# US 2021/0331663 A1 · Electric vehicle control system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0331663 A1 - Document ID: US20210331663A1 - Status: Published application - Kind: A1 - Application number: 17/240,980 - Filed: 2021-04-26 - Published: 2021-10-28 - Earliest claimed: 2020-04-26 - Applicants: Potential Motors Inc. - Inventors: Christopher NEWTON; Marcel LEBRUN; Samuel POIRIER; Isaac BARKHOUSE; Nicholas DOWLING - Web page: https://patentgrep.com/patent/US20210331663A1 ## CPC - B60W 30/02: Control of vehicle driving stability - B60W 10/08: including control of electric propulsion units, e.g. motors or generators - B60W 10/18: including control of braking systems - B60W 10/20: including control of steering systems - B60W 10/22: including control of suspension systems - B60W 30/182: Selecting between different operative modes, e.g. comfort and performance modes - B60W 40/06: Road conditions - B60W 40/08: related to drivers or passengers - B60W 50/10: Interpretation of driver requests or demands - B60W 50/14: Means for informing the driver, warning the driver or prompting a driver intervention - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 3/092: Reinforcement learning - B60W 2050/146: {Display means} - B60W 2420/50: Magnetic or electromagnetic sensors - B60W 2420/905: the sensor being an xyz axis sensor - B60W 2520/10: Longitudinal speed - B60W 2520/105: Longitudinal acceleration - B60W 2520/14: Yaw - B60W 2520/16: Pitch - B60W 2520/18: Roll - B60W 2540/10: Accelerator pedal position - B60W 2540/12: Brake pedal position - B60W 2540/18: Steering angle - B60W 2540/215: Selection or confirmation of options - B60W 2552/00: Input parameters relating to infrastructure - B60W 2552/35: Road bumpiness, e.g. potholes - B60W 2552/40: Coefficient of friction - B60W 2555/20: Ambient conditions, e.g. wind or rain - B60W 2720/10: Longitudinal speed - B60W 2720/106: Longitudinal acceleration - B60W 2720/14: Yaw - B60W 2720/16: Pitch - B60W 2720/18: Roll - G06N 3/045: Combinations of networks - G06N 20/00: Machine learning - Y02T 10/72: Electric energy management in electromobility - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - Y02T: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION ## Abstract Method and system that includes receiving data about (1) a driver's expected vehicle performance and (2) a difference between the driver's expected vehicle performance and an estimated actual vehicle performance, and based on the received data determining control signals for an electric drivetrain system to effect the driver's expected vehicle performance. A vehicle control system that incorporates one or machine learning functions to control a drivetrain that is decoupled from a driver. ## Claims ### Claim 1 A computer implemented method for assisting a human driver to control operation of a vehicle, comprising: receiving, through one or more human-vehicle control interfaces, real time driver controls input data indicating a throttle input signal and a steering angle input signal; receiving, through one or more vehicle embedded sensor systems, real-time positional state data about the vehicle; computing, based on the throttle input signal, steering angle input signal, and real-time positional state data a driver's expected vehicle performance; computing, based on the real-time positional state data, an actual vehicle performance; computing, based on the driver's expected vehicle performance and a difference between the driver's expected vehicle performance and the actual vehicle performance, control signals for an electric drivetrain system of the vehicle; applying the control signals to the electric drivetrain system to control real-time operation of the vehicle. ### Claim 2 (depends on 1) The method of claim 1 wherein the control signals include wheel torque control signals and steering angle control signals. ### Claim 3 (depends on 2) The method of claim 2 wherein the vehicle includes a suspension control system and the control signals include suspension system control signals for controlling operation of the suspension control system in real time. ### Claim 4 (depends on 2) The method of claim 2 wherein: the actual vehicle performance computed based on the real-time positional state data is based at least on data acquired by an inertial momentum unit of the embedded sensor systems and comprises: values indicating a current vehicle pitch, yaw and roll; values indicating a current vehicle velocity in three orthogonal axis; and values indicating a current vehicle acceleration in three orthogonal axis; and the driver's expected vehicle performance comprises: values indicating an expected vehicle pitch, yaw and roll; values indicating an expected vehicle velocity in three orthogonal axis; and values indicating an expected vehicle acceleration in three orthogonal axis. ### Claim 5 (depends on 2) The method of claim 2 wherein the one or more vehicle embedded sensor systems includes one or more electromagnetic (EM) wave sensors for detecting EM wave energy from an environment external to the vehicle, the method further comprising: detecting, through the one or more electromagnetic (EM) wave sensors, EM wave energy from a surface that the electric drivetrain system of the vehicle interacts with; receiving, through one or more drive system sensors, real-time drive system operating state data about an electric drive system of the vehicle; and computing road state data about the surface based on one or more of the real-time positional state data, the real-time drive system operating state data, and detected EM wave energy, wherein the control signals are computed also based on the road state data. ### Claim 6 (depends on 5) The method of claim 5 wherein the surface interface data includes road state data that includes a prediction of upcoming changes in a surface terrain for each of a plurality of contact locations between the electric drive system and the surface. ### Claim 7 (depends on 5) The method of claim 5 wherein the surface interface data includes an estimation of a grip available between a plurality of wheels of the vehicle and the surface. ### Claim 8 (depends on 5) The method of claim 5 wherein the surface interface data includes an estimation of one or more properties of water, ice or snow located on the surface. ### Claim 9 (depends on 1) The method of claim 1 comprising, prior to applying the control signals to the electric drivetrain system, predicting if execution of the control signals will result in an unsafe vehicle state, and aborting applying the control signals when an unsafe vehicle state is predicted. ### Claim 10 (depends on 1) The method of claim 1 comprising using a machine learning (ML) model to compute the control signals. ### Claim 11 A vehicle control system for assisting a human driver to control operation of a vehicle, the vehicle control system comprising one or more processors and one or more memories storing software instructions that, when executed by the one or more processors, configure the vehicle control system to perform a method comprising: receiving, through one or more human-vehicle control interfaces, real time driver controls input data indicating a throttle input signal and a steering angle input signal; receiving, through one or more vehicle embedded sensor systems, real-time positional state data about the vehicle; computing, based on the throttle input signal, steering angle input signal, and real-time positional state data a driver's expected vehicle performance; computing, based on the real-time positional state data, an actual vehicle performance; computing, based on the driver's expected vehicle performance and a difference between the driver's expected vehicle performance and the actual vehicle performance, control signals for an electric drivetrain system of the vehicle; applying the control signals to the electric drivetrain system to control real-time operation of the vehicle. ### Claim 12 (depends on 11) The vehicle control system of claim 11 wherein the control signals include wheel torque control signals and steering angle control signals. ### Claim 13 (depends on 12) The vehicle control system of claim 12 wherein the vehicle includes a suspension control system and the control signals include suspension system control signals for controlling operation of the suspension control system in real time. ### Claim 14 (depends on 12) The vehicle control system of claim 12 wherein: the actual vehicle performance computed based on the real-time positional state data is based on data acquired by an inertial momentum unit of the embedded sensor systems and comprises: values indicating a current vehicle pitch, yaw and roll; values indicating a current vehicle velocity in three orthogonal axis; and values indicating a current vehicle acceleration in three orthogonal axis; and the driver's expected vehicle performance comprises: values indicating an expected vehicle pitch, yaw and roll; values indicating an expected vehicle velocity in three orthogonal axis; and values indicating an expected vehicle acceleration in three orthogonal axis. ### Claim 15 (depends on 12) The vehicle control system of claim 12 wherein the one or more vehicle embedded sensor systems includes one or more electromagnetic (EM) wave sensors for detecting EM wave energy from an environment external to the vehicle, the method performed by the vehicle control system further comprising: detecting, through the one or more electromagnetic (EM) wave sensors, EM wave energy from a surface that the electric drivetrain system of the vehicle interacts with; receiving, through one or more drive system sensors, real-time drive system operating state data about an electric drive system of the vehicle; and computing road state data about the surface based on one or more of the real-time positional state data, the real-time drive system operating state data, and detected EM wave energy, wherein the control signals are computed also based on the road state data. ### Claim 16 (depends on 15) The vehicle control system of claim 15 wherein the surface interface data includes road state data that includes a prediction of upcoming changes in a surface terrain for each of a plurality of contact locations between the electric drive system and the surface. ### Claim 17 (depends on 15) The vehicle control system of claim 15 wherein the surface interface data includes an estimation of a grip available between a plurality of wheels of the vehicle and the surface, and the surface interface data includes an estimation of one or more properties of water, snow or ice located on the surface. ### Claim 18 (depends on 11) The vehicle control system of claim 11 wherein the method performed by the vehicle control system comprises, prior to applying the control signals to the electric drivetrain system, predicting if execution of the control signals will result in an unsafe vehicle state, and aborting applying the control signals when an unsafe vehicle state is predicted. ### Claim 19 (depends on 11) The vehicle control system of claim 11 comprising a predictive conditioner that includes a machine learning (ML) model that is trained to compute the control signals by mapping input signals that include the driver's expected vehicle performance and the difference between the driver's expected vehicle performance and the actual vehicle performance to the control signals. ### Claim 20 A vehicle comprising: an electric drivetrain system for propelling and steering the vehicle along a surface; a human-vehicle control interface that includes a throttle control input and a steering control input for receiving real time driver controls input data indicating a throttle input signal and a steering angle input signal, respectively; a plurality of vehicle embedded sensor systems for sensing real-time positional state data about the vehicle; and a vehicle control system for assisting a human driver to control operation of the vehicle, the vehicle control system comprising one or more processors and one or more memories storing software instructions that, when executed by the one or more processors, configure the vehicle control system to perform a method comprising: receiving real-time throttle input signals and steering angle input signals from the human-vehicle control interfaces; receiving real-time positional state data about the vehicle from the vehicle embedded sensor systems, real-time positional state data about the vehicle; computing, based on the throttle input signal, steering angle input signal, and real-time positional state data a driver's expected vehicle performance; computing, based on the real-time positional state data, an actual vehicle performance; computing, based on the driver's expected vehicle performance and a difference between the driver's expected vehicle performance and the actual vehicle performance, control signals for an electric drivetrain system of the vehicle; applying the control signals to the electric drivetrain system to control real-time operation of the vehicle.