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.