Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels. 

Towards Long Lifetime Battery: AI-Based Manufacturing and Management

Battery electric vehicles (BEVs) are advocated due to their environmental benign characteristic. However, the long charging time and the degradation caused by fast charging impede their further popularization. Extensive research has been carried out to optimize the charging process, such as minimizing charging time and aging, of lithium-ion batteries (LIBs). Motivated by this, a comprehensive review of existing charging optimization (ChgOp) techniques is provided in this article. First, the operation and models for LIBs are explained. Then, unexpected side effects, especially for the aging mechanism (AM) of LIB associated with unregulated fast charging, are scrutinized. This provides a solid theoretical foundation and forms the optimization problem. Following this endeavor, the general framework with critical concerns for ChgOp system design is overviewed. Within this horizon, the state-of-the-art ChgOp techniques, clustered into open- and close-loop categories, are reviewed systematically with their respective merits and shortcomings discussed. Finally, the development of an emerging charging control protocol with both real-time affordability and degradation consciousness is further discussed as an open outlook.

Charging Optimization for Li-Ion Battery in Electric Vehicles: A Review

Summary Electrochemical plating of Li metal on the graphite electrode is the key limitation behind slow charging times of Li-ion batteries (LIBs) in electric vehicles (EVs). Currently, electrochemical methods to detect the onset of Li plating while a battery is fast charging are sparse. In this study, we use operando electrochemical impedance spectroscopy to reliably detect the onset of Li plating on graphite electrodes in three-electrode LIBs. An increase in the graphite solid-electrolyte interface (SEI) resistance indicates that Li plating has occurred. By cross-validating with a highly sensitive ex situ chemical titration, we determine that this technique can detect very small amounts of plated Li (

Detecting onset of lithium plating during fast charging of Li-ion batteries using operando electrochemical impedance spectroscopy

Electrochemical impedance spectroscopy (EIS) is a widely used experimental technique for characterising materials and electrode reactions by observing their frequency-dependent impedance. Classical EIS measurements require the electrochemical process to behave as a linear time-invariant system. However, electrochemical processes do not naturally satisfy this assumption: the relation between voltage and current is inherently nonlinear and evolves over time. Examples include the corrosion of metal substrates and the cycling of Li-ion batteries. As such, classical EIS only offers models linearised at specific operating points. During the last decade, solutions were developed for estimating nonlinear and time-varying impedances, contributing to more general models. In this paper, we review the concept of impedance beyond linearity and stationarity, and detail different methods to estimate this from measured current and voltage data, with emphasis on frequency domain approaches using multisine excitation. In addition to a mathematical discussion, we measure and provide examples demonstrating impedance estimation for a Li-ion battery, beyond linearity and stationarity, both while resting and while charging. 

Electrochemical impedance spectroscopy beyond linearity and stationarity—A critical review

To render the sodium ion battery (SIB) competitive among other technologies, the processes behind sodium storage in hard carbon anodes must be understood. For this purpose, electrochemical impedance spectroscopy (EIS) is usually undervalued, since fitting the spectra with equivalent circuit models requires an a priori knowledge about the system at hand. The analysis of the distribution of relaxation times (DRT) is an alternative, which refrains from fitting arbitrarily nested equivalent circuits. In this paper, the sodiation and desodiation of a hard carbon anode is studied by EIS at different states of charge (SOC). By reconstructing the DRT function, highly resolved information on the number and relative contribution of individual electrochemical processes is derived. During the sloping part of the sodiation curve, mass transport is found to be the most dominant source of resistance but rapidly diminishes when the plateau phase is reached. An equivalent circuit model qualitatively reproducing the experimental data of the sloping region was built upon the DRT results, which is particularly useful for future EIS studies on hard carbon SIB anodes. More importantly, this work contributes to establish EIS as a practical tool to directly study electrode processes without the bias of a previously assumed model.

/pdf/insights-into-the-sodiation-mechanism-of-hard-carbon-like-1ot23zq8g9.pdf

Insights into the sodiation mechanism of hard carbon-like materials from electrochemical impedance spectroscopy.

Revealing inhomogeneities in electrode lithiation using a real-time discrete electro-chemical model

Adaptive fast charging control using impedance-based detection of lithium deposition

The operation of electrical networks, microgrids, or heterogeneous battery systems, especially the dispatch of single units within the system, requires sophisticated power flow control strategies. If objectives such as efficiency are demanded for the operation of the energy system, typical control strategies lack the ability to verify the optimality of the operation. Dynamic programming is a widely used method for determining the global optima of trajectory problems. In the context of energy systems and power flow optimization, it is restricted to applications with a low number of states and decisions. The reason for this is the rapid growth of computational effort with increasing dimensionality of the state and decision space. The approach of iterative dynamic programming (iDP) makes dynamic programming applicable to the planning and benchmarking of complex power flow optimization problems. To illustrate this, a heterogeneous battery energy storage system is introduced for which the iDP optimizes the power split at the point of common coupling to minimize the total cumulative loss of energy. The method can be adopted for a broad range of energy systems such as microgrids, utility grids, or electric vehicles. The applicability is limited only by the computation time, which depends on the model complexity and the length of the time series. To verify the functionality of the iterative dynamic programming, its results are directly compared to those of the standard dynamic programming. The total computation time can be reduced by 98% in the tested scenario. As relevant use cases, static and dynamic methods of power sharing are validated and benchmarked. The iDP offers a novel and computationally efficient method for the design and validation of power flow control strategies.

/pdf/iterative-dynamic-programming-an-efficient-method-for-the-2jf1hxfo.pdf

Iterative Dynamic Programming—An Efficient Method for the Validation of Power Flow Control Strategies

For the estimation of the state of charge of lithium‐ion batteries Kalman filters are the state of the art. To ensure precise and reliable estimations these filters use covariance matrices, which need to be tuned correctly by the developer. This process is time consuming and depends largely on the experience and skill of the developer. Hence, filter tuning is not reproducible and not optimal with regard to goals as accuracy and convergence speed. In this paper a multi‐objective optimization framework called hyper space exploration is used for the first time to automate the filter tuning procedure for an extended Kalman filter and two versions of adaptive extended Kalman filters. Four key performance indicators, including the maximum error in the estimation of the state of charge and the according root mean square error, are used to describe, validate, and compare the filter performance. Consequently, filter parameters are found, which improve the estimation behavior regarding the objective functions for all load profiles. This automated process enables optimal usage of the degrees of freedom in filter tuning and no longer requires manual tuning while the whole hyper space, including different use cases and validation scenarios, is considered in the optimization. Furthermore, the proposed approach yields a novel method for the evaluation of filter parameters and their influence on the estimation behavior.This article is protected by copyright. All rights reserved.

Kalman Filter Tuning for State Estimation of Lithium‐Ion Batteries by Multi‐objective Optimization via Hyper Space Exploration

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Revealing inhomogeneities in electrode lithiation using a real-time discrete electro-chemical model

Adaptive fast charging control using impedance-based detection of lithium deposition

Iterative Dynamic Programming—An Efficient Method for the Validation of Power Flow Control Strategies

Kalman Filter Tuning for State Estimation of Lithium‐Ion Batteries by Multi‐objective Optimization via Hyper Space Exploration