Accurate energy consumption prediction is crucial for optimizing the operation of electric commercial heavy-duty vehicles, e.g., route planning for charging. Moreover, understanding why certain predictions are cast is paramount for such a predictive model to gain user trust and be deployed in practice. Since commercial vehicles operate differently as transportation tasks, ambient, and drivers vary, a heterogeneous population is expected when building an AI system for forecasting energy consumption. The dependencies between the input features and the target values are expected to also differ across sub-populations. One well-known example of such a statistical phenomenon is Simpson’s paradox. In this paper, we illustrate that such a setting poses a challenge for existing XAI methods that produce global feature statistics, e.g., LIME or SHAP, causing them to yield misleading results. We demonstrate a potential solution by training multiple regression models on subsets of data via a divide-and-conquer approach. It not only leads to superior regression performance but also more relevant and consistent LIME explanations. Given that the employed groupings correspond to relevant sub-populations, the associations between the input features and the target values are consistent within each cluster but different across clusters. Experiments on both synthetic and real-world datasets show that such splitting of a complex problem into simpler ones yields better regression performance and interpretability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Heavy-duty battery electric vehicles rely on large and complex energy storage systems (ESS), composed of multiple battery modules, whose individual health and reliability are critical to vehicle performance and safety. This study applies an unsupervised anomaly detection framework, COSMO (Consensus Self-Organizing Models), to a naturalistic real-world dataset collected during routine operations of in-service heavy-duty vehicles. We extend the baseline COSMO by incorporating causal discovery algorithms to help detect early signs of faults in ESS across heterogeneous missions and external conditions. On-board sensors data is collected as a multivariate time series, including information such as voltage, current, temperature, state of charge, etc. Given the wide range of applications of heavy-duty vehicles, these signals typically exhibit extreme variability even under normal operation, making anomaly detection challenging. Causal graph discovery allows us to acquire latent structures that capture the underlying relationships among these influential features. The resulting learned causal graphs, for each battery module, serve as a more consistent representation that captures each battery module’s usage and behavior over time. Since battery modules within the same ESS are expected to behave similarly under comparable operating conditions, COSMO models them as a homogeneous group. We then mark as anomalous modules that are identified to exhibit causal graph representations deviating markedly from the consensus.

For detecting changes in air composition, the traditional method based on measuring the speed of sound lacks selectivity for different gaseous species and is easily influenced by environmental effects such as pressure, humidity, and temperature. Additionally, this method is difficult to be used for the quantitative analysis of air mixed with an unknown gas. In this paper, a data-driven model is developed for detecting changes in air composition from a qualitative perspective. By comparing the measured speed of sound with that theoretically calculated using the virial expansion for real air, the precise differences are used as data to construct a distance matrix, then the most typical speed difference is identified in order to calculate the z-score, from which the one-sided p-value (which is the probability of the z-score from a normal distribution) is calculated to detect a change in air composition at a given significance level. Experimental results show that the proposed data-driven model can accurately locate the time of change and determine the change intervals for air composition variations, and it has better accuracy and a lower value of RFT, almost equal to zero, compared with methods such as quartiles, standard deviation, interquartile range, and Bayesian detection and thus can be applied to domestic and industrial sensors for air monitoring, gas detection, and gas pollution alarms. © 2024 Elsevier Ltd

Suicidal behavior is relatively well studied in younger populations but remains understudied among older adults despite significant risks linked to loneliness, physical ailments, and depression. This study addresses the gap by analysing suicide among older adults in Sweden using advanced statistical and machine learning techniques. Leveraging both static and longitudinal data – including suicide attempts, psychopharmaceutical prescriptions, medical conditions, and sociodemographic factors – we tackle the challenges associated with temporal dynamics and data censoring. The proposed approach integrates survival models such as Cox Proportional Hazards (CoxPH), Random Survival Forest (RSF), and Gradient Boosting Survival Analysis (GBSA) with sequential machine learning methods, specifically Long Short-Term Memory (LSTM) networks. Our results demonstrate that combining survival analysis with sequential models significantly enhances prediction accuracy by effectively capturing time-dependent patterns and censored observations.

Accurate energy consumption prediction is crucial for optimising the operation of electric commercial heavy-duty vehicles, particularly for efficient route planning, refining charging strategies, and ensuring optimal truck configuration for specific tasks. This study investigates the application of multi-task curriculum learning to enhance machine learning models for forecasting the energy consumption of various onboard systems in electric vehicles. Multi-task learning, unlike traditional training approaches, leverages auxiliary tasks to provide additional training signals, which has been shown to enhance predictive performance in many domains. By further incorporating curriculum learning, where simpler tasks are learned before progressing to more complex ones, neural network training becomes more efficient and effective. We evaluate the suitability of these methodologies in the context of electric vehicle energy forecasting, examining whether the combination of multi-task learning and curriculum learning enhances algorithm generalisation, even with limited training data. We primarily focus on understanding the efficacy of different curriculum learning strategies, including sequential learning and progressive continual learning, using complex, real-world industrial data. Our research further explores a set of auxiliary tasks designed to facilitate the learning process by targeting key consumption characteristics projected into future time frames. The findings illustrate the potential of multi-task curriculum learning to advance energy consumption forecasting, significantly contributing to the optimisation of electric heavy-duty vehicle operations. This work offers a novel perspective on integrating advanced machine learning techniques to enhance energy efficiency in the exciting field of electromobility. © 2024 Copyright for this paper by its authors.

Batteries are a safety-critical and the most expensive component for electric buses (EBs). Monitoring their condition, or the state of health (SoH), is crucial for ensuring the reliability of EB operation. However, EBs come in many models and variants, including different mechanical configurations, and deploy to operate under various conditions. Developing new degradation models for each combination of settings and faults quickly becomes challenging due to the unavailability of data for novel conditions and the low evidence for less popular vehicle populations. Therefore, building machine learning models that can generalize to new and unseen settings becomes a vital challenge for practical deployment. This study aims to develop and evaluate feature selection methods for robust machine learning models that allow estimating the SoH of batteries across various settings of EB configuration and usage. Building on our previous work, we propose two approaches, a genetic algorithm for domain invariant features (GADIF) and causal discovery for selecting invariant features (CDIF). Both aim to select features that are invariant across multiple domains. While GADIF utilizes a specific fitness function encompassing both task performance and domain shift, the CDIF identifies pairwise causal relations between features and selects the common causes of the target variable across domains. Experimental results confirm that selecting only invariant features leads to a better generalization of machine learning models to unseen domains. The contribution of this work comprises the two novel invariant feature selection methods, their evaluation on real-world EBs data, and a comparison against state-of-the-art invariant feature selection methods. Moreover, we analyze how the selected features vary under different settings. © 2024 Copyright for this paper by its authors.

Time-To-Event (TTE) modeling using survival analysis in industrial settings faces the challenge of premature replacements of machine components, which leads to bias and errors in survival prediction. Typically, TTE survival data contains information about components and if they had failed or not up to a certain time. For failed components, the time is noted, and a failure is referred to as an event. A component that has not failed is denoted as censored. In industrial settings, in contrast to medical settings, there can be considerable uncertainty in an event; a component can be replaced before it fails to prevent operation stops or because maintenance staff believe that the component is faulty. This shows up as “no fault found” in warranty studies, where a significant proportion of replaced components may appear fault-free when tested or inspected after replacement.

In this work, we propose an expectation-maximization-like method for discovering such premature replacements in survival data. The method is a two-phase iterative algorithm employing a genetic algorithm in the maximization phase to learn better event assignments on a validation set. The learned labels through iterations are accumulated and averaged to be used to initialize the following expectation phase. The assumption is that the more often the event is selected, the more likely it is to be an actual failure and not a “no fault found”.

Experiments on synthesized and simulated data show that the proposed method can correctly detect a significant percentage of premature replacement cases.

In this work-in-progress paper two types of physics-based models, for accessing elastic and non-elastic air leakage processes, were evaluated and compared with conventional statistical methods to detect air leaks in city buses, via a data-driven approach. We have access to data streamed from a pressure sensor located in the air tanks of a few city buses, during their daily operations. The air tank in these buses supplies compressed air to drive various components, e.g. air brake, suspension, doors, gearbox, etc. We fitted three physics-based models only to the leakage segments extracted from the air pressure signal and used fitted model parameters as expert features for detecting air leaks. Furthermore, statistical moments of these fitted parameters, over predetermined time intervals, were compared to conventional statistical features on raw pressure values, under a classification setting in discriminating samples before and after the repair of air leak problems. The result of this exploratory study, on six air leak cases, shows that the fitted parameters of the physics-based models are useful for discriminating samples with air leak faults from the fault-free samples, which were observed right after the repair was performed to deal with the air leak problem. The comparison based on ANOVA F-score shows that the proposed features based on fitted parameters of physics-based models outrank the conventional features. It is observed that features of a non-elastic leakage model perform the best. © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network. © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

Batteries are a safety-critical and the most expensive component for electric vehicles (EVs). To ensure the reliability of the EVs in operation, it is crucial to monitor the state of health of those batteries. Monitoring their deterioration is also relevant to the sustainability of the transport solutions, through creating an efficient strategy for utilizing the remaining capacity of the battery and its second life. Electric buses, similar to other EVs, come in many different variants, including different configurations and operating conditions. Developing new degradation models for each existing combination of settings can become challenging from different perspectives such as unavailability of failure data for novel settings, heterogeneity in data, low amount of data available for less popular configurations, and lack of sufficient engineering knowledge. Therefore, being able to automatically transfer a machine learning model to new settings is crucial. More concretely, the aim of this work is to extract features that are invariant across different settings.

In this study, we propose an evolutionary method, called genetic algorithm for domain invariant features (GADIF), that selects a set of features to be used for training machine learning models, in such a way as to maximize the invariance across different settings. A Genetic Algorithm, with each chromosome being a binary vector signaling selection of features, is equipped with a specific fitness function encompassing both the task performance and domain shift. We contrast the performance, in migrating to unseen domains, of our method against a number of classical feature selection methods without any transfer learning mechanism. Moreover, in the experimental result section, we analyze how different features are selected under different settings. The results show that using invariant features leads to a better generalization of the machine learning models to an unseen domain.