8 research outputs found
Comparison of Three Real-Time Implementable Energy Management Strategies for Multi-mode Electrified Powertrain
No full textThree real-time implementable energy management system (EMS) strategies have been articulated for forward simulation vehicle model with an electrified powertrain. Rulebased strategy and equivalent consumption minimization strategy (ECMS) have been profoundly used as a competent real-time implementable EMS strategy for electrified powertrain. Reinforcement learning (RL) is relatively new as a real-time EMS controller. All these three controllers have been articulated for model-in-the-loop (MIL) simulation. A comparison among state-of-the art RL-based controller, widely accredited ECMS, and rule-based control strategies is very crucial in order to analyze strengths and weaknesses of each of these strategies at the MIL and to make them apposite for the subsequent phases of utilitarian controller development
Effect of coordinated control on real-time optimal mode selection for multi-mode hybrid electric powertrain
No full textAn online simulation framework is developed in this article to evaluate the performance of a multi-mode electrified powertrain equipped with more than one power source. An electrically variable transmission with two planetary gear-set has been chosen as the centerpiece of the powertrain considering the versatility and prospects of such transmissions. A novel architecture topology of the aforementioned class of transmission is selected through rigorous screening process whose workflow is presented here with brevity. The article systematically delineates the steps for deriving dynamics associated with all the feasible operating modes facilitated by the selected topology. The dynamics associated with all the feasible mode-shift events are also heeded judiciously. One of the legitimate concern of multi-mode transmission is its proclivity to contribute discontinuity of power-flow downstream of the powertrain. Mode-shift events can be predominantly held responsible for engendering such discontinuity. Many scholars in literature have substantiated the advent of dynamic coordinated control as a technique for ameliorating such discontinuity. Hence, a system-level coordinated control is employed within the energy management system (equivalent consumption minimization strategy), which governs the mode schedule of the multi-mode powertrain in real-time simulation. Simulation results corroborate the effect of coordinated control on the equivalent consumption minimization strategy in generating optimal mode schedule
A Computationally Lightweight Dynamic Programming Formulation for Hybrid Electric Vehicles
No full textPredicting the fuel economy capability of hybrid electric vehicle (HEV) powertrains by solving the related optimal control problem has been available for a few decades. Dynamic programming (DP) is one of the most popular techniques implemented to this end. Current research aims at integrating further powertrain modeling criteria that improve the fidelity level of the optimal HEV powertrain control behavior predicted by DP, thus corroborating the reliability of the fuel economy assessment. Dedicated methodologies need further development to avoid the curse of dimensionality which is typically associated to DP when increasing the number of control and state variables considered. This paper aims at considerably reducing the overall computational effort required by DP for HEVs by removing the state term associated to the battery state-of-charge (SOC). New opportunities open in this way for considering additional vehicle states, such as internal combustion engine (ICE) dynamics in terms of speed and torque, without incurring the curse of dimensionality for the proposed DP formulation. The computational lightweight DP version finds benchmarking here with the baseline DP that considers the full set of control and state variables. Obtained results demonstrate that the proposed DP formulation can remarkably improve the computational efficiency of the baseline DP formulation. Moreover, the fidelity level of the HEV simulation can be improved by limiting the overall number of ICE activations over time. Thanks to the achieved computational lightweight, the proposed method could thus be exploited to accelerate HEV powertrain design processes and to foster on-board HEV powertrain predictive controllers
Energy Management System for Input-Split Hybrid Electric Vehicle (Si-EVT) with Dynamic Coordinated Control and Mode-Transition Loss
No full textInstantaneous optimization-based energy management systems (EMS) are getting popular since they can yield near-optimal performance in unknown driving situations with minimalistic tuning parameters. However, they often disregard the drivability score of the powertrain as a performance assessment criterion, and this leads to too frequent or even infeasible mode-transitions during the multi-mode operation of a hybrid electric powertrain. Aiming to bring down the mode-transition frequency below a feasible limit, this paper proffers an instantaneous optimization-based EMS, which also accounts for the energy lost during mode-transitions into the cost function along with the electrical and chemical energy losses. The energy lost during a single mode-transition event refers to the summation of change in rotational energy for all the prime-movers, i.e., internal combustion engine and electric machines. However, this approach will add another weighting factor for weighting the mode-transition loss term in the same equivalent scale used for weighting other loss terms too. A dynamic coordinated control prescribed in literature is also employed along with the EMS to enhance the drivability score of multi-mode hybrid electric powertrains. Simulation results corroborate the efficacy of the proffered EMS framework in ameliorating the drivability issues without sacrificing much in the fuel consumption and charge sustainability performances
Adaptive Real-Time Energy Management of a Multi-Mode Hybrid Electric Powertrain
No full textMeticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle. Equivalent consumption minimization strategy is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this paper aims to propose an adaptive real-time equivalent consumption minimization strategy for a multi-mode hybrid electric powertrain. With the help of road recognition and vehicle speed prediction techniques, future driving conditions can be predicted over a certain horizon. Based on the predicted power demand, the optimal equivalence factor is calculated in advance by using bisection method and implemented for the upcoming driving period. In such a way, the equivalence factor is updated periodically to achieve charge sustaining operation and optimality. To verify the performance of the adaptive strategy, simulation has been conducted under city and highway driving cycles. Optimal solutions of the equivalence factor and the control outputs, i.e., engine speed and torque, are presented. Results show that the adaptive strategy can maintain battery charge sustaining operation, although there is a drawback that engine activation sometimes happens when vehicle is decelerating or braking. A comparative study is also conducted to verify the fuel economy of the proposed strategy. It is shown that with adaptive strategy, fuel consumption is increased by 9.737% in city driving and 2.409% in highway driving
Intelligent Energy Management Strategy for Eco-driving in Connected and Autonomous Hybrid Electric Vehicles
No full textThis thesis focuses on developing an intelligent energy management strategy for eco-driving in Connected and Autonomous Hybrid Electric Vehicles (CA-HEV's), which can be implemented in real-time. The strategy is divided into two layers, i.e. the upper level controller and the lower level controller. The upper level controller can be executed on the remote server. It is responsible for extracting the information from the driver about the trip and the vehicle information using the communication capabilities of the CA-HEV. The gathered information is then utilized by dynamic programming (DP), which is implemented in a bi-layer fashion to reduce the computation burden on the server. The outer layer of the DP algorithm and the optimal velocity trajectory and the inner layer optimizes the power distribution in the powertrain to minimize fuel consumption alongside maintaining charge balance conditions. These global optimal results are evaluated for an ideal environment without any traffic information. The lower level controller is responsible for real-time implementation on vehicles in the real world environment and is based on a well-accredited reinforcement learning (RL) strategy, i.e., Q-learning. The RL-based controller optimally distributes the power in a CA-HEV and maintains charge balance conditions. Furthermore, the RL-based controller is also trained on the remote server based on global optimal results obtained from the DP algorithm. The optimal parameter information is then resent to the vehicle's embedded controller for real-time implementation. Simulations are performed for Toyata Prius (2010) on MATLAB and Simulink, and road information is gathered from SUMO. Simulation results provide a comparative study between the global optimal and the RL-based controller. To validate the adaptiveness of the RL-based controller, it is also tested on two approximate real-world
drivecycles and its performance is compared against global optimal results evaluated using DP.ThesisMaster of Applied Science (MASc
Robust Electro-Thermal Modeling of Lithium-Ion Batteries for Electrified Vehicles Applications
No full textLithium-ion battery (LIBs) packs represent the most expensive and safety-critical components in any electric vehicle, requiring accurate real-time thermal management. This task falls under the battery management system (BMS), which plays a crucial role in ensuring the longevity, safety, and optimal performance of batteries. The BMS accurately monitors cell temperatures and prevents thermal runaway by leveraging multiple temperature sensors; however, adding a temperature sensor to each individual cell is not practical and increases the total cost of the EV. This paper provides three key original contributions: (1) the development and optimization of a new efficient electro-thermal battery model that accurately estimates the LIB voltage and temperature, which reduces the required number of temperature sensors; (2) the investigation of the ECM parameters’ dependency on the state of charge (SOC) at a wide range of ambient temperatures, including cold temperatures; (3) the testing and validation of the proposed electro-thermal model using real-world dynamic drive cycles and temperature ranges from −20 to 25 °C. Results indicate the effectiveness of the proposed electro-thermal model, which shows good estimation accuracy with an average error of 50 mV and 0.5 °C for the battery voltage and surface temperature estimation, respectively
Targeted axillary dissection: worldwide variations in clinical practice
No full textPurpose: Targeted axillary dissection (TAD) for the axillary staging of clinically node-positive (cN +) breast cancer patients converting to clinically node negative post neoadjuvant chemotherapy (NAC), has gained popularity due to its minimal false negative rate and low arm morbidity. The aim of this study is to shed more light on the variation in the clinical practice globally in terms of indications and perceived limitations of TAD. Methods: A panel of expert breast surgeons constructed a structured questionnaire comprising of 18 questions and asked surgeons worldwide for their opinions and routine practice on TAD. The questionnaire was electronically distributed and answers were collected between May 1st and August 1st 2022. Results: Responses included 137 entries from 36 countries. Of them, 73.7% consider TAD for cN + patients planned to receive NAC. Among them, the greatest number of respondents (45%) perform the procedure for tumours up to T3, whereas 27% regardless of T-stage. The majority (42%) perform TAD on patients with 1–3 positive nodes and only 30% consider TAD when matted nodes are present. HER2 positive and Triple Negative subtypes are more likely to undergo TAD than Luminal A and B (86%, 79.1%, 39.5%, and 62.8%, respectively). Maximum acceptable lymph node burden is median 3 nodes for any subtype with a tendency to accept more positive nodes for Triple Negative. Conclusion: This study demonstrates the differences in current practice regarding TAD as well as the fact that the biology of the tumour heavily affects the method of axillary staging. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024
