Charging Time Research Articles

Considerations for Exploring Nanosecond Pulsed Electric Fields (nsPEFs) for Treatments of Cancer, Benign Skin Diseases, Atrial Fibrillation, and for New Mechanistic Understandings

Pulsed power includes acquiring electrical energy, compressing it, and releasing it in instantaneous bursts that are low in energy but very high in power. When the pulse duration is near the plasma membrane charging time constant, which is the time during which the cell interior is exposed to the applied pulsed electric field, it affects intracellular structures and functions. The technology is called nanosecond Pulsed Electric Fields (nsPEFs), nanosecond electric pulses (nsEP), or Nanopulse Stimulation (NPSTM) according to Pulse Biosciences, Inc., a company taking the technology to the market. Initial studies showed the elimination of tumor cells in vitro by apoptosis, and other regulated cell death mechanisms, elimination of rodent and canine osteosarcoma, and a basal cell carcinoma clinical trial. In the rat liver and mouse breast cancers, tumor-free animals were in situ vaccinated (ISV), preventing the recurrence of the treated cancers. The technology has also focused on treating benign skin diseases, with some advantages over cryoablation. More recently, the same technology called nanosecond pulsed-field ablation (nsPFA) has been used to treat cardiac arrhythmias like Atrial Fabulation (AFib) with catheters in humans. In pre-clinical studies and now in humans, this technology is showing advantages over radiofrequency ablation and cryoablation. On a new mechanistic landscape, nonlethal nsPEFs modulation of electron transport in the plasma membrane and the mitochondria show potential for controlling redox homeostasis and metabolism. Furthermore, different nsPEF waveforms have different effects on cells; waveforms can differ by pulse duration, rise time, electric field, and/or post-pulse features. So, for nsPEFs, there is a lethal side used for ablation as with NPS and nsPFA and a more recently recognized nonlethal side indicating new possibilities to differentially modify cell physiology depending on the different nsPEF waveforms.

Optimal charging of Li-ion batteries using sparse identification of nonlinear dynamics

Optimal charging of Li-ion batteries requires careful management of charge rates, as high rates can lead to accelerated degradation, while low rates significantly extend charging times. Traditional methods for determining charge rates often rely on rule-based approaches, which typically fail to effectively balance battery performance with charging duration. To address this, we introduce a novel optimization approach that directly integrates the dual objectives of minimizing charge time and maximizing battery lifetime into the optimization process. Unlike most existing charge optimization methods that do not directly track battery lifetime and charge time simultaneously, our method employs a data-driven model that facilitates direct and dynamic estimation of both battery lifetime and charge time at each step of the optimization process. Specifically, we utilize the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to predict battery capacity and voltage dynamics, which informs the calculations of lifetime and charge time required to solve the optimization problem. This approach provides a balanced optimization strategy that enhances the effectiveness of battery’s performance while maintaining the efficiency of the charging process. We applied this method to a novel next-generation NMC811 battery, featuring a cathode comprised of 80 % nickel, 10 % manganese, and 10 % cobalt, and a lithium metal foil anode - a combination not extensively studied previously. Experimental validation demonstrated that when optimized charge rates are applied every 10 cycles in a 100-cycle operation, the method leads to more stable cycling and improved capacity retention of approximately 7.4 % over the nominal charge rate, demonstrating the potential of the developed approach.

Adaptive safe reinforcement learning‐enabled optimization of battery fast‐charging protocols

AbstractOptimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL‐based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this article proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions.

A novel energy management of public charging stations using attention-based deep learning model

Electricity grids are complex systems that must balance the supply and demand of electricity in real-time. However, with the increasing adoption of electric vehicles (EVs), managing the grid’s stability has become more challenging. EV charging can cause spikes in electricity demand, leading to peak demand periods that strain the power grid’s infrastructure. With the help of load forecasting, this effect on the grid can be mitigated by predicting the charging demand of electric vehicles in advance. This will help utilities adjust their energy supply in real-time, ensuring enough energy is available to meet demand, and preventing overloads or under utilization of the grid. Moreover, the EV charging demand is influenced by a wide range of factors, including charging station locations, weather, and time of day. Therefore, advanced deep learning models are required to learn these complex relationships and identify patterns in EV charging demand, enabling utilities to make more informed decisions. In this research, an attention-based deep learning approach is proposed for more accurate prediction of EV load demand. This novel approach integrates attention mechanisms with traditional deep learning models like LSTM and GRU, allowing the model to dynamically weight the importance of different features and focus on the most relevant information. The outcomes are compared to conventional deep learning and machine learning algorithms. To test the efficacy of the proposed framework, an actual ACN dataset for public EV charging stations is utilized.

Model and simulations of the effects of polyelectrolyte-coated electrodes in capacitive deionization.

The problem of ion transport in porous media is fundamental to many practical applications such as capacitive deionization, where ions are electrostatically attracted to a porous electrode and stored in the electric double layer, leaving a partially desalinated solution. These electrodes are functionalized to achieve maximum efficiency: it is intended that for each depleted electron one ion is removed. For this purpose, the surface is coated with a polyelectrolyte layer of the same sign as the electronic charge. In this work, the movement of ions from the solution to the soft or polyelectrolyte-coated electrodes is studied. For this purpose, a one-dimensional model is used to study the electric and diffusive fluxes produced by the application of an electric field and the storage of these ions in the micropores. The partial differential equationsgoverning the process are numerically solved using the explicit Euler method. The results of the model indicate that the number of ions removed using soft electrodes is approximately 15% greater than that achieved with bare electrodes. Ion adsorption kinetics show that coated electrodes provide slightly slower adsorption compared to bare electrodes. Regarding the charging time of the micropores, it can be seen that it is a faster process (characteristic time of 100s) compared to the time in which the ion concentration reaches equilibrium: electromigration is faster than diffusion. Comparing the situations with and without polyelectrolyte coating, it is observed that saturation in the micropores is reached earlier when the electrodes are coated. Concerning the cell geometry, it has been found that the characteristic time is proportional to the length of the spacer and inversely proportional to the length of the electrodes. With regard to microporosity, the rate of the process is approximately constant, irrespective of the number of micropores. Moreover, the number of adsorbed ions strongly depends on their initial concentration. Finally, the analysis of the ionic diffusion coefficient is determinant in the kinetics of the process: Taking into account the tortuosity of the porous electrode, which directly affects the diffusion in the channel, is fundamental to obtain model predictions close to reality.

Experimental investigation into thermal characteristics of subcooled flow boiling in horizontal concentric annuli for cooling ultra-fast electric vehicle charging cables

In an effort to reduce the charging time of electric vehicles (EVs), the thermal management of the charging system has emerged as one of the most urgent issues. The charging rate has been restricted to avoid thermal failure especially in charging cable. Recently, a direct contact cooling technique that utilizes subcooled flow boiling for charging cables has been proposed and has shown promising thermal management performance. However, there has been a lack of clear explanation regarding its thermal characteristics due to its intrinsically complicated flow features. This study investigates the thermal characteristics of subcooled flow boiling in a horizontally aligned concentric annular tube intended to simulate the direct contact cooling technique employed for charging cables. For the experimental investigation, the wall temperature measurements and high-speed flow visualization images acquired from the transparent test module annulus, with inner and outer diameters of 6.35 mm and 22.0 mm, were utilized. Three key axial thermal characteristics of subcooled flow boiling in annulus have been analyzed, and they are flow regime transition, variation of heat transfer mechanisms, and occurrence of critical heat flux (CHF). The transition of flow regimes and the variation of dominant heat transfer mechanisms in the axial direction are mutually influencing due to the coupled effects between the hydrodynamic and thermal characteristics of simultaneously developing flows. The flow regime changes from partially developed boiling (PDB) to fully developed boiling (FDB) as the intensities of nucleate boiling and bubble recondensation vary in the axial direction, while the dominant heat transfer mechanism shifts from single-phase convection to nucleate boiling, corresponding to the flow regime transition from PDB to FDB. The departure from nucleate boiling (DNB) type CHF is observed, manifested by the wavy interface propagating from the far downstream to the upstream region, and the intensities of nucleate boiling and bubble recondensation are also identified as the dominant parameters in determining the occurrence of CHF. Charging time estimation reveals that the proposed method can achieve an 80 % charge for 100-kWh EV batteries in 98 s, while maintaining the cable wire temperature below the safety limit of 80 °C. This strongly supports its potential as a promising thermal management technique for future ultra-fast charging systems.

Energy management optimization of hybrid electric vehicles based on deep learning model predictive control

In this paper, the hybrid electric vehicle (HEV) energy management optimization method is proposed based on deep learning (DL) model predictive control. Through empirical research combined with the questionnaire survey, this article not only provides a new perspective and practical basis but also improves the efficiency and accuracy of the model by improving the relevant algorithms. The study first analyzes the importance of HEV energy management and reviews the existing literature. Then, the optimization method of HEV energy management based on the deep learning model is introduced in detail, including the composition of energy management for hybrid electric vehicles, the structure and working principle of the deep learning model, especially the backpropagation neural network (BPNN) and the convolutional neural network (CNN), and the steps of application of deep learning in energy management. In the experimental part, questionnaire data from 1,500 consumers were used to design the HEV energy management optimization scheme, and consumers’ attitudes and preferences towards HEV energy optimization were discussed. The experimental results show that the proposed model can predict HEV energy consumption under different road conditions (urban roads, highways, mountain areas, suburban areas, and construction sites), and the difference between the average predicted energy consumption and the actual energy consumption is between 0.1KWH and 0.3KWH, showing high prediction accuracy. In addition, the deep learning-based energy management strategy outperforms traditional control strategies in terms of fuel consumption (6.2 L/100 km), battery charge and discharge times (814), battery life, and CO2 emissions, significantly improving the efficiency of HEV energy. These results demonstrate the great potential and practical application value of deep learning models in the optimization of energy management of HEVs, helping to drive the development of more sustainable and efficient transportation systems.

A hybrid data-driven approach for state of health estimation in lithium-ion batteries

ABSTRACT The assessment of the state of health (SOH) in lithium-ion batteries is essential for ensuring safe operation and implementing scientific management practices. This paper proposes a novel hybrid data-driven approach for accurately estimating the SOH of lithium-ion cells. Initially, the sparrow search algorithm (SSA) was implemented to optimize the structural configuration of a hybrid long-short-term memory neural network and convolutional neural network model. Second, four health indicators, namely, constant current charging time (CCCT), constant current charging capacity (CCCC), discharging time (DT), and discharging capacity (DC), were identified as inputs to the estimation model by Pearson correlation analysis. The CALCE battery dataset is utilized for conducting experiments, yielding remarkably low average values of root mean square error (RMSE) and mean absolute error (MAE), standing at merely 0.6737% and 0.5286%, respectively. And comparing with GRU, TCN, CNN, LSTM, and PSO optimization models, the proposed method has higher SOH estimation accuracy, which is more than 11% better than that. The robustness of three additional kinds of batteries is tested to validate their performance under varying current rates and temperatures. The results demonstrate that the accurate estimation of SOH, and the estimated values’ relative errors for each cycle of the three different types of batteries are all basically within 1%, with their maximum average RMSE are only 0.4840%, 0.3609%, and 0.5681%, respectively, and their maximum average MAE are only 0.3900%, 0.2828%, and 0.4149%, respectively.

User-needs-oriented shared DC charging resources optimal configuration and operation for improving EV penetration in old residential communities

The charging limitation of electric vehicles (EVs) in old residential communities (ORCs) derived from insufficient electricity capacity and parking spaces significantly impacts residents' quality of life and impedes urban sustainability. Considering economic and social factors, ORCs are likely to persist worldwide for the foreseeable future. To tackle these challenges, this paper proposes the scheme of shared DC fast charging stations to address the EV charging issues in ORCs. Specific methods involve minimizing the charging station spaces and charging infrastructure investment, while optimizing the photovoltaic (PV) and energy storage capacity. Additionally, a coordinated charging strategy based on dynamic price is designed to guide users in choosing the initial charging time, enhancing EV charging infrastructure utilization and EV penetration. This work is predominantly driven by human needs, integrating surveys on human behavior related to charging station locations and charging times. This case study is based on a real-life ORC in Shenzhen, China. By utilizing the proposed dynamic-pricing coordinated charging strategy tailored to the station capacity, the maximum EV penetration rate is to be achieved with satisfying EV charging demand. Furthermore, the implementation of dynamic pricing effectively guides users to adjust charging behavior, achieving the peak-shaving and valley-filling performance of load curves.

Experimental study on the thermal performance of a high-temperature finned latent heat storage system

The primary objective of this study is to develop and evaluate the performance of a latent heat storage (LHS) system. A tube-in-tube heat exchanger configuration was used to design the LHS system. Sodium nitrate and air were employed as the phase change material (PCM) and heat transfer fluid (HTF), respectively. To mitigate the low thermal conductivity of the PCM, six circular fins were integrated on the HTF tube surface and experiments were conducted in an in-house experimental facility. Temperature plots from the charging-discharging experiments revealed that the HTF flow direction governs the heat transfer along the axis of the system. The sensible heat gain by the PCM in the range of 270–330 °C, led to an additional ⁓0.4 MJ of heat storage beyond the storage capacity of 1 MJ. Furthermore, increasing the inlet temperature from 400 °C to 440 °C reduced the charging time by 39.6 %, while reducing the inlet temperature from 210 °C to 170 °C decreased the discharging time by 18.2 %. Despite improved heat transfer, higher inlet temperatures adversely affected the thermal uniformity whereas, the increase in inlet air velocity enhanced both the dynamics of heat transfer and the thermal uniformity within the storage system.

Self-growing bionic leaf-vein fins for high-power-density and high-efficiency latent heat thermal energy storage

Latent heat storage (LHS) has found extensive applications across various fields. However, large-scale deployment is still restricted due to the worse thermal charging performance of traditional LHS systems. In this research, the self-growing fins of the tube-shell LHS system are designed via the bionic topology optimized method to improve thermal storage performances, inspired by the structures and functions of leaf veins. Notably, the system installed with topology-optimized self-growing fins (T-FIN) demonstrates the shortest melting time, largest specific power density (SPD), and highest efficiency simultaneously. At the inlet conditions of 0.5 L min−1 and 358.15 K, the thermal charging time decreases by 46.4 % and 57.1 % compared with the systems equipped with traditional long-fins (L-FIN) and short-fins (S-FIN), while increasing the SPD by 80.6 % and 125.7 %, respectively. Moreover, the efficiency of exergy and entransy storage is 2.49 and 2.42 times, respectively, as large as those of the S-FIN. The intrinsic mechanism is attributed to enhanced synergy between the liquid-PCM temperature field and the flow field. Furthermore, outer tube shapes are optimized by the Genetic Algorithm for the first time, leading to a further decrease in charging time by 21.57 % compared with its original counterpart. These findings provide a valuable idea for designing efficient bionic LHS systems.

Multilayer Electrode Strategy Shorten Thermal Charging Time and Boost Energy Output in Gelatin‐Based i‐TE Cells

AbstractGel‐based ionic thermoelectric (i‐TE) cells provide alternative thermal energy harvesting from the environment, showing obvious advantages in voltage matching for self‐powered Internet‐of‐Things (IoT) sensors. However, the gel‐based i‐TE cells always suffer a long thermal charging time and poor output power performance. Herein, a multilayer electrode engineering strategy is proposed from the device‐design level, aiming to decrease the ions' diffusion distance, increase the electrode surface area, and facilitate the ions' reaction and recovery process. The thermal charging time is shortened from 27 to 8 min as the electrode layers increase from 2 to 8. An ultrahigh instantaneous power density of 15.8 mW m−2 K−2 and 2 h output energy density (E2h) of 403 J m−2 are achieved in an 8‐layer electrode i‐TE cell. Finally, A flexible and wearable i‐TE device with 20 units is demonstrated to generate a remarkable voltage of 3.8 V and output power of 282 µW by harvesting the human body heat. This work provides a feasible and effective route to design the i‐TE device, hopefully promoting its practical power generation application.

Fast Charging of Lithium-Ion Batteries While Accounting for Degradation and Cell-to-Cell Variability

Safety and maintaining high performance are key considerations during the operation of lithium-ion batteries. Battery degradation, in particular lithium plating and loss of active material, is often accelerated by fast charging. This study explores a strategy for the design of fast charging protocols that takes into account the influence of the variability between battery cells on factors that can impact degradation. We employ a non-intrusive polynomial chaos expansion to identify the key parameters for each degradation condition. We explore the reduction of battery degradation by adjusting constraints such as the maximum C-rate and voltage. Tight control of the key adjustable parameters contributes significantly to reducing the confidence interval of the degradation factors, allowing reduced charging time with minimal degradation. The application of our approach to two state-dependent fast charging protocols for a LiC6/LiCoO2 battery indicates the value in explicitly accounting for uncertainties when designing charging protocols that minimize degradation.

Mitigating Voltage Drop and Excessive Step-Voltage Regulator Tap Operation in Distribution Networks Due to Electric Vehicle Fast Charging

Electric vehicles (EVs) are transforming the transportation sector, driven by the rapid expansion of charging infrastructure, including fast-charging stations (FCSs), significantly reducing charging time compared to standard charging stations. Despite the advantages of faster charging, the substantial power demand of EVs poses significant technical challenges for distribution networks. In particular, the existing literature has a research gap regarding how FCSs may impact or interact with step-voltage regulators’ (SVRs) tap operations. In this study, we characterize and evaluate the effects of fast recharging at varying penetration levels (PLs) on SVRs’ tap operations using probabilistic simulations and sensitivity analysis. To address these challenges, we propose a local and innovative application of the Volt/Var control on EV fast charging. The proposed application aims to inject reactive power into the network, depending on the FCS’s nominal active power, when the bus voltage connected to the FCS exceeds a minimum value. Our research on an actual feeder in northern Brazil reveals that reducing the active power supplied to the vehicle or oversizing the charging station power converters is unnecessary. Furthermore, our strategy reduces the probability of undervoltage violations and minimizes SVR tap changes, mitigating EVs’ impact on voltage quality.

Integration and Optimization of Multisource Electric Vehicles: A Critical Review of Hybrid Energy Systems, Topologies, and Control Algorithms

Electric vehicles (EVs) are pivotal in addressing the escalating environmental crisis. While EV drivetrains excel compared to those of vehicles with internal combustion engines (ICEs), their energy storage systems are hampered by limited range, lifespan, and lengthy charging times. Hybrid energy storage systems (HESSs) present a viable current solution to these issues. This review thoroughly explores the state of the art in the emerging field of multisource EVs that utilize HESSs, incorporating any combination of batteries (BTs), supercapacitors (SCs), flywheels (FWs), fuel cells (FCs), and/or transmotors. In addition, the paper systematically categorizes and evaluates different hybrid configurations, detailing potential topologies and their respective advantages and limitations. Moreover, the paper examines diverse control algorithms used to manage these complex systems, focusing on their effectiveness and operational efficiency. By identifying current research gaps and technological challenges, this study aims to delineate future research directions that could enhance the deployment and optimization of multisource EVs, thereby addressing critical challenges such as energy density, system reliability, and cost-effectiveness.

Full QCD with milder topological freezing

We simulate Nf = 2 + 1 QCD at the physical point combining open and periodic boundary conditions in a parallel tempering framework, following the original proposal by M. Hasenbusch for 2d CPN−1 models, which has been recently implemented and widely employed in 4d SU(N) pure Yang-Mills theories too. We show that using this algorithm it is possible to achieve a sizable reduction of the auto-correlation time of the topological charge in dynamical fermions simulations both at zero and finite temperature, allowing to avoid topology freezing down to lattice spacings as fine as a ∼ 0.02 fm. Therefore, this implementation of the Parallel Tempering on Boundary Conditions algorithm has the potential to substantially push forward the investigation of the QCD vacuum properties by means of lattice simulations.

Techno-economic analysis of a novel solar-based polygeneration system integrated with vanadium redox flow battery and thermal energy storage considering robust source-load response

In this study, a novel solar-based polygeneration system incorporated with a partially covered parabolic trough photovoltaic thermal (PCPVPVT) collector, vanadium redox flow battery (VRFB), thermal energy storage, and absorption chiller/heat pump is proposed. A robust energy management strategy is developed to alter the operating states of the system to respond to the inevitable intermittent source inputs and varying demands throughout the year. Furthermore, a comprehensive comparison of the techno-economic performance differences between the novel system and the Power to Hydrogen to Power (P2H2P) combined cooling, heating, and power (CCHP) system is carried out. In addition, the sensitivity analysis of the key technical and external economic parameters as well as a multi-objective optimization are investigated. The results illustrate that: the charging, discharging, and round-trip efficiencies of the VRFB unit reach 88.99%, 88.23%, and 78.52%, respectively, and the current of the VRFB has significant effects on the termination voltage, charge and discharge time, efficiencies, and stored and output energy of the VRFB. The PV coverage ratio of the PCPVPVT and the capacity of the VRFB unit considerably affect the system's thermodynamic and economic performance. The exergy efficiency of the proposed system is always greater than that of the P2H2P CCHP system because the efficiency of the VRFB with 78.52% is significantly higher than that of the P2H2P electricity storage with 41.73%, and the maximum difference reaches 4.25%. The levelized cost of energy of the present system is 0.0503 $/kWh, which is less than that of the P2H2P CCHP system by approximately 6.85%, indicating superior economic strengths.

Strategic integration of metamaterials properties and topology optimization of gyroid metal hydride reactor for high-density hydrogen storage

Metal hydride (MH) is considered to be one of the potential materials for storing hydrogen. It has a substantial limitation to wide use in the mobility sector, which is its low gravimetric hydrogen storage density. A novel canister design utilizing the optimization of gyroid structure for MH-based hydrogen storage is proposed to enhance reactor strength and capacity, increasing the favor of using it outside stationary applications. Topology optimization (TO) of the reactor increases the capacity by 49 % while maintaining the capability of this reactor to sustain 5000 N of bending case. On top of that, changing the metamaterial properties of the gyroid results in a larger chamber size for storing a larger amount of MH by the maximum amount of 30 %. However, increasing the chamber size comes with a cost of a lower charging rate and a considerable area of non-reacted hydride at the same charging time compared to the non-modified structure. By evaluating the MH bed that consists of lanthanum nickel (LaNi5), the topologically optimized reactor design is varied with different chamber offsets of 0, 2, and 4 mm. The results show that using a 2 mm offset in this study case can increase hydrogen storage capacity by 22 % while maintaining the same charging time of less than 4500 s. The combination of both proposed methods can increase the overall MH bed chamber by 66.22 % compared to previous initial research that acts as proof of concept.

Impact of potential and temperature fluctuations on charge and heat transport in quantum Hall edges in the heat Coulomb blockade regime

We present a broad study of charge and heat transport in a mesoscopic system where one or several quantum Hall edge channels are strongly coupled to a floating Ohmic contact (OC). It is well-known that charge-current fluctuations emanating from the OC along the edge channels are highly susceptible to the OC charge capacitance in the heat Coulomb blockade regime (an impeded ability of the OC to equilibrate edge channels). Here, we demonstrate how potential and temperature fluctuations due to finite OC charge and heat capacities impact the heat-current fluctuations emitted from the OC. First, by assuming an infinite OC heat capacity, we show that the output heat-current noise is strongly dependent on the OC charge capacitance, following from a close relation between one-dimensional charge and heat currents. When also the OC heat capacity is finite, an interplay of potential and temperature fluctuations influences the heat transport. Concretely, we find that the effect of the charge capacitance on heat transport manifests in terms of a strongly increased energy relaxation time in the heat Coulomb blockade regime. Furthermore, we find expressions for a broad set of output observables, such as charge and heat auto- and cross correlations, as functions of input and OC fluctuations, depending on the relation between charge and energy relaxation times compared to the frequency of fluctuations and inverse (local) temperatures as well as on the number of edge channels attached to the OC. Finally, we show that a finite OC heat capacity transforms the full counting statistics of the output charge from Gaussian to non-Gaussian. Our findings provide opportunities to experimentally probe and harness the quantum nature of heat transport in strongly coupled electron circuits. Published by the American Physical Society 2024

Entropy-increased LiMn2O4-based positive electrodes for fast-charging lithium metal batteries

Fast-charging, non-aqueous lithium-based batteries are desired for practical applications. In this regard, LiMn2O4 is considered an appealing positive electrode active material because of its favourable ionic diffusivity due to the presence of three-dimensional Li-ion diffusion channels. However, LiMn2O4 exhibits inadequate rate capabilities and rapid structural degradation at high currents. To circumvent these issues, here we introduce quintuple low-valence cations to increase the entropy of LiMn2O4. As a result, the entropy-increased LiMn2O4-based material, i.e., LiMn1.9Cu0.02Mg0.02Fe0.02Zn0.02Ni0.02O4, when tested in non-aqueous lithium metal coin cell configuration, enable 1000 cell cycles at 1.48 A g−1 (corresponding to a cell charging time of 4 minutes) and 25°C with a discharge capacity retention of about 80%. We demonstrate that the increased entropy in LiMn2O4 leads to an increase in the disordering of dopant cations and a contracted local structure, where the enlarged LiO4 space and enhanced Mn-O covalency improve the Li-ion transport and stabilize the diffusion channels. We also prove that stress caused by cycling at a high cell state of charge is relieved through elastic deformation via a solid-solution transition, thus avoiding structural degradation upon prolonged cycling.