A study on look-ahead control and energy management strategies in hybrid electric vehicles

Fuel efficiency in a hybrid electric vehicle requires a fine balance between usage of combustion engine and battery power. Information about the geometry of the road and traffic ahead can have a great impact on optimized control and the power split between the main parts of a hybrid electric vehicle. This paper provides a survey on the existing methods of control and energy management emphasizing on those that consider the look-ahead road situation and trajectory information. Then it presents the future trends in the control and energy management of hybrid electric vehicles.<br /

Solar Energy Dependent Supercapacitor System with ANFIS Controller for Auxiliary Load of Electric Vehicles

Innovations are required for electric vehicles (EVs) to be lighter and more energy efficient due to the range anxiety issue. This article introduces an intelligent control of an organic structure solar supercapacitor (OSSC) for EVs to meet electrical load demands with solar renewable energy. A carbon fibreȬreinforced polymer, nano zinc oxide (ZnO), and copper oxide (CuO) fillers have been used in the development of OSSC prototypes. The organic solar cell, electrical circuits, converter, controller, circuit breaker switch, and batteries were all integrated for the modelling of OSSCs. A carbon fibre (CF)Ȭreinforced CuOȬdoped polymer was utilised to improve the concentration of elecȬ trons. The negative electrodes of the CF were strengthened with nano ZnO epoxy to increase the mobility of electrons as an nȬtype semiconductor (energy band gap 3.2–3.4 eV) and subsequently increased to 3.5 eV by adding 6%ȱΔȬcarbon. The electrodes of the CF were strengthened with epoxyȬ filled nanoȬCuO as a pȬtype semiconductor to facilitate bore/positive charging. They improve the conductivity of the OSSC. The OSSC power storage was controlled by an adaptive neuroȬfuzzy inȬ telligent system controller to meet the load demand of EVs and auxiliary battery charging. MoreoȬ ver, a fully charged OSSC (solar irradiance = 1000 W/m2) produced 561 Wȉh/m2 to meet the vehicle load demand with 45 A of auxiliary battery charging current. Therefore, the OSSC can save 15% in energy efficiency and contribute to emission control. The integration of an OSSC with an EV battery can minimise the weight and capacity of the battery by 7.5% and 10%, respectively

Evaporator Modeling and an Optimal Control Strategy Development of an Organic Rankine Cycle Waste Heat Recovery System for a Heavy Duty Diesel Engine Application

The Organic Rankine Cycle (ORC) has proven to be a promising technology for Waste Heat Recovery (WHR) systems in heavy duty diesel engine applications. However, due to the highly transient heat source, controlling the working fluid flow through the ORC system and maximizing the heat recovery is a challenge for real time application. To that end, this research resulted in the following main developments. The first new development is in the area of heat exchanger modeling. The heat exchanger is a key component within the WHR system and it governs the dynamics of the complete ORC system. The heat exchanger model is extended using a thermal image data to improve its phase length prediction capability. It’s shown that the new identified empirical equations help improve the phase length estimation by 43% over a set of transient experiments. As a result, the model can be used to develop an improved control oriented moving boundary model and to provide insights into evaporator design. The second new development is the advancement of the control design of an ORC system. With advanced knowledge of the heat source dynamics, there is potential to enhance power optimization from the WHR system through predictive optimal control. The proposed approach in this this dissertation is a look-ahead control strategy where, the future vehicle speed is predicted utilizing road topography and V2V connectivity. The forecasted vehicle speed is utilized to predict the engine speed and torque, which facilitates estimation of the engine exhaust conditions used in the ORC control model. In the simulation study, a reference tracking controller is designed based on the Model Predictive Control (MPC) methodology. Two variants of Non-linear MPC (NMPC) are evaluated: an NMPC with look-ahead exhaust conditions and a baseline NMPC without the knowledge of future exhaust conditions. Simulation results show no particular improvement to working fluid superheat tracking at the evaporator outlet via the look-ahead strategy for a drive cycle. However, the look-ahead control strategy does provide a substantial reduction in system control effort via dampening the heavily transient working fluid pump actuation, enhancing pump longevity, health, and reducing pump power consumption. This reduction in pump actuation helps the NMPC with preview to maintain the superheat lower than the NMPC without this feature for certain frequency of the exhaust conditions. Overall, NMPC with preview feature can help reduce parasitic losses, like pump power and improve power generation. The third development addresses the modeling errors and measurement inaccuracies for NMPC implementation. NMPC is inherently a state feedback system and for that reason an Extended Kalman Filter (EKF) is used to estimate unmeasurable states inside the ORC evaporators based on exhaust gas and working fluid temperatures. Since it is not realistic to expect that the system model will perfectly describe the behavior of the evaporator dynamics in all operating conditions, the estimator is therefore augmented with a disturbance model for offset free MPC tracking. Simulation study shows that the augmented system is perfectly capable of discarding the model errors and rejecting the measurement inaccuracies. Moreover, experimental validation confirms that no steady state error is observed during online implementation of the augmented EKF. Finally, experimental validation of the designed NMPC control strategy was conducted. The performance of the NMPC was evaluated on a heavily transient drive cycle, as well as on a sinusoidal generated heat signals. Both experimental and simulated sinusoidal exhaust condition shows that evaporator under consideration inherently helps attenuate the fluctuating exhaust conditions due to its thermal inertia especially for heat signals of shorter time periods. However for slow changing exhaust conditions, a slower rate of change of working fluid flow helps in inhibiting temperature overshoot at the evaporator outlet

Characterization Of Real-World Particle Number Emissions During Re-Ignition Events From A 2010 Light-Duty Hybrid-Electric Vehicle

Despite the increasing popularity of hybrid-electric vehicles (HEVs), few studies have quantified their real-world particle emissions from internal combustion engine (ICE) re-ignition events (RIEVs). RIEVs have been known to occur under unstable combustion conditions which frequently result in particle number emission rates (PNERs) that exceed stabilized engine operation. Tailpipe total PN (5 to 560 nm diameter) emission rates (#/s) from a conventional vehicle (CV) and hybrid electric vehicle (HEV) 2010 Toyota Camry were quantified on a 50 km (32 mi) route over a variety of roadways in Chittenden County, Vermont using the Total On-board Tailpipe Emissions Measurement System (TOTEMS). While HEVs are known to have significant fuel conserving benefits compared to conventional vehicles, less is known about the relative emissions performance of HEVs. This study is the first to characterize RIEVs under a range of real-world driving conditions and to directly compare HEV and CV PNER during driving on different road sections. A total of 28 CV and 33 HEV sampling runs were conducted over an 18-month period under ambient temperatures ranging between -4 and 35 °C. A road classification based upon speed and intersection density divided the route into four different road sections: Freeway, Rural, Urban I and Urban II. Due to the distinct on-off cycling of the HEV ICE, a new operational mode framework (ICE OpMode) was developed to characterize shutdown, off, re-ignition and stabilized HEV ICE operation. Road section was found to affect overall ICE OpMode distribution, with HEV engine-off operation averaging 57%, 36% and 5% of total operation for combined Urban, Rural and Freeway road sections, respectively. Re-ignition frequency was found to range between 11 and 133 events per hour, with spatial density ranging between 0.1 and 5.6 events per kilometer of roadway. A total of 3212 re-ignition events were observed and recorded, and mean HEV PNER during RIEVs, on average, ranged between 2.4 and 4.4 times greater than that of HEV Stabilized operation. Approximately 65% of all re-ignition events resulted in a peak PNER exceeding the 95% percentile for all ICE-on activity in both vehicles (9.3 x 1011 #/s), known as a High Emission Event Record (HEER). RIEV operation made up only 7.4% of total ICE-on operation for both vehicles but accounted for 35.4% of all HEERs. Overall, total particles emitted during HEV operation associated with re-ignition events ranged from 5% for Freeway driving to 60% for Urban I driving. Comparisons between vehicles found an average of 37% and 7% fuel conserving benefits of the HEV during Urban I and Freeway driving, respectively. However, a different effect was found for PN emissions. During Urban I driving, where RIEVs were most frequent, on average HEV PNER was 2.3 times greater than overall mean CV PNER. For Freeway driving, where the HEV operated similar to a conventional vehicle, mean CV PNER was 2.4 times greater than mean HEV PNER. PNER from partial re-ignition events following an incomplete ICE shutdown (no period of prior engine off operation) were on average 1.65 times greater than those occurring when the ICE shutdown for at least one second. The typical fuel consumption benefits of HEVs in urban driving are associated with a tradeoff in PN emissions. The HEV ICE operating behavior has implications for the spatial distribution of PN hot-spots as well as the associated micro-scale modeling of alternative vehicle technology emissions. It is likely that building a model of HEV behavior based upon CV activity will be appropriate, with consideration of a hybridization factor and, as a result of these analyses, a re-ignition factor

Intelligent control and look-ahead energy management of hybrid electric vehicles

A review of the state of knowledge in the field of control and energy management in HEVs is carried out. The key innovation of the project is the development of a model of a PHEV using the real road data with an intelligent look-ahead online controller. Another novelty of this work is the method of route planning. It combines the information of vehicle sensors such as accelerometer and speedometer with the data of a GPS to create a road grade map for use within the look-ahead energy management strategy in the vehicle. For the PHEV, an adaptive cruise controller is modelled and an optimisation method is applied to obtain the best speed profile during a trajectory. Finally, the nonlinear model of the vehicle is applied with the sliding mode controller. The effect of using this controller is compared with the universal cruise controller. The stability of the system is studied and proved

Thermal characterization, multi-scale thermal modeling and experimental validation of lithium-ion batteries for automobile application

This work focuses on studying the thermal aspects of automotive battery systems that includes developing a detailed thermal model for lithium-ion battery systems comprising an electrochemical heat generation model and a heat transfer model dynamically coupled together to form a full 3D thermal model. And finally validating the model by experimental findings