EV fleet managers struggled with limited visibility into battery degradation, leading to unexpected failures, reduced operational efficiency, and higher maintenance costs.
AI Solutions
Built a full-stack web and mobile platform integrated with IoT-enabled battery monitoring. Applied machine learning models to predict Remaining Useful Life (RUL) of EV batteries and support data-driven maintenance planning.
India
Electric vehicle fleet operators rely on battery performance for consistent service delivery and cost efficiency. Without real-time insights into battery health, managers face increased risk of breakdowns, reduced lifespan, and costly repairs. This case study examines how our IoT-integrated, AI-powered platform delivered predictive insights to help fleet operators optimize maintenance, extend battery life, and improve operational reliability.
Fleet managers lacked the tools to continuously monitor and analyze EV battery performance. Existing systems provided only basic metrics, failing to predict degradation trends or estimate remaining useful life. This reactive approach led to premature replacements, unplanned downtime, and higher operational costs.
We developed a secure, scalable platform with:
Trustometer equips non-profits with a powerful AI-driven framework for ensuring transparency, boosting donor trust, and reducing operational risks. By combining beneficiary verification, sentiment analysis, and governance scoring, it transforms the way charitable organizations manage trust and compliance, paving the way for more ethical and efficient operations.
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