Model predictive control for multimode power-split hybrid electric vehicles: Parametric internal model with integrated mode switch and variable meshing losses

Model predictive control (MPC) is one of the most promising energy management strategies for hybrid electric vehicles. However, owing to constructive complexity, the multimode power-split powertrain requires dedicated mathematical tools to model the mode switch and transmission power losses within the internal model of the controller. Thus, the transmission losses are usually neglected and the mode switch is optimised through offline simulations. This paper proposes an MPC internal model relying on a parametric approach available in the literature, which provides a unique formulation for modelling any power-split transmission and assesses the transmission meshing losses. The objectives, which cover a gap in the literature, are: 1) to integrate the discrete problem of the mode switch in a continuous formulation of the internal model; 2) to compare MPC internal models with different complexity, and evaluate how the consideration of meshing losses and efficiency of the electric machines affect the controller performance. The results on a case study vehicle, i.e., the Chevrolet Volt, suggest that a simplified internal model deteriorates the fuel consumption performance by less than 2 %, while the integrated mode switch is comparable to the offline strategy

A New Energy Management Strategy for Multimode Power-Split Hybrid Electric Vehicles

Among the hybrid electric vehicle categories, the multimode power-split allows to fully exploit the advantages related to the powertrain electrification. However, together with the increased flexibility, it comes with greater difficulty in defining an effective control strategy, both in terms of predicted fuel consumption and computational cost. To overcome the limits of the most diffused energy management strategies, slope-weighted energy-based rapid control analysis (SERCA) has been recently proposed. Nevertheless, so far, the algorithm has been applied to powertrains characterized by two operative modes solely. In this paper, we first present the inconsistency of SERCA applied to the whole set of multimode power-split arrangements. Subsequently, after correlating this divergence to the mode selection process, to overcome this draft, we introduce a novel strategy called SERCA + . This algorithm is proven to be robust and to achieve results close to the optimum benchmark with an insignificant increase in computational cost. Therefore, SERCA + could potentially find application in design methodologies for multimode power-split HEVs to accelerate the overall vehicle design process

REAL-TIME PREDICTIVE CONTROL OF CONNECTED VEHICLE POWERTRAINS FOR IMPROVED ENERGY EFFICIENCY

The continued push for the reduction of energy consumption across the automotive vehicle fleet has led to widespread adoption of hybrid and plug-in hybrid electric vehicles (PHEV) by auto manufacturers. In addition, connected and automated vehicle (CAV) technologies have seen rapid development in recent years and bring with them the potential to significantly impact vehicle energy consumption. This dissertation studies predictive control methods for PHEV powertrains that are enabled by CAV technologies with the goal of reducing vehicle energy consumption. First, a real-time predictive powertrain controller for PHEV energy management is developed. This controller utilizes predictions of future vehicle velocity and power demand in order to optimize powersplit decisions of the vehicle. This predictive powertrain controller utilizes nonlinear model predictive control (NMPC) to perform this optimization while being cognizant of future vehicle behavior. Second, the developed NMPC powertrain controller is thoroughly evaluated both in simulation and real-time testing. The controller is assessed over a large number of standardized and real-world drive cycles in simulation in order to properly quantify the energy savings benefits of the controller. In addition, the NMPC powertrain controller is deployed onto a real-time rapid prototyping embedded controller installed in a test vehicle. Using this real-time testing setup, the developed NMPC powertrain controller is evaluated using on-road testing for both energy savings performance and real-time performance. Third, a real-time integrated predictive powertrain controller (IPPC) for a multi-mode PHEV is presented. Utilizing predictions of future vehicle behavior, an optimal mode path plan is computed in order to determine a mode command best suited to the future conditions. In addition, this optimal mode path planning controller is integrated with the NMPC powertrain controller to create a real-time integrated predictive powertrain controller that is capable of full supervisory control for a multi-mode PHEV. Fourth, the IPPC is evaluated in simulation testing across a range of standard and real-world drive cycles in order to quantify the energy savings of the controller. This analysis is comprised of the combined benefit of the NMPC powertrain controller and the optimal mode path planning controller. The IPPC is deployed onto a rapid prototyping embedded controller for real-time evaluation. Using the real-time implementation of the IPPC, on-road testing was performed to assess both energy benefits and real-time performance of the IPPC. Finally, as the controllers developed in this research were evaluated for a single vehicle platform, the applicability of these controllers to other platforms is discussed. Multiple cases are discussed on how both the NMPC powertrain controller and the optimal mode path planning controller can be applied to other vehicle platforms in order to broaden the scope of this research

Electrified Powertrain with Multiple Planetary Gears and Corresponding Energy Management Strategy

Modern hybrid electric vehicles (HEVs) like the fourth generation of Toyota Prius incorporate multiple planetary gears (PG) to interconnect various power components. Previous studies reported that increasing the number of planetary gears from one to two reduces energy consumption. However, these studies did not compare one PG and two PGs topologies at their optimal operation. Moreover, the size of the powertrain components are not the same and hence the source of reduction in energy consumption is not clear. This paper investigates the effect of the number of planetary gears on energy consumption under optimal operation of the powertrain components. The powertrains with one and two PGs are considered and an optimal simultaneous torque distribution and mode selection strategy is proposed. The proposed energy management strategy (EMS) optimally distributes torque demands amongst the power components whilst also controlling clutches (i.e., mode selection). Results show that increasing from one to two PGs reduces energy consumption by 4%

Slope-weighted Energy-based Rapid Control Analysis for Hybrid Electric Vehicles

Recent studies have addressed the development of optimal control strategies for hybrid electric vehicles (HEVs). Achieving global optimality for the fuel economy prediction while minimizing the computational efficiency still is a research and development challenge. This paper aims at presenting a novel technique for managing the energy flows in a power split HEV named slope-weighted energy-based rapid control analysis (SERCA). After presenting the HEV plant model and the optimal control problem, the currently most adopted energy management strategies are analyzed. The SERCA technique is then illustrated and its operating steps are detailed. The simulation results for the considered HEV energy management strategies in the standard urban driving cycle subsequently indicate that the SERCA can efficiently achieve near-optimal fuel economy while limiting the computational costs. This suggests the potential use of SERCA for rapid component sizing of HEV powertrains

Next Generation HEV Powertrain Design Tools: Roadmap and Challenges

Hybrid electric vehicles (HEVs) represent a fundamental step in the global evolution towards transportation electrification. Nevertheless, they exhibit a remarkably complex design environment with respect to both traditional internal combustion engine vehicles and battery electric vehicles. Innovative and advanced design tools are therefore crucially required to effectively handle the increased complexity of HEV development processes. This paper aims at providing a comprehensive overview of past and current advancements in HEV powertrain design methodologies. Subsequently, major simplifications and limits of current HEV design methodologies are detailed. The final part of this paper defines research challenges that need accomplishment to develop the next generation HEV architecture design tools. These particularly include the application of multi-fidelity modeling approaches, the embedded design of powertrain architecture and on-board control logic and the endorsement of multi-disciplinary optimization procedures. Resolving these issues may indeed remarkably foster the widespread adoption of HEVs in the global vehicle market

Comparison of Three Real-Time Implementable Energy Management Strategies for Multi-mode Electrified Powertrain

Three 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

Design of Power Split Hybrid Powertrains with Multiple Planetary Gears and Clutches.

Fuel economy standards for automobiles have become much tighter in many countries in the past decades. Hybrid electric vehicles (HEVs), as one of the most promising solutions to take on these challenging standards, have been successful in the US market. In the last few years, an observed trend is to use multiple planetary gears with multiple operating modes to further improve vehicle fuel economy and driving performance. Most work in existing literature on HEV design and optimization has been based on specific configurations, rather than exhaustively searching through all possible configurations. This limitation arises from the large size of the design space–millions to trillions of possible topological candidates. In this dissertation, a systematic design methodology is presented, which enables the exhaustive search of multi-mode powertrain systems. As a first step, a systematic analysis has been performed for all 12 single PG configurations with multiple operating modes enabled by clutch operation. The Dynamic Programming (DP) technique is used to solve the optimal energy management problems for each design candidate. For multi-mode HEVs with multiple PGs, an automated modeling and mode classification methodology is developed, which makes it possible to exhaustively search all possible designs. General mode shift mechanisms are studied, while mode shift cost is evaluated using Dijkstra’s algorithm, which identifies the optimal mode shift path. For each candidate, the optimal control problem needs to be solved so that all designs can be compared based on their best possible execution. A fast and near-optimal energy management strategy is proposed. The comparison results show that it is up to 10,000 times faster than DP while achieving similar performance. To ensure acceptable launching performance of the design candidates, a fast and optimal acceleration performance test procedure is developed, which can be used to determine optimal control inputs and mode shift schedule. Combining all proposed methodologies produces a systematic and optimal design procedure. Optimization results show that the exhaustive search design method is able to identify dozens of better designs than the production hybrid vehicle models available in today’s market.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116659/1/xiaowuz_1.pd

ОДНОЗНАЧНІСТЬ-НЕОДНОЗНАЧНІСТЬ ПАРАМЕТРИЧНОЇ ОПТИМІЗАЦІЇ АВТОМОБІЛЬНИХ СИСТЕМ ЗА УМОВ КРИТЕРІЙНОЇ НЕВИЗНАЧЕСНОСТІ

Annotation. The general methodology of parametric optimization of systems is considered for two arbitrary cri-teria simultaneously. The so-called principle of expanding an optimization problem is proposed, which creates the basis for finding guaranteed unambiguous solutions, without resorting to artificial formal means of «collapse» of the two cri-teria into one. It turns out that a very common multiplicative criterion for so-called fair trade-off actually expresses the average geometric basic criteria. It is easy to reduce (lead down) it to additive. Therefore, it is certainly not known, why he should give preference to the arithmetic mean (after the appropriate coordinate) of the dimensions of the primary criteria. There are more subjective and far-fetched than objective and truthful in the criterion of a fair compromise.Perfection is a permanent process — it has a beginning but has no end. In that the new" perfections arise from time to time and each of them definitely use a certain time, then, of course, the process of perfection is a step-by-step process, an endless step to an unattainable ideal. This particular circumstance should be taken into account.Described algorithms for optimal search formally reproduce on a primitive model plane the real process of step-by-step improvement of all man-made - from acceptable to better... There are no examples when something was created immediately unconditionally optimally (and the ideal — at all not recognizable and therefore not embodied). At each step, one of the algorithms regulates minimizing the value of a single criterion, without affecting it, without changing the other. That is why there are no conflicts outside the attractor. Only within the attractor, for which the line (which is a one-dimensional attractor) rules on the model plane, the consistency disappears. Another algorithm combines a series of steps in each of which only one parameter varies, and the gain at the same time has both supporters of one perfection, and supporters of some other perfection. Consequently, there are no conflicts, until the algorithm does not attract the attractor, which this time is an area on a model plane, that is a two-dimensional attractor.Within the attractor, all solutions to the optimization problem is appropriate without a doubt, even advisable to consider completely equivalent. However, in fact, insurmountable subjectivism does not allow us to adhere to this idea (let's say, without the participation of any dictator).Розглядається загальна методологія параметричної оптимізації систем одночасно за двома довільними критеріями. Запропоновано так званий принцип розширення оптимізаційної задачі, який створює засади для пошуку гарантовано однозначних її розв’язків, не вдаючись до штучних формальних засобів «згортання» двох критеріїв в один. Виявляється, дуже поширений мультиплікативний критерій так званого справедливого компромісу насправді виражає середнє геометричне основних критеріїв. Його легко звести до адитивного. І отже достеменно не відомо, чому йому слід надавати перевагу перед, скажімо, середнім арифметичним (після відповідного погодження розмірностей)первинних критеріїв. В критерії справедливого компромісу більше суб’єктивного й надума-ного, ніж об’єктивного і правдивого. Удосконалювання — це перманентний процес: він має початок, але не має кінця. А оскільки «нові» дос-коналості виникають час від часу і з кожної з них певний час обов’язково користають, то, зрозуміло, процес удосконалювання — це покроковий процес, нескінченне крокування до недосяжного ідеалу. І саме цю обставину слід обов’язково брати до уваги. Описані в роботі алгоритми пошуку оптимального формалізовано відтворюють на примітивній модельній площині реальний процес покрокового удосконалення всього рукотворного — від прийнятного до кращого… Не існує прикладів, коли б щось було створено відразу беззастережно оптимально (а ідеальне — взагалі не пізнаване, а отже й не втілюване). На кожному кроці один з алгоритмів регламентує мінімізувати значення якогось одного критерію, не зачіпаючи, не змінюючи іншого. А тому поза атрактором жодних конфліктів не виникає. І тільки в межах атрактора, за який на модельній площині править відтинок лінії (що є одновимірним атрактором), злагода зникає. Інший алгоритм поєднує в собі низку кроків, в кожному з яких змінюється тільки один параметр і зиск при цьому мають як прихильники якоїсь одної досконалості, так і прихильники якоїсь іншої досконалості. Тож не виникає конфліктів, аж допоки алгоритм, знову ж таки, не надибує атрактор, який цього разу є областю на модельній площині, тобто двовимірним атрактором. В межах атрактора всі розв’язки оптимізаційної задачі доречно, доцільно вважати цілком рівноцінними. Проте насправді нездоланний суб’єктивізм не дозволяє пристати на цю думку (без участі якогось диктатора, скажімо)