Pulsetrain develops integrated battery management and inverter technology for electric vehicles and related applications. Their system combines advanced hardware and software to actively manage thermal risks and precisely control battery performance, extending lifespan by up to 80%. This technology optimizes charging speeds, enhances vehicle efficiency, and improves overall power delivery across automotive and non-road machinery.
Funding
Funding not disclosed
Founders
Product
Problem
Electric vehicle (EV) batteries degrade over time, limiting vehicle range and lifespan, while also posing safety risks due to potential thermal events. Traditional battery management systems (BMS) often lack the sophistication to optimize battery performance, prevent failures, and enable efficient battery reuse.
Solution
PULSETRAIN develops advanced battery management systems (BMS) that leverage AI and real-time data to extend EV battery life, enhance safety, and enable battery reuse. Their technology employs sophisticated algorithms to optimize charging and discharging cycles, minimizing degradation and maximizing battery lifespan by up to 80%. The BMS also incorporates advanced thermal management strategies to mitigate the risk of thermal events and ensure safe operation. Furthermore, PULSETRAIN's system facilitates battery reuse through seamless software updates, adapting the battery's performance profile to new applications and extending its overall lifecycle.
Target Audience
PULSETRAIN's primary customers are automotive manufacturers, particularly those focused on electric vehicles, as well as companies involved in two and three-wheelers, non-road applications, and all-terrain vehicles.
Features
- AI-driven software updates for battery re-use, adapting performance to new applications.
- Advanced thermal management to minimize the risk of thermal events.
- Integrated system architecture reducing the number of components and weight.
- Pulsed charging capabilities enabling operation at high C-rates for fast charging.
- Real-time data analysis for optimized charging and discharging cycles.