Patent Description:
EVs are vehicles powered by electrical energy, which is a renewable energy source. Improving energy efficiency improves an EV's range and make an EV cheaper to operate, more environment-friendly, and attractive to a broader range of users. An EV may use a battery pack and one or multiple electric motors for propulsion instead of an internal combustion engine. The battery pack stores electrical energy that powers the one or multiple motors to drive the EV. Nowadays, the EVs are gaining popularity due to their potential to reduce operational costs of automobiles as well as to reduce pollutant emissions of the automobiles. In some cases, the EV may correspond to a hybrid electric vehicle (HEV) that uses an internal combustion engine in combination with one or multiple electric motors. The dynamics of the internal combustion engine and the dynamics of the electric motors may vary. For instance, the electric motors may produce high torque, whereas the internal combustion engine may reach that high torque after gaining speed. The combustion engine may be slower than the electric motors, which may constrain control operation of the EV.

The EV may use a transmission, such as a continuously variable transmission (CVT) that converts power supplied by the one or multiple electric motors to momentum for driving wheels of the EV. The CVT is a type of transmission that seamlessly changes a gear regardless of the speed of the EV. However, such transmission in the EV may impact efficiency of the electric motors. The efficiency of the electric motors corresponds to a difference between a mechanical power output of the electric motors and electrical energy consumed by the electric motors. Typically, the mechanical power output is lower than the electrical energy consumed due to loss of energy during conversion of the electrical energy to the mechanical energy. This loss of energy may be higher when the transmission, such as the CVT is used for changing the gear, thereby reducing the efficiency of the electric motors. Further, the efficiency of the electric motors may be impacted by different types of the electric motors and by varying torque demand for moving the EV. For instance, one of the electric motors may correspond to a powerful electric motor and another may correspond to a weaker electric motor, whose efficiencies vary based on the torque demand. The weaker electric motor may have a low torque demand and the powerful electric motor may have a high torque demand. describes a method for energy consumption prediction and multiple driving profile hypotheses. Furthermore, <CIT> discloses a method for preventing a vehicle from being rear- ended. Hereby, data of the surrounding environment state of the vehicle as well as vehicle driving, steering, and braking control is collected. describes an energy management strategy of hybrid electric vehicles. Hereby, it is a hierarchical control proposed with multiple layers, wherein in the higher layer the optimal velocity is obtained and in the lower layer the power split and gear ratio are optimized.

Accordingly, there is a need to overcome the above-mentioned problem. More specifically, there is need to develop a method and system for controlling the electric vehicle in an efficient and feasible manner.

An EV, such as a battery-powered EV may use one or multiple electric motors to operate the EV. An electric motor may have an efficiency which is a function of torque and rotational speed of the electric motor. When the electric motor operates to achieve desired torque and rotational speed for moving the EV, there may be loss of energy that impacts the efficiency of the electric motor. The impact on the electric motor efficiency may further affect an energy-efficient control of the EV.

To that end, it is an object of the invention to use an energy-loss function in order to minimize the energy loss of the EV. The energy-loss function is a cost function that may be derived from a tabulated data of an efficiency map of the electric motor. The efficiency map may include a contour plot that determines the maximum efficiency of the electric motor for any combination of torque and a rotational speed of the electric motor. The cost function may include a pseudo-convex loss function that may be optimized for an energy-efficient control of the EV.

In particular, the pseudo-convex loss function may include energy losses of the EV that may correspond to velocity and acceleration of the EV. The energy losses include amount of dissipated energy that may not be recuperated. For example, the energy losses by the one or multiple electric motors resulting from efficiencies are dissipated and are irrecoverable. The energy losses of the EV may also include driving losses and electric motor losses of the EV. The driving losses may correspond to energy losses resulting from rolling resistance or from aerodynamic drag while driving the EV. The electric motor losses correspond to energy losses resulting from operation of the one or multiple electric motors by efficiencies of the electric motors. In some cases, energy used to accelerate the EV may be recuperated when the EV decelerates by operating the one or multiple electric motors in a generator mode. The generator mode corresponds to regenerating energy through deceleration of the EV.

It is also an objective of some embodiments to optimize the efficiency by using degrees of freedom of the EV. In particular, the efficiency of the one or multiple electric motors may be optimized by using the degrees of freedom. The degrees of freedom herein correspond to parameters of the EV, such as torque-split ratio, transmission gear ratio and/or velocity profile of the EV. The torque-split ratio corresponds to allocation of corresponding torque among the one or multiple electric motors. For example, an EV with four in-wheel electric motors may allocate or split a total torque demand among the four electric motors of the EV. The transmission gear ratio corresponds to a gear ratio for controlling transmissions of the EV. The velocity profile corresponds to braking and accelerating maneuvers tracked by the EV.

The invention is based on the realization that the velocity profile tracked by the EV may impact the energy losses. The invention uses the energy-loss function to compute the velocity profile for minimizing the energy loss of the EV. The velocity profile may include an initial velocity (i.e., a current velocity) of the EV and a target velocity for the EV. The current velocity may be determined using one or multiple sensors of the EV. The target velocity may be determined based on a motion plan of the EV and a period of time allocated for the velocity profile. In some embodiments, the velocity profile may be determined 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 the current position, the current velocity, and a predicted velocity over a period of time of the leading vehicle driving ahead of the EV. The velocity profile and the initial velocity may be recomputed at every sampling time step of an implementation of the energy-efficient control of the EV.

According to the invention, the gear ratio may be determined using a predetermined motor efficiency function. The predetermined motor efficiency function may be learned using a kernel-based regression that fits data of the efficiency map of one electric motor relating the efficiency of the electric motor to a motor speed and a motor torque. The kernel-based regression may also fit 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 the aerodynamics and rolling resistance of the EV. The model of drivetrain losses includes losses corresponding to efficiencies of using energy and regeneration of energy from the acceleration and deceleration of the EV. More specifically, the kernel-based regression learns the pseudo-convex loss function having a global minimum, i.e., a smallest overall value in the data of the one or multiple efficiency maps. In some embodiments, the global minimum may be determined using a gradient-descent method. The gradient-descent method is an iterative optimization approach.

Additionally or alternatively, some embodiments may use a predetermined torque-split function for allocating the total torque among the one or multiple motors. In some embodiments, the predetermined torque-split function may be learned using the predetermined motor efficiency function of the one or multiple electric motors.

In some embodiments, a torque profile for the one or multiple electric motors may be determined for moving the EV according to the velocity profile. The torque profile may be determined using a model corresponding to a longitudinal motion of the EV. The model of longitudinal motion relates the acceleration and velocity of the EV with a total torque of the EV. The total torque may be generated according to the torque profile by controlling the one or multiple electric motors. In some cases, the torque profile may split for each of the one or multiple electric motors. For instance, the torque profile may split into a first torque profile for a first electric motor of the one or multiple electric motors and a second torque profile for a second electric motor of the one or multiple electric motors.

Additionally or alternatively, the energy-loss function may be derived from at least the predetermined motor efficiency function, the predetermined torque-split function and the model of the longitudinal motion of the EV.

Additionally or alternatively, the energy-loss function may be optimized using a gradient-descent method. The gradient-descent method is an iterative optimization approach for determining a local minimum, i.e., a suboptimal value of the energy-loss function. In some embodiments, the energy efficiency of the EVs may be controlled based on a sequence of control inputs and an automatic feedback control mechanism for the EV. The sequence of control inputs may correspond to torque and gear ratio commands for the energy-efficient control of the EV. The automatic feedback control may include using a feedback signal that includes a sequence of measurements that may be indicative of states corresponding to the sequence of control inputs. In some example embodiments, a feedback controller may be to used to determine, at each control step, a current control input of the sequence of control inputs for controlling the energy efficiency of the EV based on the feedback signal. The feedback signal may include a current measurement of a current state of a corresponding sequence of control inputs. The feedback controller may apply a control policy for transforming the current measurement into the current control input based on current values of control parameters in a set of control parameters of the feedback controller. Further, a state of the feedback controller defined by the control parameters may be updated in iterative manner by a Kalman filter that uses a prediction model for predicting values of the control parameters subject to process noise. The predicted values may be updated based on a measurement model based on the sequence of measurements. The updated predicted values are used for determining the current values of the control parameters that explain the sequence of measurements according to a performance objective.

Additionally or alternatively, in some embodiments, the Kalman filter is used to update the velocity profile using the energy loss function. Specifically, in response to receiving a feedback signal indicative of a current state of the vehicle tracking the velocity profile, some embodiments execute 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 energy loss function. This is advantageous because the energy loss function is deterministic, while the vehicle control is probabilistic in nature. Using the probabilistic measurement model including the deterministic energy loss function modified, for example, with probabilistic noise can reflect the nature of the control, improve the efficiency of determining or updating the velocity profile, and consider imperfections of estimation of the energy loss function.

Accordingly, one embodiment discloses a computer-implemented method for controlling a motion of an electric vehicle. 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 and/or one or multiple transmissions of the EV to move the EV at corresponding acceleration and velocity values. The processor is configured to execute stored instructions in the memory for implementing the method. The instructions, when executed by the processor carry out steps of the method that include 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 controlling the one or multiple electric motors and/or the one or multiple transmissions of the EV to generate a torque for moving the EV according to the velocity profile.

Accordingly, another embodiment discloses a system for controlling a motion of an electric vehicle. The system includes 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. The processor is configured to execute stored instructions in the memory 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 control the one or multiple electric motors of the EV to generate a torque for moving the EV according to the velocity profile.

In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, to one skilled in the art that the present disclosure may be practiced without these specific details. In other instances, apparatuses and methods are shown in block diagram form only in order to avoid obscuring the present disclosure.

As used in this specification and claims, the terms "for example", "for instance", and "such as", and the verbs "comprising", "having", "including", and their other verb forms, when used in conjunction with a listing of one or more components or other items, are each to be construed as open ended, meaning that that the listing is not to be considered as excluding other, additional components or items. The term "based on" means at least partially based on. Further, it is to be understood that the phraseology and terminology employed herein are for the purpose of the description and should not be regarded as limiting. Any heading utilized within this description is for convenience only and has no legal or limiting effect.

<FIG> illustrates an environment 100A of a system <NUM> for controlling motion of an electric vehicle (EV) <NUM>, according to embodiments of the present disclosure. The EV <NUM> may correspond to a battery-operated EV, such as a four-wheeled electric vehicle 102A (e.g., an electric car), a two-wheeled electric vehicle 102B (e.g., an electric bike) or the like. The EV <NUM> may be operated by one or multiple electric motors. Each of the one or multiple electric motors may exhibit a corresponding efficiency that varies as a function of a torque and a rotation speed of corresponding one or multiple electric motors. To that end, the efficiency of the corresponding one or multiple electric motors can be optimized by using a system <NUM> for energy-efficient control of the EV <NUM>.

In some example embodiments, the EV <NUM> may be connected to the system <NUM> via a network <NUM>. The network <NUM> may include a wired network, wireless network, or the like. In some other example embodiments, the system <NUM> may be integrated to a controller or a control unit (not shown) of the EV <NUM>. The system <NUM> is further described next in <FIG>.

<FIG> shows a block diagram 100B of the system <NUM> for controlling the motion of the EV <NUM>, according to some embodiments of the present disclosure.

The system <NUM> includes a processor <NUM> and a memory <NUM> storing an energy-loss function <NUM>. The energy-loss function <NUM> maps values of acceleration and velocity of the EV <NUM> to an energy dissipation of the EV <NUM>. The energy dissipation results from controlling the one or multiple electric motors of the EV <NUM> to move the EV <NUM> at corresponding acceleration and velocity values. The processor <NUM> is configured to execute stored instructions in the memory <NUM> to cause the system <NUM> to determine a velocity profile for moving the EV <NUM> from an initial velocity over a period of time. The velocity profile is a function of time that may be determined by minimizing the energy dissipation according to the energy-loss function <NUM>.

In some example embodiments, the initial velocity is determined as a current velocity of the EV <NUM>. The current velocity may be estimated using one or multiple sensors of the EV <NUM>. The one or multiple sensors may include an accelerometer, a gyroscope, a global positioning system (GPS), a velocity sensor, a wheel speed sensor, or the like. Some embodiments are based on the realization that recomputation using sensor data can provide energy efficient motion control to the EV. To that end, the initial velocity and the velocity profile may be recomputed at every sampling time. The sampling time may correspond to a time duration that the initial velocity and the velocity profile are sampled for the energy-efficient control of the EV <NUM>. Such recomputed initial velocity and the velocity profile can improve accuracy for controlling the motion of the EV <NUM> with efficient energy consumption.

The processor <NUM> is further configured to cause the system <NUM> to control the one or multiple electric motors of the EV <NUM> to generate a torque for moving the EV <NUM> according to the velocity profile. The EV <NUM> may be moved from the initial velocity to a target velocity of the velocity profile. The target velocity may be determined based on a motion plan of the EV <NUM> and a period of time allocated for the velocity profile.

The procedure for controlling the motion of the EV <NUM> using the system <NUM> is described further with reference to <FIG>.

<FIG> shows a schematic diagram depicting a procedure <NUM> for controlling the motion of the EV <NUM>, according to some embodiments of the present disclosure.

In some example embodiments, the system <NUM> may obtain motor efficiency data <NUM> of the one or multiple electric motors of the EV <NUM>. The motor efficiency data <NUM> may be obtained from a dynamometer test <NUM> of the one or multiple electric motors of the EV <NUM>. The dynamometer test <NUM> may correspond to a set of experiments where the one or multiple electric motors are tested at a specific constant speed and a specific constant torque, a high acceleration aggressive driving schedule (US06) test, an Urban Dynamometer Driving Schedule (UDDS) test, or the like. The motor efficiency data <NUM> may be used for learning a pseudo-convex loss function <NUM>. The pseudo-convex loss function <NUM> is a cost function that may be obtained from driving loss data <NUM>. The driving loss data <NUM> may be obtained from a vehicle model <NUM> corresponding to driving losses of the EV <NUM> or from testing the EV <NUM> on a testbed. The driving losses include energy losses resulting from an aerodynamic drag and a rolling resistance of the EV <NUM>.

In some embodiments, the pseudo-convex loss function <NUM> includes both driving modes of using energy for accelerating the EV <NUM> and regenerating energy during decelerating the EV <NUM>. The pseudo-convex loss function <NUM> includes a pseudo-convex shape that enables differentiability of the energy losses in the motor efficiency data <NUM>, which makes the system <NUM> feasible for a real-time energy-optimal control <NUM> of the EV <NUM>.

To that end, the system <NUM> may minimize the energy losses of the EV <NUM> using velocity profile and enable the energy-optimal control <NUM>. The minimization of the energy losses based on the velocity profile is described further with reference to <FIG>.

<FIG> shows a schematic diagram depicting of a procedure <NUM> for controlling a motion of the EV <NUM>, according to some other embodiments of the present disclosure. The procedure <NUM> is performed by the system <NUM> to minimize efficiency of the one or multiple electric motors using degrees of freedom of the EV <NUM>. The degrees of freedom include a velocity profile <NUM>, a total torque profile <NUM>, and a gear ratio <NUM>. The velocity profile <NUM> is determined using the energy-loss function <NUM>. The velocity profile <NUM> is a function of time corresponding to braking and acceleration maneuvers tracked by the EV <NUM>. The acceleration is a time-derivative of velocity of the EV <NUM>, which implies values for the acceleration of the EV <NUM>.

The total torque profile <NUM> is determined using a model for the longitudinal motion <NUM> of the EV <NUM> (referred to hereinafter as "longitudinal motion model <NUM>"). The longitudinal motion model <NUM> relates the acceleration and velocity of the EV <NUM> with a total torque of the EV <NUM>.

Further, the gear ratio <NUM> may be determined from the velocity profile <NUM> and the total torque profile <NUM> using a predetermined motor efficiency function <NUM>. The gear ratio <NUM> is used for a transmission of the one or multiple electric motors. For example, the gear ratio <MAT> may be obtained by optimizing the efficiency of the electric motor i driving the transmission: <MAT> where m is a number of the transmissions of the EV <NUM>, ηi(ωi,k, τi,k) is the efficiency function of the electric motor driving the transmission i as function of the motor speed ωi,k and the motor torque τi,k.

According to the invention, the longitudinal motion model <NUM> may be determined based on Newtonian mechanics such as, <MAT>.

The longitudinal motion model <NUM> may be determined based on parameters, such as road grade that the EV <NUM> is travelling, wheels of different radii of the EV <NUM>, and the like. The longitudinal motion model <NUM> relates the total torque of the EV102 (Tk) and the velocity of the EV <NUM> (vk) with the acceleration of the EV <NUM> (ak).

In some example embodiments, the total torque profile <NUM> is allocated to the one or multiple electric motors for moving the EV <NUM> according to the velocity profile <NUM>. The total torque profile <NUM> is allocated among the one or multiple electric motors based on a predetermined torque-split function <NUM>. The total torque profile <NUM> is split into motor torque <NUM>, motor torque N, or the like for 'N' number of multiple electric motors of the EV <NUM>. For instance, the EV <NUM> may use first electric motor 316A and a second electric motor 316B. The total torque profile <NUM> may be allocated into a first torque profile for the first electric motor 316A and a second torque profile for the second electric motor 316B.

In some embodiments, the energy-loss function <NUM> may be used for driving losses such as aerodynamic drag and rolling resistance of the EV <NUM>. The aerodynamic drag may be modeled based on following equation, <MAT> where Adrag is a frontal area, cdrag is a drag coefficient, and ρ is an air density.

The rolling resistance may be modeled based on following equation, <MAT> where croll is a rolling coefficient and g is a gravitational constant.

Further, relationship between rotational speeds of the one or multiple electric motors of the EV <NUM>, the velocity of the EV <NUM>, and the gear ratio <NUM>, may be represented as <MAT> where ωi,k is a motor speed of one electric motor (i) of the EV <NUM> and qi,k is the gear ratio <NUM> between the electric motor i and wheels of the EV <NUM>.

In some cases, the EV <NUM> may use multiple electric motors. In such cases, relationship between the total torque and motor torques at the wheels of the multiple electric motors may be represented as, <MAT> where <MAT> is a torque at the wheel of the EV <NUM> resulting from the electric motor i and n is the number of electric motors.

In some embodiments, the velocity profile <NUM> and the longitudinal motion model <NUM> may be used to determine the total torque profile <NUM>. For example, the total torque profile <NUM> may result from (<NUM>) and determined by, <MAT>.

Such total torque profile <NUM> is shown as a graphical plot in <FIG>.

Furthermore, in order to control the longitudinal motion of the EV <NUM>, the system <NUM> sends torque and gear ratio commands <NUM> with the motor torques <MAT> for all electric motors (i = <NUM>,. , n) of the EV <NUM>, where qi,k denotes the gear ratio <NUM> between the electric motors i and the wheels of the EV <NUM>.

In some example embodiments, the system <NUM> may control the longitudinal motion of the EV <NUM> by using feedback signals <NUM>. The feedback signals <NUM> may include information corresponding to the EV <NUM> and a vehicle driving ahead of the EV <NUM>, which is described further with reference to <FIG> in conjunction with <FIG>.

<FIG> illustrates an exemplary scenario <NUM> depicting the EV <NUM> and a vehicle <NUM> driving ahead of the EV <NUM> on a road <NUM>, according to some embodiments of the present disclosure. The EV <NUM> and the vehicle <NUM> are driving on a road <NUM>. The vehicle <NUM> may include a fully EV, a fuel-driven vehicle, or the like that may be autonomous, a semi-autonomous or manually operated.

In some embodiments, the velocity profile <NUM> may be determined based on one or more of a constraint on an average velocity of the velocity profile <NUM>, a current position of the vehicle <NUM> and/or a current velocity of the vehicle <NUM>. In some other embodiments, the velocity profile <NUM> may be determined based on the current position, the current velocity, and a predicted velocity over a period of time of the vehicle <NUM>. To that end, the efficiency of the one or multiple electric motors may be optimized using an automatic feedback control. The automatic feedback control corresponds to the feedback signals <NUM> from the EV <NUM> that may include a current velocity of the EV <NUM>, a current position of the EV <NUM>, a current velocity of the vehicle <NUM>, the position of the vehicle <NUM> driving, and the like.

The energy-loss function <NUM> may be optimized, for example, as <MAT> where dk is a traveled distance by the EV <NUM>, LEV is the energy-loss function <NUM>, NVP is a prediction horizon of determined the velocity profile <NUM>, v(t) is a current velocity of the EV, and zk may include the feedback signals <NUM> of the vehicle <NUM> such as a traveled distance of the vehicle <NUM> ( <MAT>) or a velocity of the vehicle <NUM> ( <MAT>).

The velocity profile <NUM> may be optimized in (<NUM>) using inequality constraints (cVP (dk, vk, ak, zk) ≤ <NUM>) or equality constraints (ceq,VP (dk, vk, ak, zk) = <NUM>), or both, to attain certain properties for minimizing energy-loss of the EV <NUM>, which may be denoted as, <MAT> where <MAT> is the velocity profile <NUM> from time <NUM> through time NVP.

For example, inequality constraints (cVP (dk, vk, ak, zk) ≤ <NUM>) may be used to attain that a maximum velocity of the EV <NUM> is not exceeded with <MAT> where vmax is the maximum velocity. The inequality constraints cVP (dk, vk, ak, zk) ≤ <NUM> may also be used to attain that a maximum acceleration of the EV <NUM> is not exceeded with <MAT> where amax is the maximum acceleration. Further, the inequality constraints cVP (dk, vk, ak, zk) ≤ <NUM> may be used to attain a minimum average velocity for the EV <NUM> with <MAT> where vaverage is the average velocity of the velocity profile <NUM>. The inequality constraints cVP(dk, vk, ak, zk) ≤ <NUM> may also be used to attain that the EV <NUM> arrives to a complete stop within a stopping distance with <MAT> where dstop is the stopping distance. The inequality constraints <MAT> may also be used to maintains a safe distance between the EV <NUM> and the vehicle <NUM> with <MAT> where Thw is a headway time for maintaining a safe distance between the EV <NUM> and the vehicle <NUM>. The headway time increases the safe distance as the velocity of the vehicle <NUM> increases.

Further, the equality constraint ceq,VP (dk, vk, ak, zk) = <NUM> in (<NUM>) may be used to achieve a target velocity at any or all points within a horizontal length of the velocity profile <NUM>. In some example embodiments, the target velocity may be determined based on a motion plan of the EV <NUM> and a period of time allocated for the velocity profile <NUM>. The target velocity is at end of the velocity profile <NUM>. In order to achieve the target velocity (vtarget) at the period of time (k), the velocity profile <NUM> may be constrained as, <MAT>.

For example, if the EV <NUM> has to stop at the target velocity of the velocity profile <NUM>, then the velocity profile <NUM> is constrained as, ceq,VP(dk, vk, ak) = <NUM> - vNVP = <NUM>. If the EV <NUM> has to stop at a specific location, e.g., at a stop sign and a specific time, then the velocity profile <NUM> is constrained as, <MAT>.

The velocity profile <NUM>, the total torque profile <NUM> and the gear ratio <NUM> are shown in <FIG>, <FIG>, <FIG> and <FIG>.

<FIG> shows a graphical representation 500A depicting a graphical plot <NUM> corresponding to a velocity profile of the EV <NUM> (e.g., the velocity profile <NUM>) and a graphical plot <NUM> corresponding to a total torque profile of the EV <NUM> (e.g., the total torque profile <NUM>), according to some embodiments of the present disclosure.

As described in <FIG>, the total torque profile <NUM> may be determined from the velocity profile <NUM> using the longitudinal motion model <NUM>. Such total torque profile <NUM> is depicted in the graphical plot <NUM>.

There may be different torque demands by the multiple electric motors of the EV <NUM> when the EV <NUM> is driving on the road <NUM>. For instance, the torque demand may be high when the EV <NUM> drives to move ahead of the vehicle <NUM>. The torque demand may be low when the EV <NUM> slows down to take a turn. In some cases, the torque demand may be at peak when the EV <NUM> drives on an uphill road. In order to control the multiple electric motors of the EV <NUM>, the total torque profile <NUM> may allocated among the multiple electric motors using a torque-split function. The multiple electric motors may correspond to two electric motors, such as the first electric motor 316A and the second electric motor 316B.

For the multiple electric motors of the EV <NUM>, the total torque profile <NUM> is allocated using the predetermined torque-split function <NUM> and the velocity profile <NUM> in order to compute torque for corresponding wheels connected to the multiple electric motors <MAT>. The torque for the corresponding wheels may be computed as, <MAT>.

The allocation of the total torque profile <NUM> among the multiple electric motors is shown in <FIG> and <FIG>.

<FIG> shows an exemplary graphical representation 500B depicting splitting of the total torque profile <NUM>, according to some embodiments of the present disclosure. The graphical representation 500B includes a graphical plot <NUM> and a graphical plot <NUM>. The graphical plot <NUM> corresponds to a speed trajectory of the EV <NUM> when the total torque profile <NUM> is split between two electric motors of the EV <NUM>, such as the first electric motor 316A and the second electric motor 316B. The graphical plot <NUM> corresponds to splitting the torque profile <NUM> into a first torque profile 508A (τ1) for the first electric motor 316A and a second torque profile 508B (τ2) for the second electric motor 316B. For instance, the first torque profile 508A includes a maximum power <NUM> kW and the second torque profile 508B includes maximum power <NUM> kW, as shown in <FIG>.

The first electric motor 316A is controlled according to the first torque profile 508A and the second electric motor 316B is controlled according to the second torque profile 508B. Each of the first electric motor 316A and the second electric motor 316B may be used for different torque demands. For instance, the first electric motor 316A may be used for high-torque demand when the EV <NUM> drives to move ahead of the vehicle <NUM>. The second electric motor 316B may be used for low torque demand when the EV <NUM> slows down to take a turn at the road <NUM>. In some cases, both the first electric motor 316A and the second electric motor 316B may be used for peak torque demand when the EV <NUM> drives on an uphill road.

In some cases, the multiple electric motors may correspond to four electric motors of the EV <NUM>. The total torque profile <NUM> is allocated among the four electric motors of the EV <NUM>, which is shown in <FIG>.

<FIG> shows an exemplary graphical representation 500C depicting splitting of the total torque profile <NUM>, according to some other embodiments of the present disclosure. The graphical representation 500C includes a graphical plot <NUM> corresponding to the total torque profile <NUM>. The total torque profile <NUM> is allocated among four electric motors of the EV <NUM> using the predetermined torque-split function <NUM>. As shown in <FIG>, the total torque profile <NUM> is split into a first torque profile for a first electric motor depicted in a graphical plot <NUM>, a second torque profile for a second torque electric motor depicted in a graphical plot <NUM>, a third torque profile for a third electric motor depicted in a graphical plot <NUM> and a fourth torque profile for a fourth electric motor depicted in a graphical plot <NUM>.

Further, the torque profile and the velocity profile are used for computing the gear ratio for transmission of the one or multiple electric motors. The computed gear ratio is represented in a graphical plot, which is shown in <FIG>.

<FIG> shows an exemplary graphical representation 500D depicting a gear ratio (e.g., the gear ration <NUM>) for transmission of the one or multiple electric motors of the EV <NUM>, according to some embodiments of the present disclosure. The graphical representation 500D includes a graphical plot <NUM> corresponding to the gear ratio <NUM> that is computed from the first torque profile depicted in the graphical plot <NUM> and the velocity profile <NUM> depicted in the graphical plot <NUM> using the predetermined motor efficiency function <NUM>.

In an example scenario, the four electric motors of the EV <NUM> may include a first electric motor of the EV <NUM> that may be a powerful electric motor, and a second electric motor of the EV <NUM> that may be a weak electric motor. The difference in power of the electric motors may affect energy consumption. When the first electric motor and the second electric motor are accelerated, energy consumption may be high. In such scenario, the acceleration of the electric motors is modulated based on the velocity profile <NUM>. The modulation of the acceleration saves energy, which is shown in a graphical representation of <FIG>.

<FIG> shows an exemplary graphical representation 500E depicting the velocity profile <NUM> for the EV <NUM>, according to some embodiments of the present disclosure. The graphical representation 500E includes a graphical plot <NUM> and a graphical plot <NUM> that describe an energy-optimal velocity profile for minimizing energy consumption by the one or multiple electric motors of the EV <NUM>. The graphical plot <NUM> includes a curve 522A corresponding to velocity profile of the EV <NUM> with no transmission for one or multiple electric motors of the EV <NUM>, a curve 522B corresponding to velocity profile when the EV <NUM> uses one electric motor, such as the first electric motor 316A of the EV <NUM> with a transmission such as continuously variable transmission (CVT), a curve 522C corresponding to velocity profile when the EV <NUM> uses the second electric motor 316B with the CVT transmission, and a curve 522D corresponding to velocity profile when the EV <NUM> uses both the first electric motor 316A and the second electric motor 316B of the EV <NUM> with the CVT transmission, for changing gear of the EV <NUM>. In a similar manner, the graphical plot <NUM> includes a curve 524A, a curve 524B, a curve 524C and a curve 524D that describe different velocity profile of the EV <NUM> with different configurations mentioned above. As shown in <FIG>, the energy-optimal velocity profile depends on configuration of the EV <NUM>, such as use of electric motor with transmission, use of the multiple electric motors in the EV <NUM> with or without transmission, or the like.

In one example scenario, the first electric motor 316A may correspond to a powerful electric motor of the EV <NUM> and the second electric motor 316B may correspond to a weak electric motor of the EV <NUM>. In some cases, the EV <NUM> may not use the CVT for changing gear of the EV <NUM>. In such cases, the first electric motor 316A may be accelerated first, as shown in the graphical plot <NUM> (e.g., the curve 522A at <NUM>-<NUM> in time-axis) and the graphical plot <NUM> (e.g., the curve 524A at <NUM>-<NUM> in time-axis). Next, the acceleration may switch to the second electric motor 316B, as shown in the graphical plot <NUM> (e.g., the curve 522A at <NUM>-<NUM> in time-axis) and the graphical plot <NUM> (e.g., the curve 524A at <NUM>-<NUM> in time-axis). Further, the acceleration may switch back to the first electric motor 316A, as shown in the graphical plot <NUM> (e.g., the curve 522A at <NUM>-<NUM> in time-axis) and the graphical plot <NUM> (e.g., the curve 524A at <NUM>-<NUM> in time-axis).

Using the velocity profile <NUM>, the EV <NUM> can save energy, such as energy savings between <NUM>-<NUM>%. For instance, the EV <NUM> accelerates from initial velocities <NUM>/h and <NUM>/h to reach target velocities, such as <NUM>/h and <NUM>/h, respectively, within the period of time, such as <NUM> seconds. The average velocity of the velocity profile <NUM> may be constrained such that distance traveled over the period of time allocated for the velocity profile <NUM> is the same.

The derivation of the energy-loss function <NUM> for the multiple electric motors of the EV <NUM> is described further with reference to <FIG>.

<FIG> illustrates a schematic block diagram <NUM> corresponding to a procedure for learning the energy-loss function <NUM> for controlling the motion of the EV <NUM>, according to some embodiments of the present disclosure.

In some embodiments, the energy-loss function <NUM> may be learned using a kernel-based regression. The kernel-based regression fits data of one or multiple electric motor efficiencies <NUM> of the one or multiple electric motors to the energy-loss function <NUM> based on a model of drivetrain losses of the one or multiple electric motors and a model of driving losses caused by aerodynamic and rolling resistance of the EV <NUM>. The model of drivetrain losses may be determined from the one or multiple efficiencies resulting from using energy and regeneration of the energy from the acceleration and deceleration of the EV <NUM>.

In some example embodiments, the one or multiple electric motor efficiencies <NUM> may be obtained from corresponding electric motor efficiency data of the one or multiple electric motors of the EV <NUM>. For instance, the one or multiple electric motor efficiencies <NUM> may be obtained from a first electric motor efficiency data 602A, a second electric motor efficiency data 602B, and an Nth electric motor efficiency data 602N of the one or multiple electric motors of the EV <NUM>.

Each of the first electric motor efficiency data 602A, the second electric motor efficiency data 602B, and the Nth electric motor efficiency data 602N may be used to determine a motor efficiency function <NUM> of the corresponding one or multiple electric motors. The motor efficiency function <NUM> is an example of the predetermined motor efficiency function <NUM>. For instance, the first electric motor efficiency data 602A is used for determining a first electric motor efficiency function 604A for the first electric motor of the EV <NUM>, the second electric motor efficiency data 602B is used for determining a second electric motor efficiency function 604B for the second electric motor of the EV <NUM>, and the Nth electric motor efficiency data 602N is used for determining an Nth electric motor efficiency function 604N for Nth electric motor of the EV <NUM>.

Further, the motor efficiency functions <NUM> of the one or multiple electric motors of the EV <NUM> may be used with a longitudinal motion model <NUM> of the EV <NUM> to determine a torque-split function <NUM>. The longitudinal motion model <NUM> corresponds to the longitudinal motion model <NUM> and the torque-split function <NUM> corresponds to the predetermined torque-split function <NUM>. In some embodiments, all of the motor efficiency functions <NUM>, the longitudinal motion model <NUM> and the torque-split function <NUM> may be used for determining the energy-loss function <NUM>.

In some example embodiments, the kernel regression learns a model corresponding to the motor efficiency functions <NUM>, the longitudinal motion model <NUM> and the torque-split function <NUM> and the energy-loss function <NUM>. The kernel regression is described further with reference to <FIG>.

<FIG> shows an exemplary graphical representation depicting a graphical plot <NUM> corresponding to a kernel function <NUM> of a kernel regression for learning a model of the energy-loss function <NUM>, according to some embodiments of the present disclosure. The kernel regression is a data-based model fitting method that uses the kernel function <NUM>. The kernel function <NUM> may use a set of training data (X) corresponding to the one of the one or multiple electric motor efficiencies <NUM> of <FIG> for learning a continuous function from the set of training data. The training data may be obtained using a grid of data for a velocity of the EV <NUM> and an acceleration of the EV <NUM>, which is further described in <FIG>.

In particular, the kernel function <NUM> relates two data points in the training dataset (X) for determining the energy-loss function <NUM>.

The training dataset (X) may be represented as, <MAT>.

The kernel function <NUM> determines yi = L(xi) for all i=<NUM>,. , N training data points. Further, using y = L(x) = k(x, X)α + α<NUM> with the kernel function <NUM>(x, X) = [k(x, x<NUM>) k(x, x<NUM>). k(x, xN)] (<NUM>), where α and α<NUM> are weights to be learned using the training data. The kernel function <NUM> may be reformulated as, <MAT>.

The weights α and α<NUM> are learnt using the following optimization problem, <MAT> <MAT> where, <NUM>=[<NUM><NUM>. <NUM>]T ∈ RN, <. > denotes an inner product, I is an identity matrix, and σ denotes noise in the training data.

More specifically, the kernel-based function <NUM> may be used as a pseudo-convex loss function having a global minimum, i.e., a smallest overall value in the data of the one or multiple efficiencies <NUM>. To that end, equation (19b) may be used for determining the global optimum (x*), based on the pseudo-convex loss function for all x belonging to X , where the set X may include operating range of the one or multiple electric motors of the EV <NUM>. For example, the motor efficiency function <NUM> (η) may be a function of a motor speed (ω) and a motor torque (τ). The motor efficiency function <NUM> may be defined as η = k([ω, τ ]T , X)α + α<NUM>.

The kernel function <NUM> enables a flexible approximation capability for representing the model of the drivetrain losses and the model of the driving losses of the EV <NUM> in an accurate manner. To that end, the kernel function <NUM> may use a radial basis function, which is a real-valued function, may be represented as, <MAT> where l is a length scale that determines a width <NUM> of the kernel function <NUM> and x<NUM> is a kernel point on the kernel function <NUM>, and v<NUM> is an output variance determining a height <NUM> of the kernel function <NUM>. The kernel function <NUM> may be useful for a gradient-based optimization as training data points, X, that are distant from x may have little impact on the gradient.

The motor efficiency function <NUM> for an electric motor i of the EV <NUM> may be represented by <MAT> where ωi,k is the motor speed of the electric motor i at time k, τi,k is the motor torque of electric motor i at time k, αη are efficiency parameters for the motor efficiency function <NUM> that may be learned using the kernel regression, Kx,Xη is a vector kernel function <NUM> with <MAT> where xi,k = [ωi,k τi,k]T is a vector comprising the motor speed ωi,k and the motor torque τi,k of electric motor i, and <MAT> for all j = <NUM>,. , ni are the training data with <MAT>.

The kernel regression may be used to obtain the efficiency parameters αη with <MAT> where KXη,Xη(Xη, Xη) is a matrix comprising a training data corresponding to the efficiency data of the electric motor i with <MAT> and <MAT> are the one or multiple efficiencies <NUM> associated with the motor speed <MAT> and the motor torque <MAT>.

Further, the kernel regression in (<NUM>) may use a constraint to obtain the motor efficiency function <NUM> of a first electric motor (e.g., the first electric motor 314A) ηi (ωi,k, τi,k) in (<NUM>) pseudo convex.

For example, the efficiency parameters may be obtained as <MAT> where <MAT> denotes a point of global optimality, ∇x denotes a gradient with respect to x, Xdom denotes a domain for which the constraint has to be satisfied, and <MAT> denotes an inner product of a first input x<NUM> and a second input x<NUM>.

The kernel regression in (<NUM>) may be used for learning the motor efficiency function <NUM> (described in <FIG>) from a training data set, which is described further with reference to <FIG>.

<FIG> shows a graphical representation <NUM> depicting a predetermined motor efficiency function <NUM>, according to the invention of the present disclosure. The predetermined motor efficiency function <NUM> is an example of the motor efficiency function <NUM> that may be learned from a training data set Xi,η <NUM> using the kernel regression method. The kernel regression uses the constraint in equation (<NUM>) to obtain a pseudo convex function for the predetermined motor efficiency function <NUM>, as shown in <FIG>. Further, the predetermined motor efficiency function <NUM> is used along with the longitudinal motion model <NUM> to determine a torque-split function, such as the torque-split function <NUM>. In some example embodiments, the torque-split function <NUM> may correspond to an interpolation function. The interpolation function is described further with reference to <FIG>.

<FIG> shows an exemplary graphical representation depicting an interpolation function <NUM> corresponding to the torque-split function <NUM>, according to some embodiments of the present disclosure. The interpolation function <NUM> may use a grid <NUM> of sample data for a velocity of the EV <NUM> and a total torque of the EV <NUM>. For each sample grid point in the grid <NUM>, an optimal torque-split ratio is determined using the predetermined motor efficiency function <NUM> of all the electric motors of the EV <NUM> and/or optimal transmission gear ratios for any or all transmissions of the electric motors of the EV <NUM>. The optimal torque-split ratio for all sample grid points in the grid <NUM> may be stored in a memory, such as the memory <NUM> the system <NUM>. The torque-split function may use the stored grid <NUM> of the velocity and the total torque of the EV <NUM> with the corresponding torque-split ratio of the electric motors of the EV <NUM>.

For a specific velocity and a total torque ([vk Tk]T) <NUM> of the EV <NUM>, the torque-split function <NUM> may determine four closest torque grid points [vlow Tlow]T <NUM>, [vlow Thigh]T <NUM>, [vhigh Thigh]T <NUM> and [vhigh Tlow]T <NUM> in the grid <NUM>, as shown in <FIG>. The closest torque grid points [vlow Tlow]T <NUM>, [vlow Thigh]T <NUM>, [vhigh Thigh]T <NUM> and [vhigh Tlow]T <NUM> are used for determining an optimal torque-split ratio for the specific velocity and total torque ([vk Tk]T ) <NUM>. The optimal torque-split ratio for the specific velocity and total torque ([vk Tk]T) <NUM> may be computed as, <MAT> where F<NUM> = FTS(Tlow, vlow) denotes a predetermined optimal torque-split ratio corresponding to grid point [vlow Tlow]T<NUM> of the grid <NUM>, F<NUM> = FTS(Tlow, vhigh) denotes a predetermined optimal torque-split ratio corresponding to grid point [vlow Thigh]T <NUM> of the grid <NUM>, F<NUM> = FTS(Thigh, vhigh) denotes a predetermined optimal torque-split ratio corresponding to grid point [vhigh Thigh]T<NUM> of the grid <NUM>, and F<NUM> = FTS(Thigh, vlow) denotes a predetermined optimal torque-split ratio corresponding to grid point [vhigh Tlow]T <NUM> of the grid <NUM>. F<NUM>, F<NUM>, F<NUM> and F<NUM> together represent a set of predetermined optimal torque-split ratios. Further, <MAT>.

For each grid point [vi ai] in the grid <NUM>, where i denotes the i-th grid point, the energy-loss is evaluated. The energy-loss may be evaluated using the energy-loss function <NUM> that include driving losses Ldrive(vi) resulting from aerodynamic drag and rolling resistance as <MAT>.

The energy-loss function <NUM> may also include motor losses Tmotor(vi, ai ) resulting from efficiencies of electric motors of the EV <NUM> (e.g., the one or multiple electric motor efficiencies <NUM>) as, <MAT> where wheel torques <MAT> of the EV <NUM> are obtained using a torque-split function (e.g., the predetermined torque-split function <NUM> that computes torque for the corresponding wheels of the EV <NUM> using equation (<NUM>)) with a total torque Tk computed in (<NUM>), and motor efficiencies ηj are obtained using (<NUM>) <MAT> for the specific grid point with <MAT>.

In this manner, the energy-loss function <NUM> may use knowledge of the predetermined torque-split function <NUM> and the predetermined motor efficiency function <NUM>. The training data for the energy-loss function <NUM> of the EV <NUM> may be represented as, <MAT>.

The energy-loss function <NUM> that is learned using the kernel regression may be computed by, <MAT> where αL and α<NUM> are the parameters to be learned, Kx,XL is a vector kernel function with <MAT> where xL = [v a]T and <MAT> are vectors comprising the training data with i = <NUM>,. , nL with <MAT>. Further, some embodiments use kernel regression in order to obtain the energy-loss parameters αL and α<NUM> with <MAT> where KXL,XL(XL, XL) is a matrix comprising the training data of the energy-loss function <NUM> with <MAT> and LEV,i are the energy-loss values associated with an operating point of the EV <NUM>, xL = [vi ai]T.

Further, the kernel regression in (<NUM>) may use a constraint to obtain a pseudo convex shape of the energy-loss function <NUM> in (<NUM>). To that end, parameters for the energy-loss function <NUM> may be obtained as <MAT> where XL,dom denotes a domain for which the constraint has to be satisfied.

An exemplary graphical representation of the energy-loss function <NUM> is described further with reference to <FIG>.

<FIG> shows an exemplary graphical representation <NUM> depicting an energy-loss function <NUM>, according to some embodiments of the present disclosure. The energy-loss function <NUM> is an example of the energy-loss function <NUM>. The energy-loss function <NUM> is represented as function of the velocity and the acceleration of the EV <NUM> as driving losses of the EV <NUM> such as the aerodynamic drag and the rolling resistance depend on the velocity and motor losses resulting from motor efficiencies (e.g., the one or multiple motor efficiencies <NUM>) depend on the velocity of the EV <NUM> and on the motor torques, which are linked to the acceleration of the EV <NUM>.

As shown in <FIG>, the graphical representation <NUM> includes energy-loss lines separated by <NUM> Watts. For example, an energy-loss line <NUM> in the graphical representation <NUM> indicates, an energy-loss of <NUM> Watts.

<FIG> shows a method <NUM> flow diagram for controlling a motion of an EV (such as the EV <NUM>), according to some embodiments of the present disclosure. The method <NUM> may be performed by the system <NUM>.

At step <NUM>, the method <NUM> includes 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. In some embodiments, the initial velocity may be estimated as a current velocity using one or multiple sensors of the EV. The velocity profile may also include a target velocity for the EV that may be determined based on a motion plan of the EV and the period of time allocated for the velocity profile. The target velocity may be at an end of the velocity profile. In some embodiments, the velocity profile may be determined 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 of the leading vehicle driving ahead of the EV along with the current position and the current velocity.

At step <NUM>, the method <NUM> includes controlling the one or multiple electric motors of the EV to generate torque moving the EV according to the velocity profile.

In some embodiments, the controlling of the one or multiple electric motors include determining, using a model of a longitudinal motion of the EV that relate 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. In some cases, the EV may use a first electric motor (e.g., the first electric motor 316A) and a second electric motor (e.g., the second electric motor 316B). In such cases, the total torque profile is split into a first torque profile and a second torque profile based on a predetermined torque-split function. The first electric motor and the second electric motor are controlled according to the first torque profile and the second torque profile, respectively.

<FIG> shows a block diagram <NUM> of a device <NUM> for controlling an operation of the system <NUM>, according to some embodiments of the present disclosure. In some embodiments, the device <NUM> may be integrated with the system <NUM>. In some other embodiments, the device <NUM> may be connected to the system <NUM> via the network <NUM>. The device <NUM> may control the system <NUM> for controlling the motion of the EV <NUM>.

The device <NUM> may include at least one processor <NUM> and a memory <NUM> having instructions stored thereon including executable modules for being executed by the at least one processor <NUM> during the controlling of the system <NUM>. The executable modules include a transceiver <NUM>, a feedback controller <NUM> and a Kalman filter <NUM>.

In one example embodiment, the memory <NUM> may be configured to store an energy-loss function <NUM>, a torque-split function <NUM> and a motor efficiency function <NUM> for an energy-efficient control of the EV <NUM>. The energy-loss function <NUM> corresponds to the energy-loss function <NUM>, the torque-split function <NUM> corresponds to the torque-split function <NUM> and the motor efficiency <NUM> corresponds to the motor efficiency function <NUM>. In an alternative example embodiment, any or all of the energy-loss function <NUM>, the torque-split function <NUM>, or the motor efficiency function <NUM> (referred to herein as the functions <NUM>, <NUM> and <NUM>) may be accessed from the system <NUM>, via the network <NUM>.

The memory <NUM> may be embodied as a storage media such as RAM (Random Access Memory), ROM (Read Only Memory), hard disk, or any combinations thereof. For instance, the memory <NUM> may store instructions that are executable by the at least one processor <NUM>. The at least one processor <NUM> may be embodied as a single core processor, a multi-core processor, a computing cluster, or any number of other configurations. The at least one processor <NUM> may be operatively connected to the memory <NUM> and/or the transceiver <NUM> via a bus <NUM>. In an embodiment, the at least one processor <NUM> may be configured to compute a velocity profile (e.g., the velocity profile <NUM>) for the EV <NUM> using the energy-loss function <NUM>, and the torque-split function <NUM>, and/or compute a gear ratio for transmission of the EV <NUM> using the motor efficiency function <NUM>.

The computed velocity profile and/or the gear ratio are used to submit a sequence of control inputs to the system <NUM> via the transceiver <NUM> for controlling a motion, such as a longitudinal motion of the EV <NUM>. The sequence of the control inputs changes an initial velocity of the EV <NUM> to track a specific velocity reference, i.e., a target velocity in the velocity profile. In some example embodiments, the sequence of control inputs may be sent as torque commands and gear ratio commands <NUM> to the EV <NUM> via the transceiver <NUM>. The transceiver <NUM> may be a Radio Frequency (RF) transceiver, or the like.

In some cases, the EV <NUM> may be driving behind another vehicle, such as the vehicle <NUM>. In such cases, the device <NUM> may control the longitudinal motion of the EV <NUM> by using a feedback signal <NUM> that may include a velocity of the EV <NUM>, a position of the EV <NUM>, a velocity of the vehicle <NUM>, a position of the vehicle <NUM>, and the like. The feedback signal <NUM> may be received by the feedback controller <NUM>. The feedback controller <NUM> is configured to determine, at each control step, a current control input for controlling the system <NUM> based on the feedback signal <NUM> including a current measurement of a current state of the system <NUM> by applying a control policy. The control policy may correspond to a method that transforms the current measurement into the current control input based current values of control parameters in a set of control parameters of the feedback controller <NUM>. The device <NUM> may determine an optimized velocity profile based on the feedback signal <NUM> and enable the system <NUM> to control the EV <NUM> using the optimized velocity profile.

In some embodiments, the Kalman filter <NUM> is configured to iteratively update a state of the feedback controller <NUM>. The state may be defined by the control parameters using a prediction model that predict values of the control parameters subject to process noise and a measurement model updating the predicted values of the control parameters based on the sequence of measurements subject to measurement noise to produce the current values of the control parameters that explain the sequence of measurements according to a performance objective of the system <NUM>, such as an energy-efficient motion control of the EV <NUM>.

In some example embodiments, the device <NUM> may be embodied within a control system of the EV <NUM>, which is described further with reference to <FIG>.

<FIG> shows a schematic block diagram <NUM> of an EV, such as the EV <NUM> with a control system <NUM> for controlling the motion of the EV <NUM>, according to some other example embodiments of the present disclosure. The control system <NUM> is embodied within the EV <NUM>. In some example embodiments, the control system <NUM> may be connected with the system <NUM> and the device <NUM>. In some other example embodiments, the control system <NUM> may be embodied with the system <NUM> and the device <NUM>.

The EV <NUM> includes a battery <NUM>, a gearbox <NUM>, a first electric motor such as an electric motor 1308A, and a second electric motor such as an electric motor 1308B. The electric motor 1308A is connected to the gearbox <NUM> for driving a front axle of the EV <NUM> (not shown). The electric motor 1308B may use a fixed gear for driving a rear axle of the EV <NUM>.

Further, the EV <NUM> may include one or multiple sensors collectively referred to as sensors <NUM>. The sensors <NUM> may be any combination of positioning sensors such as global positioning system (GPS), acceleration sensors such as inertial measurement unit (IMU), gyroscope sensors, radar sensors, cameras, or the like. In one example embodiment, the sensors <NUM> may be used to measure a velocity and a position of the EV <NUM>. In another example embodiment, the sensors <NUM> may be used to measure the velocity and position of the EV <NUM> as well as velocity and position of a leading vehicle <NUM> driving ahead of the EV <NUM>.

The control system <NUM> may use sensor measurements from the sensors <NUM> to compute a velocity profile for the EV <NUM>. The control system <NUM> may use the computed velocity profile to output motor torque for electric motor 1308A, motor torque for electric motor 1308B, and a gear ratio for the gearbox <NUM>. In particular, the control system <NUM> determines a total torque profile for the electric motor 1308A and the electric motor 1308B. The total torque profile is split between the electric motor 1308A and the electric motor 1308B. For instance, the total torque profile is split into a first torque and a second torque for corresponding wheels of the electric motor 1308A and the electric motor 1308B. The first torque and the second torque are sent as torque commands for the electric motor 1308A and the electric motor 1308B.

<FIG> shows a use case <NUM> for controlling motion of an EV <NUM> using the system <NUM>, according to some embodiments of the present disclosure. In an illustrative example scenario, the EV <NUM> and a vehicle <NUM> are driving on a road. The vhicle <NUM> is driving ahead of the EV <NUM> as shown in <FIG>. The EV <NUM> is an example of the EV <NUM> and the vehicle <NUM> is an example of the vehicle <NUM>.

The system <NUM> determines a velocity profile for the EV <NUM> to move the EV <NUM> from an initial velocity, e.g., <NUM>/h to a target velocity, e.g., <NUM>/h within a period of time, such as <NUM> seconds. In order to maintain a minimum distance as a safe distance from the vehicle <NUM>, the system <NUM> may determine the velocity profile for the EV <NUM> based on one or more constraints (e.g., inequality constraints) on an average velocity of the velocity profile, a current position and a current velocity of the vehicle <NUM>, and/or a current position, a current velocity, and a predicted velocity within the period of time, such as <NUM> seconds. For instance, the current velocity of the vehicle <NUM> may be at <NUM>/h and the predicted velocity of vehicle <NUM> is <NUM>/h. In some example embodiments, the current position and the current velocity of the vehicle <NUM> may be obtained from a feedback signal (e.g., the feedback signal <NUM>) of the EV <NUM>.

The system <NUM> then controls one or multiple electric motors of the EV <NUM> to generate a total torque profile for moving the EV <NUM> based on the computed velocity profile. The total torque profile may be split for corresponding one or multiple electric motors of the EV <NUM> using a torque-split function (e.g., the predetermined torque-split function <NUM>). The computed velocity profile may also be used to determine a gear ratio for transmission of the one or multiple electric motors of the EV <NUM> using a predetermined motor efficiency function (e.g., the predetermined motor efficiency function <NUM>). The system <NUM> then minimizes energy consumption of the EV <NUM>, while following the vehicle <NUM> and maintaining the safe distance from the vehicle <NUM>.

Such optimal control procedure of the system <NUM> may be efficient for an EV that includes multiple electric motors with different specifications, while leveraging strengths of the different electric motors to improve the overall energy-efficiency of the EV.

Also, individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. A process may be terminated when its operations are completed, but may have additional steps not discussed or included in a figure. Furthermore, not all operations in any particularly described process may occur in all embodiments. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, the function's termination can correspond to a return of the function to the calling function or the main function.

The above-described embodiments of the present disclosure may be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code may be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers. Such processors may be implemented as integrated circuits, with one or more processors in an integrated circuit component. Though, a processor may be implemented using circuitry in any suitable format.

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 including an initial velocity of the EV and a target velocity for the EV; and
controlling the one or multiple electric motors of the EV to generate a torque for moving the EV according to the velocity profile, wherein the energy-loss function corresponds to a pseudo-convex function having a global minimum to minimize energy losses of the EV, that include driving losses and electric motor losses, 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, wherein
the energy-loss function is a cost function that is derived from a tabulated data of an efficiency map of the electric motor, wherein
the efficiency map of the electric motor includes a contour plot that determines the maximum efficiency of the electric motor for any combination of torque and a rotational speed of the electric motor and
the predetermined motor efficiency function is learned using a kernel-based regression that fits data of the efficiency map of one electric motor relating the efficiency of the electric motor to a motor speed and a motor torque and
the longitudinal motion model is determined based on forces resulting from the aerodynamic drag of the EV and forces resulting from the rolling resistance of the EV and
the predetermined torque split function is determined by using the predetermined motor efficiency function along with the longitudinal motion model and the predetermined torque split function comprises the wheel torques.