Batterylog provides custom battery engineering that combines hardware design, BMS development, and software tools such as digital twins and IoT connectivity. The company delivers integrated solutions for battery monitoring, performance optimization, and supply‑chain management across industrial and renewable‑energy applications. Its services span from simulation and consulting to full‑stack hardware‑software integration, ensuring reliable operation throughout the battery lifecycle.
Funding
$10.4K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
High total cost of ownership and uncertainty in battery lifespan calculation create challenges for businesses relying on battery-powered equipment. Lack of real-time insights into battery health leads to inefficient battery replacement planning, often resulting in replacements only upon failure.
Solution
BatteryLog offers a cloud-based digital twin solution that provides continuous monitoring of battery health and usage metrics, enabling accurate predictions of battery lifespan and identification of optimization opportunities. The platform collects relevant battery data and stores it in a digital twin tailored to specific needs. Algorithms optimized for cloud computation calculate the state-of-health of each individual battery based on its usage patterns. This allows for extending the useful life of batteries and optimizing battery replacement schedules.
Target Audience
The primary customers are businesses using battery-powered machines, particularly those seeking to reduce total cost of ownership, improve battery lifespan, and implement dynamic pricing for battery-supported electricity purchases.
Features
- Digital twin creation based on relevant measured battery values stored in the cloud.
- Matrix-based battery model that calculates the state-of-health of each battery.
- Algorithms optimized for cloud computation and individual battery usage.
- AI-assisted model optimization refines model parameters based on usage data.
- Modular structure allows data input from the battery's BMS or a customizable API.
- Encrypted data transfer for high data security.