Christofer Sundström

Adaptive Control of a Hybrid Powertrain with Map-based ECMS

To fully utilize the fuel reduction potential of a hybrid powertrain requires a careful design of the energy management control algorithms. Here a controller is created using mapbased equivalent co ...

Selecting and Utilizing Sequential Residual Generators in FDI Applied to Hybrid Vehicles

In order to obtain a realistic model of a complex system, thousands of possible residual generators need to be used for diagnosis. Based on engineering insights of the system to be monitored, certain algebraic and dynamic properties of the residual generators may be preferred, and therefore, a method for finding sequential residual generators is developed that accounts for these properties of the residual generator candidates. It is shown that only a small fraction of all residual generator candidates fulfill fundamental requirements, and thereby, proves the value of systematic methods. Furthermore, methods are devised for utilization of the residual generators, such as initialization of dynamic residual generators. A proposed method, considering the fault excitation in the residuals using the internal form of the residuals, significantly increases the diagnosis performance. A hybrid electric vehicle is used in a simulation study for demonstration, but the methods used are general in character and provides a basis when designing diagnosis systems for other complex systems.

Robust driving pattern detection and identification with a wheel loader application

Information about wheel loader usage can be used in several ways to optimize customer adaption. First, optimizing the configuration and component sizing of a wheel loader to customer needs can lead ...

Overall Monitoring and Diagnosis for Hybrid Vehicle Powertrains

Abstract Designing diagnosis systems for hybrid vehicles include new features compared to conventional vehicles, e.g. mode switches in the system. The influence of this on the performance of the diagnosis system is investigated by design and implementation of diagnosis systems on vehicle level. The diagnosis systems are based on two sensor configurations, one consisting of many sensors and one of few sensors. The diagnosis systems detect specific faults, here specifically faults in the electrical components in a hybrid vehicle driveline, but the methodology is generic. There is a connection between the design of the energy management and the diagnosis system, and this interplay is of special relevance when models of components are valid only in some operating modes. In the systems implemented, the diagnosis system based on few sensors is more complex and includes a larger part of the vehicle model than the system based on more sensors.

A Combined Data-Driven and Model-Based Residual Selection Algorithm for Fault Detection and Isolation

Selecting residual generators for detecting and isolating faults in a system is an important step when designing model-based diagnosis systems. However, finding a suitable set of residual generators to fulfill performance requirements is complicated by model uncertainties and measurement noise that have negative impact on fault detection performance. The main contribution is an algorithm for residual selection that combines model-based and data-driven methods to find a set of residual generators that maximizes fault detection and isolation performance. Based on the solution from the residual selection algorithm, a generalized diagnosis system design is proposed where test quantities are designed using multivariate residual information to improve detection performance. To illustrate the usefulness of the proposed residual selection algorithm, it is applied to find a set of residual generators to monitor the air path through an internal combustion engine.

Sequential residual generator selection for fault detection

Structural methods in model-based fault diagnosis applications are simple and efficient tools for finding candidates for residual generation. However, the structural methods do not take model uncertainties and information about fault behavior into consideration. This may result in selecting residual generators with bad performance to be included in the diagnosis system. By using the Kullback-Leibler divergence, the performance of different residual generators can be compared to find the best one. With the ability to quantify diagnostic performance, the design of residual generators can be optimized by, for example, combining several residual generators such that the diagnostic performance is maximized. The proposed method for residual generation selection is applied to a water tank system to show that the achieved residual performance is improved compared to only use a structural method.

Residual Generator Selection for Fault Diagnosis of Hybrid Vehicle Powertrains

The performance of a model based diagnosis system is affected by the selection of consistency relation in a set of equations with analytical redundancy in a non-linear system. To investigate aspect ...

Analysis of optimal energy management in smart homes using MPC

Advanced building management systems utilize future information, such as electricity spot prices, weather forecasts, and predicted electric loads and hot water consumption, to reduce the maximum electric power consumption and energy cost. A model predictive controller (MPC) is implemented for a household with one hour sample intervals, including hot water usage, charging of an electric vehicle, and domestic heating, but also an accumulator water tank to be used as an additional thermal energy storage. Both the maximum total power used in the house and the energy cost are included in the cost function to evaluate how these properties are affected by different system designs. The MPC solution is compared to the global optimal solution using dynamic programming indicating comparable performance. The robustness of the MPC is evaluated using a prediction of the future household electric consumption in the controller. Results also show that a significant part of the cost reduction is achieved for as small prediction horizons as five hours. Analysis shows that including an accumulator tank is useful for reducing the total energy cost, while reducing the peak power is mainly achieved by increasing the prediction horizon of the MPC.

Diagnostic Method Combining the Lookup Tables and Fault Models Applied on a Hybrid Electric Vehicle

A common situation in industry is to store measurements for different operating points in the lookup tables, often called maps. They are used in many tasks, e.g., in control and estimation, and therefore considerable investments in engineering time are spent in measuring them which usually make them accurate descriptions of the fault-free system. They are thus well suited for fault detection, but, however, such a model cannot give fault isolation since only the fault free behavior is modeled. One way to handle this situation would be also to map all fault cases but that would require measurements for all faulty cases, which would be costly if at all possible. Instead, the main contribution here is a method to combine the lookup model with analytical fault models. This makes good use of all modeling efforts of the lookup model for the fault-free case, and combines it with fault models with reasonable modeling and calibration efforts, thus decreasing the engineering effort in the diagnosis design. The approach is exemplified by designing a diagnosis system monitoring the power electronics and the electric machine in a hybrid electric vehicle. An extensive simulation study clearly shows that the approach achieves both good fault detectability and isolability performance. A main point is that this is achieved without the need for neither measurements of a faulty system nor detailed physical modeling, thus saving considerable amounts of development time.