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A combustion engine-driven vehicle can be made more fuel efficient over some drive cycles by, for example, introducing electric machines and solutions for electrical energy storage within the vehicle’s driveline architecture. The possible benefits of different hybridization concepts depend on the architecture, i.e., the type of energy storage, and the placement and sizing of the different driveline components. This paper examines a diesel electric plug-in hybrid truck, where the powertrain includes a diesel engine supported with two electric motors, one supporting the crank shaft and one the turbocharger. Numerical optimal control was used to find energy-optimal control strategies during two different accelerations, the trade-off between using electrical energy and diesel fuel was evaluated using a simulation platform. Fixed-gear acceleration was performed to evaluate the contribution from the two electric motors in co-operation, and individual operation. A second acceleration test case from 8 to 80 km/h was performed to evaluate the resulting optimal control behavior when taking gear changes into account. A cost factor was used to relate the cost of diesel fuel to electrical energy. The selection of the cost factor relates to the allowed usage of electrical energy: a high cost factor results in a high amplification from electrical energy input to total system energy savings, whereas a low cost factor results in an increased usage of electrical energy for propulsion. The difference between fixed-gear and full acceleration is mainly the utilization of the electric crank shaft motor. For the mid-range of the cost factors examined, the crank shaft electric motor is used at the end of the fixed-gear acceleration, but the control sequence is not repeated for each gear during the full acceleration. The electric motor supporting the turbocharger is used for higher cost factors than the crank shaft motor, and the amplification from electrical energy input to total energy savings is also the highest.

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. Funding agencies: Volvo Car Corporation Gothenburg Sweden

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.

In northern Europe the electricity price is set by hourly rates one day in advance. The price fluctuates due to supply and demand, and these fluctuations are expected to increase when solar and wind power are increased in the energy system. The potential in cost reduction for heating a house and charging of an electrified vehicle by using a smart energy management system in a household is investigated. Dynamic programming is used and a simulation study of a household in Sweden comparing this optimal control scheme with a heuristic controller is carried out. The time frame in the study is one year and a novel way of handling the fact that the vehicle is disconnected from the grid at some times is developed. A plug-in hybrid electric vehicle is considered, but the methodology is the same also for pure electric vehicles. It is found that the potential in energy cost reduction for house heating and vehicle charging is significant and that using a smart energy management system is a promising path of cost reduction, especially with the introduction of electrified vehicles.

A diagnosis system for the electric machine and the power electronics in a hybrid electric vehicle is designed, where the diagnosis system uses a map based model of the system to be monitored. Such a model gives an accurate description of the fault free system, and is therefore suited for fault detectability. However, one drawback using such a model for diagnosis is that fault isolation often requires that the model, in addition to the fault free case, also describes the faulty system, and thereby measurements of the faulty system are needed, which is costly. Another approach is to use a model including physical parameters of interest in the system to be monitored, to also describe the faults' impact on the system. To achieve good diagnostic performance such a model needs to be accurate, which also is costly. Therefore, in the new approach taken here, two models for the system are used in combination to achieve good fault detectability and isolability; one is a map based model, and one is describing the faults of the system. It is shown that the approach works well and is a promising path to achieve both good fault detectability and isolability performance, without the need for neither measurements of a faulty system nor detailed physical modeling. In a simulation study evaluating the designed diagnosis system all faults are isolated and also accurately estimated.

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.

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.

When hybridizing a vehicle, new components are added that need to be monitored due to safety and legislative demands. Diagnostic aspects due to powertrain hybridization are investigated, such as th ...

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.

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 consumption minimization strategy and implemented to function without any knowledge of the future driving mission. The optimal torque distribution is calculated oine and stored in tables. Despite only considering stationary operating conditions and average battery parameters, the result is close to that of deterministic dynamic programming. Eects of making the discretization of the tables sparser are also studied and found to have only minor eects on the fuel consumption. The controller optimizes the torque distribution for the current gear as well as assists the driver by recommending the gear that would give the lowest consumption. Two ways of adapting the control according to the battery state of charge are proposed and investigated. One of the adaptive strategies is experimentally evaluated and found to ensure charge sustenance despite poor initial values.

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 to a significant improvement in e.g. fuel efficiency and cost. Second, relevant driving cycles to be used in the development of wheel loaders can be extracted from usage data. Third, on-line usage identification opens up for the possibility of implementing advanced look-ahead control strategies for wheel loader operation. The main objective here is to develop an on-line algorithm that automatically, using production sensors only, can extract information about the usage of a machine. Two main challenges are that sensors are not located with respect to this task and that significant usage disturbances typically occur during operation. The proposed method is based on a combination of several individually simple techniques using signal processing, state automaton techniques, and parameter estimation algorithms. The approach is found to berobust when evaluated on measured data of wheel loaders loading gravel and shot rock.