arXiv subjects

Torsten Wik

Publications and source records attributed to Torsten Wik.

At least 19 recordsLinked to original sources

arXiv2026

Timescale-aware surrogate-assisted multi-objective optimization of battery cell design for energy density, fast charging, and degradation

Battery cell design must balance energy density, fast charging, and degradation, yet these metrics evolve over different time scales and are costly to optimize jointly. We develop a timescale-aware surrogate-assisted framework that evaluates beginning-of-life volumetric energy density and 10--80% charging time together with state-of-health (SOH) loss over 200 cycles. Physics-based simulations of 1501 cell designs generate 1427 quality-controlled samples across 12 manufacturing-relevant parameters. Objective-specific surrogates support total-order Sobol analysis, which reveals distinct parameter rankings across the three metrics. A cross-objective rank-union strategy then retains variables influential to at least one objective before evolutionary Pareto optimization. Re-evaluation of the optimized candidates using the original physics-based models confirms designs that outperform the reference cell in all three metrics. Among these jointly improving candidates, the objective-wise best solutions, attained by different designs, can reduce SOH loss by 99.31%, increase volumetric energy density by 12.49%, and shorten charging time by 28.73%. The framework makes Pareto exploration across disparate electrochemical timescales computationally tractable, thereby enabling systematic multi-objective optimization of battery cell design.

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arXiv2026

Model Predictive Coolant Allocation for Integrated Tab-Surface Cooling of Battery Cells

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.

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arXiv2026

Online Energy Management for Bidirectional EV Charging with Rooftop PV: An Aging-Aware MPC Approach

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% extra battery degradation. Even in the least favorable case with no remuneration for V2G energy, bidirectional operation still delivers an economic gain of EUR 355.8 through V2H. Sensitivity analyses over V2G price ratio, EV battery size, household demand, and pickup time uncertainty confirm that these benefits persist across a wide range of scenarios and highlight the potential of EVs as active energy nodes, enabling sustainable energy management and cost-effective battery usage in real-world conditions.

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arXiv2026

Reinforcement Learning-based Home Energy Management with Heterogeneous Batteries and Stochastic EV Behaviour

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%.

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arXiv2025

A Practitioner's Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications

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.

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arXiv2025

Spectral Analysis of Approximated Capacity Fade Curvature for Lithium-Ion Batteries

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.

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arXiv2025

Online Aging-Aware Energy Optimization for Vehicle-Home-Grid Integration

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% increase in battery aging. Even under unfavorable market conditions, with no V2G revenue, V2H alone provides yearly savings of EUR 425.48. Sensitivity analyses on battery capacity, household load, and price ratios confirm the consistent benefits of bidirectional energy exchange, highlighting the role of EVs as active energy nodes for sustainable management.

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arXiv2025

Smart Sensing Breaks the Accuracy Barrier in Battery State Monitoring

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.

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arXiv2025

A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction

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.

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arXiv2024

Thermal Modelling of Battery Cells for Optimal Tab and Surface Cooling Control

Optimal cooling that minimises thermal gradients and the average temperature is essential for enhanced battery safety and health. This work presents a new modelling approach for battery cells of different shapes by integrating Chebyshev spectral-Galerkin method and model component decomposition. As a result, a library of reduced-order computationally efficient battery thermal models is obtained, characterised 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 two-dimensional 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 channels, enabling effective thermal performance optimisation. 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 cell design optimisation.

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arXiv2024

Automatic Generation of Examinations in the Automatic Control Courses

Final written examination is the most important part of summative assessment in automatic control courses. Preparation of the examinations with a given number of points according to the concept of Constructive Alignment (which could be the main concept in future automatic control education) takes significant amount of time of the educator and motivates development of a toolkit for automatic compilation of examination problems. A decision support Matlab-LATEX toolkit based on random number generators for selection of examination problems is described in this report to facilitate the alignment. The toolkit allows application of Stepwise Constructive Alignment (a new method described in this report), where the alignment is achieved by a number of software runs associated with random trials. In each step the educator manually selects suitable problems before each run based on evaluation of the random choice from the previous run. Automatic generation of the examination together with solutions for the course 'Process control and measurement techniques' is presented as an example.

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arXiv2024

A Unified Model for Active Battery Equalization Systems

Lithium-ion battery packs demand effective active equalization systems to enhance their usable capacity and lifetime. Despite numerous topologies and control schemes proposed in the literature, conducting quantitative analyses, comprehensive comparisons, and systematic optimization of their performance remains challenging due to the absence of a unified mathematical model at the pack level. To address this gap, we introduce a novel, hypergraph-based approach to establish the first unified model for various active battery equalization systems. This model reveals the intrinsic relationship between battery cells and equalizers by representing them as the vertices and hyperedges of hypergraphs, respectively. With the developed model, we identify the necessary condition for all equalization systems to achieve balance through controllability analysis, offering valuable insights for selecting the number of equalizers. Moreover, we prove that the battery equalization time is inversely correlated with the second smallest eigenvalue of the hypergraph's Laplacian matrix of each equalization system. This significantly simplifies the selection and optimized design of equalization systems, obviating the need for extensive experiments or simulations to derive the equalization time. Illustrative results demonstrate the efficiency of the proposed model and validate our findings.

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arXiv2023

Analysis of potential lifetime extension through dynamic battery reconfiguration

Growing demands for electrification result in increasingly larger battery packs. Due to factors such as cell position in the pack and variations in the manufacturing process, the packs exhibit variations in the performance of their constituent cells. Moreover, due to the fixed cell configuration, the weakest cell renders the pack highly susceptible to these variations. Reconfigurable battery pack systems, which have increased control flexibility due to additional power electronics, present a promising solution for these issues. Nevertheless, to what extent they can prolong the battery lifetime has not been investigated. This simulation study analyzes the potential of dynamic reconfiguration for extending battery lifetime w.r.t. several parameters. Results indicate that the lifetime extension is larger for series than for parallel configurations. For the latter, the dominant factor is equivalent full cycles spread at the end of life, but resistance increase with age and the number of cells in parallel are also influential. Finally, for the former, the number of series-connected elements amplifies these effects.

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arXiv2023

MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling

The concept of integrating physics-based and data-driven approaches has become popular for modeling sustainable energy systems. However, the existing literature mainly focuses on the data-driven surrogates generated to replace physics-based models. These models often trade accuracy for speed but lack the generalizability, adaptability, and interpretability inherent in physics-based models, which are often indispensable in modeling real-world dynamic systems for optimization and control purposes. We propose a novel machine learning architecture, termed model-integrated neural networks (MINN), that can learn the physics-based dynamics of general autonomous or non-autonomous systems consisting of partial differential-algebraic equations (PDAEs). The obtained architecture systematically solves an unsettled research problem in control-oriented modeling, i.e., how to obtain optimally simplified models that are physically insightful, numerically accurate, and computationally tractable simultaneously. We apply the proposed neural network architecture to model the electrochemical dynamics of lithium-ion batteries and show that MINN is extremely data-efficient to train while being sufficiently generalizable to previously unseen input data, owing to its underlying physical invariants. The MINN battery model has an accuracy comparable to the first principle-based model in predicting both the system outputs and any locally distributed electrochemical behaviors but achieves two orders of magnitude reduction in the solution time.

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arXiv2023

Battery Capacity Knee-Onset Identification and Early Prediction Using Degradation Curvature

Abrupt capacity fade can have a significant impact on performance and safety in battery applications. To address concerns arising from possible knee occurrence, this work aims for a better understanding of their cause by introducing a new definition of capacity knees and their onset. A curvature-based identification of a knee and its onset is proposed, which relies on the discovery of a distinctly fluctuating behavior in the transition between an initial and a final stable acceleration of the degradation. The method is validated on experimental degradation data of two different battery chemistries, synthetic degradation data, and is also benchmarked to the state-of-the-art knee identification method in the literature. The results demonstrate that our proposed method could successfully identify capacity knees when the state-of-the-art knee identification method failed. Furthermore, a significantly strong correlation is found between knee and end of life (EoL) and almost equally strong between knee onset and EoL. As the method does not require the full capacity fade curve, this opens up online knee-onset identification as well as knee and EoL prediction.

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arXiv2022

Interpretable Battery Cycle Life Range Prediction Using Early Degradation Data at Cell Level

Battery cycle life prediction using early degradation data has many potential applications throughout the battery product life cycle. For that reason, various data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms. However, managing the rapidly increasing amounts of batteries at end-of-life with lower economic and technical risk requires prediction of cycle life with quantified uncertainty, which is still lacking. The interpretability (i.e., the reason for high prediction accuracy) of these advanced data-driven methods is also worthy of investigation. Here, a Quantile Regression Forest (QRF) model, having the advantage of not assuming any specific distribution of cycle life, is introduced to make cycle life range prediction with uncertainty quantified as the width of the prediction interval, in addition to point predictions with high accuracy. The hyperparameters of the QRF model are optimized with a proposed alpha-logistic-weighted criterion so that the coverage probabilities associated with the prediction intervals are calibrated. The interpretability of the final QRF model is explored with two global model-agnostic methods, namely permutation importance and partial dependence plot.

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arXiv2021

LQG control over unreliable channels-Full Proof

In this paper LQG control over unreliable communication links is derived. That is to say, the communication channels between the controller and the actuators and between the sensors and the controller are unreliable. Previous solutions to finite horizon discrete time hold-input LQG control for this case do not fully utilize the available information. Here a new solution is presented which resolves this limitation. The focus is to derive and present a full mathematical proof to derive the optimal control sequence.

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arXiv2020

Design and comparative analyses of optimal feedback controllers for hybrid electric vehicles

This paper presents an adaptive equivalent consumption minimization strategy (ECMS) and a linear quadratic tracking (LQT) method for optimal power-split control of combustion engine and electric machine in a hybrid electric vehicle (HEV). The objective is to deliver demanded torque and minimize fuel consumption and usage of service brakes, subject to constraints on actuator limits and battery state of charge (SOC). We derive a function for calculating maximum deliverable torque that is as close as possible to demanded torque and propose modeling SOC constraints by tangent or logarithm functions that provide an interior point to both ECMS and LQT. We show that the resulting objective functions are convex and we provide analytic solutions for their second order approximation about a given reference. We also consider robustness of the controllers to measurement noise using a simple model of noise. Simulation results of the two controllers are compared and their effectiveness is discussed.

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