# US 11,897,341 B2 · Method and system for controlling motion of an electric vehicle (EV)

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- Number: US 11,897,341 B2
- Document ID: US11897341B2
- Status: Granted
- Kind: B2
- Application number: 17/450,996
- Filed: 2021-10-15
- Published: 2023-04-20
- Granted: 2024-02-13
- Earliest claimed: 2021-10-15
- Assignees: Mitsubishi Electric Research Laboratories, Inc.
- Applicants: Mitsubishi Electric Research Laboratories, Inc.
- Inventors: Marcel Menner; Stefano Di Cairano
- Web page: https://patentgrep.com/patent/US11897341B2

## CPC

- B60L 15/2045: {for optimising the use of energy}
- B60L 15/38: with automatic control
- B60L 15/2054: {by controlling transmissions or clutches}
- B60L 50/70: using power supplied by fuel cells (in combination with batteries B60L50/75)
- B60K 1/02: comprising more than one electric motor
- B60L 2240/12: Speed
- B60L 2240/14: Acceleration
- B60L 2240/421: Speed
- B60L 2240/423: Torque
- B60L 2240/486: Operating parameters
- B60L 2240/54: related to batteries
- Y02T 10/64: Electric machine technologies in electromobility
- Y02T 10/72: Electric energy management in electromobility
- B60L: PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES (arrangements or mounting of electrical propulsion units or of plural diverse prime-movers for mutual or common propulsion in vehicles B60K1/00, B60K6/20; arrangements or mounting of electrical gearing in vehicles B60K17/12, B60K17/14; preventing wheel slip by reducing power in rail vehicles B61C15/08; dynamo-electric machines H02K; control or regulation of electric motors H02P); SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES (electric coupling devices combined with mechanical couplings of vehicles B60D1/64; electric heating for vehicles B60H1/00); ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL (control or regulation of electric motors H02P); MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60K: ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
- Y02T: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION

## Abstract

Embodiments of the present disclosure disclose a method and a system for controlling a motion of an electric vehicle (EV). The method includes determining a velocity profile moving the EV from an initial velocity over a period of time by minimizing the energy dissipation according to an energy-loss function. The energy-loss function maps values of acceleration and velocity of the EV to energy dissipation of the EV resulting from controlling one or multiple electric motors of the EV to move the EV at corresponding acceleration and velocity values. The velocity profile is a function of time. The method further includes controlling the one or multiple electric motors of the EV to generate a torque for moving the EV according to the velocity profile.

## Claims

### Claim 1

A method for controlling a motion of an electric vehicle (EV), wherein the method uses a processor coupled to a memory storing an energy-loss function mapping values of acceleration and velocity of the EV to energy dissipation of the EV resulting from controlling one or multiple electric motors of the EV to move the EV at corresponding acceleration and velocity values, wherein the processor is configured to execute instructions stored in the memory for implementing the method, wherein the instructions, when executed by the processor carry out steps of the method, comprising:
determining a velocity profile moving the EV from an initial velocity over a period of time by minimizing the energy dissipation according to the energy-loss function, wherein the velocity profile is a function of time, and wherein the energy-loss function corresponds to a pseudo-convex function having a global minimum, and the energy-loss function is determined based on at least the model of the longitudinal motion, the predetermined torque-split function, and the predetermined motor efficiency function; and
controlling the one or multiple electric motors of the EV to generate a torque for moving the EV according to the velocity profile.

### Claim 2 (depends on 1)

The method of claim 1, wherein the controlling of the one or multiple electric motors comprises:
determining, using a model of the longitudinal motion of an EV relating vehicle acceleration and vehicle velocity with a total torque profile for the one or multiple electric motors to move the EV according to the velocity profile; and
controlling the one or multiple electric motors based on the determined torque profile.

### Claim 3 (depends on 2)

The method of claim 2, wherein the one or multiple electric motors include a first electric motor of the EV and a second electric motor of the EV, further comprising
allocating the torque profile into a first torque profile and a second torque profile based on a predetermined torque-split function, the predetermined torque-split function allocates the torque profile at least between the first electric motor and the second electric motor; and
controlling the first electric motor and the second electric motor according to the first torque profile and the second torque profile, respectively.

### Claim 4 (depends on 3)

The method of claim 3, further comprising:
determining a gear ratio for a transmission of the one or multiple electric motors using a predetermined motor efficiency function; and
controlling the transmission based on the determined gear ratio.

### Claim 5 (depends on 4)

The method of claim 4, wherein the predetermined motor efficiency function is learned using a kernel-based regression that fits data of an efficiency map of the first electric motor relating the efficiency of the first electric motor to a motor speed and a motor torque of the first electric motor.

### Claim 6 (depends on 5)

The method of claim 5, wherein the predetermined torque-split function is learned using the predetermined motor efficiency function of the one or multiple electric motors.

### Claim 7 (depends on 1)

The method of claim 1, wherein the energy-loss function is learned using a kernel-based regression that fits data of one or multiple efficiency maps of the one or multiple electric motors to the energy-loss function based on a model of drivetrain losses of the one or multiple electric motors and a model of driving losses caused by aerodynamics and rolling resistance of the EV, wherein the model of drivetrain losses corresponds to efficiencies of a motor mode of the EV and a generator mode of the EV, and wherein the motor mode corresponds to usage of electric energy for the vehicle acceleration and the generator mode corresponds to regeneration of the electric energy from the vehicle acceleration and deceleration of the EV.

### Claim 8 (depends on 1)

The method of claim 1, further comprising optimizing the energy-loss function based on a gradient-descent method.

### Claim 9 (depends on 7)

The method of claim 7, further comprising utilizing the total torque profile to operate the one or multiple electric motors in at least the motor mode and the generator mode of the EV.

### Claim 10 (depends on 1)

The method of claim 1, further comprising:
determining the initial velocity as a current velocity estimated using one or multiple sensors of the EV; and
determining a target velocity based on a motion plan of the EV and the period of time allocated for the velocity profile, wherein the target velocity is at end of the velocity profile.

### Claim 11 (depends on 10)

The method of claim 10, further comprising recomputing the velocity profile and the initial velocity at every sampling time step for the controlling of the motion of the EV.

### Claim 12 (depends on 10)

The method of claim 10, wherein the recomputing the velocity profile further comprises:
receiving a feedback signal indicative of a current state of the vehicle tracking the velocity profile; and
executing a Kalman filter to update the velocity profile for the current state of the vehicle improving a likelihood of energy efficiency according to a probabilistic measurement model including the deterministic energy loss function.

### Claim 13 (depends on 9)

The method of claim 9, further comprising determining the velocity profile based on at least one or more of:
a constraint on an average velocity of the velocity profile;
a current position and a current velocity of a leading vehicle driving ahead of the EV; and
a predicted velocity over a period of time of the leading vehicle driving ahead of the EV along with the current position and the current velocity.

### Claim 14

A system for controlling a motion of an electric vehicle (EV), comprising:
a processor coupled to a memory storing an energy-loss function mapping values of acceleration and velocity of the EV to energy dissipation of the EV resulting from controlling one or multiple electric motors of the EV to move the EV at corresponding acceleration and velocity values, wherein the processor is configured to execute stored instructions in the memory to cause the system to:
determine a velocity profile moving the EV from an initial velocity over a period of time by minimizing the energy dissipation according to the energy-loss function, wherein the velocity profile is a function of time, and wherein the energy-loss function corresponds to a pseudo-convex function having a global minimum, and the energy-loss function is determined based on at least the model of the longitudinal motion, the predetermined torque-split function, and the predetermined motor efficiency function; and
control the one or multiple electric motors of the EV to generate a torque for moving the EV according to the velocity profile.

### Claim 15 (depends on 14)

The system of claim 14, wherein to control the one or multiple electric motors, the processor is configured to execute stored instructions in the memory to cause the system to:
determine, using a model of the longitudinal motion of an EV relating vehicle acceleration and vehicle velocity with a total torque profile for the one or multiple electric motors to move the EV according to the velocity profile; and
control the one or multiple electric motors based on the determined torque profile.

### Claim 16 (depends on 15)

The system of claim 15, wherein the one or multiple electric motors include a first electric motor of the EV and a second electric motor of the EV, and wherein the processor is further configured to execute stored instructions in the memory to cause the system to:
allocate the torque profile into a first torque profile and a second torque profile based on a predetermined torque-split function, the predetermined torque-split function allocates the torque profile at least between the first electric motor and the second electric motor; and
control the first electric motor and the second electric motor according to the first torque profile and the second torque profile, respectively.

### Claim 17 (depends on 16)

The system of claim 16, wherein the processor is further configured to execute stored instructions in the memory to cause the system to:
determine a gear ratio for a transmission of the one or multiple electric motors using a predetermined motor efficiency function; and
control the transmission based on the determined gear ratio.

### Claim 18 (depends on 16)

The system of claim 16, wherein the predetermined motor efficiency function is learned using a kernel-based regression that fits data of an efficiency map of the first electric motor relating the efficiency of the first electric motor to a motor speed and a motor torque of the first electric motor, and wherein the predetermined torque-split function is learned using the predetermined motor efficiency function of the one or multiple electric motors.

### Claim 19 (depends on 14)

The system of claim 14, wherein the energy-loss function is learned using a kernel-based regression that fits data of one or multiple efficiency maps of the one or multiple electric motors to the energy-loss function based on a model of drivetrain losses of the one or multiple electric motors and a model of driving losses caused by aerodynamics and rolling resistance of the EV, wherein the model of drivetrain losses corresponds to efficiencies of a motor mode of the EV and a generator mode of the EV, and wherein the motor mode corresponds to usage of electric energy for the vehicle acceleration and the generator mode corresponds to regeneration of the electric energy from the vehicle acceleration and deceleration of the EV.