EVE-Ai™ 360 Adaptive Controls

An Onboard Battery Pack Software Solution for Every Application

Energy Storage
Artificial Intelligence

Optimize Battery Pack Control Strategy with Adaptive AI

Using AI and Machine Learning (ML) to improve range, performance, lifetime, and safety of battery packs through continuous software updates.​


AI Battery Pack Controls for Superior Performance

EVE-Ai™ creates Range Extension, Fast Charging Strategy, and Lifetime Reassurance strategies for every battery pack. AI Algorithms decide the best method to get there for each unique pack.
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Fast Charging

Safely reduce fast charge time with variable pack temperature and charging current.

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Velocity Recommendations

+25% range extension

+9% lifetime extension

Display passive recommendations to coach towards efficient driving.

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Dynamic State of Charge

+12% range extension 

+11% lifetime extension

Adjust the min/max SOC based on CAN & BMS data on temperature, humidity, and cell SOH.

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+35% lifetime extension


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+20% lifetime extension

+12% range extension 

+11% lifetime extension

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Dual Chemistry

+50% range extension

+90% lifetime extension

+12% range extension 

+11% lifetime extension

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Extended Range

EVE-Ai™ uses battery pack-specific AI to increase usable energy and decrease energy consumption, extending the vehicle's range capabilities.

Velocity Recommendations provided throughout drive cycle operate in conjunction with existing route planning software

Dynamic State of Charge (SOC) Limits expand the minimum and maximum SOC during safe conditions to extend range without compromising BMS safety functions

Eco routing is unlocked with ADAS and enhanced in Autonomous Vehicle installations


EVE-Ai™ reduces the strain of charging with Dynamic Thermal Charging Strategy, a Predictive Charging Model, and Overnight Charging Optimization. 

Dynamic Thermal Charging Strategy adapts charging strategy to diverse temperatures across the battery pack for fast charging with limited damage to cell lifetime

Predictive Charging Model created for each individual based on historical and fleet data for unique user habits

Overnight Charging Optimization protects battery life by timing overnight charging with predicted morning drive time, allowing for fast charging the next day



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Lifetime Reassurance

EVE-Ai™ ensures battery packs meet service life and warranties by continuously updating cell modeling and usable lifetime predictions based on battery cell wear. 

Accurate State of Health (SOH) and State of Charge (SOC) modeling is made possible with our Adaptive Cell Model trained on laboratory cell data and improved with battery pack utilization 

Battery Fault Predictability uses AI to classify battery cell usable lifetime and alert before cell conditions become unsafe

Cloud Connectivity allows the AI model to continuously learn from worldwide fleet data for faster and more precise adaptation to specific driver profiles


EVE-Ai™ assesses fleet-wide battery pack SOH and SOC data to minimize environmental impact, maximize fiscal savings, and enhance driver safety.

V2X Capabilities​

AI Learning Using Fleet Data

Remaining Useful Life (RUL) Estimation




  • Embedded software installation in ECU, BMS, or Hybrid 

  • Learns from historical and live data

  • Cloud connectivity for Over-the-Air (OTA) updates and V2X data sharing

  • Integration with BMS strategies

  • Integration with Autonomous and ADAS Systems

  • Battery Cell Chemistry Agnostic

  • CAN Bus communication

  • AUTOSAR compliant

Neural Network Algorithms​

Proprietary strategy based on 360 degrees of data coming from the battery pack, the vehicle, the driver, and the environment, adapts to each model to create an custom controls approach. These models include:

  • Energy Storage Models

  • Vehicle Characteristic Models

  • Individual Driver Profile Models

  • Environment Models


EVE-Ai™ Onboard AI Algorithm is Hardware Agnostic

Install in eMobility vehicles, no matter the application, installation location, or cell chemistry.
  • EVE-Ai™ is hardware agnostic and functions within standard BMS safety parameters

  • Deployed in current or future ECU, BMS, or Hybrid installation

  • Electra's proprietary neural network algorithms work on the back-end to inform AI learning 

  • Data collection, cleaning, and labeling occurs in the Cloud, saving memory storage on each hardware installation 


Have software integration questions? 

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V2X Communication for Energy Storage Precision

EVE-Ai    unlocks V2V, V2I, and V2G capabilities to adapt energy storage management according to surrounding conditions and activity for a detailed control approach 



Vehicle to Vehicle

For data sharing across fleets and models to improve AI responses in situations not yet encountered by each individual vehicle

Vehicle to Infrastructure

Traffic, weather, and condition predictions implemented into AI decisions in tandem with available IoT infrastructure

Vehicle to Grid

Grid communication improves charging decisions to appropriately time charging with grid activity and save energy cost

Maximize fleet uptime with EVE-Ai™ Fleet Analytics

Receive battery pack alerts, monitor fleet performance, and push OTA updates.

Adaptive Cell Model

Use programmed and current data to control module activity and predict battery failure.
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Our cutting edge electrochemical model uses lab data, historical data, and programmed scenarios of simulation data to train recurring neural networks to make predictions and control module activity.


This cell model is updated to match the cell type, energy output rate (kWh), and specific vehicle and is continuously updated throughout the vehicle's lifetime.


The Adaptive Cell Model learns from all relevant connected systems. For example, combining data from other individual vehicles in a fleet of the same cell, pack, and vehicle type.

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