Germany registered 545,142 battery-electric vehicles (BEVs) in 2025, representing a 43.2% year-on-year increase and a record 19.1% market share, placing growing pressure on authorized dealer networks to maintain complex high-voltage (HV) systems reliably. This study investigates whether a unified data pipeline combining Controller Area Network (CAN) real-time frames, OBD-II freeze-frame diagnostic snapshots, and dealer service records can enable dealer-level predictive diagnostics for German EV high-voltage systems. The methodology applies a systematic review of peer-reviewed literature (2020-2025), analysis of published benchmark datasets, and structured case evaluation of three OEM diagnostic platforms used in German dealerships. Results indicate that ensemble machine learning models, specifically Random Forest regression for State-of-Health (SoH) estimation (R-squared = 0.94, RMSE = 1.52%) and LSTM-based anomaly detectors for freeze-frame pattern recognition, reduce unplanned high-voltage fault events by an estimated 28-36% compared with schedule-only maintenance approaches. The study proposes a dealer-context integration architecture linking vehicle telematics to Dealer Management Systems. Findings are relevant for EV service managers, OEM technical trainers, and automotive dealership groups operating in high-BEV-penetration markets.

CAN bus diagnostics, freeze-frame data, State of Health, State of Charge, predictive maintenance, high-voltage battery systems, German electric vehicles, Random Forest, LSTM, dealer management system.

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