Thijs van Keulen

Energy management in hybrid electric vehicles: benefit of prediction

Hybrid vehicles require a supervisory algorithm, often referred to as energy management strategy, which governs the drivetrain components. In general the energy management strategy objective is to minimize the fuel consumption subject to constraints on the components, vehicle performance and driver comfort. Typically, we have to deal with two difficulties in the design of an energy management strategy. Firstly, the nonlinear behavior of the components results in a nonconvex cost function, complicating the use of optimization methods. Different approaches to deal with the nonconvexity are discussed. Secondly, the future power and velocity trajectories are unknown. Prediction of the future trajectories, based upon either past or predicted vehicle velocity and road grade trajectories, could help in obtaining a solution close to optimal. The benefit of prediction, compared to a heuristic and an optimal control strategy that uses only actual vehicle data, is shown with an example of a hybrid truck at a highway trajectory in a hilly environment. Results indicate that prediction has benefits only when the slopes have sufficient grade and length, such that the battery state-of-charge boundaries are reached.

An Adaptive Sub-Optimal Energy Management Strategy for Hybrid Drive-Trains

A method and apparatus for interfacing a bus with a plurality of input/output (I/O) devices includes steps for handling transactions to and from the I/O devices. Transactions from the I/O devices includes processing that begins by receiving the transactions, where each transaction is received at a rate corresponding to the providing I/O device. The processing continues by identifying, for each transaction, a corresponding section of memory for temporarily storing the transaction. The particular section of memory is identified based on the type of transaction and/or the identity of the I/O device. The processing then continues by storing each transaction in the identified section of memory when the section has an available entry. When the bus is available and a transaction has been selected, the selected transaction is provided to the bus at the rate of the bus. Processing of transactions directed towards the I/O devices includes steps for monitoring the bus during non-I/O bus intervals for transactions relating to one of the I/O devices. When a transaction is directed towards an I/O device, the transaction is interpreted to identify the particular I/O controller that supports the particular I/O device. The processing then continues by storing, at the bus rate, the transaction in the section of memory corresponding with the I/O device. Once stored in memory, the transaction is provided to an associated I/O controller, which processes the transaction at the rate of the corresponding I/O device.

Optimal trajectories for vehicles with energy recovery options

Abstract The fuel optimal control of modern vehicles involves the control of several components: the automated manual transmission, power split between the engine and secondary power converter, vehicle velocity, clutch position and motor start-stop. These controls are often optimized separate from each other, which leads to suboptimal results. In this paper we focus on the combined optimization of hybrid system use, gearbox and vehicle velocity. A novel cost function description is used which describes the influence of the automated manual transmission, the potential of brake energy recovery, and the vehicle velocity with one control signal, and, therefore, reduces the computational complexity. The cost is modeled using a piecewise affine continuous function, which has the advantage of the control appearing affine in the Hamiltonian. Besides the standard optimal control solution for systems with an affine cost function, non-smooth optimal control theory is involved to obtain a sequence of subarcs that fulfills the necessary conditions of optimality. Since the length and cost of each subarc, that fulfills the necessary conditions of optimality, in travel time and fuel consumption, can analytically be expressed in its initial and end velocity, the fuel optimal control of a vehicle with energy recovery options is rewritten as a nonlinear optimization problem.

Implementation of an optimal control energy management strategy in a hybrid truck

Energy Management Strategies for hybrid powertrains control the power split, between the engine and electric motor, of a hybrid vehicle, with fuel consumption or emission minimization as objective. Optimal control theory can be applied to rewrite the optimization problem to an optimization independent of time. Estimation of the Lagrange parameter, e.g., by feedback on the battery State-Of-Charge (SOC), can be used to arrive at a real-time implementable strategy. Nevertheless, it is still required to solve a nonconvex optimization problem with limited onboard computational power. This paper suggests to solve this optimization problem, offline, for different values of the Lagrange parameter, crankshaft rotational speed, and torque request. The resulting strategy is evaluated with simulations of a hybrid distribution truck on two different velocity trajectories. The influence of several control parameters is investigated also.

A control oriented multivariable identification procedure for turbocharged diesel engines

In this paper, multisine frequency response function identification is used for non-parametric modelling of the air path of a turbocharged diesel engine around a fixed operating point. The variable geometry turbine (VGT) and exhaust gas recirculation (EGR) valve are used as inputs. The nitrogen oxide emissions, air-fuel equivalence ratio, and pressure difference between the intake and exhaust manifold are the considered outputs. A time-efficient and accurate identification procedure for this input-output system is devised, analysed, and executed experimentally for a range of operating points. The analysis quantifies the individual effects of noise and nonlinearities and shows that the identified linear models capture the actual local behaviour with over 97.2% accuracy.

Receding Horizon Real-Time Energy Management Strategy for Hybrid Vehicles

Indirect optimal control and dynamic programming are combined in a receding horizon controller to obtain an energy management strategy for hybrid vehicles. This combination permits the use of inaccurate predictions of the future, instead of requiring exact knowledge, and allows the use of mixed state-control constraints, like voltage constraints for batteries. The controller can run in real-time on commodity hardware and, using a prediction of the future based on geographic information only, obtains a fuel use within 0.2% of the optimal fuel use computed with the exact speed and power trajectory of the vehicle known in advance. All this for a planned distance of more than 500 [km].Copyright

Systematic calibration procedure for the air path control of diesel engines

Heavy-duty diesel engines are designed for low fuel consumption and flexibility in the engine-out emissions of Nitrogen Oxides (NOx) and Particulate Matter (PM) to meet the Euro VI en EPA13 emission legislation and market expectations on low operating cost. Generally, the amount of NOx formed in the combustion chamber exceeds the legal tailpipe limits. Therefore, a diesel engine can be equipped with a catalyst that converts the NOx to harmless N2 and O2 by the injection of a urea solution. Flexibility in engine-out NOx emissions is required to deal with the varying conversion performance of the catalyst.

Indirect solution for optimal control problems with a pure state constraint

This paper presents an algorithm that provides a regularization for the costate dynamics of state constrained optimal control problems with a scalar constraint under the assumption that the Hamiltonian is convex in the control and the state dynamics equation of the constrained state is monotonically increasing in the control variable. The algorithm is demonstrated with a classical optimal control problem.