The Electra Team
An Adaptive Battery Digital Twin: The Key to Battery Pack Precision
Updated: Nov 9, 2022
Precise battery pack controls must balance a constant tradeoff between accessing the full performance capability of the battery pack and preserving battery health for full lifetime fulfillment in every given driving scenario. To create an equilibrium between performance and health preservation, the controls must operate off of an accurate depiction of battery cell characteristics. Similarly, methods of unlocking excellent battery pack performance without compromising lifetime exist in unique scenarios that must be identified by precise pack controls by accounting for operational environmental sensitivity.
While all current battery packs utilize laboratory testing data to inform battery control decisions, this only provides a picture of cell characteristics at the start of battery life. Electra is innovating battery pack control precision by placing an Adaptive Battery Digital Twin at the center of EVE-Ai 360 Adaptive Control software, providing a clear depiction of battery health at all stages of cell life.
Electra’s Adaptive Battery Digital Twin is an electrochemical model that combines lab testing data, proprietary simulation results, and real-time driving analysis for each vehicle model, battery pack voltage, and cell type. Once on the road, the AI algorithm continuously learns the vehicle and similar vehicles in its fleet to compile the most accurate depiction of the cell’s characteristics throughout its lifetime.
EVE-Ai Adaptive Battery Digital Twin & Dynamic SOC
With a unique cell model, EVE-Ai can identify when it is possible to safely push the battery pack to exceed expectations in each area of battery performance. EVE-Ai: Dynamic State of Charge (SOC) adjusts the depth of discharge of the battery pack based on current trip demands and the state of the cell model. Since 98% of vehicle trips are under 20 miles (32.19 kilometers), the depth of discharge can be restricted without causing range anxiety in short trip scenarios. This gentler use of the battery according to current driving needs and the cell model during the majority of trips allows the algorithm to expand the depth of discharge to increase range for long trips without sacrificing battery health. Alternatively, the algorithm can reward drivers who lessen the strain on SOH through responsible driving and charging behavior by unlocking exceptional charging and driving capabilities when available.
Safe Fast Charging Using a Precise Battery Digital Twin
The EVE-Ai Battery Digital Twin also supports fast charging and overnight charging features. It is known that the ideal charging temperature to minimize cell degradation resides at a precise temperature between 40° and 60° Celsius that is unique to each battery pack. With an accurate model of the unique cells in a given battery pack, EVE-Ai controls determine the precise charging temperature appropriate for the system and communicate with the thermal management system ahead of charging to ensure safe and effective fast charging.
Understanding the gap in current battery pack performance and battery pack needs requires a transparent look inside the battery pack and inside its cells for clarity. Current battery pack systems only attempt to close this gap in the design phase when a cell is selected for the pack based on its known characteristics, and any future charge and discharge decisions are similarly based on the cell’s characteristics before use. This approach neglects inevitable degradation over the cell’s lifetime and cannot correct cell usage before detrimental damage is done.
Ready to learn more about Electra's EVE-Ai 360 Adaptive Controls? Schedule a time to meet with the Electra Team using our Contact Us page, or access the chat in the lower righthand corner to reach an Electra representative.
Subscribe to Electra's Newsletter for AI battery pack tech updates in your inbox.