The push for less emissions has driven transportation towards electrification. The electrical variable transmission is a promising emerging component that has proven to be successful in passenger vehicles and is being considered in this paper for off-highway vehicles. By electromagnetically coupling the internal combustion engine with the wheels, allowing independent rotation, the engine is kept in its optimal operating range. This paper benchmarks the electrical variable transmission to one of the most successful hybrid topologies: the Toyota hybrid system. Flanders Make's Hybrid Electric Drivetrain CoDesign framework is being used to ensure optimal control decisions for both. Results show that the electrical variable transmission may reduce fuel consumption by 30% and total cost of ownership by 10%. Index Terms-Electric variable transmission, dynamic programming, energy management, off-highway vehicles, total cost of ownership.

This paper presents real-time control strategies for peak shaving and voltage control provided by a Battery Energy Storage System in a low voltage grid. Two performance indices, one for peak shaving and one for voltage control, are proposed to quantify the results. The strategies are compared with a benchmark which is calculated as an ex post optimization that solves the control problem together with the sizing problem. First, a rule-based strategy for peak shaving is presented which only injects active power. Afterwards, the strategy is extended with voltage control through reactive power injection. In the latter case, an optimization problem is solved at each time step to decide at which rate active and reactive power should be injected.

A model predictive control framework for optimal heating of a residential building is proposed. The control inputs are applied to a virtual building emulator model using a limited amount of measurements. State estimation is implemented using moving horizon estimation to reinitialize the states of the controller model in every time step. To implement the moving horizon estimation, the Modelica equations had to be modified. A stochastic input is declared at the controller model state equations to represent the process noise (model error). The state estimation significantly improves the output matching between emulator and controller model. The JModelica optimization framework proves to be satisfactory for this first, limited case investigated here. Future work will focus on the extension to different models and prediction errors within the framework developed here.

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Annex 60 is developing and demonstrating new generation computational tools for building and community energy systems based on the nonproprietary Modelica modeling language and Functional Mockup Interface (FMI) standards. Demonstrations will include optimized design and operation of building and community energy systems. Within the Annex 60, Activity 2.3 focuses on the use of models to augment monitoring, control and fault detection and diagnostics methods. This promises to detect a degradation of equipment efficiency over time because measured performance can be compared to expected performance at the current operating conditions. Furthermore, use of models during operation allows operational sequences to be optimized in real-time to reduce energy or cost, subject to dynamic pricing. This paper will offer an overview of the work carried out within this IEA Annex 60 Activity 2.3 both in terms of approach and case studies with a particular focus on model use during operation for fault detection and diagnosis.