We introduce the Python-based open-source library Energym, a building model library to test and benchmark building controllers. The incorporated building models are presented with a brief explanation of their function, location and technical equipment. Furthermore, the library structure is described, highlighting the necessary features to provide the benchmarking and control capabilities, i.e., standardized evaluation scenarios, key performance indicators (KPIs) and forecasts of uncertain variables. We go on to characterize the evaluation scenarios for each of the models and give formal definitions of the KPIs. We describe the calibration methodologies used for constructing the models and illustrate their usage through examples.

Abstract-The multi-state Generalized Maxwell-Slip (GMS) friction model is known to describe all essential friction characteristics in presliding and sliding motion. It is also known that due to its switching state conditions between presliding and sliding, gradient-based parameter and state estimation methods cannot be implemented efficiently. Efficient on-line state and parameter estimation is essential for model-based friction compensation in order to track changes of friction characteristics in time and space. This paper presents a Smoothed GMS (S-GMS) friction model with an analytic set of differential equations well suited for gradient-based estimation techniques. Friction state estimation is implemented with an Extended Kalman Filter (EKF) to validate the S-GMS model numerically and experimentally, and compare its behavior with the GMS model.

This paper presents a smoothed friction model that closely approximates the Generalized Maxwell-Slip (GMS) model, a multi-state friction model known to describe all essential friction characteristics in presliding and sliding motion. In contrast to the GMS model, which consists of a switching structure to accommodate for its hybrid nature, the Smoothed GMS (S-GMS) model consists of an analytic set of differential equations well suited for on-line state and parameter estimation, such as in Moving Horizon Estimation (MHE). Efficient on-line state and parameter estimation is essential for model-based friction compensation in order to track friction characteristics changes in time and space. Moreover, MHE is known to better handle model nonlinearities, disturbances and constraints than Extended Kalman Filter (EKF). This paper discusses the implementation of the EKF and MHE estimators for both the GMS and the S-GMS friction models. The benefit of the combination of MHE and S-GMS model is shown.

Efficient on-line state and parameter estimation is essential for model-based friction compensation in order to track changes of friction characteristics in time and space. This paper presents a moving horizon estimation (MHE) algorithm for on-line friction state and parameter estimation using a smoothed (analytic) version of the Generalized Maxwell-Slip (GMS) model, a multi-state friction model known to describe all essential friction characteristics in presliding and sliding motion. In contrast to the GMS model, which consists of a switching structure to accommodate for its hybrid nature, the Smoothed GMS (S-GMS) model consists of an analytic set of differential equations well suited for gradient-based state and parameter estimation, as in MHE or in extended Kalman filtering (EKF). Moreover, MHE is known to better handle model nonlinearities, disturbances and constraints than EKF. This paper discusses the implementation of an MHE algorithm for the S-GMS friction model and experimentally compares its performance to an EKF implementation for joint state and parameter estimation.

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