EzyCharge Pulse - EV Battery Analytics Platform
The EV Battery Analytics Platform is designed to optimize the performance and lifespan of electric vehicle (EV) lithium-ion (Li-ion) batteries through real-time monitoring, data analytics, and predictive maintenance.
Our platform provides fleet operators, manufacturers, and end-users with actionable insights to enhance battery health, improve efficiency, and reduce operational costs.
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EzyCharge Pulse - Analytics Platform Tailored For
Two-wheeler (2W)
Three-wheeler (3W)
Use Case for 2W Operators: A two-wheeler delivery service utilizes the EV Battery Analytics Platform to monitor the battery health of its fleet in real time. By analyzing parameters like state of charge (SoC) and temperature, the platform identifies underperforming batteries and alerts operators before they fail.
Use Case for 3W Operators:
A three-wheeler ride-sharing company employs the EV Battery Analytics Platform to optimize battery performance across its fleet. By leveraging predictive analytics, the company can forecast battery degradation and schedule maintenance when it’s most needed, minimizing disruptions.
Data Analytics
Performance Analysis
Evaluate battery performance trends over time to identify efficiency bottlenecks like, State of Charge (SoC),State of Health (SoH), Cycle Count, Energy Throughput, Voltage Levels, Current Flow, Temperature
Data Analytics
Anomaly Detection
Utilize machine learning algorithms to identify and alert users to abnormal patterns that may indicate potential failures. Employ predictive algorithms to forecast battery degradation and estimate remaining useful life (RUL).
Data Analytics
Historical Data Review
Analyze historical performance data to understand long-term trends and improve future battery management strategies.Our platform collects and analyzes a comprehensive set of data parameters for lithium-ion batteries to get more insights.
Data Analytics
Visualization Tools
Interactive dashboards and visual reports provide a clear overview of battery performance metrics, facilitating easy interpretation of complex data. Supports integration with various devices and applications, providing flexibility in deployment.
EzyCharge Pulse - EV Battery Analytics Platform
Our EV Battery Analytics Platform empowers users with the tools they need to manage and optimize their lithium-ion battery systems effectively.
By leveraging real-time data, advanced analytics, and predictive maintenance strategies, we help you enhance battery performance, reduce costs, and promote sustainability in electric vehicle operations.
Real-time Monitoring: Continuously track battery performance metrics to ensure optimal functioning.
Data-Driven Insights: Leverage analytics to inform decision-making regarding battery usage, maintenance schedules, and end-of-life strategies.
Predictive Maintenance: Identify potential issues before they escalate, minimizing downtime and maintenance costs.
Sustainability: : Promote eco-friendly practices by maximizing battery lifespan and reducing waste.
Lightweight Protocol (MQTT): Ideal for low-bandwidth, high-latency environments, ensuring reliable communication with minimal overhead.
Robust Connection (TCP/IP) : Ensures reliable communication over networks with guaranteed delivery of messages.
Predictive Modeling: > Employ predictive algorithms to forecast battery degradation and estimate remaining useful life (RUL).
Customizable Thresholds: > Allow users to set personalized thresholds for critical parameters, ensuring timely interventions based on specific operational needs.
Benefits and Use Cases
EV Battery Analytics Platform
Our EV Battery Analytics Platform empowers users with the tools they need to manage and optimize their lithium-ion battery systems effectively. By leveraging real-time data, advanced analytics, and predictive maintenance strategies, we help you enhance battery performance, reduce costs, and promote sustainability in electric vehicle operations.
Fleet Management for 2Ws: Delivery services utilize real-time monitoring to ensure battery reliability, enabling timely and efficient deliveries.
Ride-Sharing Optimization for 3Ws: Ride-sharing companies harness predictive analytics for proactive maintenance, minimizing service disruptions and enhancing customer satisfaction.
Energy Consumption Insights: Operators gain valuable insights into energy usage patterns, allowing for more efficient route planning and operational strategies.
Battery Health Assessment: Regular assessments of battery health facilitate informed decisions regarding replacements and upgrades, ensuring optimal fleet performance.
Data-Driven Decision Making: Leverage advanced analytics to inform operational and maintenance decisions, leading to better resource allocation.