WO2022190059A1

WO2022190059A1 - Predictive energy management and drive advisory system for parallel hybrid electric vehicles

Info

Publication number: WO2022190059A1
Application number: PCT/IB2022/052213
Authority: WO
Inventors: Dhrupad BISWAS; Siddhartha Mukhopadhyay; Prasad WARULE; Somnath Sengupta; Prasanta Sarkar; Susenjit GHOSH
Original assignee: Indian Institute of Technology Kharagpur
Priority date: 2021-03-11
Filing date: 2022-03-11
Publication date: 2022-09-15

Abstract

The present invention is a Predictive Energy Management (PEM) based drive advisory system for Electric and Parallel Hybrid Electric vehicles (xEV) and comprises an engine (109) (only for Parallel HEV) with an Engine Control Unit (ECU)(105) (only for Parallel HEV) and a motor (111) with a Motor Control Unit (MCU)(106) connected to a transmission (113); a battery (114) with a Battery Management System (BMS) (108); plurality of clutches(110,112) based on the type of xEV; at least one gear box (113); at least one power electronic converter (106) configured to convert electrical energy; a PEM based Supervisory Controller (SC); plurality of sensors configured to perform sensing crucial information, thereby helping to implement a set of actions in the xEV; an Energy Management System (EMS)(101) which incorporates PEM; plurality of ADAS modules (100,102) such as ACC, TJAS, HDC, TLAPEM, EORSG which affect the drive behavior of the vehicle in terms of vehicle dynamics and propulsion. Further, the PEM based SC is configured to interact with the ADAS modules towards suggesting the optimal operating points (based on macroscopic and microscopic traffic information as well as optimal operating regions of the powertrain components, using two level prediction modules) considering vehicle dynamics, propulsion and travel time related subsystems of the xEV continuously for minimizing energy over a whole trip.

Description

PREDICTIVE ENERGY MANAGEMENT AND DRIVE ADVISORY SYSTEM FOR PARALLEL HYBRID ELECTRIC VEHICLES

TECHNICAL FIELD OF THE INVENTION

The present invention relates to the field of Electric and Parallel Hybrid Electric Vehicles (xEV) and more particularly, the invention relates to energy management based drive advisory system for Electric and Parallel Hybrid Electric Vehicles. BACKGROUND OF THE INVENTION

Electric and Parallel Hybrid Electric Vehicles (xEV) are well- suited for operating in urban environments. These vehicles typically have a battery and yet have higher manufacturing cost compared to conventional vehicles. Therefore, these need to achieve high energy efficiency to minimize operating costs for the users. Here, maximization of overall energy efficiency of the vehicle is performed for every trip. Energy efficiency of xEVs for a trip from a given location to another given location depends on several parameters like: a. Route characteristics such as grades, traffic density and lengths etc. b. Vehicle propulsion subsystem characteristics such as those of engine, motor, transmission etc. c. Driver choice of: i) drive cycles to achieve desired levels of travel time, speed, safety etc. and ii) driving modes of the Advanced Driver Assistance System (ADAS) such as Adaptive Cruise Control (ACC), Hill Descent Control (HDC), Traffic Jam Assist (TJAS) etc. d. Energy storage subsystem characteristics such as State of Charge

(SOC), Capacity, State of Health (SOH), etc.

The vehicle needs to be controlled in a manner that maximizes the overall energy efficiency for the whole trip while providing the desired motion. This essentially involves optimization which must consider the current state of the vehicle as well as its predicted future states over the remaining part of the trip. Thus Predictive Energy Management (PEM) is essential for such goals. An object of the present invention is to provide a prediction based energy management and advisory system for ADAS to be present in the supervisory controller (SC) of Electric and Parallel Hybrid Electric Vehicles to advise fuel / time / energy optimal vehicle operating condition in different modes of ADAS, while considering the desired drive of the vehicle. Here the energy can be due to electrical or fuel sources. The drive in this context refers to all dynamic aspects of the vehicle such as vehicle dynamics and powertrain dynamics leading to the desired and safe motion of the vehicle. The desired motion of the vehicle can be during cruising, acceleration, deceleration, parking, hill descent. Several pieces of prior work have attempted to achieve energy management addressing one or more of the above mentioned factors. Some of these are briefly described below as relevant to the invention.

US 8612077 B2 proposes a path dependent control of HEVs based on fuel economy by segmenting the original routes into segments. When the vehicle reaches the end of one trip segment one virtual route is created by considering the remaining segments. The battery SOC set points for traversing each virtual route optimally are calculated based on a Receding Horizon Control algorithm. At the end of each segment this calculation is repeated. This may be advantageous considering that a trip may be hours long. So recursively computing for both level- 1 and level-2 may be useful, although computationally more complex.

US8612077 B2 discloses a path-dependent control of a hybrid electric vehicle (HEV) which includes dividing the trip route into segments. A virtual route based on the remaining portion of the original trip route is generated once the HEV reaches the end of the last segment of the previous virtual route. Receding horizon control over the road segment is used to reduce the computational complexity.

US 8190318 B2 discloses a system and method of determining and applying PSRs to power sources within hybrid vehicles. The PSR is determined using a two-stage DP technique to achieve optimal SOC depletion over the course of a trip. On the macroscopic scale level, a global SOC profile is created for the entire trip route. On the microscopic scale level, the SOC profile and accompanying PSR is recalculated at the end of each segment as the vehicle proceeds along the trip. Various trip modeling techniques are used to provide constraints for the DP algorithm.

WO2019094843 A1 discloses advanced systems and methods that facilitate autonomous driving functionality, including a platform for autonomous driving Levels 3, 4, and/or 5. In preferred embodiments, the technology provides an end- to-end platform with a flexible architecture, including an architecture for autonomous vehicles that leverages computer vision and known ADAS techniques, providing diversity and redundancy, and meeting functional safety standards. The technology provides for a fast, reliable, safe, energy-efficient and space-efficient System-on-a-Chip, which may be integrated into a flexible, expandable platform that enables a wide-range of autonomous vehicles, including cars, taxis, trucks, and buses, as well as watercraft and aircraft.

US8108136 discloses an intelligent advisory system, which may be based on fuzzy rule -based logic to guide a vehicle driver in selecting an optimal driving strategy to achieve best fuel economy. The advisory system includes separate controllers for providing advisory information regarding driver demand for power and advisory information regarding vehicle braking, which are conveyed to the driver.

US 10399555 B2 discloses a hybrid vehicle and a method of controlling a charge mode therefore. The control method includes determining a first torque, which is a currently requested torque and determining a second torque, which is predicted to be generated in the near future from the present time, or predicted acceleration. Additionally, the method includes releasing a lock-up charge mode when the first torque is less than a first threshold value relevant to a reference for determining coasting driving and the second torque or the predicted acceleration is less than a second threshold value relevant to a driving mode change reference. US8108136 B2 discloses fuzzy logic based controllers which are used to provide guidance for selecting an optimal driving strategy that result in optimal fuel economy. Two controllers are used to determine the maximum driver demand (accelerator or brake). This controller takes the driver input as feedback and sends the driver signals to maximize fuel economy without significantly reducing vehicle speed.

Further the method reported in the paper by B. Asadi and A. Vahidi, "Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time," published in IEEE Transactions on Control Systems Technology, vol. 19, no. 3, pp. 707-714, May 2011 plans the trip, for a conventional vehicle, accordingly so that there is a timely arrival of green lights during the trip with minimal use of braking while maintaining safe distance from near vehicles. This planning helps to reduce idle time and fuel consumption. Oncoming traffic signal information and short range radar signal are used to formulate the control problem. This work minimizes the time of travel, fuel consumption during idling and braking in cruise control mode for a conventional vehicle based on prior traffic light Information. This does not consider the operation of other ADAS modes like Automated Parking (AP), Hill Descent Control (HDC), Traffic Jam Assist (TJA), Traffic light aware predictive energy management (TLAPEM) etc. It also does not consider optimal torque split based SOC management as it is a conventional vehicle not an HEV, while not predicting future load. Moreover, it does not advise the ACC module of ADAS on the optimal operating speed.

Another method reported in the paper by G. Katsargyri et al., "Optimally controlling Hybrid Electric Vehicles using path forecasting," 2009 American Control Conference, St. Louis, MO, 2009, pp. 4613-4617 proposes a path dependent control strategy for HEVs based on fuel economy of the forecasted route. To do that it divides the route into countable route segments. The approach for segmenting a route is discussed. The set points for battery SOC in each route segment is calculated based on the expected vehicle speed trajectory. Then it uses Dynamic Programming (DP) to achieve fuel economy.

Another publication by Tunnell, J.A., Asher, Z.D., Pasricha, S., Bradley, T. H., “Toward Improving Vehicle Fuel Economy with ADAS,” SAE Technical Paper 2018-01-0593, 2018 discloses a predictive approach to get optimal EMS using ADAS technology. Two ADAS strategies using Connected Vehicles (CVs) are developed for improvement in fuel economy. Implementation of CV based ADAS is also shown.

A further publication by Baker, D., Asher, Z.D., and Bradley, T., “V2V Communication Based Real-World Velocity Predictions for Improved HEV Fuel Economy,” SAE Technical Paper 2018-01-1000, 2018 discloses Future vehicle operation for optimal energy management is determined using multiple data sources and signals like previous drive cycle info, current vehicle state, current position from GPS, perception using Advanced Driver Advisory System (ADAS) Signal, travel time data. A nonlinear Autoregressive ANN with external input (such as GPS data, current travel time etc.) is used to model velocity prediction model. It is a short range prediction. This is then used to derive an optimal energy management strategy using dynamic programming. But this strategy requires several data sources with a high demand of memory as well as a fast embedded system.

Another publication by Plianos, A., Jokela, T., and Hancock, M., “Predictive Energy Optimization for Connected and Automated HEVs,” SAE Technical Paper 2018-01-1179, 2018 discloses two stage optimization for velocity prediction and fuel optimization using the predicted velocity for connected and automated HEVs. In each route segment, the way to obtain SOC at the end of the segment, is not given. In microscopic traffic behavior prediction model, all the vehicles are assumed to be connected vehicles which are not implementable for unconnected not automated vehicles. It also has not considered lane changing. The torque split is based on a static rule based algorithm which would result in less energy- efficient torque splits. Green Light Optimized Speed Advisory (GLOSA) is not considered for minimizing emission, fuel consumption/electrical energy consumption during Red light traffic signal.

A further publication by D. Rojdestvenskiy, M. Cvetkovic and P. Bouchner, "Real-time driver advisory system for improving energy economy based on advance driver assistant systems interface," 2018 Smart City Symposium Prague (SCSP), Prague, 2018, pp. 1-6 discloses road map data with added functionality of ADAS which provides a preview of road characteristics ahead of the vehicle. One real time driver advisory system is presented to achieve higher fuel economy, comfort and safety when a vehicle is decelerating. It predicts the decelerating drive cycle based on Electronic Horizon (provides future road and traffic profile). It then divides the decelerating force between mechanical braking and regenerating braking.

A further publication by Calefato, Caterina, et al. "The modularisation design approach applied to the ADAS domain: the DESERVE project experience." Transportation Research Procedia 14 (2016): 2265-2273 focuses on the innovative strength that the DESERVE platform has brought on the Advanced Driver Assistance Systems (ADAS) market in terms of major safety and economic affordability. DESERVE is a project aimed at designing and implementing a low- cost, integrated platform for ADAS: the creation of innovative software and hardware modules to be integrated in ADAS applications will pave the way to a standardization of the single components in order to achieve a full integration of diversified models despite their complexity. The achievement of such objective will end up in an increase of the reliability level of the system and in a cost reduction for ADAS functions and for development costs as well. In this paper the results of the application of the modularization philosophy to the DESERVE platform architecture and to the HMI concepts is presented. It would be apparent to a person skilled in the art that some of the above mentioned documents divide the future path of travel into different segments and based on expected vehicle speed trajectory and specific SOC reference set points. They do not consider any set points for fuel and time for each segment. They cannot provide (considering future conditions) any suggestions of operating conditions to ADAS of the route based on fuel, electrical energy and time optimality. Further, some of the above mentioned works have done a short range prediction based on past drive cycle, ADAS and GPS data. These are also not giving any suggestions to ADAS through Supervisory Control (SC) based on fuel or time optimization. There is no mention of taking any predictive control actions based on prior traffic light sequence information.

Moreover, most of the documents described herein above considered two-stage optimization for velocity prediction and fuel optimization for connected and automated xEVs. Though velocity prediction is not based on fuel optimization, in GLOSA (Green Light Optimized Speed Advisory) minimizing emission, fuel consumption/electrical energy consumption during Red light traffic signal are not considered. Velocity prediction during such operations is also not been shown. Also, some documents do not consider traffic light information for predictive control action. These documents also does not consider SPORTS and ECO mode for the speed limit suggestion.

The above described prior art systems suffer from many disadvantages, which the instant invention effectively eliminates. For this reason, there still exist a need to provide an improved and efficient energy management based drive advisory for the Electric and Hybrid Electric Vehicles.

SUMMARY OF THE INVENTION

The following disclosure presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention. It is not intended to identify the key /critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concepts of the invention in a simplified form as a prelude to a more detailed description of the invention presented later.

An object of the present invention is to overcome the problems of prior art.

An object of the present invention is to provide a predictive energy management based advisory system for ADAS to be present in the supervisory controller (SC) of Electric and Parallel Hybrid Electric Vehicles to advise fuel/time/energy optimal vehicle operating condition in different modes of ADAS, which impacts the drive of the vehicle. The drive in this context refers to all dynamic aspects of the vehicle such as vehicle dynamics and powertrain dynamics leading to the desired motion of the vehicle. The desired motion of the vehicle can be during cruising, acceleration, deceleration, parking, hill descent, approaching traffic light etc.

One aspect of the present invention is to provide a Predictive Energy Management based advisory system for ADAS in Electric and Parallel Hybrid Electric Vehicles. It discloses, without limitation, a prediction based supervisory controller (SC) running in an xEV which advises fuel/time/energy optimal vehicle operating condition suggestion in different modes of ADAS. The different modes of ADAS, which the SC will interact with, are Hill descent control (HDC), Energy/time optimal route suggestion in GPS (EORSG), Intelligent Adaptive cruise control (IACC), Traffic Jam Assist System (TJAS), Traffic light aware predictive energy management (TLAPEM). SC will also actuate the different power sources like motor and engine (only motor for EV) based on the Energy Management Strategy (EMS). Two levels of prediction is used in the supervisory controller to advise the ADAS for optimal velocity of a running xEV and SOC targets for road segments. In the present invention, a prediction based energy management strategy is designed which not controls the power based on driver and autonomous ADAS mode’s demand, it also gives optimal velocity advisory to some ADAS modules. The primary objective of this prediction based EMS is to control the power optimally and also select the optimal gear ratio along with optimal speed and SOC set points along the routes. Two levels of predictors are used in this invention.

In one implementation of the first aspect as described above, the present invention provides a Predictive Energy Management (PEM) based Drive Advisory for Electric and Hybrid Electric vehicle (xEV), said system comprising an engine and a motor (HEV) or only motor (EV) connected to a transmission, a battery with a Battery Management System (BMS), can have a plurality of clutches, a Motor Control Unit (MCU), a PEM based Supervisory Controller (SC), plurality of sensors, configured to perform decision making and to implement a set of actions in the xEV, an Energy Management System (EMS) and plurality of ADAS modes.

In another implementation of the first aspect as described above, the PEM based SC is configured to interact with the ADAS modules and to choose optimal operating points for all propulsion and energy related subsystems of the xEV continuously over a whole trip, based on macroscopic and microscopic traffic information.

In another implementation of the first aspect as described above, the xEV comprises user selectable ECO and SPORTS driving modes and based on two levels of prediction parameters PEM, SC implements the power control in the xEV. ECO mode is only focused on minimizing energy consumption where, SPORTS mode considers energy and travel time while considering control inputs.

In a further implementation of the first aspect as described above, the plurality of ADAS modes comprise a Hill Descent Control (HDC) mode, an Energy/time optimal route suggestion in GPS (EORSG) mode, an Intelligent Adaptive cruise control (IACC) mode, a Traffic Jam Assist (TJA) mode and a Traffic light aware predictive energy management (TLAPEM) module In another implementation of the first aspect as described above, the TLAPEM module is configured to take predictive action and to suggest optimal operating points to the ADAS modules based on traffic light information.

In further implementation of the first aspect of the present invention, the PEM based Supervisory Controller (SC) interacts with ADAS in two modes like semi- autonomous mode and advisory mode, wherein in the semiautonomous mode the PEM based SC advises operating conditions to HDC, ACC modules and receive accelerator and brake commands based from the ADAS modules and wherein in the advisory mode SC only gives advice to EORSG and TLAPEM modules.

Other aspects, advantages, and salient features of the invention will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the annexed drawings, discloses exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

The above and other aspects, features and advantages of the embodiments of the present disclosure will be more apparent in the following description taken in conjunction with the accompanying drawings, in which:

Figure 1 illustrates the block diagram of the Predictive Energy Management based drive advisory System for Parallel Hybrid Electric Vehicles, according to one of the embodiments of the present invention.

Figure 2 illustrates the block diagram of the two levels of predictors used in the PEM based drive advisory system, according to one of the embodiments of the present invention. Figure 3 illustrates two different routes of level one prediction for the same source and destination, according to one of the embodiments of the present invention.

Figure 4 illustrates a normal road divided in multiple segments of level one prediction and for each segment there are optimal initial SOC, according to one of the embodiments of the present invention.

Figure 5 illustrates battery charging and discharging of the xEV to attain the required SOC_ref/Fue to successfully traverse the distance, according to one of the embodiments of the present invention.

Figure 6 illustrates the state flow of the two different EMS, according to one of the embodiments of the present invention.

Figure 7 illustrates Energy management based HDC, according to one of the embodiments of the present invention.

Figure 8 illustrates the block diagram of working of EORSG module, according to one of the embodiments of the present invention.

Figure 9 illustrates the block diagram of working of the ACC and TJAS module, according to one of the embodiments of the present invention.

Figure 10 illustrates the block diagram of the two levels of predictors used in the PEM based drive advisory system, according to one of the embodiments of the present invention along with Traffic light information.

Figure 11 illustrates the block diagram of the Predictive Energy Management based drive advisory System for Electric Vehicles, according to one of the embodiments of the present invention. Figure 12 illustrates the results after implementation of the proposed framework in a parallel HEV with optimal TJAS operating under ECO and SPORTS mode, in terms of savings of energy and time.

Figure 13 illustrates the results after implementation of proposed framework in a parallel HEV with optimal ACC operating under ECO and SPORTS mode, in terms of savings of energy and time.

Figure 14 illustrates the results after implementation of proposed framework in a parallel HEV with optimal TLAPEM-ACC mode under two controller settings corresponding to (a) only energy, (b) energy and time. This figure also illustrates that optimal TLAPEM-ACC functionality also enables crossing the traffic light signal without stopping.

Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may not have been drawn to scale. For example, the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure. Throughout the drawings, it should be noted that like reference numbers are used to depict the same or similar elements, features, and structures.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the present disclosure as defined by the claims and their equivalents. It includes various specific details to assist in that understanding, but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the various embodiments described herein can be made without departing from the scope and spirit of the present disclosure. In addition, descriptions of well-known functions and constructions may be omitted for clarity and conciseness.

The terms and words used in the following description and claims are not limited to the bibliographical meanings, but, are merely used by the inventor to enable a clear and consistent understanding of the present disclosure. Accordingly, it should be apparent to those skilled in the art that the following description of various embodiments of the present disclosure is provided for illustration purpose only and not for the purpose of limiting the present disclosure as defined by the appended claims and their equivalents.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments belong. Further, the meaning of terms or words used in the specification and the claims should not be limited to the literal or commonly employed sense but should be construed in accordance with the spirit of the disclosure to most properly describe the present disclosure.

The terminology used herein is for the purpose of describing particular various embodiments only and is not intended to be limiting of various embodiments. As used herein, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising" used herein specify the presence of stated features, integers, steps, operations, members, components, and/or groups thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, members, components, and/or groups thereof. Also, expressions such as "at least one of," when preceding a list of elements, modify the entire list of elements and do not modify the individual elements of the list. The present disclosure will now be described more fully with reference to the accompanying drawings, in which various embodiments of the present disclosure are shown. The present disclosure may, however, be embodied in many different forms and should not be construed as being limited to the various embodiments set forth herein, rather, these various embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the present disclosure. Furthermore, a detailed description of other parts will not be provided not to make the present disclosure unclear. Like reference numerals in the drawings refer to like elements throughout.

In an embodiment, the present invention provides a prediction based supervisory controller (SC) running in an xEV which advises fuel / time / energy optimal vehicle operating condition suggestion in different modes of ADAS. The different modes of ADAS which SC will interact with are Hill Descent control (HDC), Energy/time optimal route suggestion in GPS (EORSG), Intelligent Adaptive cruise control (IACC), Traffic Jam Assist (TJAS), Traffic light aware predictive energy management (TLAPEM). SC will also actuate the different power sources like motor (111) and engine (109) for Parallel HEV (only motor for EV) based on Energy Management Strategy (EMS). Two levels of prediction is used in the supervisory controller to advise ADAS and for optimal velocity of a running xEV. In this invention, a prediction based energy management strategy is designed which not only controls the power based on driver and autonomous ADAS mode’s demand, it also gives advisory to some ADAS modules. The primary objective of this prediction based EMS is to control the power optimally and also select the optimal gear ratio. Two levels of predictors are used in this invention.

Figure 1 of the present invention is general block representation of a Prediction based Advanced Supervisory Controller (101) for Parallel HEV architecture (116) (engine, motor are connected to the transmission), which will interact with other ECUs (MCU(106), BMS(108), TCU (107), EMS (105) etc.) and ADAS modules (100,102). SC will provide power demand to other subsystems based on different levels of traffic prediction and give advisory to different ADAS modules for different driving modes.

Figure 11 is a general block representation of a Prediction based Advanced Supervisory Controller (1101) for Electric Vehicle architecture (motor are connected to the transmission), which will interact with other ECUs (MCU(1106), BMS(1105), TCU (1112) etc.) and ADAS modules (1100,1102). SC will provide power demand to other subsystems based on different levels of traffic prediction and give advisory to different ADAS modules for different driving modes.

The prediction based energy management system of the present invention is designed, which not only controls the power based on driver and autonomous ADAS module’s demand, it also gives advisory to some ADAS modules. The primary objective of this prediction based EMS is to split the power optimally between the motor (228) and engine (229) for Parallel HEVs (only motor for Evs) and also select the optimal gear ratio (230). Two levels of predictors are used in this invention, i.e. Level 1 Prediction (LIP) (211), Level 1.1 Prediction (LI. IP) (216) and Level 2 Prediction (L2P) (218) which are shown in Figure 2 and Figure 10. LIP partitions the routes in multiple segments and generates SOC (215), fuel (214), time (213) of travel reference at the starting of each segment considering (218) macroscopic traffic condition, gradient, weather etc. LI. IP further partitions each segment of the route into predicted sub-segment points based on predicted fuel optimal distance before subsequent segment so that vehicle can attain the required SOC target at the starting of the subsequent segment to traverse at the desired velocity under the conditions of traffic, grade, traffic light, low emission zones etc. L2P predicts optimal velocity of the ego vehicle based on microscopic traffic information (218,1016) and sends the information to ADAS. The EMS uses reference parameters generated in LIP to consider future power requirements. ADAS sensor (203,1000) outputs are used for the L2P to predict optimal velocity for short duration and identification of microscopic traffic modes (223) (car following/ Lane Changing/ Lree flowing) of the ego vehicle, which is used for optimal power split for predictive energy management of xEV. LIP and LI. IP are not affected by the microscopic traffic disturbances where L2P is not sensitive to macroscopic traffic and weather changes. That is why two levels of predictors are used in this invention. As the driver and ADAS autonomous modules are unaware of the fuel optimal and time- optimal operating conditions, the Prediction-based EMS advises driver and ADAS to improve the fuel economy or travel time based on ECO/SPORTS mode, respectively.

Level 1 Prediction ( LIP) ( 211,1012 )

Source and destination of the route will be partitioned into multiple segments. These segments are divided based on traffic density, traffic light, grade, low emission zones etc shown in figure 3. Starting of each road segment will be assigned with a SOC ,Fuel_ref and Time_ref further arbitrated based on assigned priority. These reference parameters may vary with time based traffic flow direction change brought about by dynamic routes such as weather condition, special event etc. Here SOCW is the minimum (or maximum) value of SOC to be maintained before reaching the starting point of the respective segment. Similarly, Fuel_ref is the minimum fuel level in the tank that needs to be present at the beginning of the segment. The Time_ref parameter refers to the relative maximum time before which the vehicle must reach the segment end. These parameters will be estimated based on average velocity, grade, traffic density, distance of the road segment. In Figure 3 three different routes are shown for the same source and destination. SOCi_j, Fuek_j, Timei_j is mentioned for each i^th segment of j^Lhroutc. In Figure 4 a normal road is divided in multiple segments.

Level 1.1 Prediction (LI. IP) (216)

Each Segment of the route will be further divided into predicted sub-segment points based on predicted fuel optimal distance / minimum distance before subsequent segment so that vehicle can attain the required SOC , Fuel_ref, time_ref at the start of the subsequent segment, to traverse at a desired velocity under the conditions of traffic, grade (518), traffic light, low emission zones (506) etc.

In Figure 5, which is only valid for Parallel HEVs from Di(511, 513) to D_ref (513,523) the Parallel HEV will charge / discharge the battery to attain the required SOCW (517) to successfully traverse the distance. Here, D_min(505,517) is the estimated point from which the SOC tracking (508,520) are managed through the energy management system to achieve the respective SOC_ref.

Level 2 Prediction (L2P)(218,1016)

In this prediction method Self- optimal velocity of the vehicle is predicted for a short duration in the immediate future which will be useful for model based optimal energy management strategy and send this information to ADAS(212,1004). This velocity prediction is based on microscopic traffic information (223,224). The microscopic traffic information like lane changing, car following, free flowing, start-stop are detected using ADAS functionality along with traffic light information (1017) . To detect these modes, it uses the velocity, acceleration and position of the vehicle and its nearest surrounding vehicles.

Based on two levels of prediction parameters EMS in xEV SC will implement the torque split between motor and engine (only for HEVs). This EMS has two modes. EMS Mode 1(600): This mode is used during car following and free flowing mode based on Level 2 prediction. The mode is characterized by short term prediction based optimization. EMS Mode 2 (601): This mode is used during lane changing mode and also when prediction error in Level 2 prediction is higher than threshold prediction error. State flows of the two EMSs are shown in Figure 6.

Based on the stratified EMS functionalities and prediction parameters the resultant energy management function in the algorithm of the SC further generates fuel/electric energy/time optimal suggestions for operation to the ADAS modules. This additional algorithm inside SC will advise ADAS of more refined operating conditions (upon the actuation of ADAS modules) targeted to achieve better performance in terms of fuel economy, emissions, electric energy consumption and time while driving.

The effect of this functionality on the various ADAS modules are explained as follows: EMS based supervisory controller will interact with ADAS in two modes, namely, semi-autonomous mode and advisory mode. In semi-autonomous mode SC will advise energy optimal operating conditions such as velocity to HDC, ACC, TJAS modules and receive accelerator and brake commands based from ADAS modules. In advisory mode SC will give advice to EORSG, TLAPEM modules and selects power splits(for a Parallel HEV between motor and engine while there will be no power split in battery electric vehicles) and gear optimally.

HDC (703): During this mode first LI prediction estimates SOC targets such that kinetic energy of this vehicle can be extracted through regenerative braking to the maximum extent possible. In this mode of the ADAS, braking is mostly used while going downhill. During this mode SC will advise the ADAS module, HDC (703) such that energy consumption and regeneration is optimal while regenerative braking is used by considering jerk, as well as optimal efficiency zones, and optionally further suggesting gear changes. The EV/ Parallel HEV EMS (701) provides information to the HDC (703) related to suggestive high efficiency/maximum braking torque and speed profile considering SOC, SOH, jerk, headways etc. Energy management based HDC is shown in Figure 7. The actuators (704) as shown in figure 7 are regenerative brake and friction brake. Further, the sensors (702) are vehicle speed sensor, slope sensor, accelerator pedal position sensor and some of the other sensors may include LIDAR/radar camera sensor fusion based headway/pothole/blind-spot and bump detection. As shown in figure 7, the predictive SOC correction is based on prior hill descent information. EQRSG: From LI Prediction the trip will be divided in multiple routes. Each route will be divided in multiple segments. SC will suggest best route based on time or fuel optimality/ Electric Energy optimality (Figure 8). Figure 8 illustrates the block diagram of working of EORSG module. This figure shows the EV/ Parallel HEV EMS (801) in communication with the GPS navigation based route suggestion display interface (803), suggestions for the best route is based on the distance, terrain, traffic condition considering fuel, emission, SOC, travel time and driving comfort. Predictive SOC correction is done on chosen route. Sensors and data driven predictors (802) as shown in this figure comprises traffic information and prediction, data based driver behavior prediction and GPS navigation terrain information. It further shows a dashboard display (804) which provides information regarding terrain-traffic aware route suggestion considering emission, SOC, fuel efficiency, travel time and driving comfort.

ACC: Optimal fuel and electrical energy based operating conditions, gear will be suggested by the SC to ACC module such that xEV can attain targeted SOC for the subsequent segment (Figure 9). Figure 9 illustrates the block diagram of working of the ACC and TJAS module. It shows a EV/ Parallel HEV EMS (901) in communication with the adaptive cruise control/ traffic jam assist/rough road handling (903). EV/ Parallel HEV EMS (901) passes the information to the adaptive cruise control (903) regarding suggestive optimal future velocity profile to be followed based on fuel, electric energy consumption, emission, SOC and SOH of battery. The actuators (904) comprises of regenerative brake, friction brake and accelerator. The sensors (902) comprises of vehicle speed, slope sensor, accelerator and brake pedal position sensor, lidar/radar, camera sensor fusion based headway /pothole/blind- spot detection. Predictive SOC correction is based on prior slope/ traffic information.

TJAS: Using L2P parameters fuel and electrical energy optimal operating conditions, gear will be suggested by SC to TJAS (Figure 9). TLAPEM: The targeted SOC, Fuel, time can be generated using prior traffic light sequence information in LIP. Based on this SC will take predictive action by giving appropriate optimal velocity set points from L2P to ADAS, so that it not only crosses the traffic light during green light as well as it minimizes the energy consumption. TLAPEM also take the predictive action based on prior traffic light sequence information so that it can minimize emission during unavoidable red traffic light. Based on these references optimal fuel, electrical energy consumption, emission based operating condition, gear shifts can be suggested to the ADAS/driver.

Two driving modes can be added to provide more flexible driving and energy management conditions. SPORTS mode: If driver selects sports mode, SC will suggest time optimal prediction based operating condition and gear shifts to driver/ AD AS. ECO mode: If driver selects ECO mode, SC will suggest energy optimal prediction based operating condition and gear to driver/ AD AS.

PEM-based SC in present disclosure will choose optimal operating points for ah propulsion and energy related subsystems of the xEV continuously over the whole trip, based on macroscopic and microscopic traffic information such as road grade, traffic density, average velocity, time of travel etc., using various data sources as well as prediction. This results in the best possible over ah energy efficiency over the whole trip for the vehicle in the context of the available information.

A modem xEV utilizes an ADAS in the various modes of a running xEV like AP, HDC, EORSG, IACC, TJA etc. The PEM-based SC proposed here will advise ADAS on optimal operating points for various subsystems considering fuel, emissions, electric energy and time of journey. Thus ADAS functionality shah be able to take into consideration these factors, while performing in the above modes for easy drivability. Further, in case traffic light sequence schedules are available, the method also enables a new ADAS module, namely the Traffic Light Aware Predictive Energy Management (TLAPEM) to be introduced. This will take predictive action and suggest optimal operating points to ADAS modules utilizing traffic light information in addition to the ones mentioned above. This can further improve trip performance criteria, such as journey time or emission.

It is a great advantage for the vehicle user to be able to endow the same vehicle with flexible characteristic like high energy efficiency with compromised drivability, or high drivability with compromised energy efficiency at various times. This invention makes it possible that two driving modes, namely, ECO and SPORT can be added to provide more flexible driving and energy management conditions. Two user-selectable driving modes for the vehicle, namely ECO and SPORT, can be realized to provide flexible drivability and energy efficiency- based policies for SC.

Figure 12 shows the comparative results after implementation of conventional TJAS (TJAS) and proposed strategy (Optimal TJAS) in a parallel HEV under ECO and SPORTS mode of the vehicle and it is evident from the figure of results that the proposed strategy is exhibiting improved energy savings while taking little more time. It is further shown in the figure of results that the optimal TJAS under ECO Mode consumes less energy and more time than the optimal TJAS under SPORTS Mode.

Figure 13 shows the comparative results after implementation of conventional ACC (ACC) and proposed strategy (Optimal ACC) in a parallel HEV under ECO and SPORTS mode of the vehicle and it is evident from the figure of results that the Optimal energy aware ACC strategy is exhibiting improved energy savings while taking little more time. It is further shown in the figure of results that the optimal ACC under ECO Mode consumes less energy and more time than the optimal ACC under SPORTS Mode. Figure 14 shows the comparative results after implementation of conventional ACC (ACC) and proposed strategy (Optimal TLAPEM-ACC) in a parallel HEV and it is evident from the figure of results that the Optimal energy aware TLAPEM-ACC strategy is exhibiting improved energy savings for different controller parameter settings (both under ECO Mode) and crosses traffic light signal without stopping.

In another embodiment of the present invention the Predictive Energy Management (PEM) (104) based drive advisory for Electric and Parallel Hybrid Electric vehicle (xEV) comprises an engine (109) and a motor (111) for HEV (only motor for EV) connected to a transmission, a battery (114) with a Battery Management System (BMS)(108), plurality of clutches (110, 112), at least one gear box (113); at least one power electronics converter (106) configured to convert electrical energy, a Motor Control Unit (MCU)(106), a PEM based Supervisory Controller (SC) (101,104), plurality of sensors configured to perform decision making and to implement a set of actions in the xEV, an Energy Management System (EMS) and plurality of ADAS modules.

In another embodiment, the PEM based SC is configured to interact with the ADAS modules and to choose optimal operating points for all propulsion and energy related subsystems of the xEV continuously over a whole trip, based on macroscopic and microscopic traffic information.

In another embodiment of the present invention, the xEV comprises user selectable ECO driving mode and SPORTS driving mode and based on two levels of prediction parameters PEM, SC implements the optimal power demand in the xEV.

In another embodiment of the present invention, the plurality of ADAS modules comprises a Hill Descent Control (HDC) module, an Energy/time optimal route suggestion in GPS (EORSG) module, an Intelligent Adaptive cruise control (IACC) module, a Traffic Jam Assist (TJA) module and a Traffic light aware predictive energy management (TLAPEM) module.

In another embodiment of the present invention, the TLAPEM module is configured to take predictive action and to suggest optimal operating points to the ADAS modules based on traffic light information.

In an implementation of further embodiment of the present invention, the PEM based SC also actuates the different power source like the motor (111) and the engine (109) (for Parallel HEV) through MCU(113) an ECU(105) respectively based on Energy Management System (EMS).

In a further embodiment of the present invention, the PEM based Supervisory Controller (SC) interacts with ADAS modules in two modes like semi- autonomous mode and advisory mode, wherein in the semiautonomous mode the PEM based SC advises operating conditions to HDC, ACC modules and receive accelerator and brake commands based from the ADAS modules and wherein in the advisory mode SC only gives advice to EORSG and TLAPEM modules.

In another embodiment, the system comprises a Transmission control unit (TCU) and the xEV is a parallel HEV.

In another embodiment, the system comprises a Transmission control unit (TCU) without engine and the xEV is an EV.

Some of the advantages of the present invention, without limitations, are as follows:

1. PEM-based SC will choose optimal operating points for ah propulsion and energy related subsystems of the xEV continuously over the whole trip, based on macroscopic and microscopic traffic information such as road grade, traffic density, average velocity, time of travel etc., using various data sources as well as predicted stares of the system. This results in the best possible over all energy efficiency over the whole trip for the vehicle in the context of the available information.

2. A modem xEV utilizes an ADAS in the various modes of a running xEV like HDC, EORSG, I ACC, TJAS, TLAPEM. The PEM-based SC proposed here will advise ADAS on optimal operating points for various subsystems considering fuel, emissions, electric energy and time of journey. Thus the PEM based SC functionality will coordinate with the ADAS to optimize these factors, while interacting in the ADAS modules. PEM based SC will also actuate engine, motor (while considering battery constraints) and transmission during HDC, IACC, TJAS semi- autonomous modes for ensuring better energy minimization and better driv ability.

3. In case traffic light sequence schedules are available, the method also enables a new ADAS module, namely the Traffic Light Aware Predictive Energy Management (TLAPEM) to be introduced. This will take predictive action and suggest optimal operating points to ADAS modules utilizing traffic light information in addition to the ones mentioned above. This can further improve trip performance criteria, such as journey time or emission.

4. It is a great advantage for the vehicle user to be able to endow the same vehicle with flexible characteristic like high energy efficiency with compromised drivability, or high drivability with compromised energy efficiency at various times. This invention makes it possible that two driving modes, namely, Eco and Sport can be added to provide more flexible driving and energy management conditions. The illustrations of overview of the system as described herein are intended to provide a general understanding of the structure of various embodiments, and they are not intended to serve as a complete description of all the elements and features of apparatus and systems that might make use of the structures described herein. Many other arrangements will be apparent to those skilled in the art upon reviewing the above description. Other arrangements may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Figures are also merely representational and may not be drawn to scale. Certain proportions thereof may be exaggerated, while others may be minimized. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.

Thus, although specific figures have been illustrated and described herein, it should be appreciated that any other designs calculated to achieve the same purpose may be substituted for the specific arrangement shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments of the present invention. Combinations of the above designs/structural modifications not specifically described herein, will be apparent to those skilled in the art upon reviewing the above description. Therefore, it is intended that the disclosure not be limited to the particular method flow, apparatus, system disclosed as the best mode contemplated for carrying out this invention, but that the invention will include all embodiments and arrangements falling within the scope of the appended claims.

Claims

CLAIMS:

1. A Predictive Energy Management (PEM) based Drive Advisory System for Electric and Parallel Hybrid Electric Vehicle (xEV), said system comprising: an engine (109) (for only Parallel HEV) and a motor (111) connected to a transmission mechanism of the xEV; a battery (114) operatively coupled to a Battery Management System (BMS)(108); plurality of clutches (110, 112); at least one gear assembly (113); a Motor Control Unit (MCU) (113) ; Engine Control Unit (ECU) (105) (only for Parallel HEV); a PEM based Supervisory Controller (SC) (101); plurality of sensors configured to perform decision making and corresponding actions in the xEV ; an Energy Management System (EMS) (101); plurality of ADAS modules ; wherein the PEM based SC is configured to interact with the ADAS modules and to choose optimal operating points for propulsion and energy related subsystems of the xEV continuously over a trip, based on macroscopic and microscopic traffic information using two level prediction modules (L1P(211,216,1012), L2P(218,1016)); and wherein the xEV further comprises a user-selectable ECO driving mode and a SPORTS driving mode which are based on at least two hierarchical levels incorporated in the prediction based EMS which implements optimal power demand in the xEV and further optimally distributes the power based on the configuration.

2. The system as claimed in claim 1, wherein the plurality of ADAS modules comprises a Hill Descent control (HDC) module, an Energy/Time Optimal Route Suggestion in GPS (EORSG) module, an Intelligent Adaptive cruise control (IACC) module, a Traffic Jam Assist (TJA) module and a Traffic light aware predictive energy management (TLAPEM) module which operate in co-ordination with the PEM based SC wherein the PEM based SC provides drive suggestions to ADAS modules based on predicted drive patterns.

3. The system as claimed in claim 2, wherein the TLAPEM module is configured to take predictive action and to suggest corresponding optimal operating points to the ADAS modules based on traffic light information for saving energy while considering travel time.

4. The system as claimed in claim 1, wherein the PEM based SC also actuates the different power sources like the motor (111) and the engine (109) (for Parallel HEV only) through MCU(113) an ECU(105) (for Parallel HEV only) respectively.

5. The system as claimed in claim 1, wherein the PEM based SC interacts with ADAS modules in semi-autonomous modes like IACC, HDC, and TJAS, where the PEM based SC sends drive suggestions to these semi-autonomous modes for saving energy while considering travel time.

6. The system as claimed in claim 5, wherein in the semi-autonomous mode, the PEM based SC advises operating conditions to HDC, ACC and TJAS modules for saving energy while considering travel time and also provides actuation signals to the engine, motor, and transmission after considering their optimal operating regains as well as the vehicle's future driving scenarios and its surrounding traffic during HDC, ACC and TJAS modes.

7. The system as claimed in claim 5, wherein in the PEM-based SC advises on EORSG and TLAPEM modules in terms of SOC_ref /Fuel_ref and route suggestions based on their optimal operating regions as well as predicted future driving scenarios.

8. The system as claimed in claim 2, wherein the TLAPEM is adapted to take predictive action by giving appropriate optimal velocity from L2P to ADAS, so that it not only crosses the traffic light during green/yellow light without stopping, thereby saving time as well as it minimizes the energy consumption.

9. The system as claimed in claim 8, wherein the TLAPEM additionally is adapted to take the predictive action based on prior traffic light sequence information so that it can minimize energy and emission (only for Parallel HEV) during unavoidable red traffic light.

10. The system as claimed in claim 1, wherein the EMS has two modes, EMS mode 1 and EMS mode 2.

11. The system as claimed in claim 10, wherein the EMS mode 1 is adapted for car following and free flowing mode based on level 2 prediction; and wherein the EMS mode 2 is characterized by short term prediction based or instantaneous optimization, wherein the EMS mode 2 is adapted for lane changing mode and also when prediction error in level 2 prediction is higher than threshold prediction error.

12. The system as claimed in any of the preceding claims, wherein the PEM based supervisory controller of the EV/HEV can work individually with the functionalities of TLAPEM, IACC, TJAS, HDC in ECO/SPORTS mode or operate in coordination with multiple of these ADAS modules during their simultaneous operation.

13. The system as claimed in claim 1, wherein the system comprises a Transmission Control Unit (107) (TCU) which the SC co-ordinates with for suggesting optimal gear command along with providing the clutch command.

14. The system as claimed in claim 1, wherein the xEV is an Electric or Hybrid Electric Vehicle.