Smart charging load management
Optimization models allocate power across chargers via OCPP smart charging profiles, balancing grid limits, time-of-use tariffs and vehicle departure times at depots and public sites.
Predictive AnalyticsAI for EMobility
AI in e-mobility that keeps fleets charged.
We build AI in e-mobility for charge point operators, micromobility companies and electric fleets. Our models balance charging loads through OCPP, predict battery wear, rebalance shared scooters and plan electrification so vehicles stay available and energy costs stay under control.
- OCPP-aware smart charging
- Battery health prediction
- Micromobility rebalancing
01 Overview
Smarter charging, batteries and fleets for electric mobility
Electric mobility depends on infrastructure and assets that behave very differently from fuel-based fleets. Charge points fail, grid capacity is limited, tariffs change by the hour and batteries degrade based on how they are used. Our work connects charger telemetry, vehicle data and energy prices so operators can act before a broken connector strands a driver or a depot exceeds its power limit.
Applying ai in e-mobility spans the whole network. Smart charging schedules sessions around grid constraints and departure times. Predictive maintenance flags chargers likely to fault. Battery models estimate state of health for warranty and resale decisions. For shared e-scooters and e-bikes, demand forecasts guide rebalancing and battery swaps, while route and charge planning helps delivery and transit fleets electrify with confidence.
02 Challenges
What AI solves in EMobility.
The recurring problems we hear from EMobility teams — and where AI makes the biggest difference.
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CH-01
Charger downtime frustrates drivers
Faulty connectors, payment errors and communication drops leave drivers stranded. Predictive models analyze OCPP status logs and error codes to flag failing chargers before they go offline.
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CH-02
Grid limits and demand charges
Depots and sites with many chargers risk exceeding connection capacity or paying peak demand charges. Smart charging optimizes power allocation across sessions while meeting each vehicle's departure needs.
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CH-03
Battery degradation uncertainty
Fleet operators struggle to forecast battery replacement and residual value. State-of-health models use charge cycles, temperature and depth of discharge to estimate degradation per vehicle.
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CH-04
Unbalanced micromobility fleets
Shared scooters pile up in some areas and vanish from others. Demand forecasts by zone and hour guide rebalancing crews and battery swap routes more efficiently.
03 Use cases
AI use cases for EMobility.
Proven applications we design, build and run in production — each delivered through one of our core services.
Charger predictive maintenance
Models trained on charger status notifications, error codes and session histories predict hardware faults and dispatch technicians before chargers go offline.
ML DevelopmentBattery state-of-health estimation
Machine learning on BMS telemetry estimates remaining capacity and degradation trends, informing warranty claims, second-life decisions and charging policies that extend battery lifespan.
ML DevelopmentE-scooter rebalancing and swap routing
Demand forecasts by zone and hour drive optimized routes for rebalancing vans and battery swap crews, keeping vehicles available where riders open the app.
Predictive AnalyticsDriver and rider support assistant
A conversational assistant helps drivers start sessions, troubleshoot connectors, find working chargers and resolve billing issues, escalating hardware problems to the operations center.
AI Chatbot DevelopmentOn-vehicle and on-charger edge models
Lightweight models running on charger controllers or scooter IoT modules detect tampering, abnormal temperature and parking violations locally, even when connectivity is weak.
Edge AI & IoT04 Services
AI services for EMobility.
Predictive Analytics
Forecasting, churn, demand and risk models.
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ML Development
Custom machine learning models for prediction and classification.
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Edge AI & IoT
On-device AI for cameras, sensors and embedded hardware.
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Data Engineering for AI
Pipelines, vector stores and feature stores that feed AI.
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AI Chatbot Development
LLM chatbots for support, sales and internal knowledge.
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MLOps Services
CI/CD, monitoring and retraining pipelines for ML in production.
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05 How we deliver
From use case to production.
Every EMobility AI project follows the same five stages, so you validate value early and scale with confidence.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
06 Built for your sector
Designed for EMobility realities.
Data, regulation and the systems you already run shape every architecture decision we make.
Location, payment and grid data
Driver locations, charging histories and payments are sensitive. We minimize retained personal data, segment payment flows and design systems around GDPR, CCPA and applicable energy data and cybersecurity requirements.
OCPP, OCPI and fleet integration
We integrate with charge point management systems via OCPP 1.6 and 2.0.1, roaming platforms via OCPI, telematics providers, BMS data and energy management systems used by utilities and depots.
Hardware diversity and field conditions
Networks mix charger brands, firmware versions and vehicle models, all exposed to weather and vandalism. We normalize data across vendors and validate models under real field conditions before scaling.
How does AI improve EV charging networks?
AI helps charge point operators predict faults before chargers fail, balance power across sites to respect grid limits, forecast demand to plan new locations and support drivers through conversational assistants. Together these raise uptime and make the network more reliable for drivers and site hosts.
Can your smart charging work with our existing chargers?
In most cases, yes. Chargers that support OCPP smart charging profiles can receive power limits from our optimization service through your charge point management system. We review your hardware mix and firmware versions to confirm which features each charger supports.
How accurate is AI battery health prediction?
Accuracy depends on the telemetry available. With BMS data on cycles, temperature and voltage curves, models can track degradation trends well and improve as more history accumulates. We validate estimates against capacity tests where possible and report confidence ranges rather than single numbers.
How does AI help e-scooter and e-bike operators?
Demand forecasting tells operators where riders will need vehicles by hour and zone, and routing optimization plans rebalancing and battery swap trips. Edge models can also detect improper parking or tampering, helping operators meet city permit requirements. Insights feed directly into the dispatch tools field crews already use each shift.
Can AI help plan fleet electrification?
Yes. By analyzing existing routes, duty cycles and depot power capacity, models can recommend which vehicles to electrify first, how many chargers each depot needs and how to schedule charging around shifts and energy tariffs. Scenarios can be compared side by side before committing capital to vehicles or grid upgrades.