton exchange membrane fuel cell (PEMFC). This paper proposes a new cost-minimizing power-allocating technique with fuel cell/battery health-aware control to optimize the economic potential of fuel cell/battery hybrid buses. The proposed framework quantifies the fuel cell (FC) deterioration of the whole working zone in a real hybrid electric bus using a long short-term memory network (LSTM), which reduces the time required to get the key lifetime parameters. A new FC lifespan model is embedded into the control framework, together with a battery aging model, to balance hydrogen consumption and energy source durability. In addition, in the speed prediction step, an enhanced online Markov prediction approach with stochastic disturbances is presented to increase the forecast accuracy for future disturbances. Finally, comparative analysis is used to verify the efficacy of the suggested approach, which shows that when compared to the benchmark method, the proposed strategy may extend the FC lifetime and lower operating costs by 5.04% and 3.76%, respectively.     

Gridable vehicles and second life batteries for generation side asset management in the Smart Grid

As numerous generation side assets, such as the generating units and the plant equipments, in the Smart Grid system have already aged, well planned maintenance and operational scheduling is needed to maintain their expected longevity. Forced outages and short-notice/temporary maintenance of thermal units should also be considered as likely events. Backup energy sources are required to replace the generating units during the maintenance period. Using conventional storage devices for this purpose is feasible, but costly. Electric vehicles and plug-in hybrid electric vehicles, with vehicle-to-grid capability, referred to as “gridable vehicles”, are a useful choice as storage devices for the same. Second life batteries, disassembled from gridable vehicles after passing their automotive life, are another candidate for this purpose from the economic perspective. A system model that aggregates gridable vehicles and second life batteries together in an intelligent way to provide such backup energy can reduce significant storage costs. Such a system model is presented in this paper for a Smart Grid environment. Our simulation results suggest that using gridable vehicles and second life batteries together can save up to 70% of conventional storage energy costs and recover capital costs for the second life batteries in only 1.5 years.     

Review of gas diffusion layer for proton exchange membrane-based technologies with a focus on unitised regenerative fuel cells

Proton exchange membrane (PEM) based technologies (fuel cells and electrolysers) offer promising sustainable power generation and storage solutions for a diverse range of stationary and mobile applications. Unitised regenerative fuel cell (URFC) is an electrochemical cell that can operate both as a fuel cell (FC) and an electrolyser (E). However, for a widespread commercialisation, further improvements are required that address the durability, performance, and cost limitations. One of the main challenging components in developing URFCs is the gas diffusion layer (GDL) as it plays different vital roles, some of which are paradoxical in FC and E-modes. Therefore, in this paper, the published research on GDL of PEM-URFCs as well as relevant studies on PEM fuel cells and electrolysers are critically reviewed. The materials and novel methods to address the corrosion in E-mode are discussed. This is followed by presenting and discussing different properties of GDLs affecting the performance in FC and E-modes: i.e. porosity, thickness, pore size, transport properties, thermal and electrical conductivity, and the GDL compressibility. Finally, the main modifications of the GDLs, such as hydrophobisation and microporous layer application, to improve the performance of a URFC are analysed and discussed.     

The large discretization step method for time-dependent partial differential equations

A new method for the acceleration of linear and nonlinear time-dependent calculations is presented. It is based on the large discretization step (LDS, in short) approximation, defined in this work, which employs an extended system of low accuracy schemes to approximate a high accuracy discrete approximation to a time-dependent differential operator.These approximations are efficiently implemented in the LDS methods for linear and nonlinear hyperbolic equations, presented here. In these algorithms the high and low accuracy schemes are interpreted as the same discretization of a time-dependent operator on fine and coarse grids, respectively. Thus, a system of correction terms and corresponding equations are derived and solved on the coarse grid to yield the fine grid accuracy. These terms are initialized by visiting the fine grid once in many coarse grid time steps. The resulting methods are very general, simple to implement and may be used to accelerate many existing time marching schemes.The efficiency of the LDS algorithms is defined as the cost of computing the fine grid solution relative to the cost of obtaining the same accuracy with the LDS methods. The LDS method’s typical efficiency is 16 for two-dimensional problems and 28 for three-dimensional problems for both linear and nonlinear equations. For a particularly good discretization of a linear equation, an efficiency of 25 in two-dimensional and 66 in three-dimensional was obtained.     

Variable structure battery-based fuel cell hybrid power system and its incremental fuzzy logic energy management strategy

A hybrid power system consists of a fuel cell and an energy storage device like a battery and/or a supercapacitor possessing high energy and power density that beneficially drives electric vehicle motor. The structures of the fuel cell-based power system are complicated and costly, and in energy management strategies (EMSs), the fuel cell's characteristics are usually neglected. In this study, a variable structure battery (VSB) scheme is proposed to enhance the hybrid power system, and an incremental fuzzy logic method is developed by considering the efficiency and power change rate of fuel cell to balance the power system load. The principle of VSB is firstly introduced and validated by discharge and charge experiments. Subsequently, parameters matching of the fuel cell hybrid power system according to the proposed VSB are designed and modeled. To protect the fuel cell as well as ensure the efficiency, a fuzzy logic EMS is formulated via setting the fuel cell operating in a high efficiency and generating an incremental power output within the affordable power slope. The comparison between a traditional deterministic rules-based EMS and the designed fuzzy logic was implemented by numerical simulation in three different operation conditions: NEDC, UDDS, and user-defined driving cycle. The results indicated that the incremental fuzzy logic EMS smoothed the fuel cell power and kept the high efficiency. The proposed VSB and incremental fuzzy logic EMS may have a potential application in fuel cell vehicles.     

四轮轮毂电机驱动智能电动汽车转向失效容错控制研究

四轮轮毂电机驱动电动汽车各轮驱动力矩独立可控,可通过控制前轴左右两轮的力矩差实现前轮转向。以四轮轮毂电机驱动智能电动汽车为研究对象,针对线控转向系统执行机构失效时的轨迹跟踪和横摆稳定性协同控制问题,提出一种基于差动转向与直接横摆力矩协同的容错控制方法。该方法采用分层控制架构,上层控制器首先基于时变线性模型预测控制方法求解期望前轮转角和附加横摆力矩,然后考虑转向执行机构建模不确定性以及路面干扰,设计基于滑模变结构控制的前轮转角跟踪控制策略。下层控制器以轮胎负荷率最小化为目标,利用有效集法实现四轮转矩优化分配。最后,分别在高速换道和双移线工况下仿真验证了该控制方法的有效性和实时性。     

On-line measurement of the size distribution of particles in a gas–solid two-phase flow through acoustic sensing and advanced signal analysis

Acoustic emission (AE) technology is a promising approach to non-intrusively measure the size distribution of particles in a pneumatic suspension. This paper presents an experimental study of the AE sensing technology coupled with signal processing algorithms for on-line particle sizing. The frequency characteristics of the AE signals under different experimental conditions are studied and compared. Initially, the characteristics of the background noise and AE signals are compared in the frequency domain for different air velocities and particle feeding rates. Through short-term energy analysis the working features of the suction unit and the vibration feeder are revealed. To find the effective characteristic frequency band of the AE signals, a multiple scanning and accumulation method assisted with a Savitzky–Golay smoothing filter is used to denoise the power spectra of the signals. Wavelet analysis is also deployed to denoise the signals. The denoising performance of different wavelet parameters (wavelet function, decomposition level and thresholding) is compared in terms of signal-to-noise ratio and signal smoothness. Finally, particle size is predicted through a neural network with energy fraction extracted through wavelet analysis. Experimental results demonstrate that the relative error of the particle sizing system is no greater than 23%.     

Predictive energy management of hybrid long-haul trucks

This paper presents a novel predictive control scheme for energy management in hybrid trucks that drive autonomously on the highway. The proposed scheme uses information from GPS together with information about the speed limits along the planned route to schedule the charging and discharging of the battery, the vehicle speed, the gear, and when to turn off the engine and drive electrically. The proposed control scheme divides the predictive control problem into three layers that operate with different update frequencies and prediction horizons. The top layer plans the kinetic and electric energy in a convex optimization problem. In order to avoid a mixed-integer problem, the gear and the switching decision between hybrid and pure electric mode are optimized in a lower layer in a dynamic program whereas the lowest control layer only reacts on the current state and available references. The benefits of the proposed predictive control scheme are shown by simulations between Frankfurt and Koblenz. The simulations show that the predictive control scheme is able to significantly reduce the mechanical braking, resulting in fuel reductions of 4% when allowing an over and under speed of 5 km/h.     

Energy dispatch schedule optimization and cost benefit analysis for grid-connected,photovoltaic-battery storage systems

A linear programming (LP) routine was implemented to model optimal energy storage dispatch schedules for peak net load management and demand charge minimization in a grid-connected, combined photovoltaic-battery storage system (PV+ system). The LP leverages PV power output and load forecasts to minimize peak loads subject to elementary dynamical and electrical constraints of the PV+ system. Battery charge/discharge were simulated over a range of two PV+ system parameters (battery storage capacity and peak load reduction target) to obtain energy cost for a time-of-use pricing schedule and the net present value (NPV) of the battery storage system. The financial benefits of our optimized energy dispatch schedule were compared with basic off-peak charging/on-peak discharging and real-time load response dispatch strategies that did not use any forecast information. The NPV of the battery array increased significantly when the battery was operated on the optimized schedule compared to the off-peak/on-peak and real time dispatch schedules. These trends were attributed to increased battery lifetime and reduced demand charges attained under the optimized dispatch strategy. Our results show that Lithium-ion batteries can be a financially viable energy storage solution in demand side, energy cost management applications at an installed cost of about $400–$500 per kW h (approximately 40–50% of 2011 market prices). The financial value of forecasting in energy storage dispatch optimization was calculated as a function of battery capacity ratio.