Automating EV Battery Degradation Reports with n8n

Electric vehicle (EV) fleet operators and battery engineers require consistent, repeatable insights into battery health. This reference guide describes a production-ready EV battery degradation report workflow template in n8n. The automation ingests telemetry and diagnostic text through a webhook, splits and embeds content for semantic search, persists vectors in Redis, and generates human-readable reports with an AI agent. The design emphasizes reproducibility, auditability, and integration with Google Sheets for logging and downstream analysis.

1. Workflow Overview

This n8n workflow automates the full lifecycle of EV battery degradation reporting, from raw input to logged report output:

1. Receive battery telemetry and diagnostic notes via an HTTP Webhook (POST).
2. Segment long text into smaller chunks using a Text Splitter node.
3. Generate semantic embeddings for each text chunk using Cohere.
4. Insert embeddings into a Redis vector index named ev_battery_degradation_report.
5. On report generation, query Redis for the most relevant chunks and expose them to an AI Agent as a tool.
6. Use an LLM-based Agent to assemble a structured, human-readable degradation report.
7. Append the generated report and key metadata to a Google Sheet for logging and audit.

The template is suitable for both small pilot deployments and large-scale fleet scenarios where many vehicles stream telemetry and diagnostic information.

2. Architecture & Data Flow

2.1 High-level Architecture

• Ingress: Webhook node receives JSON payloads from devices or upstream ingestion services.
• Pre-processing: Text Splitter node normalizes and chunks diagnostic text.
• Vectorization: Cohere Embeddings node converts each chunk into a vector representation.
• Storage: Redis node stores vectors and associated metadata in a vector index.
• Retrieval: Redis Query node retrieves semantically similar chunks for a given query.
• Reasoning: Agent node combines Redis results, short-term memory, and prompt logic to generate a report using an LLM.
• Logging: Google Sheets node appends report summaries and key metrics for audit and downstream processing.

2.2 Data Flow Sequence

1. Incoming request: A POST request hits the Webhook endpoint with telemetry and technician notes.
2. Text extraction: The workflow extracts relevant textual fields (for example, technician_notes or free-form diagnostic logs).
3. Chunking: The Text Splitter splits large text into overlapping segments to preserve context.
4. Embedding generation: Each chunk is passed to Cohere, which returns a high-dimensional embedding vector.
5. Vector insertion: Embeddings and metadata are inserted into Redis under the index ev_battery_degradation_report.
6. Report request: When the workflow needs to generate a report, it uses Redis to retrieve the most relevant context for the current vehicle or query.
7. Agent execution: The Agent node consumes:
   • Retrieved context from Redis (via a Tool interface).
   • Conversation state from a Memory node.
   • Prompt instructions for structuring the EV battery degradation report.
8. Report logging: The final report and selected fields (vehicle ID, timestamp, key metrics) are appended as a new row in Google Sheets.

3. Node-by-Node Breakdown

3.1 Webhook (Trigger)

The Webhook node is the entry point of the workflow. It is configured to accept HTTP POST requests, typically with a JSON body that includes both structured telemetry and unstructured diagnostic text.

• Path: Example path /ev_battery_degradation_report.
• Method: POST.
• Typical payload:
  • vehicle_id – Unique identifier of the EV.
  • timestamp – ISO 8601 timestamp indicating when measurements were taken.
  • telemetry – Object containing metrics such as cycle count, state of health (SOH), maximum temperature, average voltage, and other relevant parameters.
  • technician_notes – Free-text notes describing observed issues, degradation patterns, or test results.

Integration points include direct device uploads, existing ingestion services, or internal APIs that forward telemetry to the webhook. For production, you can secure this endpoint with tokens, IP allowlists, or gateway-level authentication.

3.2 Text Splitter (Text Chunking)

The Text Splitter node prepares unstructured text for embedding by dividing it into smaller segments.

• Input: Fields such as technician_notes or full diagnostic logs.
• Chunk size: 400 characters.
• Chunk overlap: 40 characters.

This configuration strikes a practical balance between semantic completeness and embedding cost. Overlap ensures that information that spans boundaries is not lost. For longer technical reports, you can adjust chunkSize and chunkOverlap based on average document length and the level of detail required in retrieval.

3.3 Embeddings (Cohere)

The Cohere Embeddings node converts each text chunk into a numerical vector suitable for semantic search.

• Provider: Cohere.
• Input: Array of text chunks from the Text Splitter node.
• Output: Embedding vectors for each chunk, typically a high-dimensional float array.

These embeddings allow the workflow to perform similarity search over technical content, so the AI Agent can retrieve relevant historical notes, similar failure modes, or comparable degradation profiles when generating new reports.

The node requires a valid Cohere API key configured as n8n credentials. Rate limits and model selection are managed through the Cohere account, so ensure that the chosen model is suitable for technical language and the expected volume.

3.4 Insert (Redis Vector Store)

The Redis node (Insert mode) persists embeddings in a vector index that supports approximate nearest neighbor queries.

• Index name: ev_battery_degradation_report.
• Data stored:
  • Embedding vector for each text chunk.
  • Associated metadata such as vehicle_id, timestamp, and possibly a summary of telemetry values.
  • Original text chunk for later retrieval and display.

Redis acts as a fast, scalable vector database. The index configuration (for example, vector type, dimension, and distance metric) is handled in Redis itself and must match the embedding model used by Cohere. If the index is not correctly configured, inserts may fail or queries may return no results.

3.5 Query & Tool (Vector Retrieval)

When the workflow needs to generate a report, it uses a Redis Query node to retrieve the most relevant chunks.

• Query input: Typically a text query derived from the current vehicle context, telemetry values, or analyst request.
• Retrieval: The node searches the ev_battery_degradation_report index for nearest neighbors based on the embedding space.
• Results: A set of text chunks and metadata that are most semantically similar to the query.

These results are then exposed to the Agent as a Tool. The Tool wrapper makes Redis retrieval accessible during the LLM reasoning process, so the Agent can explicitly call the vector store to fetch context rather than relying solely on the prompt.

3.6 Memory (Buffer Window)

The Memory node provides short-term conversational context, usually implemented as a buffer window.

• Purpose: Preserve recent user inputs and agent outputs across multiple workflow runs or iterative queries.
• Use case: When an analyst refines a report, asks follow-up questions, or requests additional detail, the Agent can reference prior exchanges without re-ingesting all data.

This memory is especially useful for incremental reporting workflows, where an engineer may run several iterations on the same vehicle or dataset.

3.7 Chat & Agent (OpenAI / LLM)

The Chat node and Agent node work together to generate the final natural-language degradation report.

• LLM provider: OpenAI or any compatible LLM API configured in n8n.
• Inputs to Agent:
  • Context retrieved from Redis via the Tool interface.
  • Recent conversation history from the Memory node.
  • Prompt template that defines the desired structure of the EV battery degradation report.
• Output: A structured, human-readable report that summarizes degradation status, key metrics, possible causes, and recommended actions.

The Agent orchestrates calls to tools (Redis), merges retrieved context with current telemetry, and applies the prompt logic to ensure a consistent report structure. The Chat node handles the actual LLM interaction, including passing messages and receiving the generated text.

3.8 Sheet (Google Sheets Logging)

The Google Sheets node provides persistent logging for each generated report.

• Operation: Append row.
• Data logged (typical):
  • Vehicle identifier.
  • Timestamp of the analysis.
  • Key telemetry values (SOH, cycle count, maximum temperature, average voltage).
  • High-level report summary or full report text.

This log acts as a simple audit trail for engineering teams. It can also trigger downstream workflows, such as alerts, dashboards, or further analysis pipelines.

4. Configuration & Setup

4.1 Prerequisites

• An n8n instance (cloud or self-hosted).
• A Cohere API key for generating embeddings.
• A Redis instance with vector search capabilities enabled.
• An OpenAI or compatible LLM API key for natural-language generation.
• A Google account with the Sheets API enabled and a target spreadsheet created.

4.2 Node Configuration Steps

1. Webhook
   • Create a Webhook node.
   • Set the path, for example /ev_battery_degradation_report.
   • Configure the HTTP method as POST.
2. Text Splitter
   • Connect the Text Splitter node directly after the Webhook.
   • Set chunkSize to approximately 400 characters.
   • Set chunkOverlap to approximately 40 characters.
   • Point the node to the field that contains diagnostic or technician text.
3. Cohere Embeddings
   • Add the Cohere Embeddings node after the Text Splitter.
   • Configure Cohere credentials with your API key.
   • Map the array of text chunks to the node input.
4. Redis Vector Store (Insert)
   • Configure a Redis node for vector insertion.
   • Set the index name to ev_battery_degradation_report or a project-specific variant.
   • Ensure metadata such as vehicle_id and timestamp is included alongside each vector.
5. Redis Query
   • Add a Redis Query node for retrieval.
   • Use the same index name as the insert node.
   • Configure it to return the top N most similar chunks for a given query.
6. Agent & Chat
   • Configure the Agent node to:
     • Use the Chat node with your OpenAI (or compatible) credentials.
     • Register the Redis Query as a Tool.
     • Connect the Memory node to maintain context.
     • Set a prompt template that specifies report sections, such as metrics, degradation assessment, causes, and recommendations.
7. Google Sheets
   • Add a Google Sheets node at the end of the workflow.
   • Configure credentials and select the target spreadsheet and worksheet.
   • Map the Agent output and key metadata fields to the appropriate columns.

5. Sample Webhook Payload & Output

5.1 Example POST Payload

You can test the workflow by sending the following JSON payload to the configured webhook path:

{  "vehicle_id": "EV-1024",  "timestamp": "2025-08-30T12:34:56Z",  "telemetry": {  "cycle_count": 1200,  "soh": 82.5,  "max_temp_c": 45.1,  "avg_voltage": 3.67  },  "technician_notes": "Observed increased internal resistance after repeated fast charging sessions. Capacity delta ~3% last 100 cycles."
}

5.2 Expected Report Contents

Given this input, the Agent typically returns a structured degradation report that includes:

• High-level assessment – For example, indication of accelerated degradation due to frequent fast charging or elevated temperature exposure.
• Key metrics – Cycle count, SOH, maximum temperature, average voltage, and any notable trends.
• Possible causes and recommendations – Potential root causes such as repeated fast charging, plus suggested actions like pack balancing, cell-level diagnostics, or changes in charging strategy.
• Contextual references – Mentions of similar historical events or patterns retrieved from the Redis vector store.

The full report text is then logged to Google Sheets alongside the raw metrics, enabling quick review and cross-vehicle comparison.

6. Best Practices & Tuning

6.1 Chunk Size & Overlap

• Use chunk sizes in the range of 200 to 500 characters to maintain semantic granularity.
• Set overlap to roughly 10 percent of the chunk size to avoid splitting critical context across boundaries.
• For very long diagnostic reports, consider slightly larger chunks to reduce total embedding calls, while monitoring accuracy.

6.2 Embedding Model Selection

• Choose a Cohere embedding model that balances cost, latency, and performance on technical language.
• For highly domain-specific terminology, evaluate specialized or fine-tuned models if available in your Cohere plan.
• Monitor vector quality by spot-checking retrieval results for relevance.

6.3 Indexing Strategy in Redis

• Store metadata such as:
  • vehicle_id for per-vehicle retrieval.
  • timestamp to filter by time range.
  • Optional telemetry summaries (for example, cycle count bucket) to support more targeted queries.
• Use metadata