.png){kind=small}
.png)
PREPARE YOUR BATTERY PACK FOR EVERY SCENARIO
The power of battery intelligence onboard the vehicle.
Energy Storage
Vehicle
Environment
Artificial
Intelligence
EVE-Ai 360 Adaptive Controls is an executable code installed onboard electric vehicles that utilize 360 degrees of battery, vehicle, and environmental data to optimize battery range, charging, and lifetime. The Adaptive Battery Digital Twin is at the heart of the technology as it continuously updates cell modeling based on historical and real-time data to inform charge and discharge decisions.
Range
Charging
Lifetime
THE ADAPTIVE BATTERY
DIGITAL TWIN
Use historical and real-time data to control battery activity and predict battery failure.
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.
The digital twin is continuously updated to match the cell type, energy output rate (kWh), and unique vehicle characteristics throughout the vehicle's lifetime.
The Adaptive Battery Digital Twin learns from all relevant connected systems by combining data from other individual vehicles in a fleet of the same cell, pack, and vehicle type.
USE AI AND ML TO EXTEND RANGE, OPTIMIZE CHARGING, & REASSURE LIFETIME
AI algorithms assess the vehicle and environmental data to maximize battery function.
EXTEND RANGE
EVE-Ai™ uses AI algorithms to increase usable energy and decrease energy consumption, extending the vehicle's range capabilities.
Dynamic State of Charge (SOC) Limits expand the minimum and maximum SOC during safe conditions to extend range without compromising BMS safety functions
Velocity Recommendations are provided throughout a drive cycle to maximize range and operate in conjunction with existing route planning software
Eco routing is unlocked with ADAS and enhanced in Autonomous Vehicle installations
EVE-Ai reduces the strain of all charging instances with a dynamic thermal charging strategy, a predictive charging model, and Overnight Charging Optimization to allow for fast charging with limited State of Health (SOH) impact.
Dynamic Thermal Charging adapts diverse temperatures across the battery pack for fast charging with limited harm to cell lifetime
Predictive Charging Model analyzes unique user behavior patterns to customize charging strategy to each user and fleet
Overnight Charging Optimization considers predicted user behavior to reduce the impact on battery life and allow for fast charging the next day
OPTIMIZE CHARGING
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 based on Adaptive Battery Digital Twin trained on laboratory cell data and improved with real-time battery pack utilization
Battery Fault Predictability uses AI to classify battery cell usable lifetime and predict unsafe cell conditions
Cloud Connectivity allows the AI model to continuously learn from worldwide electric fleet data for faster and more precise adaptation to specific driver profiles
CUSTOMIZED INSTALLATION STRATEGY
Electra works with technical teams to embed Controls effectively and efficiently.
EVE-Ai 360 Adaptive Controls makes decisions for battery pack performance and safety based on environmental data and vehicle activity. Data collection, cleaning, and labeling occurs in the Cloud, saving memory storage on each hardware installation. Data can be collected with EIS, Acoustic Measurement, and other advanced sensors for the most accurate ML model training.
Three deployment options are available to customers based on the company's current hardware setup:
Active Controls & Analytics Embedded in BMS Microcontroller
Active Controls & Analytics with Cloud Connectivity Embedded in ECU
Onboard Analytics & Cloud Connectivity via Embedded ECU
EVE-Ai can work with any cell chemistry (i.e. Li-ion, Solid-State, Ni-Rich NMC, Silicon-based Anode), hardware (i.e. ECU, VCU, BMS), and sensor (i.e. EIS, Acoustic Measurement) for seamless integration.
