Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet

Integrated Thermal and Energy Management of Connected Hybrid Electric Vehicles Using Deep Reinforcement Learning

The climate-adaptive energy management system holds promising potential for harnessing the concealed energy-saving capabilities of connected plug-in hybrid electric vehicles. This research focuses on exploring the synergistic effects of artificial intelligence control and traffic preview to enhance the performance of the energy management system (EMS). A high-fidelity model of a multi-mode connected PHEV is calibrated using experimental data as a foundation. Subsequently, a model-free multistate deep reinforcement learning (DRL) algorithm is proposed to develop the integrated thermal and energy management (ITEM) system, incorporating features of engine smart warm-up and engine-assisted heating for cold climate conditions. The optimality and adaptability of the proposed system is evaluated through both offline tests and online hardware-in-the-loop tests, encompassing a homologation driving cycle and a real-world driving cycle in China with real-time traffic data. The results demonstrate that ITEM achieves a close to dynamic programming fuel economy performance with a margin of 93.7%, while reducing fuel consumption ranging from 2.2% to 9.6% as ambient temperature decreases from 15°C to -15°C in comparison to state-of-the-art DRL-based EMS solutions

Real-Time Optimal Control of a Plug-in Hybrid Electric Vehicle Using Trip Information

The plug-in hybrid electric vehicle (PHEV) is a promising option for future sustainable transportation. It offers better fuel economy and lower emissions than conventional vehicles. This thesis has developed a novel energy-optimal powertrain controller for PHEVs. The controller will be broadly applicable to all PHEV models; however, it will be fine-tuned to the Toyota Prius Plug-in Hybrid for testing and validation. The controller will take advantage of advancements in vehicle intelligent and communications technologies, such as Global Positioning System (GPS), Intelligent Transportation System (ITS), Geographic Information System (GIS), radar, and other on-board sensors, to provide look-ahead trip data. These data are critical to increasing fuel economy as well as driving safety. This PhD research has developed three energy-optimal systems for PHEVs: Trip Planning module, Route-based Energy Management System (Route-based EMS), and Ecological Cruise (Eco-Cruise) Controller. The main objective of these energy-optimal systems is to minimize the total energy cost, including both electricity derived from the grid and fuel. The upper-level system is Trip Planning, using an algorithm designed to take advantage of previewed trip information to optimize State of Charge (SOC) profiles. The Route-based EMS optimally distributes propulsion power between the batteries and engine. Finally, the Eco-Cruise controller adjusts the speed considering upcoming trip data. Real-time implementation has remained a major challenge in the design of complex control systems. To address this hurdle, simple and efficient models and fast optimization algorithms are developed for each energy-optimal strategy. A Real-time Cluster-based Optimization is developed to solve the Trip Planning problem in real-time. The Route-based EMS is developed based on Equivalent Consumption Minimization Strategy (ECMS) to optimally distribute propulsion power between two energy sources. And, a Nonlinear Model Predictive Control (NMPC) is utilized to obtain optimum traction or regenerative torques in Eco-Cruise controller. Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL) testing are critical steps in control validation and in ensuring real-time implementation capability. The MIL results show that the novel energy-optimal powertrain controller can improve the total energy cost by up to %20 compare to benchmark rule-based controller. The HIL test results demonstrate that the computational time for energy-optimal strategies are less than the target sampling-time, and they can be implemented in real-time

Intelligent Transportation Systems, Hybrid Electric Vehicles, Powertrain Control, Cooperative Adaptive Cruise Control, Model Predictive Control

Information obtainable from Intelligent Transportation Systems (ITS) provides the possibility of improving the safety and efficiency of vehicles at different levels. In particular, such information has the potential to be utilized for prediction of driving conditions and traffic flow, which allows us to improve the performance of the control systems in different vehicular applications, such as Hybrid Electric Vehicles (HEVs) powertrain control and Cooperative Adaptive Cruise Control (CACC). In the first part of this work, we study the design of an MPC controller for a Cooperative Adaptive Cruise Control (CACC) system, which is an automated application that provides the drivers with extra benefits, such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as interfering vehicles cutting-into the CACC platoons or hard braking by leading cars. To address this problem, we first propose a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme. Then, the predicted trajectory of each vehicle in the adjacent lanes is used to estimate the probability of that vehicle cutting-into the CACC platoon. To consider the calculated probability in control system decisions, a Stochastic Model Predictive Controller (SMPC) needs to be designed which incorporates this cut-in probability, and enhances the reaction against the detected dangerous cut-in maneuver. However, in this work, we propose an alternative way of solving this problem. We convert the SMPC problem into modeling the CACC as a Stochastic Hybrid System (SHS) while we still use a deterministic MPC controller running in the only state of the SHS model. Finally, we find the conditions under which the designed deterministic controller is stable and feasible for the proposed SHS model of the CACC platoon. In the second part of this work, we propose to improve the performance of one of the most promising realtime powertrain control strategies, called Adaptive Equivalent Consumption Minimization Strategy (AECMS), using predicted driving conditions. In this part, two different real-time powertrain control strategies are proposed for HEVs. The first proposed method, including three different variations, introduces an adjustment factor for the cost of using electrical energy (equivalent factor) in AECMS. The factor is proportional to the predicted energy requirements of the vehicle, regenerative braking energy, and the cost of battery charging and discharging in a finite time window. Simulation results using detailed vehicle powertrain models illustrate that the proposed control strategies improve the performance of AECMS in terms of fuel economy by 4\%. Finally, we integrate the recent development in reinforcement learning to design a novel multi-level power distribution control. The proposed controller reacts in two levels, namely high-level and low-level control. The high-level control decision estimates the most probable driving profile matched to the current (and near future) state of the vehicle. Then, the corresponding low-level controller of the selected profile is utilized to distribute the requested power between Electric Motor (EM) and Internal Combustion Engine (ICE). This is important because there is no other prior work addressing this problem using a controller which can adjust its decision to the driving pattern. We proposed to use two reinforcement learning agents in two levels of abstraction. The first agent, selects the most optimal low-level controller (second agent) based on the overall pattern of the drive cycle in the near past and future, i.e., urban, highway and harsh. Then, the selected agent by the high-level controller (first agent) decides how to distribute the demanded power between the EM and ICE. We found that by carefully designing a training scheme, it is possible to effectively improve the performance of this data-driven controller. Simulation results show up to 6\% improvement in fuel economy compared to the AECMS

Hybrid and Electric Vehicles Optimal Design and Real-time Control based on Artificial Intelligence

L'abstract è presente nell'allegato / the abstract is in the attachmen

Real-time Optimal Energy Management System for Plug-in Hybrid Electric Vehicles

Air pollution and rising fuel costs are becoming increasingly important concerns for the transportation industry. Hybrid electric vehicles (HEVs) are seen as a solution to these problems as they off er lower emissions and better fuel economy compared to conventional internal combustion engine vehicles. A typical HEV powertrain consists of an internal combustion engine, an electric motor/generator, and a power storage device (usually a battery). Another type of HEV is the plug-in hybrid electric vehicle (PHEV), which is conceptually similar to the fully electric vehicle. The battery in a PHEV is designed to be fully charged using a conventional home electric plug or a charging station. As such, the vehicle can travel further in full-electric mode, which greatly improves the fuel economy of PHEVs compared to HEVs. In this study, an optimal energy management system (EMS) for a PHEV is designed to minimize fuel consumption by considering engine emissions reduction. This is achieved by using the model predictive control (MPC) approach. MPC is an optimal model-based approach that can accommodate the many constraints involved in the design of EMSs, and is suitable for real-time implementations. The design and real-time implementation of such a control approach involves control-oriented modeling, controller design (including high-level and low-level controllers), and control scheme performance evaluation. All of these issues will be addressed in this thesis. A control-relevant parameter estimation (CRPE) approach is used to make the control-oriented model more accurate. This improves the EMS performance, while maintaining its real-time implementation capability. To reduce the computational complexity, the standard MPC controller is replaced by its explicit form. The explicit model predictive controller (eMPC) achieves the same performance as the implicit MPC, but requires less computational effort, which leads to a fast and reliable implementation. The performance of the control scheme is evaluated through different stages of model-in-the-loop (MIL) simulations with an equation-based and validated high-fidelity simulation model of a PHEV powertrain. Finally, the CRPE-eMPC EMS is validated through a hardware-in-the-loop (HIL) test. HIL simulation shows that the proposed EMS can be implemented to a commercial control hardware in real time and results in promising fuel economy figures and emissions performance, while maintaining vehicle drivability

Energy management strategies for fuel cell vehicles: A comprehensive review of the latest progress in modeling, strategies, and future prospects

Fuel cell vehicles (FCVs) are considered a promising solution for reducing emissions caused by the transportation sector. An energy management strategy (EMS) is undeniably essential in increasing hydrogen economy, component lifetime, and driving range. While the existing EMSs provide a range of performance levels, they suffer from significant shortcomings in robustness, durability, and adaptability, which prohibit the FCV from reaching its full potential in the vehicle industry. After introducing the fundamental EMS problem, this review article provides a detailed description of the FCV powertrain system modeling, including typical modeling, degradation modeling, and thermal modeling, for designing an EMS. Subsequently, an in-depth analysis of various EMS evolutions, including rule-based and optimization-based, is carried out, along with a thorough review of the recent advances. Unlike similar studies, this paper mainly highlights the significance of the latest contributions, such as advanced control theories, optimization algorithms, artificial intelligence (AI), and multi-stack fuel cell systems (MFCSs). Afterward, the verification methods of EMSs are classified and summarized. Ultimately, this work illuminates future research directions and prospects from multi-disciplinary standpoints for the first time. The overarching goal of this work is to stimulate more innovative thoughts and solutions for improving the operational performance, efficiency, and safety of FCV powertrains

Online Control of Automotive systems for improved Real-World Performance

[ES] La necesidad de mejorar el consumo de combustible y las emisiones de los sistemas propulsivos de automoción en condiciones reales de conducción es la base de esta tesis. Para ello, se exploran dos ejes: En primer lugar, el control de los sistemas de propulsión. El estado del arte de control en los sistemas propulsivos de automoción se basa en gran medida en el uso de técnicas de optimización que buscan las leyes de control que minimizan una función de coste en un conjunto de condiciones de operación denidas a priori. Estas leyes se almacenan en las ECUs de producción en forma de mapas de calibración de los diferentes actuadores del motor. Las incertidumbres asociadas al conjunto limitado de condiciones en el proceso de calibración dan lugar a un funcionamiento subóptimo del sistema de propulsión en condiciones de conducción real. Por lo tanto, en este trabajo se proponen métodos de control adaptativo que optimicen la gestión de la planta propulsiva a las condiciones esperadas de funcionamiento para un usuario y un caso determinado en lugar de a un conjunto genérico de condiciones. El segundo eje se reere a optimizar, en lugar de los parámetros de control del sistema propulsivo, la demanda de potencia de este, introduciendo al propio conductor en el bucle de control, sugiriéndole las acciones a tomar. En particular, este segundo eje se reere al control de la velocidad del vehículo (conocido popularmente como Eco-Driving en la literatura) en condiciones reales de conducción. Se proponen sistemas de aviso en tiempo real al conductor acerca de la velocidad óptima para minimizar el consumo del vehículo. Los métodos de control desarrollados para cada aplicación se describen en detalle en la tesis y se muestran ensayos experimentales de validación en los casos de estudio diseñados. Ambos ejes representan un problema de control óptimo, denido por un sistema dinámico, unas restricciones a cumplir y un coste a minimizar, en este sentido las herramientas desarrolladas en la tesis son comunes a los dos ejes: Un modelo de vehículo, una herramienta de predicción del ciclo de conducción y métodos de control óptimo (Programación Dinámica, Principio Mínimo de Pontryagin y Estrategia de Consumo Equivalente Mínimo). Dependiendo de la aplicación, los métodos desarrollados se implementaron en varios entornos experimentales: un motor térmico en sala de ensayos simulando el resto del vehículo, incluyendo el resto del sistema de propulsión híbrido y en un vehículo real. Los resultados muestran mejoras signicativas en el rendimiento del sistema de propulsión en términos de ahorro de combustible y emisiones en comparación con los métodos empleados en el estado del arte actual.[CA] La necessitat de millorar el consum de combustible i les emissions dels sistemes propulsius d'automoció en condicions reals de conducció és la base d'aquesta tesi. Per a això, s'exploren dos eixos: En primer lloc, el control dels sistemes de propulsió. L'estat de l'art de control en els sistemes propulsius d'automoció es basa en gran manera en l'ús de tècniques d'optimització que busquen les lleis de control que minimitzen una funció de cost en un conjunt de condicions d'operació denides a priori. Aquestes lleis s'emmagatzemen en les Ecus de producció en forma de mapes de calibratge dels diferents actuadors del motor. Les incerteses associades al conjunt limitat de condicions en el procés de calibratge donen lloc a un funcionament subòptim del sistema de propulsió en condicions de conducció real. Per tant, en aquest treball es proposen mètodes de control adaptatiu que optimitzen la gestió de la planta propulsiva a les condicions esperades de funcionament per a un usuari i un cas determinat en lloc d'un conjunt genèric de condicions. El segon eix es refereix a optimitzar, en lloc dels paràmetres de control del sistema propulsiu, la demanda de potència d'aquest, introduint al propi conductor en el bucle de control, suggerint-li les accions a prendre. En particular, aquest segon eix es refereix al control de la velocitat del vehicle (conegut popularment com Eco-*Driving en la literatura) en condicions reals de conducció. Es proposen sistemes d'avís en temps real al conductor sobre la velocitat òptima per a minimitzar el consum del vehicle. Els mètodes de control desenvolupats per a cada aplicació es descriuen detalladament en la tesi i es mostren assajos experimentals de validació en els casos d'estudi dissenyats. Tots dos eixos representen un problema de control òptim, denit per un sistema dinàmic, unes restriccions a complir i un cost a minimitzar, en aquest sentit les eines desenvolupades en la tesi són comunes als dos eixos: Un model de vehicle, una eina de predicció del cicle de conducció i mètodes de control òptim (Programació Dinàmica, Principi Mínim de *Pontryagin i Estratègia de Consum Equivalent Mínim). Depenent de l'aplicació, els mètodes desenvolupats es van implementar en diversos entorns experimentals: un motor tèrmic en sala d'assajos simulant la resta del vehicle, incloent la resta del sistema de propulsió híbrid i en un vehicle real. Els resultats mostren millores signicatives en el rendiment del sistema de propulsió en termes d'estalvi de combustible i emissions en comparació amb els mètodes emprats en l'estat de l'art actual.[EN] The need of improving the real-world fuel consumption and emission of automotive applications is the basis of this thesis. To this end, two verticals are explored: First is the online control of the powertrain systems. In state-of-the-art Optimal Control techniques (such as Dyanmic Programming, Pontryagins Minimum Principle, etc...) are extensively used to formulate the optimal control laws. These laws are stored in the production ECUs in the form of feedforward calibration maps. The unaccounted uncertainities related to the real-world during the powertrain calibration result in suboptimal operations of the powertrain in actual driving. Therefore, adaptive control methods are proposed in this work which, optimise the energy management of the conventional and the HEV powertrain control on real driving mission. The second vertical is regarding the vehicle speed control (popularly known as Eco-Driving in the literature) methods in real driving condition. In particular, speed advisory systems are proposed for real time application on a vehicle. The control methods developed for each application are described in details with their verication and validation on the designed case studies. Apart from the developed control methods, there are three tools that were developed and used at various stages of this thesis: A vehicle model, A driving cycle prediction tool and optimal control methods (dynamic programming, PMP and ECMS). Depending on the application, the developed methods were implemented on the Hardware-In-Loop Internal Combustion Engine testing setup or on a real vehicle. The results show signicant improvements in the performance of the powertrain in terms of fuel economy and emissions in comparison to the state-of-the-art methods.Pandey, V. (2021). Online Control of Automotive systems for improved Real-World Performance [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/173716TESI