Vehicle Parameters Estimation and Driver Behavior Classification for Adaptive Shift Strategy of Heavy Duty Vehicles

Commercial vehicles fulfill the majority of inland freight transportation in the United States, and they are very large consumers of fuels. The increasingly stringent regulation on greenhouse-gas emission has driven manufacturers to adopt new fuel efficient technologies. Among others, advanced transmission control strategy can provide tangible improvement with low incremental cost. An adaptive shift strategy is proposed in this work to optimize the shift maps on-the-fly based on the road load and driver behavior while reducing the initial calibration efforts. In addition, the adaptive shift strategy provides the fleet owner a mean to select a tradeoff between fuel economy and drivability, since the drivers are often not the owner of the vehicle. In an attempt to develop the adaptive shift strategy, the vehicle parameters and driver behavior need to be evaluated first. Therefore, three research questions are addressed in this dissertation: (i) vehicle parameters estimation; (ii) driver behavior classification; (iii) online shift strategy adaption. In vehicle parameters estimation, a model-based vehicle rolling resistance and aerodynamic drag coefficient online estimator is proposed. A new Weighted Recursive Least Square algorithm was developed. It uses a supervisor to extracts data during the constant-speed event and saves the average road load at each speed segment. The algorithm was tested in the simulation with real-world driving data. The results have shown a more robust performance compared with the original Recursive Least Square algorithm, and high accuracy of aerodynamic drag estimation. To classify the driver behavior, a driver score algorithm was proposed. A new method is developed to represent the time-series driving data into events represented by symbolic data. The algorithm is tested with real-world driving data and shows a high classification accuracy across different vehicles and driving cycles. Finally, a new adaptive shift scheme was developed, which synthesizes the information about vehicle parameters and driver score developed in the previous steps. The driver score is used as a proxy to match the driving characteristics in real time. Drivability objective is included in the optimization through a torque reserve and it is subsequently evaluated via a newly developed metric. The impact of the shift maps on the objective drivability and fuel economy metrics is evaluated quantitatively in the vehicle simulation. The algorithms proposed in this dissertation are developed with practical implementation in mind. The methods can reduce the initial calibration effort and provide the fleet owner a mean to select an appropriate tradeoff between fuel economy and drivability depending on the vocation

Development of Real-time Optimal Control Strategy of Hybrid Transit Bus Based on Predicted Driving Pattern

The control strategy of a hybrid electric vehicle (HEV) has been an active research area in the past decades. The main goal of the optimal control strategy is to maximize the fuel economy and minimize exhaust emissions while also satisfying the expected vehicle performance. Dynamic programming (DP) is an algorithm capable of finding the global optimal solution of HEV operation. However, DP cannot be used as a real-time control approach as it requires pre-known driving information. The equivalent consumption minimization strategy (ECMS) is a real-time control approach, but it always results in local optima due to the non-convex cost function. In my research, a ECMS with DP combined model (ECMSwDP) was proposed, which was a compromise between optimality and real-time capability. In this approach, the optimal equivalent factor (lambda) for a real-time ECMS controller can be derived using ECMSwDP for a given driving condition. The optimal lambda obtained using ECMSwDP was further processed to derive the lambda map, which was a function of vehicle operation and driving information. Six lambda maps were generated corresponding to the developed representative driving patterns. At each distance segment of a drive cycle, the suitable lambda value is available from one of the six lambda maps based on the identified driving pattern and current vehicle operation.;An adaptive ECMS (A-ECMS) model with a driving pattern identification model is developed to achieve the real-time optimal control for a HEV. A-ECMS was capable of controlling the ratio of power provided by the ICE and battery of a hybrid vehicle by selecting the lambda based on the identified lambda map. The effect on fuel consumption of the control strategies developed using the rule-based controller, ECMSwDP, A-ECMS, and DP was simulated using the parallel hybrid bus model developed in this research. The control strategies developed using A-ECMS are able to significantly improve the fuel economy while maintaining the battery charge sustainability. The corrected fuel economy observed with A-ECMS with a penalty function and the average lambda of RDPs was 5.55%, 13.67%, and 19.19% gap to that observed with DP when operated over the Beijing cycle, WVU-CSI cycle, and the actual transit bus route, respectively. The corrected fuel economy observed with A-ECMS with lambda maps of the RDPs was 4.83%, 10.61%, and 14.33% gap to that observed with DP when operated on the Beijing cycle, WVU-CSI cycle, and actual transit bus route, respectively. The simulation results demonstrated that the proposed A-ECMS approaches have the capability to achieve real time suboptimal control of a HEV while maintaining the charge sustainability of the battery

Look-ahead vehicle energy management with traffic predictions

This paper presents a vehicle energy management system that uses information about upcoming topography and speed limits along the planned route to schedule the speed and the gear shifts of a heavy diesel truck. The proposed control scheme divides the predictive control problem into two layers that operate with different update frequencies and prediction horizons. The focus in the paper is on the top layer that plans the vehicle speed in a convex optimization problem leaving the gear decision to be optimized in the lower layer in a dynamic program. The paper describes how predictive information of the movement pattern of surrounding vehicles can be incorporated into the convex optimization of the vehicle speed by using a moving time window constraint

Gear shift strategies for automotive transmissions

The development history of automotive engineering has shown the essential role of transmissions in road vehicles primarily powered by internal combustion engines. The engine with its physical constraints on the torque and speed requires a transmission to have its power converted to the drive power demand at the vehicle wheels. Under dynamic driving conditions, the transmission is required to shift in order to match the engine power with the changing drive power. Furthermore, a gear shift decision is expected to be consistent such that vehicle can remain in the next gear for a period of time without deteriorating the acceleration capability. Therefore, an optimal conversion of the engine power plays a key role in improving the fuel economy and driveability. Moreover, the consequences of the assumptions related to the discrete state variable-dependent losses, e.g. gear shifting, clutch slippage and engine starting, and their e¿ect on the gear shift control strategy are necessary to be analyzed to yield insights into the fuel usage. The ¿rst part of the thesis deals with the design of gear shift strategies for electronically controlled discrete ratio transmissions used in both conventional vehicles and Hybrid Electric Vehicles (HEVs). For conventional vehicles, together with the fuel economy, the driveability is systematically addressed in a Dynamic Programming (DP) based optimal gear shift strategy by three methods: i) the weighted inverse of the power re¬serve, ii) the constant power reserve, and iii) the variable power reserve. In addition, a Stochastic Dynamic Programming (SDP) algorithm is utilized to optimize the gear shift strategy, subject to a stochastic distribution of the power request, in order to minimize the expected fuel consumption over an in¿nite horizon. Hence, the SDP-based gear shift strategy intrinsically respects the driveability and is realtime implementable. By per¬forming a comparative analysis of all proposed gear shift methods, it is shown that the variable power reserve method achieves the highest fuel economy without deteriorating the driveability. Moreover, for HEVs, a novel fuel-optimal control algorithm, consist-ing of the continuous power split and discrete gear shift, engine on-o¿ problems, based on a combination of DP and Pontryagin’s Minimum Principle (PMP) is developed for the corresponding hybrid dynamical system. This so-called DP-PMP gear shift control approach benchmarks the development of an online implementable control strategy in terms of the optimal tradeo¿ between calculation accuracy and computational e¿ciency. Driven by an ultimate goal of realizing an online gear shift strategy, a gear shift map design methodology for discrete ratio transmissions is developed, which is applied for both conventional vehicles and HEVs. The design methodology uses an optimal gear shift algorithm as a basis to derive the optimal gear shift patterns. Accordingly, statis¬tical theory is applied to analyze the optimal gear shift pattern in order to extract the time-invariant shift rules. This alternative two-step design procedure makes the gear shift map: i) respect the fuel economy and driveability, ii) be consistent and robust with respect to shift busyness, and iii) be realtime implementation. The design process is ¿exible and time e¿cient such that an applicability to various powertrain systems con¿gured with discrete ratio transmissions is possible. Furthermore, the study in this thesis addresses the trend of utilizing the route information in the powertrain control system by proposing an integrated predictive gear shift strategy concept, consisting of a velocity algorithm and a predictive algorithm. The velocity algorithm improves the fuel economy in simulation considerably by proposing a fuel-optimal velocity trajectory over a certain driving horizon for the vehicle to follow. The predictive algorithm suc¬cessfully utilizes a prede¿ned velocity pro¿le over a certain horizon in order to realize a fuel economy improvement very close to that of the globally optimal algorithm (DP). In the second part of the thesis, the energetic losses, involved with the gear shift and engine start events in an automated manual transmission-based HEV, are modeled. The e¿ect of these losses on the control strategies and fuel consumption for (non-)powershift transmission technologies is investigated. Regarding the gear shift loss, the study ¿rstly ever discloses a perception of a fuel-e¿cient advantage of the powershift transmissions over the non-powershift ones applied for commercial vehicles. It is also shown that the engine start loss can not be ignored in seeking for a fair evaluation of the fuel economy. Moreover, the sensitivity study of the fuel consumption with respect to the prediction horizon reveals that a predictive energy management strategy can realize the highest achievable fuel economy with a horizon of a few seconds ahead. The last part of the thesis focuses on investigating the sensitivity of an optimal gear shift strategy to the relevant control design objectives, i.e. fuel economy, driveability and comfort. A singu¬lar value decomposition based method is introduced to analyze the possible correlations and interdependencies among the design objectives. This allows that some of the pos¬sible dependent design objective(s) can be removed from the objective function of the corresponding optimal control problem, hence thereby reducing the design complexity

Ultracapacitor Heavy Hybrid Vehicle: Model Predictive Control Using Future Information to Improve Fuel Consumption

This research is concerned with the improvement in the fuel economy of heavy transport vehicles through the use of high power ultracapacitors in a mild hybrid electric vehicle platform. Previous work has shown the potential for up to 15% improvement on a hybrid SUV platform, but preliminary simulations have shown the potential improvement for larger vehicles is much higher. Based on vehicle modeling information from the high fidelity, forward-looking modeling and simulation program Powertrain Systems Analysis Toolkit (PSAT), a mild parallel heavy ultracapacitor hybrid electric vehicle model is developed and validated to known vehicle performance measures. The vehicle is hybridized using a 75kW motor and small energy storage ultracapacitor pack of 56 Farads at 145 Volts. Among all hybridizing energy storage technologies, ultracapacitors pack extraordinary power capability, cycle lifetime, and ruggedness and as such are well suited to reducing the large power transients of a heavy vehicle. The control challenge is to effectively manage the very small energy buffer (a few hundred Watt-hours) the ultracapacitors provide to maximize the potential fuel economy. The optimal control technique of Dynamic Programming is first used on the vehicle model to obtain the \u27best possible\u27 fuel economy for the vehicle over the driving cycles. A variety of energy storage parameters are investigated to aid in determining the best ultracapacitor system characteristics and the resulting effects this has on the fuel economy. On a real vehicle, the Dynamic Programming method is not very useful since it is computationally demanding and requires predetermined vehicle torque demands to carry out the optimization. The Model Predictive Control (MPC) method is an optimization-based receding horizon control strategy which has shown potential as a powertrain control strategy in hybrid vehicles. An MPC strategy is developed for the hybrid vehicle based on an exponential decay torque prediction method which can achieve near-optimal fuel consumption even for very short prediction horizon lengths of a few seconds. A critical part of the MPC method which can greatly affect the overall control performance is that of the prediction model. The use of telematic based \u27future information\u27 to aid in the MPC prediction method is also investigated. Three types of future information currently obtainable from vehicle telematic technologies are speed limits, traffic conditions, and traffic signals, all of which have been incorporated to improve the vehicle fuel economy

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

Model predictive eco-driving control for heavy-duty trucks using Branch and Bound optimization

Eco-driving (ED) can be used for fuel savings in existing vehicles, requiring only a few hardware modifications. For this technology to be successful in a dynamic environment, ED requires an online real-time implementable policy. In this work, a dedicated Branch and Bound (BnB) model predictive control (MPC) algorithm is proposed to solve the optimization part of an ED optimal control problem. The developed MPC solution for ED is based on the following ingredients. As a prediction model, the velocity dynamics as a function of distance is modeled by a finite number of driving modes and gear positions. Then we formulate an optimization problem that minimizes a cost function with two terms: one penalizing the fuel consumption and one penalizing the trip duration. We exploit contextual elements and use a warm-started solution to make the BnB solver run in real-time. The results are evaluated in numerical simulations on two routes in Israel and France and the long haul cycle of the Vehicle Energy consumption Calculation Tool (VECTO). In comparison with a human driver and a Pontryagin's Minimum Principle (PMP) solution, 25.8% and 12.9% fuel savings, respectively, are achieved on average

On Optimal Mission Planning for Vehicles over Long-distance Trips

This thesis proposes a mission planner for vehicles over long-distance trips, for finding the optimal trade-off between trip time, energy efficiency, anddriver comfort, subject to road information, traffic situations, and weather conditions. The mission planner consists of three components, i.e. logisticsplanner, eco-driving supervisor, and thermal and charging supervisor. The logistics planner aims at optimising the mission start and/or finish time byminimising energy consumption and trip time. The eco-driving supervisor computes the velocity profile of the driving vehicle, by optimising the energyconsumption and penalising driver discomfort. To do so, an online-capable algorithm has been formulated in a model predictive control framework, subject to road and traffic information, and the pre-optimised mission start and/or finish time. This algorithm is computationally efficient and enables the driving vehicle to adapt and optimally respond to predicted disturbances within a short amount of time. Eco-driving has also been achieved for a vehicleconfronted with wind, by applying stochastic dynamic programming method. The thermal and charging supervisor regulates battery temperature and state of charge by coordinating the energy use of different thermal components. Within the thermal and charging supervisor design, a heat pump has been included for waste heat recovery purposes. Also, the charging stops have been optimally planned, in favour of energy efficiency and trip time. The performance of the proposed algorithms over a road with a hilly terrain is assessed using simulations. According to the simulation results, it is observed that total travel time is reduced up to 5.5 % by optimising the mission start time, when keeping an average cruising speed of about 75 km/h. Also, compared to standard cruise control, the energy savings of using this algorithm is up to 11.6 %. Furthermore, total charging time and energy consumption are reduced by up to 19.4 % and 30.6 %, respectively by developing the thermal and charging supervisor, compared to a case without the heat pump activated and without charge point optimisation