Zinc ion batteries using metallic zinc as the negative electrode have gained considerable interest for electrochemical energy storage, whose development is crucial for the adoption of renewable energy technologies, as zinc has a very high volumetric capacity (5845 mA h cm−3), is inexpensive and compatible with aqueous electrolytes. However, the divalent charge of zinc ions, which restricts the choice of host material due to hindered solid-state diffusion, can also pose a problem for interfacial charge transfer. Here, we report our findings on reversible intercalation of up to two Zn2+ ions in layered V3O7·H2O. This material exhibits very high capacity and power (375 mA h g−1 at a 1C rate, and 275 mA h g−1 at an 8C rate) in an aqueous electrolyte compared to a very low capacity and slow rate capabilities in a nonaqueous medium. Operando XRD studies, together with impedance analysis, reveal solid solution behavior associated with Zn2+-ion diffusion within a water monolayer in the interlayer gap in both systems, but very sluggish interfacial charge transfer in the nonaqueous electrolyte. This points to desolvation at the interface as a major factor in dictating the kinetics. Temperature dependent impedance studies show high activation energies associated with the nonaqueous charge transfer process, identifying the origin of poor electrochemical performance.

Aqueous vs. nonaqueous Zn-ion batteries: consequences of the desolvation penalty at the interface

With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs.

A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems

https://www.cell.com/joule/pdf/S2542-4351(19)30585-9.pdf

Battery Lifetime Prognostics

The current electric grid is an inefficient system that wastes significant amounts of the electricity it produces because there is a disconnect between the amount of energy consumers require and the amount of energy produced from generation sources. Power plants typically produce more power than necessary to ensure adequate power quality. By taking advantage of energy storage within the grid, many of these inefficiencies can be removed. When using battery energy storage systems (BESS) for grid storage, advanced modeling is required to accurately monitor and control the storage system. A battery management system (BMS) controls how the storage system will be used and a BMS that utilizes advanced physics-based models will offer for much more robust operation of the storage system. The paper outlines the current state of the art for modeling in BMS and the advanced models required to fully utilize BMS for both lithium-ion batteries and vanadium redox-flow batteries. In addition, system architecture and how it can be useful in monitoring and control is discussed. A pathway for advancing BMS to better utilize BESS for grid-scale applications is outlined.

Battery Energy Storage System (BESS) and Battery Management System (BMS) for Grid-Scale Applications

State of charge (SOC) estimation is critical to the safe and reliable operation of Li-ion battery packs, which nowadays are becoming increasingly used in electric vehicles (EVs), Hybrid EVs, unmanned aerial vehicles, and smart grid systems. We introduce a new method to perform accurate SOC estimation for Li-ion batteries using a recurrent neural network (RNN) with long short-term memory (LSTM). We showcase the LSTM-RNN's ability to encode dependencies in time and accurately estimate SOC without using any battery models, filters, or inference systems like Kalman filters. In addition, this machine-learning technique, like all others, is capable of generalizing the abstractions it learns during training to other datasets taken under different conditions. Therefore, we exploit this feature by training an LSTM-RNN model over datasets recorded at various ambient temperatures, leading to a single network that can properly estimate SOC at different ambient temperature conditions. The LSTM-RNN achieves a low mean absolute error (MAE) of 0.573% at a fixed ambient temperature and an MAE of 1.606% on a dataset with ambient temperature increasing from 10 to 25 $^{\circ }$ C.

Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries

State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

The growing interest and recent breakthroughs in artificial intelligence and machine learning (ML) have actively contributed to an increase in research and development of new methods to estimate the states of electrified vehicle batteries. Data-driven approaches, such as ML, are becoming more popular for estimating the state of charge (SOC) and state of health (SOH) due to greater availability of battery data and improved computing power capabilities. This paper provides a survey of battery state estimation methods based on ML approaches such as feedforward neural networks (FNNs), recurrent neural networks (RNNs), support vector machines (SVM), radial basis functions (RBF), and Hamming networks. Comparisons between methods are shown in terms of data quality, inputs and outputs, test conditions, battery types, and stated accuracy to give readers a bigger picture view of the ML landscape for SOC and SOH estimation. Additionally, to provide insight into how to best approach with the comparison of different neural network structures, an FNN and long short-term memory (LSTM) RNN are trained fifty times each for 3000 epochs. The error is somewhat different for each training repetition due to the random initial values of the trainable parameters, demonstrating that it is important to train networks multiple times to achieve the best result. Furthermore, it is recommended that when performing a comparison among estimation techniques such as those presented in this review paper, the compared networks should have a similar number of learnable parameters and be trained and tested with identical data. Otherwise, it is difficult to make a general conclusion regarding the quality of a given estimation technique.

/pdf/machine-learning-applied-to-electrified-vehicle-battery-4n39y9roqx.pdf

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

Two of the main challenges for electric vehicle (EV) adoption include limited range and long recharge times. Ultrafast charging can help to mitigate both these concerns. However, for typical 400-V battery EVs (BEVs), the charging rate is limited by the practical cable size required to carry the charging current. To reach ultrahigh charge rates of 350 or 400 kW, 800-V BEVs are a promising alternative. However, the design of an 800-V EV requires careful new considerations for all electrical systems. This article reviews the current state of 800-V vehicle powertrain electrical design and performs an analysis of benefits, challenges, and future trends regarding multiple vehicle powertrain components. Specifically, detailed benefits and challenges related to the battery, propulsion motor, inverter, auxiliary power unit, and on- and off-board chargers are discussed.

800-V Electric Vehicle Powertrains: Review and Analysis of Benefits, Challenges, and Future Trends

The purpose of this paper is to review the recent literature regarding the effects of low temperatures on Lithium ion (Li-ion) batteries for electric vehicle, plug-in hybrid electric vehicle, and hybrid electric vehicle applications. Special consideration is given to nine major effects that directly or indirectly impact the battery use at low temperatures and it is presented that they are correlated to each other. The main discussions in this paper are capacity loss, power loss, life degradation, safety hazard, unbalanced capacity, charging difficulty, thermal management system complexity, battery model and state estimation method complexity, and incremental cost. The effects correlations and possible solutions are explained to provide a detailed, yet broader understanding of the Li-ion batteries low-temperature operating scenarios.

Phillip J. Kollmeyer

Papers

Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries

State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

800-V Electric Vehicle Powertrains: Review and Analysis of Benefits, Challenges, and Future Trends

xEV Li-Ion Battery Low-Temperature Effects—Review