This n8n workflow automates the collection, analysis, and logging of electric vehicle (EV) battery degradation data. It begins with a webhook that receives battery data, processes the information into manageable text segments, and creates embeddings for advanced analysis. The workflow integrates with a Redis vector store for efficient data retrieval and uses AI models from OpenAI and Cohere for natural language understanding and processing. An agent interprets the data, generating insights into battery health. Finally, the results are logged into a Google Sheet for record-keeping and ongoing monitoring. This workflow is ideal for EV manufacturers, fleet management firms, or technicians seeking automated, intelligent battery health assessments.
EV Battery Degradation Analysis and Reporting Workflow
| Node Count | 11 – 20 Nodes |
|---|---|
| Nodes Used | @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.embeddingsCohere, @n8n/n8n-nodes-langchain.lmChatOpenAi, @n8n/n8n-nodes-langchain.memoryBufferWindow, @n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter, @n8n/n8n-nodes-langchain.toolVectorStore, @n8n/n8n-nodes-langchain.vectorStoreRedis, googleSheets, stickyNote, webhook |
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