Predicting Battery Failure with Precision

Forecasting Battery Failure Through Global Crowdsourcing

Predicting Battery Failure with Precision

Forecasting Battery Failure Through Global Crowdsourcing

The Challenge

A global tech leader needed a reliable way to forecast battery failure across a large fleet of devices to enhance product performance, reduce support incidents, and protect end users.

With over 100,000 records from 588 batteries—each with detailed measurements like voltage, temperature, and capacity—they required accurate models to determine how many days remained before a battery would degrade or fail.

The Solution

Topcoder hosted a global data science challenge, inviting participants to build forecasting models that could assess current battery condition and predict time to failure. Over 200 data scientists explored patterns across 18 battery attributes to generate solutions that classified battery risk and forecasted degradation.

The customer evaluated models based on classification accuracy and error margin in risk prediction, selecting the most effective for potential deployment.

Challenge we ran:

• Battery Risk Prediction Challenge

214

Participants

56

Submissions

5

Winners

The Impact

The challenge equipped the customer with powerful forecasting models, enabling earlier detection of battery issues and improved planning for support and maintenance.

The speed, diversity, and rigor of the crowdsourced competition delivered high-performing results—helping the global technology leader enhance reliability, reduce risk, and boost customer trust in their devices.

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Achieve high-quality outcomes with Topcoder.

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