CAUSAL ANALYTICS FOR POWERTRAIN MANAGEMENT

Abstract

Methods for management of a powertrain system in a vehicle. The methods receive data or signals from multiple sensors associated with the vehicle. Optimum thresholds for classifications of the sensor data can be changed based injecting signals into the powertrain system and receiving responsive signals. Expected priorities for the sensor signals can be altered based upon attributes of the signals and confirming actual priorities for the signals. Look-up tables for engine management can be modified based upon injecting signals into the powertrain system and measuring a utility of the responsive signals. The methods can thus dynamically alter and modify data for powertrain management, such as look-up tables, during vehicle operation under a wide range of conditions.

Claims

1. A method for automatically generating and applying causal knowledge to the management of a powertrain system in a vehicle, comprising steps of: injecting randomized controlled signals in powertrain control decisions; ensuring the signal injections occur within normal operational ranges and constraints; receiving data from a plurality of sensors and electronic control units associated with the vehicle in response to the signal injections and parsing those data into system responses associated with the injected signals; computing confidence intervals about the causal relationships between powertrain signals and a measured utility based on the signal injections and received data; and selecting optimal signals for the powertrain control decisions based on the computed confidence intervals about their effects and expected utility.

2. The method of claim 1, wherein the powertrain control signals comprise actions, queue orders, sensor calibrations, or any combination thereof.

3. The method of claim 1, wherein the normal operational ranges comprise a multidimensional space of possible control states generated based on control information and operational constraints.

4. The method of claim 1, wherein the parsing of sensor data in space and time is continuously adjusted to maximize variance across signal injections.

5. The method of claim 1, wherein the causal knowledge is continuously updated throughout the lifetime of the vehicle to account for dynamic effects such as aging, component replacement, or changing environments.

6. The method of claim 1, wherein the causal knowledge is accrued collaboratively and shared across vehicles.

7. The method of claim 1, wherein at least one placebo signal is injected in the powertrain control decisions as a mean to control a quality of the causal learning generated by monitoring whether a confidence interval around the causal effect of the placebo signal overlap with 0.

8. The method of claim 1, wherein the utility function changes over time depending on a driver's preference, road condition, atmospheric condition, traffic condition, vehicle condition, or any combination thereof.

9-11. (canceled)

12. The method of claim 1, wherein the signal injections are changes in the classification of the state of the powertrain sub systems.

13. The method of claim 12, wherein the powertrain management relies on a look-up table in an engine control unit to optimize control decisions based on the inferred state of the powertrain sub systems.

14-22. (canceled)

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings are incorporated in and constitute a part of this specification and, together with the description, explain the advantages and principles of the invention. In the drawings,

[0010] FIG. 1 is a diagram illustrating a causal analytics system for powertrain management in a vehicle;

[0011] FIG. 2 is a block diagram of software modules and core processes for the causal analytics system;

[0012] FIG. 3 is a flow chart of a search space method for the system;

[0013] FIG. 4 is a flow chart of a signal injection method for the system;

[0014] FIG. 5 is a flow chart of a continuous learning method for the system; and

[0015] FIG. 6 is a flow chart of a memory management method for the system.

DETAILED DESCRIPTION

[0016] Embodiments of this invention include methods and systems for implementing experimental trials on powertrains in motor vehicles or other transportation vehicles. Variations in control parameters are selected to be introduced into powertrains to improve the value of learning from each experimental trial and promoting improved powertrain performance by computing expected values for both learning and performance. Those trials are used to manage the opportunity costs and constraints that affect the introduction of variations in powertrain control parameters and the generation of valid data that can be attributed to particular variations in those parameters.

[0017] The methods enable real-time fine-tuning of powertrain look-up tables that are initially calibrated for a broad range of use conditions. Most cars are used the vast majority of the time in a very specific geographic location associated with various unique characteristics including fuel composition, weather, elevation/air density, road types and conditions, congestion levels, and at fairly predictable times of the day. Experimental signal injection allows the vehicle control unit to continuously learn the optimum settings based on local and real-time conditions resulting in performance improvements over baseline look-up tables. The approach can also be used to automate initial vehicle calibration beyond what is done today at a domain/functional group level (e.g. powertrain, body control, safety) rather than at an individual function level (e.g. active fuel injection, Anti-lock Braking System), resulting in significant cost savings and shorter development time. Causal knowledge being a highly-transferable type of learning, collaborative learning among vehicles can further be used to reduce the development time and requirements ahead of launching a new model as well as eliminating the trade-off between local and global optimization, for example by allowing vehicles to share knowledge about optimum powertrain management under a particular load or in a particular or geographic area. Unlike other big data approaches, these methods rely on a relatively small data size, commensurate with existing data tables in vehicle powertrain systems, and therefore require relatively low computing power and capability, a significant source of power draw in modern vehicles.

[0018] FIG. 1 is a diagram illustrating a causal analytics system for powertrain management in a vehicle 10. The system includes a processor 20 receiving inputs from multiple sensors such as sensors 12, 14, and 16 within or associated with vehicle 10. Processor 20 can also receive or access on-board data 18 stored within vehicle 10 and external data 30 received from a source external to vehicle 10. Processor 20, under software or firmware control, provides output data for a powertrain management module 22, which can include look-up tables 24 for powertrain management and control, and can provide for data storage 26 for storage of historical data relating to powertrain management. Processor 20 can also provide for data transmission 28, including wireless transmission of powertrain management or other data to other vehicles 32 and external locations 34.

[0019] FIG. 2 is a block diagram of software modules and core processes for the causal analytics system and for execution by processor 20. The system includes the following modules: an objective goals (optimization knowledge and goals) module 40; a control system hard constraints module 42; a normative operational data module 44; a minimum/maximum temporal reach data module 46; a minimum/maximum spatial reach data module 48; a deep causal learning (DCL) algorithmic processes module 50; an operational protocol module 52; an operational/sensor data module 54; a causal knowledge module 56; and a continuous optimization module 58.

[0020] The DCL core processes 60 include the following: a generation of experimental units process 62; a treatment assignment process 64; an explore/exploit management process 66; a baseline monitoring process 68; a data inclusion window management process 70; and a clustering of experimental units process 72.

[0021] FIG. 3 is a flow chart of a search space method for the modules and processes in sections 74 and 76 of the system. The search space method includes the following steps: receive control information (including costs) 90; construct multidimensional space of all possible control states 92; constrain space of potential control spaces 94; determine normal/baseline sampling distribution 96; determine highest utility sampling distribution 98; and automated control selection within constrained space 100.

[0022] FIG. 4 is a flow chart of a signal injection method for the modules and processes in sections 78 and 80 of the system. The signal injection method includes the following steps: receive set of potential signal injections 102; compute spatial and temporal reaches of signal injections 104; coordinate signal injections in space and time 106; implement signal injections 108; collect response/sensor data 110; and associate response data with signal injections 112.

[0023] FIG. 5 is a flow chart of a continuous learning method for the modules and processes in sections 80 and 82 of the system. The continuous learning method includes the following steps: receive set of potential signal injections 114; receive current belief states 116; compute learning values for signal injections 118; receive costs for signal injections 120; select and coordinate signal injections 122; implement signal injections 124; collect response/sensor data 126; and update belief states 128.

[0024] FIG. 6 is a flow chart of a memory management method for the modules and processes in section 84 of the system. The memory management method includes the following steps: receive set of historical clusters 130; receive set of historical signal injections 132; and compute temporal stability of signal injections for current clusters 134. If the signal injections from step 134 are stable 136, then the memory management method executes the following steps: receive set of historical external factor states 138; compute stability of signal injections versus external factors states 140; select two states to split cluster across 142; and update set of historical clusters 144.

[0025] Table 1 provides descriptions of key processes for the system. Tables 2-5 provide exemplary use cases for applying the methods of the causal analytics system.

[0026] The signal injections are changes in powertrain controls such as variables and parameters relating to powertrain management and control. Tables 2-5 provide examples of variables that can be leveraged for signal injection, the sensors that can be monitored to measure utility, the utility functions that can be used to drive the optimization of control decisions, and external factors that can influence the optimality of those decisions. The responses to signal injection are typically powertrain safety and performance measures resulting or related to the changes in powertrain controls from the signal injection. For example, a particular value can be inserted as a signal injection into the Engine Control Unit to the subcomponents of the powertrain, and the inserted value can be tracked within a normal or typical range. Also in this example, the value can be continuously changed and re-inserted in an iterative manner as a signal injection based upon the responses to previous values of the signal in order to find the optimal value under particular conditions. The signal injections typically occur while a vehicle is in operation but can also occur within the vehicle at other times. The temporal and spatial reaches of signal injections relate to, respectively, when and where to measure the response signals to those signal injections that are used for computing causal relationships. The cost of signal injection typically relates to how the signal injection affects vehicle performance, for example signal injection can result in lower vehicle performance, and is controlled by the specified experimental range. The queue for signal injection involves the order and priority of signal injections and relies on blocking and randomization to guarantee high internal validity at all times, even when optimizing utility. The utility of responses to signal injection involves the effectiveness of the signal injections or other measures of utility.

TABLE-US-00001 TABLE 1 Key Process Description and Examples Objective goals Power, torque, top speed, fuel economy, gas and particulate emissions, thermal efficiency, volumetric efficiency, brake power, engine life, noise/vibrations, combustion stability, battery life Control systems hard Physical constraints associated with existing design (e.g., crank constraints angle range, maximum cylinder pressure, rev limit), operating ranges prone to knocking (e.g., low fuel-air ratio) Normative operational data Existing engine calibration look-up tables. Signal injection is designed to mimic normal operations until variance in utility is detected and exploited Minimum/maximum temporal Time delays between fuel injection, spark ignition, pressure rise reach and gas exhaust, battery/capacitor charge/discharge time Minimum/maximum spatial Reaction and oxidation of particulate matter/soot along catalytic reach exhaust path Generation of experimental Identify stochastically equivalent spatial-temporal units, i.e. units where the experimental conditions are equivalent and where the units' duration is pareto optimize to minimize carry-over effects while maximizing statistical power Treatment assignment Random and blocked assignment of control variations with assignment frequencies following normal operations until variance in utility is detected and exploited Explore/exploit management Probability matching of confidence interval (CI) overlaps to explore frequencies where smaller overlaps between CIs result in more frequent use of the level associated with the highest utility Baseline monitoring Baseline is monitored in real-time through periodic random assignment to provide an unbiased measure of utility improvement Data inclusion window Confidence intervals are computed over a pareto optimum data inclusion window that provides a trade-off between precision (narrow confidence intervals) and accuracy as conditions change over time, for example as the engine ages. Clustering of experimental Signal injection and treatment assignment can be optimized units conditionally based on external factors outside of experimental control, for example altitude (oxygen content), weather (external temperature), engine temperature (cold start), load, fuel composition

TABLE-US-00002 TABLE 2 Real-time control and optimization of internal combustion engine for fuel consumption, performance, or other factors Controls Sensors Figures of merit External factors Variable spark timing, Crankshaft position Power, Fuel composition, Variable valve timing, sensor, Torque, Engine speed, Variable compression In-cylinder pressure, Top speed, Load, ratio, Exhaust gas Fuel economy, Engine temperature Variable displacement temperature, Particulate emissions, (cold start vs warmed (cylinder Engine, oil, coolant Thermal efficiency, up), deactivation), temperature, Volumetric efficiency, Friction losses Variable air-fuel ratio, Oxygen sensor, Heat release rate, (including tire Variable idle speed Combustion phase Engine wear/life pressure), (including stop/start), indicator Weather (including Variable boost external temperature), (turbocharger) Location (including altitude and oxygen content)

TABLE-US-00003 TABLE 3 Real-time control and optimization of homogeneous charge compression ignition (HCCI) Controls Sensors Figures of merit External factors Variable compression Knock sensor, Combustion stability, Fuel composition ratio, Crankshaft position Fuel sensitivity, (including fuel Variable air-fuel sensor, Power, additives), mixture preparation, In-cylinder pressure Torque, Engine speed, Variable air-fuel ratio, Exhaust gas Top speed, Load, Variable spark timing, temperature, Fuel economy, Engine temperature Variable valve timing, Engine, oil, coolant Particulate emissions, (cold start vs warmed Variable intake air temperature, Thermal efficiency, up), pressure and Oxygen sensor, Volumetric efficiency, Friction losses temperature, Combustion phase Heat release rate, (including tire Variable exhaust gas indicator Engine wear/life pressure), recirculation, Weather (including Variable idle speed external temperature), Variable boost Location (including (turbocharger) altitude and O2 content)

TABLE-US-00004 TABLE 4 Real-time control and optimization of reactivity- controlled compression ignition (RCCI) Controls Sensors Figures of merit External factors Variable number of Knock sensor, Combustion stability, Fuel composition injections, Crankshaft position Fuel sensitivity, (including fuel Variable injection sensor, Power, additives, fuel blends), timing and duration, In-cylinder pressure, Torque, Engine speed, Variable compression Exhaust gas Top speed, Load, ratio, temperature, Fuel economy, Engine temperature Variable air-fuel Engine, oil, coolant Particulate emissions, (cold start vs warmed mixture preparation, temperature, Thermal efficiency, up), Variable air-fuel ratio, Oxygen sensor, Volumetric efficiency, Friction losses Variable spark timing, Combustion phase Heat release rate, (including tire Variable valve timing, indicator Engine wear/life pressure), Variable intake air Weather (including pressure and external temperature), temperature, Location (including Variable exhaust gas altitude and O2 recirculation, content) Variable idle speed Variable boost (turbocharger)

TABLE-US-00005 TABLE 5 Real-time control and optimization of hybrid powertrain Controls Sensors Figures of merit External factors Variable mode: Battery charge meter, Power, HVAC load electric only, Fuel level sensor, Torque, Engine temperature hybrid/electric assist, Throttle position, Top speed, (cold start vs warmed battery charging, Engine speed, Fuel economy, up), regenerative braking, Efficiency of the Particulate emissions, Weather (including Variable use of internal combustion Battery life, external temperature), electric motor, engine Thermal efficiency, Location (including Variable use and Volumetric efficiency, altitude and O2 optimization of Engine wear/life content) internal combustion engine (use case 1), Variable regenerative braking

[0027] Table 6 provides an algorithm of an embodiment for automatically generating and applying causal knowledge to the management of a powertrain system in a vehicle. This algorithm can be implemented in software or firmware for execution by processor 20.

TABLE-US-00006 TABLE 6 1 inject randomized controlled signals in powertrain control decisions; provide signal injections via processor 20 into powertrain management module 22 or powertrain subcomponents 2 ensure signal injections occur within normal operational ranges and constraints 3 receive data from the plurality of sensors 12, 14, and 16 associated with the vehicle 10 and parse those data into system responses associated with injected signals 4 compute causal knowledge about the relationship between powertrain signals and measured utility 5 select optimal signals for the powertrain management module 22 based on current causal knowledge and uncertainty about expected utility

[0028] Collaborative learning can greatly improve the granularity and accuracy of causal knowledge by allowing stochastically equivalent trials across vehicles resulting in increased statistical power. Analysis of variance (ANOVA) is then used to identify dimensions where causal knowledge differs across vehicles and cluster experimental units across those dimensions.

EXAMPLES

Example 1Automated Component Calibration

[0029] A number of components in the powertrain (e.g., sensors, electronic control units) are calibrated so that the measured analog signals associated with those components are properly interpreted to accurately represent their current state of operation. Such calibration typically includes classifying combinations of sensor readings into different classes indicative of a particular state or goal, for example Good/Fair/Bad or Sport/Comfort/Eco. Aging, vehicle-to-vehicle variations and environmental factors all contribute to reducing the classification accuracy for a given vehicle at a given time. Accuracy is typically evaluated and reported as a confusion matrix that quantifies type I and II error rates (i.e. false positive and false negatives). The outcome of component calibration is the determination of an optimum parameterknown as the criterion for classification (beta) in signal detection theorywhich can be thought of as the optimum threshold value that delimitates two distinct classes based on the received signal value and minimizes the rate and/or cost of misclassification.

[0030] In today's distributed Electronic/Electrical (E/E) architecture, feedback loop control systems are used whenever possible to continuously maintain high classification accuracy for individual components. Such strategy works well when accuracy itself is directly knowable (i.e. the delta between the desired state and the actual state of the component is measurable) and there is a direct one-to-one causal relationship between controls (e.g. gain) and accuracy. As the E/E architecture migrates toward a more centralized approach, characterization and optimization of classification accuracy from multi-modal sensor data will become more ambiguous due to the increase in system complexity and interconnectivity. Provided that the utility of accurate versus inaccurate classification can be measured through safety, performance or other utility metrics, the present method continuously perturbs the value of beta within an operationally acceptable range (beta +/dbeta, dbeta<<beta) and measures its impact on utility over time. Based on this learning, it continuously recalibrates the optimum value for beta to maximize classification accuracy when knowable or utility when accuracy itself isn't directly measurable and must be inferred. Such optimization can be conducted even under non-stationarity conditions, e.g. sensor aging, changing atmospheric conditions or changing goals with different false positive and false negative costs. Thus, while vehicles may start with pre-programmed rules and models to interpret various sensor inputs, the present approach continuously improves on those rules and models over the life of the vehicle as well as collaboratively across vehicles by fine-tuning all classification criteria across more granular states of world.

[0031] Example: the measured signal corresponding to Signal absent (or state #1) and the measured signal corresponding to Signal present (or state #2) can overlap due to noise in the signal. The noise distribution, the center value of the measured signal, and the costs associated with Miss and False alarm may change over time, resulting in different optimum values for the classification criterion beta. By continuously varying beta through small perturbations (i.e. signal injections) and measuring utility, the criterion value can continuously be re-optimized.

Example 2Automated Queue Prioritization

[0032] Under operations, modern vehicles ECUs (Engine/Electronic Control Unit) receive many signals from multiple components and sensors in the powertrain. Sometimes these signals may be uncertain or contradictory when they carry information indicating conflicting goals or cumulative goals that exceed the available resources. In these situations, the ECU needs to determine which signals receive priority over others in driving decision making based on their attributes and other external factors. Pre-defined general rules can be used to prioritize certain input signals over others, for example signals related to safety receive priority over signals related to performance. Large subsets of input signals may still be perceived as having equal priority under these general rules because further prioritization is contingent on the specific state of the world at that particular instant. Within each of these pools of seemingly equal priority signals having different attributes and addressing different goals, the present method randomly assigns different levels of priority to different signals. Over time, it develops knowledge about the causal relationships between signal attributes and utility of prioritizing such signals under various circumstances thus enabling improved prioritization of input signals conditional on their attributes as well as other external factors (e.g., operational goals, load, weather, and other factors). Maximizing utility in this case can generally be defined as minimizing opportunity cost, i.e. maximizing the benefits (including reducing risk) associated with prioritizing certain signals given the available resources. A common example for this sort of problem is predictive maintenance: conducting maintenance too early ties up resources (human, financial, material) that would be better deployed elsewhere while conducting maintenance too late can be very resource consuming and costly (loss of productivity).

[0033] Example: the ECU receives a number of input signals from 8 sensors. Pre-determined criteria are used to pool these signals into a Highest Priority, Middle Priority, and Lowest Priority. Within each pool, multiple signals of seemingly equal priority may compete for attention and resources. By continuously altering their priority (i.e. their order in the scheduled queue) based on their attributes/characteristics, the system learns which ones of those attributes and characteristics are most indicative of priority given the current level of resources, external conditions, and possibly other factors. While the initial criteria to define the three main priority pools may be general enough to apply under all driving conditions (e.g., safety vs. performance), criteria used within each pool are likely to be conditional on a number of dynamic factors.

Example 3Automated System Optimization

[0034] Optimum powertrain management today is achieved through extensive testing under a wide variety of conditions to try and develop exhaustive look-up tables that cover any and all driving conditions a driver may encounter. In practice, most vehicles will encounter only a very small subset of those driving conditions, and within the conditions actually sampled the pre-defined look-up tables may lack the granularity (across existing dimensions) or the dimensionality (across additional factors) necessary to further optimize operations for performance, reliability, comfort, and safety. In addition, the optimum look-up table for engine management is likely to evolve from beginning to end of life of the vehicle due to component aging. The present method continuously experiments on combinations and timing of system controls to learn their effects on utility, and in effect continuously re-estimate the local gradient of the response surface associated with the pre-calibrated look-up table. This knowledge can in turn be used to seamlessly optimize powertrain operations in real-time even when subject to significant changes in both task and environment.

[0035] Practical implementation can be accomplished in a number of ways. The least disruptive and least sophisticated approach consists in not changing the look-up table, which is typically stored on the ECU's firmware, and rather to experiment on which setting is selected within the existing table (e.g. pick the nearest-neighbor to the recommended setting). The next approach consists in storing multiple variations of the look-up table on the firmware and to experiment on which table is optimum for driving decision making. As RAM memories and over-the-air programming become more mainstream and enable live tuning of ECU's look-up tables, it is now possible to experiment on individual look-up table values and continuously update the entire table. Finally, the most disruptive and sophisticated implementation is to self-generate the look-up table as causal knowledge accumulates and forms the new basis for decision making.

[0036] Example: a vehicle has a pre-defined look-up table for engine management that was develop by the manufacturer after years of testing. The table represents the average optimum setting given all possible driving conditions a driver may encounter through the life of the vehicle. At any particular instant though, such settings may be sub-optimal given the available operational range. By continuously varying the recommended setting through small perturbations (i.e. signal injections) and measuring utility, the look-up table can be continuously updated. In addition, different look-up tables can be developed through clustering corresponding to different driving conditions or state of the vehicle.