18,163 research outputs found

    Operation Simulation and Control of a Hybrid Vehicle Based on a Dual Clutch Configuration

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    Today, the world thrives on making more fuel-efficient vehicles that consume less energy, emit fewer emissions and have enhanced overall performance. Hybrid Electric Vehicles (HEVs) offer the advantages of improved fuel economy and emissions without sacrificing vehicle performance factors such as safety, reliability and other features. The durability and performance enhancements of HEVs have encouraged researchers to develop various hybrid power-train configurations and improve associated issues, such as component sizing and control strategies. HEVs with dual clutch transmissions (HDCT) are used in operation modes to improve fuel efficiency and dynamic performance for both diesel engines and high-speed gas engines. Dual clutch transmissions (DCTs) are proved to be the first automatic transmission type to provide better efficiency than manual transmissions. DCTs also provide reduced shift shocks and shift time that result in better driving experience. In addition, advanced software allows more simplistic approaches and tunable launch strategies in HDCT development. In this dissertation, an innovative approach to develop a desired mode controller for a HDCT configuration is proposed. This mode controller allows the driver to select the desired driving style of the vehicle. The proposed controller was developed based on adaptive control theory for the overall HDCT system. The proposed Model Reference Adaptive Control (MRAC) was applied to a parallel hybrid electric vehicle with dual clutch transmission (HDCT), and yielded good performance under different conditions. This implies that the MRAC is adaptive to different torque distribution strategies. The current study, which was performed on adaptive control applications, revealed that the Lyapunov method was effective and yielded good performance. The MRAC method was also applied to the mode transition of an HDCT bus. The simulation results confirmed that the MRAC outperformed the conventional operation method for an HDCT with reduced vehicle jerk and the torque interruption for the driveline and with improved fuel efficiency.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145173/1/Final Dissertation Elzaghir.pdfDescription of Final Dissertation Elzaghir.pdf : Dissertatio

    Simulation of hybrid electric vehicle based on a series drive train layout

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    This paper provided a validated modeling and a simulation of a 6 degree freedom vehicle longitudinal model and drive-train component in a series hybrid electric vehicle. The 6-DOF vehicle dynamics model consisted of tire subsystems, permanent magnet synchronous motor which acted as the prime mover coupled with an automatic transmission, hydraulic brake subsystem, battery subsystem, alternator subsystem and internal combustion engine to supply the rotational input to the alternator. A speed and torque tracking control systems of the electric power train were developed to make sure that the power train was able to produce the desired throttle torque in accelerating the vehicle. A human-in-the-loop-simulation was utilized as a mechanism to evaluate the effectiveness of the proposed hybrid electric vehicle. The proposed simulation was used as the preliminary result in identifying the capability of the vehicle in terms of the maximum speed produced by the vehicle and the capability of the alternator to recharge the battery. Several tests had been done during the simulation, namely sudden acceleration, acceleration and braking test and unbounded motion. The results of the simulation showed that the proposed hybrid electric vehicle can produce a speed of up to 70 km/h with a reasonable charging rate to the battery. The findings from this study can be considered in terms of design, optimization and implementation in a real vehicle

    A review of intelligent road preview methods for energy management of hybrid vehicles

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    Due to the shortage of fuel resources and concerns of environmental pressure, vehicle electrification is a promising trend. Hybrid vehicles are suitable alternatives to traditional vehicles. Travelling information is essential for hybrid vehicles to design the optimal control strategy for fuel consumption minimization and emissions reduction. In general, there are two ways to provide the information for the energy management strategy (EMS) design. First is extracting terrain information by utilizing global positioning system (GPS) and intelligent transportation system (ITS). However, this method is difficult to be implemented currently due to the computational complexity of extracting information. This leads to the second method which is predicting future vehicle speed and torque demand in a certain time horizon based on current and previous vehicle states. To support optimal EMS development, this paper presents a comprehensive review of prediction methods based on different levels of trip information for the EMS of hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV)

    The novel application of optimization and charge blended energy management control for component downsizing within a plug-in hybrid electric vehicle

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    The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%โ€“9% being realizable

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

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    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

    Automotive Powertrain Control โ€” A Survey

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    This paper surveys recent and historical publications on automotive powertrain control. Control-oriented models of gasoline and diesel engines and their aftertreatment systems are reviewed, and challenging control problems for conventional engines, hybrid vehicles and fuel cell powertrains are discussed. Fundamentals are revisited and advancements are highlighted. A comprehensive list of references is provided.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72023/1/j.1934-6093.2006.tb00275.x.pd

    ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ฐจ๋Ÿ‰์˜ ์ฃผํ–‰ ์ •๋ณด ๊ธฐ๋ฐ˜ ์—๋„ˆ์ง€ ๊ด€๋ฆฌ ์ „๋žต์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2019. 8. ์ฐจ์„์›.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.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ฐจ๋Ÿ‰์˜ ์—ฐ๋น„ ํ–ฅ์ƒ์„ ์œ„ํ•ด ์ฃผํ–‰ ์ •๋ณด ๊ธฐ๋ฐ˜ ์˜ˆ์ธก ๋ชจ๋ธ์„ ํ™œ์šฉํ•œ ์—๋„ˆ์ง€ ๊ด€๋ฆฌ ์ „๋žต์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ฐจ๋Ÿ‰์€ ์—”์ง„๊ณผ ๋ชจํ„ฐ๋ฅผ ๋™์‹œ์— ์‚ฌ์šฉํ•˜๋Š” ์ฐจ๋Ÿ‰์œผ๋กœ, ๊ธฐ์กด์˜ ๋‚ด์—ฐ๊ธฐ๊ด€ ์ฐจ๋Ÿ‰์— ๋น„ํ•ด ์—ฐ๋น„์™€ ํšจ์œจ์ด ๋†’์€ ๋Œ€ํ‘œ์ ์ธ ์นœํ™˜๊ฒฝ ์ฐจ๋Ÿ‰์ด๋‹ค. ์ด๋Ÿฌํ•œ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ฐจ๋Ÿ‰์˜ ํšจ์œจ ํ–ฅ์ƒ์„ ์œ„ํ•ด์„œ๋Š” ์—”์ง„๊ณผ ๋ชจํ„ฐ๋ฅผ ํฌํ•จํ•œ ๋‹ค์–‘ํ•œ ํŒŒ์›ŒํŠธ๋ ˆ์ธ ๊ตฌ์„ฑ์š”์†Œ๋ฅผ ์ œ์–ดํ•˜๋Š” ์ƒ์œ„์ œ์–ด๊ธฐ์˜ ์—๋„ˆ์ง€ ๊ด€๋ฆฌ ์ „๋žต์ด ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์— ์‚ฌ์šฉ๋œ ๋“ฑ๊ฐ€ ์†Œ๋ชจ ์ตœ์†Œํ™” ์ „๋žต์€ ์—ฐ๋ฃŒ์˜ ์†Œ๋ชจ๋Ÿ‰๊ณผ ๋ฐฐํ„ฐ๋ฆฌ์˜ ์ „๊ธฐ์—๋„ˆ์ง€ ์†Œ๋ชจ๋Ÿ‰์„ ๋“ฑ๊ฐ€ํ™”ํ•œ ๋“ฑ๊ฐ€ ์—๋„ˆ์ง€๋ฅผ ๊ณ ๋ คํ•œ ์‹ค์‹œ๊ฐ„ ์ตœ์ ํ™” ๊ธฐ๋ฐ˜ ์ œ์–ด ์ „๋žต์ด๋‹ค. ๋“ฑ๊ฐ€ ์†Œ๋ชจ ์ตœ์†Œํ™” ์ „๋žต์€ ๊ฐœ๋ฐœ์ด ์šฉ์ดํ•˜๊ณ  ์‹ค์‹œ๊ฐ„ ์ ์šฉ์„ฑ์ด ์ข‹์€ ํŽธ์ด์ง€๋งŒ, ๋‘ ์—๋„ˆ์ง€๊ฐ„์˜ ๋“ฑ๊ฐ€ํ™”๋ฅผ ์กฐ์ •ํ•˜๋Š” ๋“ฑ๊ฐ€ ๊ณ„์ˆ˜์— ์˜ํ•ด ์„ฑ๋Šฅ์ด ํฌ๊ฒŒ ์ขŒ์šฐ๋œ๋‹ค. ํŠนํžˆ ๋Œ€๋ถ€๋ถ„์˜ ์ตœ์ ํ™” ๊ธฐ๋ฐ˜ ์ œ์–ด ์ „๋žต๊ณผ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ, ๋ฏธ๋ž˜์˜ ์ „์ฒด ์ฃผํ–‰์†๋„ ํ”„๋กœํŒŒ์ผ์„ ์•Œ๊ณ  ์žˆ์„ ๋•Œ๋งŒ์ด ์ „์—ญ ์ตœ์ ํ™”๋œ ๋“ฑ๊ฐ€๊ณ„์ˆ˜๋ฅผ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํŠน์ • ์‹œ๊ฐ„์ฃผ๊ธฐ๋ณ„๋กœ ๋“ฑ๊ฐ€๊ณ„์ˆ˜๋ฅผ ๋ณ€ํ™”์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ํ˜„์žฌ์‹œ์ ์˜ ์ฃผํ–‰ ์ •๋ณด๋ฅผ ํ†ตํ•ด ๋‹ค์Œ ์‹œ๊ฐ„์ฃผ๊ธฐ์˜ ๋“ฑ๊ฐ€๊ณ„์ˆ˜๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ์˜ˆ์ธก ๋ชจ๋ธ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์˜ˆ์ธก ๋ชจ๋ธ์€ ํ˜„์žฌ์‹œ์  ์ฃผํ–‰ ์ •๋ณด์˜ ์‹œ๊ณ„์—ด ๋ฐ์ดํ„ฐ์™€ ์ด๋กœ๋ถ€ํ„ฐ ์ถ”์ถœ๋œ ๋ช‡ ๊ฐœ์˜ ํŠน์„ฑ ๊ฐ’๋“ค์„ ์ž…๋ ฅ๋ฐ›์•„, ๋‹ค์Œ ์‹œ๊ฐ„์ฃผ๊ธฐ์— ๋Œ€ํ•ด ์ตœ์ ํ™”๋œ ๋“ฑ๊ฐ€๊ณ„์ˆ˜๋ฅผ ์˜ˆ์ธกํ•œ๋‹ค. ๋ชจ๋ธ์€ ์žฅ๋‹จ๊ธฐ ๊ธฐ์–ต ์ˆœํ™˜ ์‹ ๊ฒฝ๋ง๊ณผ ๋‹ค์ธต ์‹ ๊ฒฝ๋ง์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. ์˜ˆ์ธก ๋ชจ๋ธ์˜ ํ•™์Šต์„ ์œ„ํ•œ ๋ฐ์ดํ„ฐ ์ค€๋น„๋ฅผ ์œ„ํ•ด, ๋ˆ„์ ๋œ ๋Œ€๋Ÿ‰์˜ ์ฃผํ–‰ ์ •๋ณด๋ฅผ ํŠน์ • ์‹œ๊ฐ„์ฃผ๊ธฐ๋ณ„๋กœ ๋‚˜๋ˆ„์–ด ๊ฐ ์‹œ๊ฐ„์ฃผ๊ธฐ์— ๋Œ€ํ•œ ์ตœ์  ๋“ฑ๊ฐ€๊ณ„์ˆ˜๋ฅผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฐ˜์œผ๋กœ ์ˆ˜์ง‘ํ•˜์˜€๋‹ค. ์ˆ˜์ง‘๋œ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์˜ˆ์ธก๋ชจ๋ธ์„ ํ•™์Šตํ•œ ํ›„ ๋ณ„๋„์˜ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•˜์—ฌ ์‹œํ—˜ํ•ด๋ณธ ๊ฒฐ๊ณผ, ์˜ˆ์ธก๋œ ๊ณ„์ˆ˜์™€ ์ตœ์  ๊ณ„์ˆ˜ ๊ฐ„์— ๋†’์€ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ฐจ๋Ÿ‰ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒ€์ฆ์„ ์œ„ํ•˜์—ฌ ํ•™์Šต๋œ ์˜ˆ์ธก ๋ชจ๋ธ์„ ๋“ฑ๊ฐ€ ์†Œ๋ชจ ์ตœ์†Œํ™” ์ „๋žต์„ ์ด์šฉํ•œ ์—๋„ˆ์ง€ ๊ด€๋ฆฌ ์ „๋žต ์ œ์–ด ๋ชจ๋ธ๊ณผ ๊ฒฐํ•ฉํ•˜๊ณ , ์ฐจ๋Ÿ‰ ๋ชจ๋ธ๊ณผ ์šด์ „์ž ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ „๋ฐฉํ–ฅ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์—ฐ๋น„ ์‹œํ—˜ ์‚ฌ์ดํด์— ๋Œ€ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ ๊ธฐ์กด์˜ ๊ทœ์น™๊ธฐ๋ฐ˜ ์ œ์–ด์ „๋žต ๋Œ€๋น„ ๊ฐ์†Œ๋œ ์—๋„ˆ์ง€ ์‚ฌ์šฉ๋Ÿ‰์„ ๋ณด์˜€์œผ๋ฉฐ, ์ „์—ญ ์ตœ์ ํ™”๋œ ๋“ฑ๊ฐ€๊ณ„์ˆ˜๋ฅผ ์‚ฌ์šฉํ•œ ๊ฒฝ์šฐ์— ๋ณด๋‹ค ๊ฐ€๊นŒ์šด ๊ฒฐ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์—ฐ๊ตฌ๋œ ์ œ์–ด ์ „๋žต์€ ์ฃผํ–‰ ์ •๋ณด ๊ธฐ๋ฐ˜์˜ ์˜ˆ์ธก๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ ์—๋„ˆ์ง€ ํšจ์œจ์„ ํ–ฅ์ƒ ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ์ตœ์ ํ™” ๊ธฐ๋ฐ˜ ์ œ์–ด ์ „๋žต์ด๋‹ค. ์ง€์†์ ์ธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ตœ์ ํ™” ๊ธฐ๋ฐ˜ ์ œ์–ด ์ „๋žต์˜ ์ƒ์šฉํ™”๊ฐ€ ๊ฐ€๋Šฅํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.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
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