ARL

During a full-system scan, ARL's AI Diagnostic Engine groups related DTCs, identifies root cause and prioritizes checks — relay, sensor, wiring or control module.

Example: for an EV charging system fault, AI suggests checking charge-port interlock, OBC, BMS and HV cabling in a safe logical sequence, with power-down warnings before high-voltage work.

Technicians save 30–50% diagnostic time versus manual lookup — with lower risk of missing critical test steps.

The ARL Neural Network EV Module is trained on millions of live data points from BMS, MCU, OBC and thermal systems — learning normal vs abnormal patterns for each vehicle model.

When technicians scan live data, AI automatically highlights channels deviating from baseline, suggests highest-probability causes and links related DTCs — cutting manual waveform analysis time by 40%.

This technology is especially powerful for EVs: HV batteries have hundreds of cells, temperatures, voltages — too many parameters for humans to analyze in real time. AI does it in seconds.

The Predictive EV Module uses time-series analysis and machine learning on historical data — detecting degradation trends on batteries (abnormal SOH decline), OBC efficiency drop, BMS cell drift.

Garages can proactively recommend maintenance before limp-home mode — increasing service revenue and trust with fleet, electric taxi and commercial EV customers.

This predictive technology draws from Industry 4.0 and fleet telematics — but ARL brings it to Mobile diagnostics, accessible to every independent workshop.

Traditional lookup: DTC → manual TSB search → find wiring diagram → guess repair path. ARL Knowledge Graph: AI traversal from DTC through graph edges to TSB, wiring, parts, labor time — ranked by probability and repair history.

The graph is built from OEM data, aftermarket repair cases and continuous learning from every ARL diagnostic case worldwide — the more it's used, the smarter it gets.

This is the high-tech core of the difference: AI doesn't just answer questions — it understands relationships between every component on an electric vehicle.