Electronic horizon: road information used by energy management strategies

Road information from a navigation database system is incorporated into existing EM strategies and translated into a preferred reference trajectory for the battery energy. The EM system can schedule energy among different road segments, optimising the energy efficiency of the vehicle. It turns out that, in many driving situations, the potential fuel benefits from the e-horizon are restricted, due to the excellent performance of existing EM strategies. Driving cycles using a larger range of the battery capacity benefit more from an e-horizon. Nevertheless, the potential gains are limited and will decrease if errors appear in the telematics information

Plug-in hybrid electric vehicles in dynamical energy markets

The plug-in hybrid electric vehicle allows vehicle propulsion from multiple internal power sources. Electric energy from the grid can be utilized by means of the plug-in connection. An on-line energy management (EM) strategy is proposed to minimize the costs for taking energy from each power source. Especially in a dynamical energy market, an on-line optimization algorithm is desirable since energy prices change over time. By construction, the proposed EM system can operate with, and without prediction information. If predictions are available, an electronic horizon is applied to anticipate on up-coming events and further optimize the strategy. Illustrative examples are given to explain the added value for both solutions. Also the situation where energy is transferred back to the grid is considere

Model development for air conditioning system in heavy duty trucks

This chapter presents a modelling approach for the air conditioning (AC) system in heavy duty trucks. The presented model entails two major elements: a mechanical compressor model and a thermal AC model. The compressor model describes the massflow of the refrigerant as well as the mechanical power requested from the combustion engine. The thermal AC model predicts how ambient air flow cools down when it passes the AC system. This model also includes the latent heat emerging from water condensation. Both elements of the model have been validated with experimental data. The compressor parameters follow from hardware-in-the-loop experiments where the AC compressor is measured under various load profiles. Validation of the thermal AC model is done by climate chamber testing with a DAF XF heavy duty truck on a roller dynamometer

Vehicle energy management for on/off controlled auxiliaries : fuel economy vs. switching frequency

In this paper, an integrated approach for designing energy management strategies concerning vehicle auxiliaries with on/off control is proposed. This approach provides the possibility of making different trade-offs between fuel economy and switching frequency. In this paper, we demonstrate the validity of this approach by using game theory for obtaining the online implementable energy management strategy while the design philosophy can be generally applied to other existing energy management strategies. A case study is carried out on a model of a heavy-duty truck with an electric power take off (ePTO) connected to a refrigerated semitrailer for which the cooling unit can only be switched on or off. The results show that different trade-offs can be made between fuel savings and switching frequency

Adaptive ECMS : a causal set-theoretic method for equivalence factor estimation

In this paper, we propose a novel approach for online estimating the equivalence factor in the equivalent consumption minimization strategies (ECMS). The solution concept is divided into two parts: the state unconstrained energy management problem where one constant equivalence factor is to be estimated and the state constrained energy management problem where the estimation needs to be refreshed from time to time. Under suitable assumptions, the estimation procedure localizes a non-increasing interval containing the correct equivalence factor. The proposed method is simulated and compared to various strategies on several drive-cycles where a robust performance in terms of fuel economy is observed

Integrated powertrain control to meet low CO2 emissions for a hybrid distribution truck with SCR-deNOx system

This article presents a cost-based optimization strategy that explicitly deals with the requirements for fuel consumption and emissions. Based on the Integrated Powertrain Control (IPC) approach, the overall powertrain performance is optimized by integrated energy and emission management. The potential of this strategy is demonstrated for a parallel hybrid diesel truck with a Selective Catalytic Reduction (SCR) de-NOx system. New results are presented for a challenging city cycle; although the average power demand is low, IPC is able to keep the SCR catalyst temperature relatively high. With this IPC approach, the CO2-NOx trade-off is optimized in a systematic way. It is demonstrated that CO2 emissions and related operating costs are reduced by 3.5% or 24.9% NOx emission reduction is achieved, depending on the applied IPC calibration

Integrated powertrain control for hybrid electric vehicles with electric variable transmission

The electric variable transmission (EVT) offers a powersplit for hybrid electric vehicles by integrating two motor/ generator sets into one electric machine. This double rotor concept implements a continuously variable transmission between the engine and the driveline, including the possibility for electric propulsion. To guarantee good energy efficiency of the overall vehicle configuration, an integrated powertrain control (IPC) strategy is developed. First, optimization of the transmission ratio is analyzed by considering energy losses in the EVT. Next, an energy management strategy is presented incorporating the complete hybrid functionality of the EVT. Simulation results demonstrate feasibility of this IPC strategy and support the design process for optimal component specifications

A distributed optimization approach to energy management for a heavy-duty truck

Energy management systems (EMS) aim at minimizing the\u3cbr/\u3evehicle fuel consumption and tailpipe emissions under the\u3cbr/\u3ewide range of driving conditions. Classical energy management\u3cbr/\u3esystems for hybrid vehicles control the powersplit between\u3cbr/\u3ethe internal combustion engine (ICE) and the electric\u3cbr/\u3emotor (EM) [1]. In the last decade, this research topic is\u3cbr/\u3eextended towards integrating (thermal) battery management\u3cbr/\u3esystems and engine aftertreatment systems (EAS). The next\u3cbr/\u3echallenge is to extend energy management to incorporate all\u3cbr/\u3eenergy flows present in the truck [2]. Figure 1 shows the energy\u3cbr/\u3eflows schematically for a Hybrid Truck. In this figure,\u3cbr/\u3ethere is one primary energy source, the ICE, and multiple energy\u3cbr/\u3econverters and energy buffers. The goal is to minimize\u3cbr/\u3ethe fuel consumption whilst meeting the minimum power request\u3cbr/\u3eof each individual energy consumer. Distributed control\u3cbr/\u3eis a promising technique for this problem, as it will enhance\u3cbr/\u3emodularity of the system in the sense that components\u3cbr/\u3ecan be removed or added to the system without affecting the\u3cbr/\u3eoptimality and complexity of the system. The main research\u3cbr/\u3equestions on this topic are i) what fuel consumption reduction\u3cbr/\u3ecan be achieved by increasing the number of controlled\u3cbr/\u3eenergy flows and ii) can we develop a complete vehicle energy\u3cbr/\u3emanagement system (CVEMS) that enables this with\u3cbr/\u3emanageable complexity resulting in an acceptable development\u3cbr/\u3etime

Online energy management for hybrid electric vehicles

Hybrid electric vehicles (HEVs) are equipped with multiple power sources for improving the efficiency and performance of their power supply system. An energy management (EM) strategy is needed to optimize the internal power flows and satisfy the driver's power demand. To achieve maximum fuel profits from EM, many solution methods have been presented. Optimal solution methods are typically not feasible in an online application due to their computational demand and their need to have a priori knowledge about future vehicle power demand. In this paper, an online EM strategy is presented with the ability to mimic the optimal solution but without using a priori road information. Rather than solving a mathematical optimization problem, the methodology concentrates on a physical explanation about when to produce, consume, and store electric power. This immediately reveals the vehicle characteristics that are important for EM. It is shown that this concept applies to many existing HEVs as well as possible future vehicle configurations. Since the method only focuses on typical vehicle characteristics, the underlying algorithm requires minor computational effort and can be executed in real time. Clear directions for online implementation are given in this paper. A parallel HEV with a 5-kW integrated starter/generator (ISG) is selected to demonstrate the performance of the EM strategy. Simulation results indicate that the proposed EM strategy exhibits similar behavior as an optimal solution obtained from dynamic programming. Profits in fuel economy primarily arise from engine stop/start and energy obtained during regenerative braking. This latter energy is preferably used for pure electric propulsion where the internal combustion engine is switched off

Real-time distributed economic model predictive control for complete vehicle energy management

\u3cp\u3eIn this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints in the optimization problem. Furthermore, a novel on/off control concept for the control of the refrigerated semi-trailer, the air supply system and the climate control system is introduced. Simulation results on a low-fidelity vehicle model show that close to optimal fuel reduction performance can be achieved. The fuel reduction for the on/off controlled subsystems strongly depends on the number of switches allowed. By allowing up to 15-times more switches, a fuel reduction of 1.3% can be achieved. The approach is also validated on a high-fidelity vehicle model, for which the road slope is predicted by an e-horizon sensor, leading to a prediction of the propulsion power and engine speed. The prediction algorithm is demonstrated with measured ADASIS information on a public road around Eindhoven, which shows that accurate prediction of the propulsion power and engine speed is feasible when the vehicle follows the most probable path. A fuel reduction of up to 0.63% is achieved for the high-fidelity vehicle model.\u3c/p\u3