A low order model of SCR-in-DPF systems with proper orthogonal decomposition

© 2018 SAE International. All Rights Reserved. This paper presents a method to achieve a low order system model of the urea-based SCR catalyst coated filter (SCR-in-DPF or SCRF or SDPF), while preserving a high degree of fidelity. Proper orthogonal decomposition (POD), also known as principal component analysis (PCA), or Karhunen-Loéve decomposition (KLD), is a statistical method which achieves model order reduction by extracting the dominant characteristic modes of the system and devises a low-dimensional approximation on that basis. The motivation for using the POD approach is that the low-order model directly derives from the high-fidelity model (or experimental data) thereby retains the physics of the system. POD, with Galerkin projection, is applied to the 1D + 1D SCR-in-DPF model using ammonia surface coverage and wall temperature as the dominant system states to achieve model order reduction. The performance of the low-order POD model (with only a few basis modes) shows good agreement with the high fidelity model in steady and transient states analyses. This shows the promise of the application of POD in exhaust after-treatment system (EATS) modelling to achieve high fidelity low order models. In addition system control design is easier for the reduced order model using a modern approach. We demonstrate the performance of a model-based controller applied to the low-order POD model to minimize ammonia slip for a transient test cycle

Optimal charging of EVs in a real time pricing electricity market

The idea of grid friendly charging is to use electricity from the grid to charge batteries when electricity is available in surplus and cheap. There are several ways of achieving this, for example using droop control, using night time electricity tariffs, or using smart metering. The goal is twofold: to avoid putting additional load on the electricity grid and power generation, and to reduce the cost to the consumer. This paper looks at the saving potential when charging an electric car using real time tariffs provided by a smart meter, using the Ameren tariffs in Illinois as an example. If prices are known in advance (day-ahead pricing), the optimization only requires picking the cheapest time slots for charging the battery. Further savings can be made by using real time prices that are not known in advance, but the optimization problem then depends on price prediction models, and it becomes much more difficult to solve. This paper presents a simple suboptimal approach, and it quantifies the potential improvements that could be made using more sophisticated price predictions. The result is that cost savings in the order of about 50 USD (1/3 of the electricity costs) are feasible if a fast charger is used using real time pricing. The scale of the savings is such that complex optimization strategies are not worthwhile, and for the foreseeable future simple solutions are expected to be more cost effective

Optimal charging of electric vehicles using a stochastic dynamic programming model and price prediction

Copyright © 2015 SAE International. The idea of grid friendly charging is to use electricity from the grid to charge batteries when electricity is available in surplus and cheap. The goal is twofold: to avoid putting additional load on the electricity grid and to reduce the cost to the consumer. To achieve this, a smart meter and a tariff with variable electricity prices has to be in place. In Day Ahead tariff (DA), prices are announced in advance for the next day, and this information can be used to select the cheapest times to charge the battery by the required amount. The optimization method is very simple, and it only has to be run once per day. However, the balance of supply and demand is not fully known in advance. Therefore Real Time Pricing (RTP) tariff supplies electricity at spot market rate depending on the current balance. This makes the charging process less predictable because it adds a stochastic element, but it does offer the potential of higher savings if future prices can be predicted with a reasonable degree of accuracy. This paper proposes an optimal controller based on a stochastic dynamic program (SDP), which predicts future price changes from available data. The controller takes into account price variability via a simple grid model that allows of unexpected price rises and a gradual return to a normal grid price. The DP algorithm has two variables, the state of charge (SoC) and the current electricity cost. It traces the expected total cost based on the stochastic model and makes a decision ‘to charge or not’ to minimize the expected (average) total cost. The results show that in case of a positive probability of price rises, the time to charge is chosen slightly before the lowest expected cost during the night. This is a rational solution, because waiting longer does increase the risk of an unexpected price spike. In the trivial case of a zero probability of unexpected price rises, the solution converges to the one found by the previous deterministic optimization algorithm

Benefits of stochastic optimisation with grid price prediction for electric vehicle charging

© 2017 SAE International. The goal of grid friendly charging is to avoid putting additional load on the electricity grid when it is heavily loaded already, and to reduce the cost of charging to the consumer. In a smart metering system, Day Ahead tariff (DA) prices are announced in advance for the next day. This information can be used for a simple optimization control, to select to charge at cheapest times. However, the balance of supply and demand is not fully known in advance and the Real-Time Prices (RTP) are therefore likely to be different at times. There is always a risk of a sudden price change, hence adding a stochastic element to the optimization in turn requiring dynamic control to achieve optimal time selection. A stochastic dynamic program (SDP) controller which takes this problem into account has been made and proven by simulation in a previous paper. Since there are differences between the DA and the RTP tariff, this paper proposes a (1) predictor to create an unbiased estimate of the RTP tariff based on available data. It uses a regression on historical data to find the best prediction of the expected price. Finally, a (2) case study based on data from the Illinois Electricity Grid prices is presented to validate the SDP controller over several years of data. The stochastic optimization uses the RTP prices effectively, getting very close to the globally optimal charging price. However, the predictor achieves only a slight reduction in prediction uncertainty with this data sate, and it has a negligible effect on cost. This means that DA prices can be used as a fair prediction of RTP charging cost here. The SDPM successfully reacts in the case study and leads to savings on charging costs over the years presented

Review of selection criteria for sensor and actuator configurations suitable for internal combustion engines

This literature review considers the problem of finding a suitable configuration of sensors and actuators for the control of an internal combustion engine. It takes a look at the methods, algorithms, processes, metrics, applications, research groups and patents relevant for this topic. Several formal metric have been proposed, but practical use remains limited. Maximal information criteria are theoretically optimal for selecting sensors, but hard to apply to a system as complex and nonlinear as an engine. Thus, we reviewed methods applied to neighboring fields including nonlinear systems and non-minimal phase systems. Furthermore, the closed loop nature of control means that information is not the only consideration, and speed, stability and robustness have to be considered. The optimal use of sensor information also requires the use of models, observers, state estimators or virtual sensors, and practical acceptance of these remains limited. Simple control metrics such as conditioning number are popular, mostly because they need fewer assumptions than closed-loop metrics, which require a full plant, disturbance and goal model. Overall, no clear consensus can be found on the choice of metrics to define optimal control configurations, with physical measures, linear algebra metrics and modern control metrics all being used. Genetic algorithms and multi-criterial optimisation were identified as the most widely used methods for optimal sensor selection, although addressing the dimensionality and complexity of formulating the problem remains a challenge. This review does present a number of different successful approaches for specific applications domains, some of which may be applicable to diesel engines and other automotive applications. For a thorough treatment, non-linear dynamics and uncertainties need to be considered together, which requires sophisticated (non-Gaussian) stochastic models to establish the value of a control architecture

Unified backwards facing and forwards facing simulation of a hybrid electric vehicle using MATLAB Simscape

This paper presents the implementation of a vehicle and powertrain model of the parallel hybrid electric vehicle which can be used for several purposes: as a model for estimating fuel consumption, as a model for estimating performance, and as a control model for the hybrid powertrain optimisation. The model is specified as a multi-domain physical model in MATLAB Simscape, which captures the key electrical, mechanical and thermal energy flows in the vehicles. By applying hand crafted boundary conditions, this model can be simulated either in the forwards or backwards direction, and it can easily be simplified as required to address specific control problems. Modelling in the forwards direction, the driver inputs are specified, and the vehicle response is the model output. In the backwards direction, the vehicle velocity as a function of time is the specified input, and the engine torque, and fuel consumption are the model outputs. The model represents a parallel hybrid vehicle, which is being developed in the TC48 project. The project goal is to produce a prototype of a plug-in parallel hybrid system which is integrated into existing front wheel drive powertrains with modest additional engineering, cost, volume, and mass requirements. This paper explains the motivation for the project, and presents examples of the simulations which were used to guide the design. The vehicle simulation models used to evaluate the layout options are described and discussed. Sensitivity analyses are presented which informed the design decisions. A novel use of the Simscape component of MATLAB/Simulink which allows the same model structure to be used for both forwards and backwards simulations is demonstrated. This method has the possibility for more general application, and a toolbox is being developed which assists the generation of mathematical models of this type

SCR-filter model order reduction (1): Development and validation of the base “high-fidelity” model

Catalysed diesel particulate filters (c-DPF) have been described as multifunctional reactor systems. Integration of selective catalytic reduction (SCR) functionality in the DPF enhances filter performance to achieve nitrous oxides (NOx) treatment along with particulate matter (PM) collection. The physical and chemical aspects of the integrated SCR-filter make modelling difficult. The goal of this work is to develop a low-complexity model of the SCR-filter system with good fidelity. The first part of our work—presented in this paper—lays out the structure of the SCR-filter model and highlights a new approach to implement faster than real-time solution to the “full-order” or “high-complexity” model. The validated model was applied to evaluate the impact of diffusion on deNOx functionality of the SCR-filter system in a simulated characterisation exercise for the SCR-filter unit. We found that internal (pore) diffusion (effective diffusivity coefficient) and external channel to wall diffusion (mass transfer coefficient) orthogonal to the channel direction are significant for accurate characterisation of the deNOx performance of the SCR-coated filter system. System modelling can be used to select the geometric properties of the monolith (length and density of the SCR-coated filter system) and micro-properties of the washcoat (catalyst loading and zoning) to optimise the influence of diffusion on the system performance. The main contribution of this paper is the presentation of a different approach to implementing the solution to the cDPF model and in enough detail so that it can be easily replicated

Evaluation of indoor environment system performance for airport buildings

Airport terminals are energy intensive buildings. They are mostly thought to operate on a 24/7 scale and so indoor environment systems run on full schedules and do not have fine control based on detailed passenger flow information. While this assumption of round-the-clock operation may be true for the public areas of the airport building and so opportunity for complete shut-down of HVAC and lighting systems are limited especially in a busy airport terminals, there are many passenger exclusive area within the airport in which occupancy varies strictly with flight schedules. This paper presents the results of indoor environment measurement and flight schedules to identify such opportunities and to implement energy conservation measure in the passenger exclusive areas of the airport building. It also uses building simulation to assess the benefits of such energy saving interventions in terms of comfort, energy and carbon emission savings

The state of the art in selective catalytic reduction control

Selective Catalytic Reduction (SCR) is a leading after treatment technology for the removal of nitrogen oxide (NOx) from exhaust gases (DeNOx). It presents an interesting control challenge, especially at high conversion, because both reagents (NOx and ammonia) are toxic, and therefore an excess of either is highly undesirable. Numerous system layouts and control methods have been developed for SCR systems, driven by the need to meet future emission standards. This paper summarizes the current state-of-the-art control methods for the SCR aftertreatment systems, and provides a structured and comprehensive overview of the research on SCR control. The existing control techniques fall into three main categories: traditional SCR control methods, model-based SCR control methods, and advanced SCR control methods. For each category, the basic control technique is defined. Further techniques in the same category are then explained and appreciated for their relative advantages and disadvantages. Thus this paper presents a snapshot of the current state of the art for the research area of SCR control. This is a very active field, and it is hoped that by providing a better understanding of the different control strategies already developed for SCR control, future areas of interest will be identified and developed with the ultimate aim of satisfying the increasingly stringent emissions legislation. Copyright © 2014 SAE International

Fuzzy supervisory control strategies to minimize energy use of airport terminal buildings

Airport terminal buildings are among energy most consuming buildings and this presents huge opportunities for implementing energy saving strategies. Achieving satisfactory control of these buildings using classical controllers alone is difficult because they contain components that are complex, nonlinear but dynamically related. Therefore, this paper presents and appraises fuzzy control strategies for reducing energy consumptions while simultaneously providing comfort for passengers in an airport terminal building. The inputs into this fuzzy supervisory controller are the time schedule for arrival and departure of passenger planes as well as the expected number of passengers during each flight, zone illuminance and external temperature. The controller outputs optimised temperature, airflow rates and lighting setpoints for the conventional controllers. Simulation studies in MATLAB/SIMULINK confirmed the capacity of this control strategy to provide comfort setpoints for the passengers at reduced energy