Predictive energy management
for EV

Maximizes energy efficiency and elevates the performance of electric vehicles, reducing the need for frequent recharges.

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What you will get

Efficient energy consumption and reduced
frequency of recharges

Personal

Ana and John want to be sure they have sufficient battery charge for daily city commutes, so they can complete their round trip without any worries.

Android-powered tablet integrated with Google Maps gives personalized recommendations based on individual driving patterns.

Business

Ms. Johnston is primarily focused on optimizing logistics operations within her company, as some drivers tend to use excessive climate control or high speeds, leading to more frequent truck recharging.

A web-based admin application provides real-time insights into driver statuses and live Google Maps tracking.

Solution Overview

01

AMPERE by N-iX is a proprietary software specifically engineered to revolutionize energy consumption predictability in electric vehicles. By harnessing the power of advanced computing, we aim to bring a new level of efficiency and reliability to the EV industry.

02

This software directly addresses the critical issue of range uncertainty in EVs, a major concern for drivers globally. By optimizing battery utilization, our solution ensures that EV owners can enjoy longer journeys without the constant worry of finding the next charging station.

03

At the core of our solution is a sophisticated system that integrates real-time telemetry data, algorithmic computations, and an analysis of driver behavior metrics. This tri-fold approach allows our software to accurately predict energy needs and adapt to various driving conditions and styles.

Core benefit

The primary advantage of our software is its ability to maximize the energy efficiency in EVs significantly. By doing so, it reduces the necessity for frequent recharges, thereby saving time and elevating the overall driving experience. This not only benefits individual drivers but also contributes to the broader goal of sustainable and efficient energy use in transportation.

User journey

01

User authorization

02

Route planning

03

Data is acquired on driving patterns (i.e. speed, driving harshness, climate control preferences), route attributes (i.e. road surface, traffic, weather), and vehicle specifications (i.e. HVAC, suspension and battery temperature)

04

Acquired data is analysed and correlated with the established driver’s behavior patterns

05

Assessment of the energy use and route requirements

06

Prediction engine provides driving recommendations

07

Prediction engine continuously calibrates the recommendations for optimal energy use in real-time

08

If the driver ignores recommendations, the prediction engine suggests moving to the nearest EV charging station

09

Prediction engine integrates with the cloud-based data warehouse and generates the BI reports with the assessment of each driver’s battery performance and provides a list of recommendations to improve energy efficiency.

not released but could be done