Battery Wear Research Articles

Charging scheduling optimisation of battery electric buses with charging setup time

Battery electric buses (BEBs) are recognised as sustainable modes of transportation. Because of its increasing range, efficient and convenient overnight charging has become crucial. The limited number of charging stations and variability in setup times require the optimisation of BEB charging schedules. This study proposes an optimal overnight centralised charging schedule that considers setup time and battery-degradation costs. We model this as a multi-travelling salesman problem with sojourn time to minimise operating costs, including electricity, setup time, and battery wear, while adhering to the bus-schedule constraints. We introduce a local search grouping genetic algorithm with a 2-opt operator local search to address the complexities of public-transport networks. Our extensive numerical analysis, grounded in real-world data, shows a 4.48% reduction in operating costs using our optimised strategy compared with current methods. Moreover, our charging-station allocation analysis provides insights for resource optimisation, advancing sustainable public transport, and charging strategies. This study contributes to the field of sustainable public transportation and charging optimisation.

Electrochemical and aging model of li-ion batteries and estimation of total number of cycles during the lifecycle of the battery

Fast charging is a critical factor for the widespread adoption of electric vehicles (EVs), yet it poses challenges to the durability of lithium-ion batteries (LiBs). This study develops an aging model for a 10 Ah LiB cell using COMSOL Multiphysics, integrating a one-dimensional electrochemical model with a zero-dimensional thermal model. The model is constructed using cell design and electrochemical parameters derived from existing literature, tailored to the specific electrode and electrolyte materials utilized in this study. Key aging phenomena, such as the Solid Electrolyte Interface (SEI) growth and lithium plating, are embedded within the model to simulate the aging patterns and the interplay between these factors across various C-rates and temperatures. Simulation results suggest that while higher charging rates can reduce charging time, they also lead to increased degradation, with lithium plating becoming more severe at these elevated rates. The model also indicates that temperature management during cell operation can influence cycle life, with strategic temperature increases potentially mitigating some negative effects of fast charging. The study also explores the concept of total charging time as an alternative metric for battery wear, examining its relationship with the charging current profile and temperature. Results indicate that while fast charging offers convenience, it significantly impacts cycle life, underscoring the need for optimized charging strategies. The insights from this work enhance the understanding of Li-ion cell aging mechanisms, shedding light on the complex trade-offs between charging speed, temperature management, and lifespan. This contributes to the development of more sophisticated battery management systems and charging protocols that aim to extend the operational life of LiBs while accommodating the demands of fast charging.

Joint Concern over Battery Health and Thermal Degradation in the Cruise Control of Intelligently Connected Electric Vehicles Using a Model-Assisted DRL Approach

Eco-driving aims to enhance vehicle efficiency by optimizing speed profiles and driving patterns. However, ensuring safe following distances during eco-driving can lead to excessive use of lithium-ion batteries (LIBs), causing accelerated battery wear and potential safety concerns. This study addresses this issue by proposing a novel, multi-physics-constrained cruise control strategy for intelligently connected electric vehicles (EVs) using deep reinforcement learning (DRL). Integrating a DRL framework with an electrothermal model to estimate unmeasurable states, this strategy simultaneously manages battery degradation and thermal safety while maintaining safe following distances. Results from hardware-in-the-loop simulation testing demonstrated that this approach reduced overall driving costs by 18.72%, decreased battery temperatures by 4 °C to 8 °C in high-temperature environments, and reduced state-of-health (SOH) degradation by up to 46.43%. These findings highlight the strategy’s superiority in convergence efficiency, battery thermal safety, and cost reduction compared to existing methods. This research contributes to the advancement of eco-driving practices, ensuring both vehicle efficiency and battery longevity.

Minimizing Voltage Ripple of a DC Microgrid via a Particle-Swarm-Optimization-Based Fuzzy Controller

DC microgrids play a crucial role in both industrial and residential applications. This study focuses on minimizing output voltage ripple in a DC microgrid, including power supply resources, a stochastic load, a ballast load, and a stabilizer. The solar cell serves as the power supply, and the stochastic load represents customer demand, whereas the ballast load includes a load to safeguard the boost circuits against the overvoltage in no-load periods. The stabilizer integrates components such as electrical vehicle batteries for energy storage and controlling long-time ripples, supercapacitors for controlling transient ripples, and an over-voltage discharge mechanism to prevent overcharging in the storage. To optimize the charging and discharging for batteries and supercapacitors, a multi-objective cost function is defined, consisting of two parts—one for ripple minimization and the other for reducing battery usage. The battery charge and discharge are considered in the objective function to limit its usage during transient periods, providing a mechanism to rely on the supercapacitor and protect the battery. Particle swarm optimization is employed to fine-tune the fuzzy membership function. Various operational scenarios are designed to showcase the DC microgrid’s functionality under different conditions, including scenarios where production exceeds and falls below consumption. The study demonstrates the improved performance and efficiency achieved by integrating a PSO-based fuzzy controller to minimize voltage ripple in a DC microgrid and reduce battery wear. Results indicate a 42% enhancement in the integral of absolute error of battery current with our proposed PSO-based fuzzy controller compared to a conventional fuzzy controller and a 78% improvement compared to a PI controller. This translates to a respective reduction in battery activity by 42% and 78%.

Ageing of Electric Vehicle Battery considering Mobility Needs for Urban Areas

This work seeks to analyse battery wear in electric vehicles (EV) based on daily usage and battery temperature. An agent-based model (ABM) was built, using Matlab® software, in order to evaluate users’ behaviour and represent the output data. Travel input data is obtained from Barcelona’s driving survey, and different models of battery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV), and electric motorcycles (EM) have been taken into consideration. Battery size characteristics and consumption are different for every model of vehicle, although only lithium ion (Li-ion) battery technology has been modelled and the same voltage profile has been used in all of them. The model successfully represents battery wear as a function of temperature and battery usage.

Smart Decentralized Electric Vehicle Aggregators for Optimal Dispatch Technologies

The number of electric vehicles (EVs) is growing exponentially, which presents the power grid with new challenges to turn their reliance to renewable energy sources (RESs). Coordination between the available generations from RESs and the charging time should be managed to optimally utilize the available generation from RESs. The dispatch scheduling of EVs can significantly reduce the impact of these challenges on power systems. Three different technologies can be used to manage the dispatch of EV batteries which are unregulated charging (UC), unidirectional grid-to-vehicle (G2V), and bidirectional vehicle-to-grid (V2G) technologies. This study aims to address the primary reason for EV owners’ disbelief in the accuracy of battery wear models, which is impeding their involvement in V2G technology. This paper introduces a novel accurate EV battery wear model considering the instantaneous change in the operation of the EV battery. Moreover, an effective musical chairs algorithm (MCA) is used to reduce everyday expenses and increase revenue for V2G technologies in a short convergence time with accurate determination of optimal power dispatch scheduling. The results obtained from these three strategies are compared and discussed. The salient result from this comparison is that V2G technology increases wear and reduces the battery lifespan in comparison with the UC and G2V. The yearly expenses of G2V are reduced by 33% compared to the one associated with the UC. Moreover, the use of V2G technology provides each EV owner with USD 3244.4 net yearly profit after covering the charging and wear costs. The superior results extracted from the proposed model showed the supremacy of V2G usage, which is advantageous for both EV owners and the power grid.

Optimal Dispatch Strategy for Electric Vehicles in V2G Applications

The overutilization of electric vehicles (EVs) has the potential to result in significant challenges regarding the reliability, contingency, and standby capabilities of traditional power systems. The utilization of renewable energy distributed generator (REDG) presents a potential solution to address these issues. By incorporating REDG, the reliance of EV charging power on conventional energy sources can be diminished, resulting in significant reductions in transmission losses and enhanced capacity within the traditional power system. The effective management of the REDG necessitates intelligent coordination between the available generation capacity of the REDG and the charging and discharging power of EVs. Furthermore, the utilization of EVs as a means of energy storage is facilitated through the integration of vehicle-to-grid (V2G) technology. Despite the importance of the V2G technology for EV owners and electric utility, it still has a slow progress due to the distrust of the revenue model that can encourage the EV owners and the electric utility as well to participate in V2G programs. This study presents a new wear model that aims to precisely assess the wear cost of EV batteries, resulting from their involvement in V2G activities. The proposed model seeks to provide EV owners with a precise understanding of the potential revenue they might obtain from participating in V2G programs, hence encouraging their active engagement in such initiatives. Various EV battery wear models are employed and compared. Additionally, this study introduces a novel method for optimal charging scheduling, which aims to effectively manage the charging and discharging patterns of EVs by utilizing a day-ahead pricing technique. This study presents a novel approach, namely, the gradual reduction of swarm size with the grey wolf optimization (GRSS-GWO) algorithm, for determining the optimal hourly charging/discharging power with short convergence time and the highest accuracy based on maximizing the profit of EV owners.

Electric Vehicle Charge Scheduling with Flexible Service Operations

Operators who deploy large fleets of electric vehicles often face a challenging charge scheduling problem. Specifically, time-ineffective recharging operations limit the profitability of charging during service operations such that operators recharge vehicles off duty at a central depot. Here, high investment cost and grid capacity limit available charging infrastructure such that operators need to schedule charging operations to keep the fleet operational. In this context, flexible service operations, that is, allowing delayed or expedited vehicle departures, can potentially increase charger utilization. Beyond this, jointly scheduling charging and service operations promises operational cost savings through better utilization of time-of-use energy tariffs and carefully crafted charging schedules designed to minimize battery wear. Against this background, we study the resulting joint charging and service operations scheduling problem accounting for battery degradation, nonlinear charging, and time-of-use energy tariffs. We propose an exact branch-and-price algorithm, leveraging a custom branching rule and a primal heuristic to remain efficient during the branch-and-bound phase. Moreover, we develop an exact labeling algorithm for our pricing problem, constituting a resource-constrained shortest path problem that considers variable energy prices and nonlinear charging operations. We benchmark our algorithm in a comprehensive numerical study and show that it can solve problem instances of realistic size with computational times below one hour, thus enabling its application in practice. Additionally, we analyze the benefit of jointly scheduling charging and service operations. We find that our integrated approach lowers the amount of charging infrastructure required by up to 57% besides enabling operational cost savings of up to 5%. Funding: This work was supported by the German Federal Ministry for Economic Affairs and Energy [Grant 01MV21020B]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0272 .

Interaction Harvesting

Whenever a user interacts with a device, mechanical work is performed to actuate the user interface elements; the resulting energy is typically wasted, dissipated as sound and heat. Previous work has shown that many devices can be powered entirely from this otherwise wasted user interface energy. For these devices, wires and batteries, along with the related hassles of replacement and charging, become unnecessary and onerous. So far, these works have been restricted to proof-of-concept demonstrations; a specific bespoke harvesting and sensing circuit is constructed for the application at hand. The challenge of harvesting energy while simultaneously sensing fine-grained input signals from diverse modalities makes prototyping new devices difficult. To fill this gap, we present a hardware toolkit which provides a common electrical interface for harvesting energy from user interface elements. This facilitates exploring the composability, utility, and breadth of enabled applications of interaction-powered smart devices. We design a set of "energy as input" harvesting circuits, a standard connective interface with 3D printed enclosures, and software libraries to enable the exploration of devices where the user action generates the energy needed to perform the device's primary function. This exploration culminated in a demonstration campaign where we prototype several exemplar popular toys and gadgets, including battery-free Bop-It--- a popular 90s rhythm game, an electronic Etch-a-sketch, a "Simon-Says"-style memory game, and a service rating device. We run exploratory user studies to understand how generativity, creativity, and composability are hampered or facilitated by these devices. These demonstrations, user study takeaways, and the toolkit itself provide a foundation for building interactive and user-focused gadgets whose usability is not affected by battery charge and whose service lifetime is not limited by battery wear.

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

In urban settings, the prevalence of traffic lights often leads to fluctuations in traffic patterns and increased energy utilization among vehicles. Recognizing this challenge, this research addresses the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs) through the introduction of a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD). This innovative strategy is designed to enhance various aspects of urban mobility, including vehicle energy efficiency, traffic flow optimization, and battery longevity, all while ensuring a satisfactory driving experience. The M-EAD strategy unfolds in two distinct stages: First, it optimizes eco-friendly green signal windows at traffic lights, with a primary focus on minimizing travel delays by solving the shortest path problem. Subsequently, it employs a receding horizon framework and leverages an iterative dynamic programming algorithm to refine speed trajectories. The overarching objective is to curtail energy consumption and reduce battery wear by identifying the optimal speed trajectory for EVs in urban environments. Furthermore, the research substantiates the real-world efficacy of this approach through on-road vehicle tests, attesting to its viability and practicality in actual road scenarios. In the proposed case, the simulation results showcase notable achievements, with energy consumption reduced by 0.92% and battery wear minimized to a mere 0.0017%. This research, driven by the pressing issue of urban traffic energy efficiency, not only presents a solution in the form of the M-EAD strategy but also contributes to the fields of sustainable urban mobility and EV performance optimization. By tackling the challenges posed by traffic lights, this work offers valuable insights and practical implications for improving the sustainability and efficiency of urban transportation systems.

Optimal intermittent electrification and its effect on battery sizing and energy saving using a high-fidelity train model

The fastest way to decarbonise traction energy of a railway network is full electrification of the network. However, full electrification can be costly and is often delayed due to complicated planning procedures. Often, it is not financially viable to electrify certain sections of the network due to geographical challenges such as hillsides and requirements to raise or lower the tracks. Intermittent electrification can provide a way to reduce emissions on a route without fully electrifying it. This paper presents an optimal intermittent electrification strategy and compares its effects on required battery size to a ‘common-sense’ approach. The “common-sense” approach is a heuristic electrification method where high-power sections of the route are electrified first. Energy savings are demonstrated using a validated high-fidelity bi-mode train model on the Newbury-Plymouth route of Great Western Railway in the UK. Unlike prior research, the optimal electrification strategy considers energy consumptions for both directions of a journey on the route and is therefore optimal regardless of travel direction. The 278-km Newbury-Plymouth route was divided into 50 equal discrete sections for the optimization process, which then identified sections that should be prioritised for electrification as they consume the maximum energy. A high charge/discharge lithium titanite-oxide battery was modelled and installed on the virtual vehicle to determine the required battery sizing for given optimal percentages of intermittent electrification. The battery sizing strategy includes battery wear effects from charge/discharge cycle life and shows the battery life to be 1.42 years if kept at 55°C and 6 years if kept at 25°C. At 36% electrification, using the ‘common-sense’ approach, the battery weighs 10,500 kg and the battery-OLE train reduces the CO2 emissions and energy usage on the route by 83% when compared to the diesel-only train.

Multi-Objective Energy Management Strategy for Hybrid Electric Vehicles Based on TD3 with Non-Parametric Reward Function

The energy management system (EMS) of hybridization and electrification plays a pivotal role in improving the stability and cost-effectiveness of future vehicles. Existing efforts mainly concentrate on specific optimization targets, like fuel consumption, without sufficiently taking into account the degradation of on-board power sources. In this context, a novel multi-objective energy management strategy based on deep reinforcement learning is proposed for a hybrid electric vehicle (HEV), explicitly conscious of lithium-ion battery (LIB) wear. To be specific, this paper mainly contributes to three points. Firstly, a non-parametric reward function is introduced, for the first time, into the twin-delayed deep deterministic policy gradient (TD3) strategy, to facilitate the optimality and adaptability of the proposed energy management strategy and to mitigate the effort of parameter tuning. Then, to cope with the problem of state redundancy, state space refinement techniques are included in the proposed strategy. Finally, battery health is incorporated into this multi-objective energy management strategy. The efficacy of this framework is validated, in terms of training efficiency, optimality and adaptability, under various standard driving tests.

Universal Simulation Model of Battery Degradation with Optimization of Parameters by Genetic Algorithm

Modeling of batteries is necessary to control their operating mode and diagnose their condition. It is important to model the life cycle, i. e. degradation of basic parameters over a long service life. This is due to the fact that the cost of buffering electricity by batteries is associated with their cycling resource, which can be increased by optimizing the mode of operation of the drive in the energy system. The existing models of battery degradation are characterized by specificity, limited work on standardized charge-discharge cycles, and mathematical cumbersomeness. The article proposes a universal approach devoid of the above disadvantages. The concept of continuous battery wear during the service life is used. A simple empirical model is presented that does not consider in detail the characteristics of the state of batteries during a separate charge-discharge cycle, and does not include voltaic variables. The model considers the intensity of the current wear of the battery as a function of the state of its charge, temperature, the current of the external circuit and the current of self-discharge, the full charge that has flowed through the battery since the beginning of its operation. In this case, the amount of wear (degradation) is determined by the integral of the function of the intensity of current wear over the battery life. To optimize the parameters of the model, a random search method is used in combination with a genetic selection algorithm. The corresponding model of degradation of parameters for the Delta GEL-12-55 lead-acid battery has been constructed, in which the data on degradation of capacity given in the technical description from the manufacturer are used. The efficiency of the parameter optimization algorithm and the adequacy of the resulting model are shown. The model developed by the authors can be used for technical and economic calculations of generator – storage –consumer systems, hybrid power storage systems, and compact representation of large volumes of experimental data on the degradation of specific batteries.

Reliability cost/worth assessment of emergency B2G services in two modes of battery swap technology

This paper presents a cost/worth assessment framework for evaluating the cost-effectiveness of using emergency battery-to-grid (B2G) services in battery swap mode for improving the reliability of active distribution systems. Two integration modes are considered: the decentralized mode, in which the replaced batteries are charged upon receiving in the battery swap stations (BSSs), and the centralized mode, in which the replaced batteries are regularly collected from BSSs and transferred to the central charging stations (CCSs) for charging. In doing so, probabilistic models are presented to estimate the available energy of batteries in the two modes, and evaluate the associated reliability benefits. Moreover, different implementation costs of emergency B2G services are analyzed, and appropriate models are proposed for estimating the battery wear cost as well as battery charging costs in the centralized and decentralized modes. Finally, the reliability cost/worth method is used to evaluate the cost-effectiveness of emergency B2G programs in both modes of battery swap integration, and the major factors that affect the viability of such services in either mode are identified through extensive case studies. • Reliability Cost/Worth Assessment of B2G Services in EVs’ Battery Swap Mode. • Modeling two modes of battery swap technology: centralized and decentralized. • Probabilistic models to estimate the available energy in the two modes. • Simple models for estimating the implementation cost of emergency B2G programs. • Comparative analyses between the centralized and decentralized modes.

In-motion charged vehicle simulation considering traffic and power grid interactions

When designing new electric vehicles for urban transport, both vehicle producers and operators need to establish and verify requirements that the vehicle has to fulfil regarding, e.g., energy storage capacity, driving range, or battery wear. These requirements are typically verified using simulation tools that concentrate mostly on electrical quantities, but disregard the influence of road infrastructure, surrounding traffic and detours. At the same time, an expansion of the electric vehicle fleet may have a negative impact on the existing power grid. Sufficient dimensioning of the grid elements and their verification are therefore necessary to keep the electric transport service reliable. This paper demonstrates a tool suitable for verification of battery-assisted trolleybus fleet and power infrastructure parameters, based on extensions to the SUMO traffic simulator. The tool carries out a joint simulation of electric and traffic-related quantities. It is demonstrated on seven use-cases inspired by real-life problems. • A tool to verify battery-assisted trolleybus fleet configuration is presented. • Includes fast DC power grid solver for electric grid analysis. • Combines traffic and electric circuit simulation to study mutual interactions. • The effect of surrounding traffic on vehicle power demands is considered. • Study of different parameters of a sample trolleybus network is presented.

A cost of ownership analysis of batteries in all-electric and plug-in hybrid vehicles

The ever-faster transformation of road vehicles from traditional fuel engines to electric motors, is leading to increasingly widespread research on and development of electric vehicles and related infrastructures. In this context, this article addresses the cost aspect of batteries from the owner’s perspective. Specifically, it proposes an analysis of the optimal usage cost of batteries in order to maximize the benefit-cost ratio and battery replacement intervals. In order to analyze battery degradation, various tests were utilized for both a full-battery electric vehicle (BEV) and a plug-in hybrid electric vehicle (PHEV). The results demonstrate greater wear of the PHEV battery when the vehicle is under charge-sustaining mode, that is, when using the combustion engine, while driving with frequent starts and stops. On the other hand, the degradation costs of the BEV battery are generally close to optimal in every scenario, in which the main parameter affecting battery wear is average daily mileage.

On the role of battery degradation in en-route charge scheduling for an electric bus system

Electric buses (EB) are widely promoted as a clean alternative to conventional fleets to mitigate greenhouse gas emissions from ground transportation. En-route charging that takes advantage of bus dwell time is an efficient and promising charging strategy to address mileage limitations and long energy replenishment time. However, the charging and discharging behavior of electric buses during operation has a vital impact on battery degradation, grid stability, and schedule robustness. The present study proposes an optimal electric bus charge scheduling model that addresses the grid and battery issues by introducing peak-to-average power ratio, time-of-use electricity price, and battery wear formulation while ensuring the original bus schedules. The model is formulated as a mixed-integer program that can be solved using off-the-shelf solvers even for large-scale problems. Extensive numerical studies based on real-world bus networks are introduced to demonstrate the performance and to gain insights from sensitive analysis. The result shows that the optimal schedule achieves a balanced spatial and temporal distribution of charging demand. Besides, it reveals that the battery wear costs have a greater impact on operations than charging costs, accounting for about 87.26% of total operating costs. However, this impact can be well controlled by controlling the battery state-of-charge (SOC) intervals.

Impact of battery degradation on energy cost and carbon footprint of smart homes

Optimal energy management in residential buildings with battery storage devices largely relies on the implementation of accurate battery degradation models. A proper wear model to use for the battery scheduling at homes should be universally applicable to different battery technologies and precisely model the impact of the major parameters that contribute to the capacity loss. In this work, a technology-agnostic battery wear model has been proposed to address the degradation caused by the irregular cycling of batteries in residential buildings. The proposed wear model provides the DoD-associated degradation for charging/discharging events between random SoCs only by employing the lifecycle curve of batteries which is usually provided by battery manufactures. This model has been formulated in the framework of mixed-integer programming (MIP) such that it can be directly incorporated into MIP optimization models to provide optimal unit commitment solutions without sacrificing other objectives of the problem. To facilitate the implementation of the nonlinear wear coefficient in MIP models, a curve-fitted version of the wear coefficient is used in the MIP problem. A comprehensive study on the costs and carbon footprint of a smart home has been carried out in this paper by producing a MIP problem that incorporates the proposed battery wear model. The problem has been solved with different approaches to minimize costs, capacity loss, and emissions of the system. The optimization results show that the application of the presented wear model managed to lower the cost, carbon footprint, and battery degradation by 78%, 30%, and 81% respectively.

Simultaneous charging station location-routing problem for electric vehicles: Effect of nonlinear partial charging and battery degradation

Recently, electric vehicles (EVs) have gained attention in the field of logistics owing to the strong support received from the government and continuous increase in social environmental awareness. Compared to traditional logistics vehicles, EVs incur additional charging costs, such as charging time and battery wear costs. In this study, the routing problem of EVs is formulated as an integer programming model based on a nonlinear charging model and practical battery wear model. Subsequently, a three-phase algorithm called CWIGALNS was proposed to solve this problem. Based on the proposed model, a series of instances was generated, showing the benefits of combining charging time, battery wear, and distribution. Finally, sensitivity analyses were systematically conducted on the wear cost and charging time under a realistic background. The results show that the optimal planning of an EV network considering time and wear costs is in line with the practical needs of EV logistics enterprises, which can help reduce the operating costs.

Intact, Commercial Lithium-Polymer Batteries: Spatially Resolved Grating-Based Interferometry Imaging, Bragg Edge Imaging, and Neutron Diffraction

We survey several neutron imaging and diffraction methods for non-destructive testing and evaluation of intact, commercial lithium-ion batteries. Specifically, far-field interferometry was explored as an option to probe a wide range of autocorrelation lengths within the batteries via neutron imaging. The dark-field interferometry images change remarkably from fresh to worn batteries, and from charged to discharged batteries. When attempting to search for visual evidence of battery degradation, neutron Talbot-Lau grating interferometry exposed battery layering and particle scattering through dark-field imaging. Bragg edge imaging also reveals battery wear and state of charge. Neutron diffraction observed chemical changes between fresh and worn, charged and discharged batteries. However, the utility of these methods, for commercial batteries, is dependent upon battery size and shape, with 19 to 43 mAh prismatic batteries proving most convenient for these experimental methods. This study reports some of the first spatially resolved, small angle scattering (dark-field) images showing battery degradation.