Efficiency enhancement strategy implementation in hybrid electric vehicles using sliding mode control

Introduction. Hybrid electric vehicles are offering the most economically viable choices in today's automotive industry, providing best solutions for a very high fuel economy and low rate of emissions. The rapid progress and development of this industry has prompted progress of human beings from primitive level to a very high industrial society where mobility used to be a fundamental need. However, the use of large number of automobiles is causing serious damage to our environment and human life. At present most of the vehicles are relying on burning of hydrocarbons in order to achieve power of propulsion to drive wheels. Therefore, there is a need to employ clean and efficient vehicles like hybrid electric vehicles. Unfortunately, earlier control strategies of series hybrid electric vehicle fail to include load disturbances during the vehicle operation and some of the variations of the nonlinear parameters (e.g. stator’s leakage inductance, resistance of winding etc.). The novelty of the proposed work is based on designing and implementing two robust sliding mode controllers (SMCs) on series hybrid electric vehicle to improve efficiency in terms of both speed and torque respectively. The basic idea is to let the engine operate only when necessary keeping in view the state of charge of battery. Purpose. In proposed scheme, both performance of engine and generator is being controlled, one sliding mode controllers is controlling engine speed and the other one is controlling generator torque, and results are then compared using 1-SMC and 2-SMC’s. Method. The series hybrid electric vehicle powertrain considered in this work consists of a battery bank and an engine-generator set which is referred to as the auxiliary power unit, traction motor, and power electronic circuits to drive the generator and traction motor. The general strategy is based on the operation of the engine in its optimal efficiency region by considering the battery state of charge. Results .Mathematical models of engine and generator were taken into consideration in order to design sliding mode controllers both for engine speed and generator torque control. Vehicle was being tested on standard cycle. Results proved that, instead of using only one controller for engine speed, much better results are achieved by simultaneously using two sliding mode controllers, one controlling engine speed and other controlling generator torque.Вступ. Гібридні електромобілі пропонують найбільш економічно доцільний вибір у сучасній автомобільній промисловості, надаючи найкращі рішення для дуже високої економії палива та низького рівня викидів. Швидкий прогрес та розвиток цієї галузі підштовхнули людей до переходу від примітивного рівня до дуже високого індустріального суспільства, де мобільність була фундаментальною потребою. Однак використання великої кількості автомобілів завдає серйозної шкоди довкіллю та життю людини. Нині більшість транспортних засобів покладаються на спалювання вуглеводнів задля досягнення потужності руху на провідних колесах. Отже, необхідно використовувати чисті та ефективні транспортні засоби, такі як гібридні електромобілі. На жаль, раніше стратегії управління серійним гібридним електромобілем не враховували збурення навантаження під час роботи автомобіля і деякі зміни нелінійних параметрів (наприклад, індуктивність розсіювання статора, опір обмотки і т.д.). Новизна запропонованої роботи заснована на розробці та реалізації двох надійних контролерів ковзного режиму (SMC) на серійному гібридному електромобілі для підвищення ефективності з точки зору швидкості та моменту, що крутить, відповідно. Основна ідея полягає в тому, щоб дозволити двигуну працювати тільки тоді, коли це необхідно з урахуванням стану заряду акумулятора. Мета. У пропонованій схемі контролюються характеристики як двигуна, так і генератора, один контролер ковзного режиму регулює швидкість двигуна, а інший регулює крутний момент генератора, а потім результати порівнюються з використанням режимів 1-SMC і 2-SMC. Метод. Силова установка серійного гібридного електромобіля, що розглядається в даній роботі, складається з акумуляторної батареї та установки двигун-генератор, яка називається допоміжною силовою установкою, тяговим двигуном та силовими електронними схемами для приводу генератора та тягового двигуна. Загальна стратегія заснована на роботі двигуна в області оптимальної ефективності з урахуванням рівня заряду акумуляторної батареї. Результати. Математичні моделі двигуна та генератора були прийняті до уваги для розробки регуляторів ковзного режиму як для керування частотою обертання двигуна, так і для керування крутним моментом генератора. Транспортний засіб випробовувався за стандартним циклом. Результати показали, що замість використання лише одного регулятора частоти обертання двигуна набагато кращі результати досягаються при одночасному використанні двох регуляторів ковзного режиму, один з яких керує частотою обертання двигуна, а інший - моментом, що крутить, генератора

Efficiency enhancement strategy implementation in hybrid electric vehicles using sliding mode control

Introduction. Hybrid electric vehicles are offering the most economically viable choices in today's automotive industry, providing best solutions for a very high fuel economy and low rate of emissions. The rapid progress and development of this industry has prompted progress of human beings from primitive level to a very high industrial society where mobility used to be a fundamental need. However, the use of large number of automobiles is causing serious damage to our environment and human life. At present most of the vehicles are relying on burning of hydrocarbons in order to achieve power of propulsion to drive wheels. Therefore, there is a need to employ clean and efficient vehicles like hybrid electric vehicles. Unfortunately, earlier control strategies of series hybrid electric vehicle fail to include load disturbances during the vehicle operation and some of the variations of the nonlinear parameters (e.g. stator’s leakage inductance, resistance of winding etc.). The novelty of the proposed work is based on designing and implementing two robust sliding mode controllers (SMCs) on series hybrid electric vehicle to improve efficiency in terms of both speed and torque respectively. The basic idea is to let the engine operate only when necessary keeping in view the state of charge of battery. Purpose. In proposed scheme, both performance of engine and generator is being controlled, one sliding mode controllers is controlling engine speed and the other one is controlling generator torque, and results are then compared using 1-SMC and 2-SMC’s. Method. The series hybrid electric vehicle powertrain considered in this work consists of a battery bank and an engine-generator set which is referred to as the auxiliary power unit, traction motor, and power electronic circuits to drive the generator and traction motor. The general strategy is based on the operation of the engine in its optimal efficiency region by considering the battery state of charge. Results .Mathematical models of engine and generator were taken into consideration in order to design sliding mode controllers both for engine speed and generator torque control. Vehicle was being tested on standard cycle. Results proved that, instead of using only one controller for engine speed, much better results are achieved by simultaneously using two sliding mode controllers, one controlling engine speed and other controlling generator torque.Вступ. Гібридні електромобілі пропонують найбільш економічно доцільний вибір у сучасній автомобільній промисловості, надаючи найкращі рішення для дуже високої економії палива та низького рівня викидів. Швидкий прогрес та розвиток цієї галузі підштовхнули людей до переходу від примітивного рівня до дуже високого індустріального суспільства, де мобільність була фундаментальною потребою. Однак використання великої кількості автомобілів завдає серйозної шкоди довкіллю та життю людини. Нині більшість транспортних засобів покладаються на спалювання вуглеводнів задля досягнення потужності руху на провідних колесах. Отже, необхідно використовувати чисті та ефективні транспортні засоби, такі як гібридні електромобілі. На жаль, раніше стратегії управління серійним гібридним електромобілем не враховували збурення навантаження під час роботи автомобіля і деякі зміни нелінійних параметрів (наприклад, індуктивність розсіювання статора, опір обмотки і т.д.). Новизна запропонованої роботи заснована на розробці та реалізації двох надійних контролерів ковзного режиму (SMC) на серійному гібридному електромобілі для підвищення ефективності з точки зору швидкості та моменту, що крутить, відповідно. Основна ідея полягає в тому, щоб дозволити двигуну працювати тільки тоді, коли це необхідно з урахуванням стану заряду акумулятора. Мета. У пропонованій схемі контролюються характеристики як двигуна, так і генератора, один контролер ковзного режиму регулює швидкість двигуна, а інший регулює крутний момент генератора, а потім результати порівнюються з використанням режимів 1-SMC і 2-SMC. Метод. Силова установка серійного гібридного електромобіля, що розглядається в даній роботі, складається з акумуляторної батареї та установки двигун-генератор, яка називається допоміжною силовою установкою, тяговим двигуном та силовими електронними схемами для приводу генератора та тягового двигуна. Загальна стратегія заснована на роботі двигуна в області оптимальної ефективності з урахуванням рівня заряду акумуляторної батареї. Результати. Математичні моделі двигуна та генератора були прийняті до уваги для розробки регуляторів ковзного режиму як для керування частотою обертання двигуна, так і для керування крутним моментом генератора. Транспортний засіб випробовувався за стандартним циклом. Результати показали, що замість використання лише одного регулятора частоти обертання двигуна набагато кращі результати досягаються при одночасному використанні двох регуляторів ковзного режиму, один з яких керує частотою обертання двигуна, а інший - моментом, що крутить, генератора

PHYSICS-BASED MODELING AND CONTROL OF POWERTRAIN SYSTEMS INTEGRATED WITH LOW TEMPERATURE COMBUSTION ENGINES

Low Temperature Combustion (LTC) holds promise for high thermal efficiency and low Nitrogen Oxides (NOx) and Particulate Matter (PM) exhaust emissions. Fast and robust control of different engine variables is a major challenge for real-time model-based control of LTC. This thesis concentrates on control of powertrain systems that are integrated with a specific type of LTC engines called Homogenous Charge Compression Ignition (HCCI). In this thesis, accurate mean value and dynamic cycleto- cycle Control Oriented Models (COMs) are developed to capture the dynamics of HCCI engine operation. The COMs are experimentally validated for a wide range of HCCI steady-state and transient operating conditions. The developed COMs can predict engine variables including combustion phasing, engine load and exhaust gas temperature with low computational requirements for multi-input multi-output realtime HCCI controller design. Different types of model-based controllers are then developed and implemented on a detailed experimentally validated physical HCCI engine model. Control of engine output and tailpipe emissions are conducted using two methodologies: i) an optimal algorithm based on a novel engine performance index to minimize engine-out emissions and exhaust aftertreatment efficiency, and ii) grey-box modeling technique in combination with optimization methods to minimize engine emissions. In addition, grey-box models are experimentally validated and their prediction accuracy is compared with that from black-box only or clear-box only models. A detailed powertrain model is developed for a parallel Hybrid Electric Vehicle (HEV) integrated with an HCCI engine. The HEV model includes sub-models for different HEV components including Electric-machine (E-machine), battery, transmission system, and Longitudinal Vehicle Dynamics (LVD). The HCCI map model is obtained based on extensive experimental engine dynamometer testing. The LTC-HEV model is used to investigate the potential fuel consumption benefits archived by combining two technologies including LTC and electrification. An optimal control strategy including Model Predictive Control (MPC) is used for energy management control in the studied parallel LTC-HEV. The developed HEV model is then modified by replacing a detailed dynamic engine model and a dynamic clutch model to investigate effects of powertrain dynamics on the HEV energy consumption. The dynamics include engine fuel flow dynamics, engine air flow dynamics, engine rotational dynamics, and clutch dynamics. An enhanced MPC strategy for HEV torque split control is developed by incorporating the effects of the studied engine dynamics to save more energy compared to the commonly used map-based control strategies where the effects of powertrain dynamics are ignored. LTC is promising for reduction in fuel consumption and emission production however sophisticated multi variable engine controllers are required to realize application of LTC engines. This thesis centers on development of model-based controllers for powertrain systems with LTC engines

Context and driver dependent hybrid electrical vehicle operation

This paper studies the driver and context changes during the operation of a hybrid electric vehicle (HEV) and their influence on fuel consumption. Firstly, a context estimation model to recognize driving styles is developed based on machine learning techniques, for which a realistic scenario with simulation of urban mobility (SUMO) and car modeling platform (IPG Carmaker) integration is designed. Secondly, a novel context-aware control strategy based on model predictive control with extended prediction self-adaptive control (MPC-EPSAC) strategy is proposed. The control objective is to achieve optimal torque-split distribution, while optimizing fuel consumption in the parallel HEV. The simulation results suggest that an improvement in fuel economy can be achieved when the driving style in the control loop is adequately considered. Copyright (C) 2020 The Authors

INCORPORATING DRIVER’S BEHAVIOR INTO PREDICTIVE POWERTRAIN ENERGY MANAGEMENT FOR A POWER-SPLIT HYBRID ELECTRIC VEHICLE

The goal of this series of research is to advance hybrid electric vehicle (HEV) energy management by incorporating driver’s driving behavior and driving cycle information. To reduce HEV fuel consumption, the objectives of this research are divided into the following three parts. The first part of the research investigates the impact of driver’s behavior on the overall fuel efficiency of a hybrid electric vehicle and the energy efficiency of individual powertrain components under various driving cycles. Between the sticker number fuel economy and actual fuel economy, it is well known that a noticeable difference occur when a driver drives aggressively. To simulate aggressive driving, the input driving cycles are scaled up from the baseline driving cycles to higher levels of acceleration/deceleration. The simulation study is conducted using Autonomie®, a powertrain simulation and analysis software. The performance of the major powertrain components is analyzed when the HEV is operated at different level of aggressiveness. In the second part of the study, the vehicle driving cycles affect the performance of a hybrid vehicle control strategy and the corresponding overall performance of the vehicle. By identifying the driving cycles of a vehicle, the HEV supervisor controller system will be dynamically adapt the control strategy to the changes of vehicle driving patterns. With pattern recognition method, a driving cycle is represented by feature vectors that are formed by a set of parameters to which the driving cycle is sensitive. To establish reference driving cycle database, the representative feature vectors of four federal driving cycles are generated using feature extraction method. The performance of the presented adaptive control strategy based on driving pattern recognition is evaluated using Autonomie. In the last part of the study, a predictive control method is developed and investigated for hybrid electric vehicle energy management in effort to improve HEV fuel economy. Model Predictive Control (MPC), a predictive control method, is applied to improve the fuel economy of a power-split HEV. The study compares the performance of MPC method and conventional rule-base control method. A parametric study is conducted to understand the influence of 3 weighting factors in MPC formulation on the performance of the vehicles

STUDY OF CONTROL SCHEMES FOR SERIES HYBRID-ELECTRIC POWERTRAIN FOR UNMANNED AERIAL SYSTEMS

Hybrid-Electric aircraft powertrain modeling for Unmanned Aerial Systems (UAS) is a useful tool for predicting powertrain performance of the UAS aircraft. However, for small UAS, potential gains in range and endurance can depend significantly on the aircraft flight profile and powertrain control logic in addition to the subsequent impact on the performance of powertrain components. Small UAS aircraft utilize small-displacement engines with poor thermal efficiency and, therefore, could benefit from a hybridized powertrain by reducing fuel consumption. This study uses a dynamic simulation of a UAS, representative flight profiles, and powertrain control logic approaches to evaluate the performance of a series hybrid-electric powertrain. Hybrid powertrain component models were developed using lookup tables of test data and model parameterization approaches to generate a UAS dynamic system model. These models were then used to test three different hybrid powertrain control strategies for their ability to provide efficient IC engine operation during the charging process. The baseline controller analyzed in this work does not focus on optimizing fuel efficiency. In contrast, the other two controllers utilize engine fuel consumption data to develop a scheme to reduce fuel consumption during the battery charging operation. The performance of the powertrain controllers is evaluated for a UAS operating on three different representative mission profiles relevant to cruising, maneuvering, and surveillance missions. Fuel consumption and battery state of charge form two metrics that are used to evaluate the performance of each controller. The first fuel efficiency-focused controller is the ideal operating line (IOL) strategy. The IOL strategy uses performance maps obtained by engine characterization on a specialized dynamometer. The simulations showed the IOL strategy produced average fuel economy improvements ranging from 12%-15% for a 30-minute mission profile compared to the baseline controller. The last controller utilizes fuzzy logic to manage the charging operations while maintaining efficient fuel operation where it produced similar fuel saving to the IOL method but were generally higher by 2-3%. The importance of developing detailed dynamic system models to capture the power variations during flight with fuel-efficient powertrain controllers is key to maximizing small UAS hybrid powertrain performance in varying operating conditions

Development of predictive energy management strategies for hybrid electric vehicles

2017 Fall.Includes bibliographical references.Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into the impact of real-world prediction error on FE improvements, and whether near-term technologies can be utilized to improve FE. This study seeks to research the effect of prediction error on FE. First, a speed prediction method is developed, and trained with real-world driving data gathered only from the subject vehicle (a local data collection method). This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a high-fidelity model of the FE of a Toyota Prius. A tradeoff analysis between prediction duration and prediction fidelity was completed to determine what duration of prediction resulted in the largest FE improvement. Results demonstrate that 60-90 second predictions resulted in the highest FE improvement over the baseline, achieving up to a 4.8% FE increase. A second speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication was developed to understand if incorporating near-term technologies could be utilized to further improve prediction fidelity. This prediction method produced lower variation in speed prediction error, and was able to realize a larger FE improvement over the local prediction method for longer prediction durations, achieving up to 6% FE improvement. This study concludes that speed prediction and prediction-informed optimal vehicle energy management can produce FE improvements with real-world prediction error and drive cycle variability, as up to 85% of the FE benefit of perfect speed prediction was achieved with the proposed prediction methods

REAL-TIME PREDICTIVE CONTROL OF CONNECTED VEHICLE POWERTRAINS FOR IMPROVED ENERGY EFFICIENCY

The continued push for the reduction of energy consumption across the automotive vehicle fleet has led to widespread adoption of hybrid and plug-in hybrid electric vehicles (PHEV) by auto manufacturers. In addition, connected and automated vehicle (CAV) technologies have seen rapid development in recent years and bring with them the potential to significantly impact vehicle energy consumption. This dissertation studies predictive control methods for PHEV powertrains that are enabled by CAV technologies with the goal of reducing vehicle energy consumption. First, a real-time predictive powertrain controller for PHEV energy management is developed. This controller utilizes predictions of future vehicle velocity and power demand in order to optimize powersplit decisions of the vehicle. This predictive powertrain controller utilizes nonlinear model predictive control (NMPC) to perform this optimization while being cognizant of future vehicle behavior. Second, the developed NMPC powertrain controller is thoroughly evaluated both in simulation and real-time testing. The controller is assessed over a large number of standardized and real-world drive cycles in simulation in order to properly quantify the energy savings benefits of the controller. In addition, the NMPC powertrain controller is deployed onto a real-time rapid prototyping embedded controller installed in a test vehicle. Using this real-time testing setup, the developed NMPC powertrain controller is evaluated using on-road testing for both energy savings performance and real-time performance. Third, a real-time integrated predictive powertrain controller (IPPC) for a multi-mode PHEV is presented. Utilizing predictions of future vehicle behavior, an optimal mode path plan is computed in order to determine a mode command best suited to the future conditions. In addition, this optimal mode path planning controller is integrated with the NMPC powertrain controller to create a real-time integrated predictive powertrain controller that is capable of full supervisory control for a multi-mode PHEV. Fourth, the IPPC is evaluated in simulation testing across a range of standard and real-world drive cycles in order to quantify the energy savings of the controller. This analysis is comprised of the combined benefit of the NMPC powertrain controller and the optimal mode path planning controller. The IPPC is deployed onto a rapid prototyping embedded controller for real-time evaluation. Using the real-time implementation of the IPPC, on-road testing was performed to assess both energy benefits and real-time performance of the IPPC. Finally, as the controllers developed in this research were evaluated for a single vehicle platform, the applicability of these controllers to other platforms is discussed. Multiple cases are discussed on how both the NMPC powertrain controller and the optimal mode path planning controller can be applied to other vehicle platforms in order to broaden the scope of this research

Energy management of hybrid and battery electric vehicles

This work focuses on improving the fuel economy of parallel Hybrid Electric Vehicles (HEVs) and dual-motor Electric Vehicles (EVs) through energy management strategies. Both vehicle models have two propulsion branches, each powering a separate axle: An engine and an electric motor in the HEV and two electric motors in the EV. This similarity in the vehicle models emphasises the need for similar energy management solutions. In Part Energy Management of HEVs of this thesis, a high-fidelity parallel Through-The-Road (TTR) HEV model is developed to study and test conventional control strategies. The traditional control strategies serve as a guide for developing novel heuristic control strategies. The Equivalent Consumption Minimisation Strategy (ECMS) is an optimisation-based control strategy used as the benchmark in this part of the work. A family of rule-based energy management strategies is proposed for parallel HEVs, including the Torque-levelling Threshold-changing Strategy (TTS) and its simplified version, the Simplified Torque-levelling Threshold-changing Strategy (STTS). The TTS applies a concept of torque-levelling, which ensures the engine works efficiently by operating with a constant torque as the load demand crosses a certain threshold, unlike the load-following approach commonly used. However, the TTS requires finely tuned constant torque and threshold parameters, making it unsuitable for real-time applications. To address this, two feedback-like updating laws are incorporated into the TTS to determine the constant torque and threshold online for real-time applications. Real-time versions of these strategies, Real-time Torque-levelling Threshold-changing Strategy (RTTS) and Real-time Simplified Torque-levelling Threshold-changing Strategy (RSTTS) are developed using a novel Driving Pattern Recognition (DPR) algorithm. The effectiveness of the RTTS is demonstrated by implementing it on a high-fidelity parallel hybrid passenger car and benchmarking it against ECMS. In Part Energy Management of EVs of the thesis, a low-fidelity model of a novel EV powertrain with two electric propulsion systems, one at each axle, has been developed to study and test its energy management with one of the main conventional optimal control methods, Dynamic Programming (DP). The EV model uses two differently sized traction motors at the front and rear axles. The thermal dynamics of the utilised Permanent Magnet Synchronous Motors (PMSMs) are studied. DP is first implemented onto the Baseline model that does not include any PMSM thermal dynamics, referred to as the Baseline DP, which acts as a benchmark since it is the conventional case. The thermal dynamics of the traction motors are then introduced in the second DP problem formulation, referred to as the Thermal DP, which is compared against the Baseline DP to evaluate the possible benefits of energy efficiency by the more informed energy management optimisation formulation. The best method is chosen to include these thermal dynamics in the overall energy management control strategy without significantly compromising computational time.Open Acces