
Worked on enhancing the Zewotherm Lambda integration within the evcc-io/evcc repository, focusing on improving energy accounting accuracy and deployment flexibility for embedded systems. Developed support for handling negative excess energy values by introducing a polarity parameter and corresponding scaling adjustments, enabling more precise energy data processing. Additionally, replaced the previously hardcoded network port with a configurable option, simplifying deployment across varied network environments. The work leveraged Go and YAML for implementation, applying configuration management and Modbus protocol expertise. These enhancements improved interoperability and onboarding for Lambda-based charger integrations, addressing real-world deployment challenges with targeted, maintainable engineering solutions.
July 2025 — Zewotherm Lambda integration enhancements in evcc (evcc-io/evcc) delivered to improve energy accounting accuracy and deployment flexibility. Implemented handling for negative excess energy values via a new polarity parameter with corresponding scaling adjustments, and introduced configurable port support to replace the previous hardcoded value (default 502). No major bugs fixed this period. Impact: enables more accurate energy data processing across diverse Lambda deployments and simplifies network configuration, improving onboarding and reliability for Lambda-based charger integrations. Technologies demonstrated: Go-based Lambda integration, parameterization for energy polarity and scaling, and network/configuration management with traceable commits.
July 2025 — Zewotherm Lambda integration enhancements in evcc (evcc-io/evcc) delivered to improve energy accounting accuracy and deployment flexibility. Implemented handling for negative excess energy values via a new polarity parameter with corresponding scaling adjustments, and introduced configurable port support to replace the previous hardcoded value (default 502). No major bugs fixed this period. Impact: enables more accurate energy data processing across diverse Lambda deployments and simplifies network configuration, improving onboarding and reliability for Lambda-based charger integrations. Technologies demonstrated: Go-based Lambda integration, parameterization for energy polarity and scaling, and network/configuration management with traceable commits.

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