The electrification of transport relies heavily on lithium-ion batteries, yet conventional fixed-configuration battery packs suffer from structural inefficiencies such as cell-to-cell variability and premature failure. Dynamic battery reconfiguration, enabled by intelligent control of switching hardware around individual cells, provides an alternative pack architecture. Here we present a systematic, battery technology-agnostic evaluation of the lifetime and economic benefits of reconfigurable battery packs using a statistically grounded framework based on detailed cell modelling across diverse design and usage conditions. We show that reconfigurable battery packs can extend battery lifetime by over 20%, particularly in high-voltage applications such as electric trucks and long-range passenger vehicles. Despite higher upfront costs, reconfigurable packs can reduce lifetime cost by deferring replacements and retaining greater residual value. A sensitivity analysis identifies robust thresholds for economic viability: battery capacities above approximately 50 kWh, annual driving distances below 12,150 km, and additional upfront costs under 7.16%. These findings position dynamic reconfiguration as a scalable, cost-optimised architecture for next-generation battery platforms, and provide a quantitative foundation for future hardware design, management software, and life-cycle sustainability assessments.

Knowledge of the internal state of lithium-ion batteries is crucial for the development of safe charging control algorithms. Most commonly, graphite is used as the negative electrode, possessing a high specific capacity and cycling stability. When such lithium-ion batteries are charged, lithium intercalates into the layered carbon structure, and the electrode material transitions through a series of phases differentiated by the number of carbon layers between each lithium layer. Each phase has different electrochemical properties, making it interesting to accurately track the phase content during lithiation. However, this quantity can only be measured through spectroscopic experiments, which cannot be included in any battery application. In this work, a method based on incremental capacity analysis and kernel smoothing is introduced to estimate the phase content of graphite electrodes from the electrode potential during constant-current charging. This kernel density function (KDF) method is validated at low currents using physics-based simulations of multiphase electrode dynamics, achieving an average phase estimation error below one percentage point per phase. Furthermore, we apply the KDF-method to experimentally measured coin cell data, for which the estimated phase content closely agrees with simulations in the mid to high range state of charge.

This paper investigates the economic impact of vehicle-home-grid integration in the presence of rooftop PV, by proposing an online, aging-aware energy management strategy for an electric vehicle (EV), a household, and the electrical grid. The model predictive control-based framework explicitly exploits vehicle-to-grid (V2G) and vehicle-to-home (V2H) operation to perform energy arbitrage, increase self-consumption, while respecting user-driven driving requirements. The framework optimizes power flows over a shrinking horizon using a detailed battery aging model that captures both calendar and cycle degradation, and a Transformer-based forecaster that provides short-term predictions of household load and solar irradiance. For a one-year horizon, the proposed strategy yields the lowest annual cost among all evaluated strategies. Adding PV increases the annual profit by EUR 1060.7 compared to operating without PV, and yields an economic gain of up to EUR 2410.5 over smart unidirectional charging, at the expense of only 1.27

This paper presents a novel algorithmic framework for efficiently solving the pseudo-two-dimensional (P2D) model of lithium-ion batteries. The proposed approach reformulates the original P2D model, typically expressed as a system of coupled nonlinear partial differential-algebraic equations, into a system of quasilinear partial integro-differential equations (PIDEs). Through this reformulation, intermittent algebraic states, such as local potential and current terms, are effectively eliminated, thereby reducing the model complexity. This enables the identification of a generic fixed-point iterated function for solving the P2D model's nonlinear algebraic equations. To implement this iterated function, the finite volume method is employed to spatially discretize the PIDE system into a system of ordinary differential equations. An implicit-explicit (IMEX) time integration scheme is adopted, and the resulting quasilinear structure facilitates the development of a single-step numerical integration scheme that admits a closed-form update, providing stable, accurate, and computationally efficient solutions. Unlike traditional gradient-based approaches, the proposed framework does not require the Jacobian matrix and is insensitive to the initial guess error of the solution, making it easier to implement and more robust in practice. Due to its significantly reduced computational cost, the proposed framework is particularly well-suited for simulating large-scale battery systems operated under advanced closed-loop control strategies.

Biogas production through anaerobic digestion (AD) presents a sustainable energy alternative with significant potential to reduce global warming. However, AD is a complex, nonlinear, and dynamic process influenced by time-varying parameters and non-stationary disturbances. These challenges, together with the limited availability of reliable online measurements for key concentration variables, hinder effective real-time monitoring. To address these limitations, this study proposes a joint state and parameter estimation approach based on a modified Advanced Monitoring and Control (AMOCO) model with a Particle Swarm Optimization (PSO)-tuned Extended Kalman Filter (EKF), a combination not previously applied to anaerobic digestion processes. The modified AMOCO model, originally developed for control applications, is adapted to better align with both simulated and experimental data. Sensitivity analysis identifies three key parameters whose estimation significantly improves system reconstruction. To further enhance estimation performance, PSO is employed to tune the noise covariance matrices of a discrete EKF. Validation using the Anaerobic Digestion Model No. 1 (ADM1) as a benchmark plant confirms reliable state and parameter estimation and accurate output predictions. Robustness is assessed by applying the EKF tuned for nominal noise to different measurement-noise levels, demonstrating stable performance under moderate noise mismatch and limited degradation under severe mismatch. Results show that the proposed PSO-EKF approach achieves a 70%-80% reduction in augmented state-estimation RMSE compared with a conventionally tuned EKF. The methodology provides a foundation for monitoring and control in AD and has potential for adaptation to other complex, non-linear bioprocesses, thus supporting more sustainable and efficient waste-to-energy systems.

The widespread adoption of photovoltaic (PV), electric vehicles (EVs), and stationary energy storage systems (ESS) in households increases system complexity while simultaneously offering new opportunities for energy regulation. However, effectively coordinating these resources under uncertainties remains challenging. This paper proposes a novel home energy management framework based on deep reinforcement learning (DRL) that can jointly minimise energy expenditure and battery degradation while guaranteeing occupant comfort and EV charging requirements. Distinct from existing studies, we explicitly account for the heterogeneous degradation characteristics of stationary and EV batteries in the optimisation, alongside stochastic user behaviour regarding arrival time, departure time, and driving distance. The energy scheduling problem is formulated as a constrained Markov decision process (CMDP) and solved using a Lagrangian soft actor-critic (SAC) algorithm. This approach enables the agent to learn optimal control policies that enforce physical constraints, including indoor temperature bounds and target EV state of charge upon departure, despite stochastic uncertainties. Numerical simulations over a one-year horizon demonstrate the effectiveness of the proposed framework in satisfying physical constraints while eliminating thermal oscillations and achieving significant economic benefits. Specifically, the method reduces the cumulative operating cost substantially compared to two standard rule-based baselines while simultaneously decreasing battery degradation costs by 8.44

Battery electrical tab cooling is effective at reducing internal thermal gradients by exploiting the high thermal conductivity of the current collectors, whereas surface cooling is effective at reducing temperature rise because of its large heat transfer area. Using either strategy alone, however, limits the achievable trade-off between thermal uniformity and temperature rise reduction. This work proposes an integrated tab-surface cooling (ITSC) system in which coolant is dynamically allocated among the lateral surface and tab channels. The allocation is formulated as an optimal control problem in which the battery temperature is regulated towards a desired reference and thermal gradients are minimised. To support this formulation, a first-principles coolant model is developed and coupled with battery and valve-actuation models. The resulting optimal coolant-allocation problem is solved using a computationally efficient real-time iteration model predictive control (RTI-MPC) scheme, with a nonlinear MPC serving as a closed-loop performance benchmark. Evaluation results under realistic driving conditions showed that RTI-MPC reproduces the nonlinear MPC thermal response with absolute errors below 0.0035 degC while reducing the computational cost from several seconds to 19.3 ms, indicating strong potential for real-time implementation. Additionally, evaluation of the proposed ITSC system against conventional cooling configurations demonstrates that ITSC achieves the best overall trade-off between temperature regulation and thermal gradient reduction.

Retired lithium-ion batteries are an important resource for second-life energy storage, but their practical reuse requires rapid and efficient state assessment under unknown usage histories. To address this problem, we develop a pulse-based framework for joint state-of-health (SOH) and state-of-charge (SOC) estimation of randomly retired lithium-ion battery cells. A set of voltage-response features is extracted from short bipolar pulse tests and used as diagnostic descriptors. Based on these features, two machine learning models, extremely randomized trees (ExtraTrees) and a tabular prior-data fitted network (TabPFN), are employed for battery state estimation and evaluated using data from 270 retired cells that cover three chemistry types and four capacity classes. The results show that TabPFN clearly outperforms ExtraTrees and achieves mean absolute errors ranging from 0.0076 to 0.0306 for SOH estimation and below 0.01 for SOC estimation across all cell groups. To reduce the testing burden, a subset of SOC-dependent pulse tests is removed, and the missing feature points are reconstructed using interpolation methods. The reduced-test results show that near-baseline estimation accuracy can be retained even after removing 40%–70% of SOC-dependent pulse tests, depending on the target state and accuracy requirements. These results demonstrate a practical route toward faster, lower-cost, and scalable state assessment for second-life battery applications.

Optimal cooling that minimizes thermal gradients and the average temperature is essential for enhanced battery safety and health. This work presents a new modeling approach for battery cells of different shapes by integrating the Chebyshev spectral-Galerkin (CSG) method and model component decomposition. As a result, a library of reduced-order, computationally efficient battery thermal models is obtained, characterized by different numbers of states. These models are validated against a high-fidelity finite-element model and are compared with a thermal equivalent circuit (TEC) model under real-world vehicle driving and battery cooling scenarios. Illustrative results demonstrate that the proposed model with four states can faithfully capture the 2-D thermal dynamics, while the model with only one state significantly outperforms the widely used two-state TEC model in both accuracy and computational efficiency, reducing computation time by 28.7%. Furthermore, our developed models allow for independent control of tab and surface cooling (SC) channels, enabling effective thermal performance optimization. Additionally, the proposed model’s versatility and effectiveness are demonstrated through various applications, including the evaluation of different cooling scenarios, closed-loop temperature control, and the thermal assessment of cell aspect ratio.

Free-energy landscapes and chemical potentials govern the dynamics of phase transitions, transport, and stability in functional materials, yet they remain experimentally inaccessible under realistic operating conditions. Here we introduce a Bayesian model-integrated neural network (BMINN) that embeds physics-based formulations of non-autonomous partial differential-algebraic equations into probabilistic learning. This approach reconstructs hidden thermodynamics directly from macroscopic current-voltage data, providing quantitative access to metastable states, staging transitions, and energy barriers without synchrotron probes. Demonstrated on lithium-graphite electrodes, BMINN recovers full Gibbs free-energy landscapes with fidelity validated against operando X-ray diffraction. The framework generalizes across dynamical regimes, enabling accurate voltage prediction, internal state estimation, and inference of governing parameters. Beyond batteries, BMINN exemplifies a broadly applicable strategy for learning missing physics in multiphase, non-equilibrium systems, offering a new pathway to uncover hidden thermodynamic functions across condensed matter and materials physics.

In order to avoid excess waste generation and provide much needed energy storage capacity, lithium ion (Li-ion) batteries, when retired from their 1st-life, can be repurposed or given a 2nd-life in lower-stress storage roles. To do so, and to determine for what purpose, accurately predicting the degradation rate of 2nd-life Li-ion batteries' state of health is highly important, yet difficult, owing to the lack of available data from cells of sufficient aging variety. Additionally, as there are no formal standards on what information may come with potential 2nd-life batteries, it is hard to predict their subsequent behavior. While certain models do exist for predicting degradation of certain cell types/chemistries, such models typically rely on extensive data from the battery's 1st-life and do not generalize well over different types of cell. This work aims to establish a novel entropy-based theoretical approach, and a novel entropy-based algorithm, for predicting 2nd-life batteries' behavior. The proposed model hybridizes simple machine learning methods with a light weight model based on physics, centered around approximating the amounts of generated irreversible thermodynamic entropy and Shannon entropy. Tests of this model on three different Li-ion battery types (LFP, LCO, NMC) show that the model is able to make accurate predictions on 2nd-life battery lifetime while only requiring data from one single cycle. Subsequent sampling is shown to further improve model accuracy, placing this novel algorithm on par with state of the art ML-estimates, but without the need for extensive training or reliance on extensive data from 1st-life.

The techno-economic benefits of incorporating battery degradation into advanced control strategies necessitate the development of degradation diagnosis as an advanced function in battery management systems (BMSs). To address this, a curvature-based knee identification method was proposed in our previous work [1]. Here, we further validate its effectiveness on a new battery aging dataset under a realistic driving profile and conduct spectral analysis of the approximated capacity fade curvature. The curvature-based method shows consistent knee identification performance on this dataset and the approximated curvature is found to correlate with underlying degradation modes and a shift of electrode material phase transition points. The method uses capacity data as the only input, which is easy to acquire in the lab and it is applicable in battery energy storage systems for grid applications.

The techno-economic and safety concerns of battery capacity knee occurrence call for developing online knee detection and prediction methods as an advanced battery management system (BMS) function. To address this, a transferable physics-informed framework that consists of a histogram-based feature engineering method, a hybrid physics-informed model, and a fine-tuning strategy, is proposed for online battery degradation diagnosis and knee-onset detection. The hybrid model is first developed and evaluated using a scenario-aware pipeline in protocol cycling scenarios and then fine-tuned to create local models deployed in a dynamic cycling scenario. A 2D histogram-based 17-feature set is found to be the best choice in both source and target scenarios. The fine-tuning strategy is proven to be effective in improving battery degradation mode estimation and degradation phase detection performance in the target scenario. Again, a strong linear correlation was found between the identified knee-onset and knee points. As a result, advanced BMS functions, such as online degradation diagnosis and prognosis, online knee-onset detection and knee prediction, aging-aware battery classification, and second-life repurposing, can be enabled through a battery performance digital twin in the cloud.

Precisely forecasting the energy consumption of electric vehicles not only alleviates the anxiety associated with driving range but also serves as the foundation for progressive advancements, including optimizing charging strategy and energy utilization. The main challenge lies in the inaccuracy of current methods, whether they are empirical models, physics-based models, or data-driven models. Based on newly constructed and engineered physics-informed features, this paper introduces a machine learning-based prediction framework, employing a synergy of offline global models and vehicle-based online adaptation. This combination aims to elevate accuracy in point predictions and also provide valuable information on prediction uncertainties. The developed framework is trained and extensively tested using data from a fleet of real-world electric vehicles. The leading global model, quantile regression neural network (QRNN), demonstrates an average error of 6.30%. Subsequent online adaptation results in a notable reduction to 5.04%, with both surpassing the performance of existing models significantly. Concurrently, the online QRNN exhibits a strong capability in enhancing the coverage probability and decreasing the average width of prediction intervals.

Tab cooling offers more uniform temperature distribution across the battery because of high thermal conductivity, while surface cooling is more effective at removing heat due to a larger cooling contact area. This work formulates the optimal integration of tab and surface cooling methods to synergise their unique strengths. This optimal integration control problem is solved within the model predictive control (MPC) framework, leading to minimised thermal gradients and average temperature rise. The proposed MPC scheme, which includes an advanced thermal model containing the cooling system’s converter switching mechanism, is evaluated against conventional side-only and base-only battery cooling schemes under the urban dynamometer driving schedule. Results demonstrate significant thermal performance improvements of the proposed MPC scheme over the conventional cooling methods.

The problems of immunodeficiency, as well as the possibility of controlling the processes of carbohydrate metabolism in the patient’s body, are one of the key tasks of modern immunology. The complexity of calculating and predicting the results of insulin therapy leads to the need to search for new solutions and methods for predicting insulin resistance processes and assessing the impact of various drugs on the patient’s body. One of the possible directions for improving this analysis is the use of mathematical statistics, and in particular, modeling biomedical processes in the patient’s body. Methods of mathematical statistics allow us to determine the dependencies of the results of these processes on the factors influencing them with external and internal an influence, which contributes to more effective diagnostics of the condition and prediction of treatment results. The use of computer methods for processing statistical information can significantly increase the efficiency of biomedical research, expand the scope of their application and provide a higher quality result. The authors analyze this possibility using the MatLab application package. This study is based on the initial data of a full-scale experiment conducted in 2022–2024 at Sahlgrenska Academy Gothenburg, Sweden. The aim of this study is to determine the features of modeling biomedical processes of carbohydrate metabolism in the patient’s body and to create a methodological basis for subsequent scientific medical research in this area.

This paper investigates the economic impact of vehicle-home-grid integration through an online optimization algorithm that manages energy flows between an electric vehicle, a household, and the electrical grid. The algorithm exploits vehicle-to-home (V2H) for self-consumption and vehicle-to-grid (V2G) for energy trading, adapting in real-time via a hybrid long short-term memory (LSTM) network for household load prediction and a nonlinear battery degradation model including cycle and calendar aging. Simulations show annual economic benefits up to EUR 3046.81 compared to smart unidirectional charging, despite a modest 1.96

Gaussian process (GP) models have been used in a wide range of battery applications, in which different kernels were manually selected with considerable expertise. However, to capture complex relationships in the ever-growing amount of real-world data, selecting a suitable kernel for the GP model in battery applications is increasingly challenging. In this work, we first review existing GP kernels used in battery applications and then extend an automatic kernel search method with a new base kernel and model selection criteria. The GP models with composite kernels outperform the baseline kernel in two numerical examples of battery applications, i.e., battery capacity estimation and residual load prediction. Particularly, the results indicate that the Bayesian Information Criterion may be the best model selection criterion as it achieves a good trade-off between kernel performance and computational complexity. This work should, therefore, be of value to practitioners wishing to automate their kernel search process in battery applications.

To enable a shift from fossil fuels to renewable and sustainable transport, batteries must allow fast charging and exhibit extended lifetimes—objectives that traditionally conflict. Current charging technologies often compromise one attribute for the other, leading to either inconvenience or diminished resource efficiency in battery-powered vehicles. For lithium-ion batteries, the way to meet both objectives is for the lithium plating potential at the anode surface to remain positive. In this study, we address this challenge by introducing a novel method that involves real-time monitoring and control of the plating potential in lithium-ion battery cells throughout their lifespan. Our experimental results on three-electrode cells reveal that our approach can enable batteries to charge at least 30% faster while almost doubling their lifetime. To facilitate the adoption of these findings in commercial applications, we propose a machine learning-based framework for lifelong plating potential estimation, utilizing readily available battery data from electric vehicles. The resulting model demonstrates high fidelity and robustness under diverse operating conditions, achieving a mean absolute error of merely 3.37 mV. This research outlines a practical methodology to prevent lithium plating and enable the fastest health-conscious battery charging.

Accurate state-of-charge (SOC) estimation is essential for optimizing battery performance, ensuring safety, and maximizing economic value. Conventional current and voltage measurements, however, have inherent limitations in fully inferring the multiphysics-resolved dynamics inside battery cells. This creates an accuracy barrier that constrains battery usage and reduces cost-competitiveness and sustainability across industries dependent on battery technology. In this work, we introduce an integrated sensor framework that combines novel mechanical, thermal, gas, optical, and electrical sensors with traditional measurements to break through this barrier. We generate three unique datasets with eleven measurement types and propose an explainable machine-learning approach for SOC estimation. This approach renders the measured signals and the predictive result of machine learning physically interpretable with respect to battery SOC, offering fundamental insights into the time-varying importance of different signals. Our experimental results reveal a marked increase in SOC estimation accuracy--enhanced from 46.1% to 74.5%--compared to conventional methods. This approach not only advances SOC monitoring precision but also establishes a foundation for monitoring additional battery states to further improve safety, extend lifespan, and facilitate fast charging.