A COMPREHENSIVE METHODOLOGY for the OPTIMIZATION of the OPERATING STRATEGY of HYBRID ELECTRIC VEHICLES

The sustainable exploitation of energy and reduction of pollutant emissions are main concerns in our society. Driven by more stringent international standards, automobile manufacturers are developing new technologies such as the Hybrid Electric Vehicles (HEVs). These innovative systems combine the main benefits of traditional Internal Combustion Engines (ICEs) with those of Battery Electric Vehicles (BEVs), while overcoming their main drawbacks. HEVs can offer significant improvements in the efficiency of the propulsion system, but they also lead to higher complexities in the design and in the control. In order to exploit all the expected advantages, a dedicated optimization of the Hybrid Operating Strategy (HOS) is required. In this framework, simulation plays a key role in identifying the optimal HOS, where the primary design targets are the fuel economy, emission reduction and improvement in the vehicle performance (including acceleration, driving range, operational flexibility and noise). With such a perspective, a simulation study was performed involving the implementation, in Matlab environment, of zero-dimensional models of a Series Hybrid Electric Vehicle (SHEV) and a Parallel Hybrid Electric Vehicle (PHEV). As far as the hybrid operating strategy is concerned, three different approaches were investigated: _ A novel Benchmark Optimizer (BO), that determines the best possible operating strategy for the selected target, mission profile and powertrain design. The single solution is characterized by a vector, in which every scalar independently defines the mechanical power of the electric machine, for the PHEV, or the engine speed, for the SHEV, at each time step of the selected driving cycle _ A real-time optimizer based on the Minimization of the Total system Losses (TLM). It involves a vector-approach, in order to select, at each time step, the power split that guarantees the minimum system losses. It requires a reduced number of calibration parameters and, therefore, is computationally fast and adequate to work in real-world applications. Based on this technique, two different methodologies concerning the engine component are considered: the Total engine losses (TLM TOT) and the Recoverable (with respect to the optimal operating point) engine losses (TLM REC) _ A real-time optimizer based on the Total Load Switch Thresholds. It switches the operating mode depending on the load and speed signals. It involves a scalar-approach and requires a reduced number of calibration parameters. It is by far the method that requires the least computational effort In all the three cases, the numerical optimizer is based on Genetic Algorithm (GA) techniques. GAs are inspired by the mechanism of natural selection, in which better individuals are likely to be the winners in a competing environment. It is a statistical approach able to solve optimization problems whose objective function is non-continuous, non-differentiable, stochastic and highly non-linear. The study analyses the optimization of the well-to-wheel CO2 emissions of a Parallel and a Series Hybrid Electric Vehicles along the New European Driving Cycle (NEDC) and the Artemis Driving Cycles. In the case of the only compression ignition engine, also NOx emissions were considered as optimization criteria along the NED

A real time energy management strategy for plug-in hybrid electric vehicles based on optimal control theory

Abstract Plug-in hybrid electric vehicles are commonly designed to work in Charge Depleting/Charge Sustaining (CD/CS) mode, depleting the battery by driving in only-electrical mode until the SoC reaches its minimum acceptable threshold, and then sustaining the state of charge till the end of the mission, operating as a traditional hybrid vehicle. Nonetheless, a simple application of an optimal control framework suggests a blended discharge strategy, in which the powertrain is operated as to gradually deplete the SoC and reach the lower threshold only at the end of the trip. Such an algorithm has the drawback that the optimal solution can only be reached offline, depending on the a-priori knowledge of the driving event, making it unsuitable to be implemented online, as it is. The paper presents a methodology to design a heuristic controller, to be used online, based on rules extracted from the analysis of the powertrain behavior under the optimal control solution. The application is a parallel plug-in vehicle, derived from a re- engineered engine-only driven powertrain, and the optimal problem is solved with the Pontryagin's Minimum Principle. Results are also compared to the same vehicle in its standard internal combustion engine version, as well as the commonly implemented Charge Depleting/Charge Sustaining strategy

Predictive Energy Management in Connected Vehicles: Utilizing Route Information Preview for Energy Saving

This dissertation formulates algorithms that use preview information of road terrain and traffic flow for reducing energy use and emissions of modern vehicles with conventional or hybrid powertrains. Energy crisis, long term energy deficit, and more restrictive environmental protection policies require developing more efficient and cleaner vehicle powertrain systems. An alternative to making advanced technology engines or electrifying the vehicle powertrain is utilizing ambient terrain and traffic information in the energy management of vehicles, a topic which has not been emphasized in the past. Today\u27s advances in vehicular telematics and advances in GIS (Geographic Information System), GPS (Global Positioning Systems), ITS (Intelligent Transportation Systems), V2V (Vehicle to Vehicle) communication, and VII (Vehicle Infrastructure Integration ) create more opportunities for predicting a vehicle\u27s trip information with details such as the future road grade, the distance to the destination, speed constraints imposed by the traffic flow, which all can be utilized for better vehicle energy management. Optimal or near optimal decision-making based on this available information requires optimal control methods, whose fundamental theories were well studied in the past but are not directly applicable due to the complexity of real problems and uncertainty in the available preview information. This dissertation proposes the use of optimal control theories and tools including Pontryagin minimum principle, Dynamic Programming (DP) which is a numerical realization of Bellman\u27s principle of optimality, and Model Predictive Control (MPC) in the optimization-based control of hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and conventional vehicles based on preview of future route information. The dissertation includes three parts introduced as follows: First, the energy saving benefit in HEV energy management by previewing future terrain information and applying optimal control methods is explored. The potential gain in fuel economy is evaluated, if road grade information is integrated in energy management of hybrid vehicles. Real-world road geometry information is taken into account in power management decisions by using both Dynamic Programming (DP) and a standard Equivalent Consumption Minimization Strategy (ECMS), derived using Pontryagin minimum principle. Secondly, the contribution of different levels of preview to energy management of plug-in hybrid vehicles (PHEVs) is studied. The gains to fuel economy of plug-in hybrid vehicles with availability of velocity and terrain preview and knowledge of distance to the next charging station are investigated. Access to future driving information is classified into full, partial, or no future information and energy management strategies for real-time implementation with partial future preview are proposed. ECMS as well as Dynamic Programming (DP) is systematically utilized to handle the resulting optimal control problems with different levels of preview. We also study the benefit of future traffic flow information preview in improving the fuel economy of conventional vehicles by predictive control methods. According to the time-scale of the preview information and its importance to the driver, the energy optimization problem is decomposed into different levels. In the microscopic level, a model predictive controller as well as a car following model is employed for predictive adaptive cruise control by stochastically forecasting the driving behavior of the lead car. In the macroscopic level, we propose to incorporate the estimated macroscopic future traffic flow information and optimize the cost-to-go by utilizing a two-dimension Dynamic Programming (2D-DP). The algorithm yields the optimal trip velocity as the reference velocity for the driver or a low level controller to follow. Through the study, we show that energy use and emissions can be reduced considerably by using preview route information. The methodologies discussed in this dissertation provide an alternative mean for the automotive industry to develop more efficient and environmentally friendly vehicles by relying mostly on software and information and with minimal hardware investments

Adaptive Equivalent Consumption Minimization Strategy with Rule-based Gear Selection for the Energy Management of Hybrid Electric Vehicles Equipped with Dual Clutch Transmissions

Based on observations of the behaviour of the optimal solution to the problem of energy management for plug-in hybrid electric vehicles, a novel real-time Energy Management Strategy (EMS) is proposed. In particular, dynamic programming results are used to derive a set of rules aiming at reproducing the optimal gearshift schedule in electric mode while the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) is employed to decide the powertrain operating mode and the current gear when power from the internal combustion engine is needed. In terms of total fuel consumption, simulations show that the proposed approach yields results that are close to the optimal solution and also outperforms those of the A-ECMS, a well-known EMS. One of the main aspects that differentiates the strategy here proposed from previous works is the introduction of a model to use physical considerations to estimate the energy consumption during gearshifts in dual-clutch transmissions. This, together with a series of properly tuned fuel penalties allows the controller to yield results in which there is no gear hunting behaviour

Fuel Optimal Control Algorithms for Connected and Automated Plug-In Hybrid Vehicles

Improving the fuel economy of light-duty vehicles (LDV) is a compelling solution to stabilizing Greenhouse Gas (GHG) emissions and decreasing the reliance on fossil fuels. Over the years, there has been a considerable shift in the market of LDVs toward powertrain electrification, and plug-in hybrid electric vehicles (PHEVs) are the most cost-effective in avoiding GHG emissions. Meanwhile, connected and automated vehicle (CAV) technologies permit energy-efficient driving with access to accurate trip information that integrates traffic and charging infrastructure. This thesis aims at developing optimization-based algorithms for controlling powertrain and vehicle longitudinal dynamics to fully exploit the potential for reducing fuel consumption of individual PHEVs by utilizing CAV technologies. A predictive equivalent minimization strategy (P-ECMS) is proposed for a human-driven PHEV to adjust the co-state based on the difference between the future battery state-of-charge (SOC) obtained from short-horizon prediction and a future reference SOC from SOC node planning. The SOC node planning, which generates battery SOC reference waypoints, is performed using a simplified speed profile constructed from segmented traffic information, typically available from mobile mapping applications. The PHEV powertrain, consisting of engine and electric motors, is mathematically modeled as a hybrid system as the state is defined by the values of the continuous variable, SOC, and discrete modes, hybrid vehicle (HV), and electric vehicle (EV) modes with the engine on/off. As a hybrid system, the optimal control of PHEVs necessitates a numerical approach to solving a mixed-integer optimization problem. It is of interest to have a unified numerical algorithm for solving such mixed-integer optimal control problems with many states and control inputs. Based on a discrete maximum principle (DMP), a discrete mixed-integer shooting (DMIS) algorithm is proposed. The DMIS is demonstrated in successfully addressing the cranking fuel optimization in the energy management of a PHEV. It also serves as the foundation of the co-optimization problem considered in the remaining part of the thesis. This thesis further investigates different control designs with an increased vehicle automation level combining vehicle dynamics and powertrain of PHEVs in within-a-lane traffic flow. This thesis starts with a sequential (or decentralized) optimization and then advances to direct fuel minimization by simultaneously optimizing the two subsystems in a centralized manner. When shifting toward online implementation, the unique challenge lies in the conflict between the long control horizon required for global optimality and the computational power limit. A receding horizon strategy is proposed to resolve the conflict between the horizon length and the computation complexity, with co-states approximating the future cost. In particular, the co-state is updated using a nominal trajectory and the temporal-difference (TD) error based on the co-state dynamics. The remaining work aims to develop a unified model predictive control (MPC) framework from the powertrain (PT) control of a human-driven to the combined vehicle dynamics (VD) and PT control of an automated PHEV. In the unified framework, the cost-to-go (the fuel consumption as the economic cost) is represented by the co-state associated with the battery SOC dynamics. In its application to automated PHEVs, a control barrier function (CBF) is augmented as an add-on block to modify the vehicle-level control input for guaranteed safety. This unified MPC framework allows for systematically evaluating the fuel economy and drivability performance of different levels and structures of optimization strategies.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169876/1/dichencd_1.pd

Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge

When developing an energy management strategy (EMS) including a battery aging model for plug-in hybrid electric vehicles, the trade-off between the energy consumption cost (ECC) and the equivalent battery life loss cost (EBLLC) should be considered to minimize the total cost of both and improve the life cycle value. Unlike EMSs with a lower State of Charge (SOC) boundary value given in advance, this paper proposes a model predictive control of EMS based on an optimal battery depth of discharge (DOD) for a minimum sum of ECC and EBLLC. First, the optimal DOD is identified using Pontryagin's Minimum Principle and shooting method. Then a reference SOC is constructed with the optimal DOD, and a model predictive controller (MPC) in which the conflict between the ECC and EBLC is optimized in a moving horizon is implemented. The proposed EMS is examined by real-world driving cycles under different preview horizons, and the results indicate that MPCs with a battery aging model lower the total cost by 1.65%, 1.29% and 1.38%, respectively, for three preview horizons (5, 10 and 15 s) under a city bus route of about 70 km, compared to those unaware of battery aging. Meanwhile, global optimization algorithms like the dynamic programming and Pontryagin's Minimum Principle, as well as a rule-based method, are compared with the predictive controller, in terms of computational expense and accuracy

하이브리드 차량의 주행 정보 기반 에너지 관리 전략에 대한 연구

학위논문(석사)--서울대학교 대학원 :공과대학 기계항공공학부,2019. 8. 차석원.In this thesis, an energy management strategy (EMS) using prediction model based on driving information is proposed to improve the fuel efficiency of hybrid electric vehicle (HEV). HEV uses both an engine and a motor, and is a representative eco-friendly vehicle with high fuel efficiency. To improve the efficiency of a HEV, the EMS of the supervisory controller that controls various powertrain components is very important. An equivalent consumption minimization strategy (ECMS) used in this study is a real-time optimization-based strategy that considers equivalent energy consumption of fuel and battery. A ECMS is easy to develop and have good real-time applicability, but a performance is largely dependent on the equivalent factor that equalize between the two energies. As with most optimization-based control strategies, the optimal equivalent factor can be obtained only when the entire future driving profile is known. In this thesis, a method of changing the equivalent factor at every specific time period is used, and a prediction model that predicts the factor of the next time window through the current driving information is proposed. The prediction model receives the time series data of the current time window driving information and several feature values extracted from it, and predicts an optimized equivalent factor for the next time window. The model was developed based on recurrent neural network (RNN) using long short-term memory (LSTM) and multi-layer perceptron (MLP). In order to prepare the data for the training of the prediction model, the cumulative driving information is divided into specific time windows, and the optimal equivalent factors for each time window are obtained based on the simulation. After training the prediction model using the collected data and testing it on separate data, it is confirmed that there is a high correlation between the predicted factor and the optimal factor. For the verification of vehicle simulation, the prediction model is combined with the EMS model using the ECMS to construct predictive-ECMS, and the forward simulation is performed using the vehicle and the driver model. Simulation results for test cycle showed less energy use compared to existing rule-based strategy and were more similar to the global optimized factor case. The control strategy proposed in this thesis is an optimization-based control strategy that can improve the energy efficiency by using prediction model based on driving information. It is expected that the optimization -based control strategy will be realized through continuous research.본 논문에서는 하이브리드 차량의 연비 향상을 위해 주행 정보 기반 예측 모델을 활용한 에너지 관리 전략을 제안하였다. 하이브리드 차량은 엔진과 모터를 동시에 사용하는 차량으로, 기존의 내연기관 차량에 비해 연비와 효율이 높은 대표적인 친환경 차량이다. 이러한 하이브리드 차량의 효율 향상을 위해서는 엔진과 모터를 포함한 다양한 파워트레인 구성요소를 제어하는 상위제어기의 에너지 관리 전략이 매우 중요하다. 본 연구에 사용된 등가 소모 최소화 전략은 연료의 소모량과 배터리의 전기에너지 소모량을 등가화한 등가 에너지를 고려한 실시간 최적화 기반 제어 전략이다. 등가 소모 최소화 전략은 개발이 용이하고 실시간 적용성이 좋은 편이지만, 두 에너지간의 등가화를 조정하는 등가 계수에 의해 성능이 크게 좌우된다. 특히 대부분의 최적화 기반 제어 전략과 마찬가지로, 미래의 전체 주행속도 프로파일을 알고 있을 때만이 전역 최적화된 등가계수를 알 수 있다. 본 논문에서는 특정 시간주기별로 등가계수를 변화시키는 방법을 사용하였으며, 현재시점의 주행 정보를 통해 다음 시간주기의 등가계수를 예측하는 예측 모델을 제안하였다. 예측 모델은 현재시점 주행 정보의 시계열 데이터와 이로부터 추출된 몇 개의 특성 값들을 입력받아, 다음 시간주기에 대해 최적화된 등가계수를 예측한다. 모델은 장단기 기억 순환 신경망과 다층 신경망을 기반으로 개발되었다. 예측 모델의 학습을 위한 데이터 준비를 위해, 누적된 대량의 주행 정보를 특정 시간주기별로 나누어 각 시간주기에 대한 최적 등가계수를 시뮬레이션 기반으로 수집하였다. 수집된 데이터를 사용하여 예측모델을 학습한 후 별도의 데이터에 대하여 시험해본 결과, 예측된 계수와 최적 계수 간에 높은 상관관계가 있음을 확인하였다. 차량 시뮬레이션 검증을 위하여 학습된 예측 모델을 등가 소모 최소화 전략을 이용한 에너지 관리 전략 제어 모델과 결합하고, 차량 모델과 운전자 모델을 사용하여 전방향 시뮬레이션을 수행하였다. 연비 시험 사이클에 대한 시뮬레이션 결과 기존의 규칙기반 제어전략 대비 감소된 에너지 사용량을 보였으며, 전역 최적화된 등가계수를 사용한 경우에 보다 가까운 결과를 나타내었다. 본 논문에서 연구된 제어 전략은 주행 정보 기반의 예측모델을 활용하여 에너지 효율을 향상 시킬 수 있는 최적화 기반 제어 전략이다. 지속적인 연구를 통해 최적화 기반 제어 전략의 상용화가 가능할 것으로 기대된다.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Background Studies 4 1.3 Contributions 7 1.4 Thesis Outlines 8 Chapter 2. Vehicle Model Development 9 2.1 Target Vehicle 9 2.2 Vehicle Modeling 11 2.2.1 Engine Model 11 2.2.2 Motor Model 12 2.2.3 Battery Model 13 2.2.4 Vehicle Model 15 2.3 Energy Management Strategy 17 2.3.1 Rule-Based Strategy 17 2.3.2 Equivalent Consumption Minimization Strategy 18 2.3.3 Implementation of ECMS 19 2.4 Forward Simulation Environment 22 Chapter 3. Prediction Model Development 23 3.1 Problem Definition 23 3.1.1 Optimal Equivalent Factor 23 3.1.2 Periodic Application of Optimal Equivalent Factor 26 3.1.3 Training Data Preprocessing 31 3.2 Prediction Model based on Driving Information 33 3.2.1 LSTM Model using Time Series Data 33 3.2.2 MLP Model using Feature Data 35 3.2.3 LSTM-MLP Model using Multiple Data 36 Chapter 4. Simulation Analysis 38 4.1 Prediction Model Training 38 4.1.1 LSTM Model using Time Series Data 38 4.1.2 MLP Model using Feature Data 39 4.1.3 LSTM-MLP Model using Multiple Data 41 4.2 Vehicle Simulation using Energy Management Strategy based on Predictive ECMS 43 Chapter 5. Conclusion 53 5.1 Conclusion 53 5.2 Future Work 55Maste