A method for quickly formulating an energy recovery strategy

Abstract

The present application relates to the technical field of energy recovery, and discloses a method for quickly formulating an energy recovery strategy, comprising the following steps: 1, obtaining the coasting resistance curve of a benchmark mass whole vehicle, and generating the approximate coasting resistance curve of different mass whole vehicles of the same vehicle type; 2, based on the coasting resistance curves, formulating the coasting deceleration target value of each operating mode of the whole vehicle through shallow throttle coasting resistance testing, and setting the shallow recovery strategy parameters; 3, real-time predicting the whole vehicle operating mass when the new vehicle is running, and obtaining the real-time zero-torque coasting deceleration; 4, obtaining the compensation amount, dynamically adjusting the coasting recovery torque according to the compensation amount, and matching the shallow recovery strategy based on the adjusted recovery torque. The present application only relies on the single path input of the target deceleration and the pre-calibration resistance model, and realizes the quick deployment and performance confirmation of the strategy without the need of complex real vehicle calibration, so as to meet the high balance demand of drivability and economy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy recovery, in particular to a method for quickly formulating an energy recovery strategy. BACKGROUND

[0002] In the development of new energy vehicles, especially electric vehicles and hybrid vehicles, energy recovery technology is a key link to improve vehicle range and optimize driving experience. The energy recovery strategy currently widely used in the industry mainly converts the driving motor into a generator mode during vehicle deceleration or braking, converting the kinetic energy of the vehicle into electrical energy and feeding it back to the power battery.

[0003] However, when formulating an energy recovery scheme for a new vehicle model, the energy recovery scheme needs to meet the two core demands of vehicle drivability and vehicle economy, which often restrict each other, making it difficult to quickly find the best balance point. Currently, existing solutions require a high degree of reliance on a large amount of real vehicle calibration work. Engineers need to adjust strategy parameters under different operating conditions through repeated road tests to try to balance drivability and economy. This process is time-consuming and labor-intensive, and is inefficient, not only significantly extending the development cycle and increasing development costs, but also because of the complexity and variability of the operating conditions, the calibration results often fail to cover all scenarios, resulting in a suboptimal final solution.

[0004] In addition, due to development cycle, cost and calibration complexity, most solutions generally adopt a relatively simple implementation method, i.e. only providing a limited number of fixed energy recovery intensity levels for users to choose from. There is usually only a preset fixed recovery torque or deceleration value difference between different levels, although the coverage is wide, but it is difficult to achieve more refined energy recovery adjustment, resulting in unstable recovery effect, poor user experience, and failure to maximize recovery potential.

[0005] In order to enhance the cost-effectiveness of vehicle design, many models, especially low-end or economy products, have optimized the cost of the vehicle's overall controller design, which usually does not have an acceleration sensor or an advanced electro-hydraulic braking system. The absence of such key hardware greatly limits the sensing ability and execution accuracy of the energy recovery strategy, making it difficult to accurately assess the strength of the driver's braking request or achieve ideal deceleration control, limiting the maximum recovery capacity and affecting the consistency of the brake pedal feel. SUMMARY

[0006] The present application aims to provide a method for quickly formulating an energy recovery strategy to solve the problem of long development time and low efficiency of energy recovery strategy formulation for new vehicles under hardware cost constraints.

[0007] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0008] A method for quickly formulating an energy recovery strategy, comprising the following steps:

[0009] Step 1, obtain the coasting resistance curve of the vehicle model benchmark mass vehicle through the power economy performance parameter calculation of the whole vehicle development process, and generate the approximate coasting resistance curve of different mass vehicles of the same vehicle model from the coasting resistance curve;

[0010] Step 2, based on the coasting resistance curve, formulate the coasting deceleration target value of each operating mode of the vehicle through the shallow throttle coasting resistance test; set the shallow recovery strategy parameters based on the target value;

[0011] Step 3, take the maximum difference of throttle opening <5% and the maximum difference of slope <1% within 2s as the effective observation interval, record the throttle state, drive motor output torque, vehicle speed and slope value in the interval in real time, predict the running mass of the new vehicle when running, and obtain the real-time zero-torque coasting deceleration;

[0012] Step 4, according to the real-time zero-torque coasting deceleration and the set operating mode of the current vehicle, calculate the compensation amount of the electric brake deceleration, dynamically adjust the coasting recovery torque according to the compensation amount, and match the shallow recovery strategy based on the adjusted recovery torque.

[0013] The principle of the scheme is:

[0014] The scheme takes the deceleration target value as the core control point when there is no acceleration sensor, electro-hydraulic brake system and other hardware of the whole vehicle, as the key bridge connecting the driving demand and the economy target. Based on the vehicle resistance characteristics and the dynamics principle, the total brake force required to maintain the target deceleration is calculated in real time, and the regenerative brake torque component that can be recovered by the motor is separated from the total brake force, so that the driver's coasting deceleration expectation is directly and efficiently converted into accurate motor power generation instructions.

[0015] The scheme bypasses the dependence on expensive hardware and complex multi-parameter coupling calibration. By skillfully using the target deceleration as the only input and based on the offline-fitted resistance model for online calculation, the dynamic adjustment of the whole vehicle coasting energy recovery torque can be quickly completed, and the quick matching and implementation confirmation of the whole vehicle coasting recovery torque strategy can be realized.

[0016] In the prior art, for high-cost vehicles, since they are equipped with acceleration sensors, electro-hydraulic brake systems and other hardware, in the case of low cost control requirements, the matching of energy recovery strategy will give priority to real-time and comfort, etc., so it will combine hardware facilities to calculate and adjust through multiple parameters. For low-cost vehicles, due to cost constraints, the industry generally prioritizes economy and sacrifices some comfort. This results in the energy recovery strategy in low-cost vehicles being set to a fixed mode or a few fixed gears, without considering the balance of comfort and economy. This scheme breaks out of the inherent research and development thinking and wants to balance comfort and economy in low-cost vehicles, so that most low-cost vehicles can achieve a balance between drivability and economy. Therefore, this scheme does not require economy or drivability to be optimal, but to achieve the best balance between the two during driving. At the same time, without acceleration sensors, electro-hydraulic brake systems and other hardware devices, this scheme can achieve rapid energy recovery strategy matching by obtaining deceleration as the only judgment value, further reducing production costs while shortening the time, achieving rapid matching, balancing economy and comfort, and improving overall efficiency.

[0017] The advantages of the present scheme are:

[0018] 1. The shallow recovery strategy set by the present scheme can effectively reduce the impact of the energy recovery mode on drivability and optimize the driver's throttle control to maximize the energy recovery strategy's improvement of the vehicle's economy, achieving maximum cost-effective recovery potential and balancing drivability and economy.

[0019] 2. By measuring and calculating the output speed, torque, throttle opening and slope angle of the electric drive, the speed change trend of the vehicle at 0 torque output is predicted, and the size of the target energy recovery drive negative torque is dynamically adjusted to meet the different needs of the driver in different energy recovery modes.

[0020] 3. The control parameter values of energy recovery are obtained through theoretical calculation, which improves the accuracy and reliability, reduces the development cycle of energy recovery function, improves the adaptation efficiency, and reduces the vehicle development and hardware cost. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The flow framework diagram of the method for quickly formulating and implementing the energy recovery strategy. DETAILED DESCRIPTION

[0022] The following will be further described in detail through specific embodiments:

[0023] Example 1

[0024] In this embodiment, the target deceleration is used as a unified control parameter. The total braking force required to maintain the target deceleration is calculated in real time using a vehicle drag model, and the recoverable motor regenerative torque component is accurately extracted. This efficiently integrates drivability requirements with economic goals, improving the balance between the two in low-cost vehicles and achieving optimal results. This embodiment also provides a method for rapidly developing and implementing an energy recovery strategy, as shown in the attached figure. Figure 1 As shown, it includes the following steps:

[0025] S1 calculates the sliding resistance curve of the vehicle with a reference mass by using the power and economic performance parameters of the whole vehicle development process, and then generates approximate sliding resistance curves of different masses of the same vehicle model.

[0026] In this embodiment, the baseline mass of the new vehicle is obtained by fitting actual vehicle coasting measurements. The sliding resistance curve is expressed as:

[0027] ;

[0028] In the formula, For constant resistance; For vehicle speed; For transmission losses; For wind resistance loss; , These are coefficients.

[0029] The drive system, based on the whole-vehicle coasting resistance curve of a benchmark mass vehicle, fits the resistance curves for other mass states of the same vehicle model, generating approximate coasting resistance curves for vehicles of different masses. These approximate coasting resistance curves for vehicles of different masses are then expressed as follows:

[0030] ;

[0031] In the formula, The Nth type of vehicle mass within the same vehicle model; The reference mass is the vehicle's baseline mass. In this embodiment, different vehicle masses refer to different mass states within the same vehicle model due to different configurations. The baseline mass is the basic vehicle mass without any additional configurations. This represents the Nth vehicle quality state for the same model.

[0032] S2, based on each coasting resistance curve, sets the coasting deceleration target value for each operating mode of the vehicle through shallow throttle coasting resistance test; and sets the shallow recovery strategy parameters based on the target value.

[0033] In this embodiment, based on the obtained sliding resistance curve, the sliding deceleration target value of each operating mode of the vehicle is determined by the shallow throttle sliding resistance test. Among them, the operating mode includes high comfort mode and strong economy mode, and the corresponding sliding deceleration target value is set, and the corresponding sliding deceleration target value is , , the value is positive, that is, the vehicle deceleration of high comfort target electric brake is , and the vehicle deceleration of strong economy target electric brake is . By setting different operating modes, the driver can freely choose the desired operating mode according to the demand, which can be more flexible in demand selection, and can meet the actual use demand of the user, and can ensure that the best energy recovery strategy is quickly matched based on the demand in the set operating mode, further considering the comfort and economy.

[0034] At the same time, in order to improve the energy recovery strategy to improve the economic performance, the shallow recovery strategy parameters are set. In this embodiment, the parameters of the shallow recovery strategy include the throttle depth threshold and the shallow electric brake torque value .

[0035] S3, taking the maximum difference value of throttle opening within 2s <5% and the maximum difference value of slope <1% as the effective observation interval, recording the throttle state, drive motor output torque, vehicle speed and slope value in this interval in real time, predicting the vehicle running quality when the new vehicle is running, and obtaining the real-time zero-torque sliding deceleration .

[0036] In this embodiment, when predicting the vehicle running quality when the current vehicle is running, the maximum difference value of throttle opening within 2s <5% and the maximum difference value of slope <1% are taken as the effective observation interval, the drive motor output torque, vehicle speed and slope value in this interval are recorded, the average torque , vehicle speed V and slope value are calculated, the change of rolling resistance component on the slope is ignored, and the estimated current vehicle running quality is calculated by the following formula . In this embodiment, combined with the driving habits of different drivers, the dynamic observation interval is set, so as to ensure that the effective parameters are obtained to evaluate the current vehicle running quality in real time, so as to flexibly match the best energy recovery strategy according to different driving behaviors, and ensure that in the case of different drivers, the comfort and economy can be considered, so that the two can be balanced as much as possible, and the driving demand of low-cost vehicles is improved.

[0037] In this embodiment, the throttle state, drive motor output torque and slope sensor feedback value in the vehicle running process are detected and obtained through the CAN bus, the vehicle running quality when the current vehicle is running is predicted, and the vehicle running quality when the current vehicle is running is predicted is expressed as

[0038] ;

[0039] wherein, is the average torque; is the transmission ratio; is the tire radius; is the transmission efficiency; is the vehicle reference mass; is the gravity acceleration; is the ramp angle.

[0040] Meanwhile, when the vehicle is running and valid estimated mass data cannot be obtained within a certain time duration, the vehicle running mass is calculated according to the reference mass, i.e. = .

[0041] According to the vehicle's estimated current running mass , the vehicle's zero-torque flat road sliding deceleration is calculated , i.e. . According to the ramp angle obtained by the ramp sensor, the vehicle's weight ramp component acceleration is calculated , i.e. . The vehicle's real-time zero-torque sliding deceleration during running is calculated by and , i.e. , which is obtained by vector addition of and , and is represented as , which has positive and negative values. A positive value represents that the vehicle is in a climbing state according to the ramp recognition, and a negative value represents that the vehicle is in a downhill state according to the ramp recognition.

[0042] S4, according to the real-time zero-torque sliding deceleration and the current vehicle's set running mode, the compensation amount of the electric brake deceleration is calculated, the sliding recovery torque is dynamically adjusted according to the compensation amount, and the shallow recovery strategy is matched based on the adjusted recovery torque.

[0043] In this embodiment, the value calculated in real time during the vehicle's running process and the current vehicle's driver's set running mode are used to calculate the compensation amount of the electric brake deceleration according to the current vehicle's set running mode, and thus the electric brake deceleration that the drive system should compensate is calculated.

[0044] When it is a high comfort mode, the compensation amount is represented as ;

[0045] When it is a strong economy mode, the compensation amount is represented as ;

[0046] wherein, The glide deceleration target value for the high comfort mode; The glide deceleration target value for the strong economy mode.

[0047] The glide recovery torque is dynamically adjusted according to the real-time calculated compensation amount:

[0048] When ≥ 0, the glide recovery torque is set to the torque value in the shallow recovery strategy parameter, that is, ;

[0049] When < 0, the glide recovery torque is

[0050] .

[0051] Through the above steps, the vehicle glide energy recovery torque is dynamically adjusted, and the vehicle glide recovery torque strategy scheme is quickly confirmed and implemented by setting the deceleration target value. When the vehicle decelerates, the kinetic energy is efficiently converted and stored as electric energy for subsequent vehicle driving or other electrical equipment, to improve the recovery potential, reduce energy waste and consumption of the battery. It also reduces the burden on traditional brake discs and brake pads, prolonging their service life.

[0052] In this embodiment, only the target deceleration single path input and the pre-calibrated resistance model are relied on, bypassing the hardware limitations of the acceleration sensor and the electro-hydraulic braking system. Without complex real vehicle calibration, the strategy is quickly deployed and the performance is confirmed, achieving a breakthrough balance between hardware cost and development efficiency, thus achieving the best balance between comfort and economy.

[0053] Due to the cognitive limitations of public demand, it is generally believed that high-cost vehicles should give more consideration to comfort, which can obtain multi-parameter matching strategies with more hardware support. In low-cost vehicles, comfort is naturally reduced and economy is sought. and In the development of low-cost electric vehicles, existing technologies lack the core hardware support of acceleration sensors and electro-hydraulic braking systems, and are trapped in a systematic cognitive dilemma of target deceleration control. Since real-time deceleration closed-loop control relies on accurate acceleration signals, the traditional scheme must rely on high-cost sensors or complex observers to achieve the condition of hardware deficiency, which directly leads engineers to abandon the deceleration control dimension and degenerate into an open-loop fixed torque mapping strategy. This approach avoids hardware shortcomings, but causes a deep split between drivability and economy.

[0054] Moreover, the traditional regenerative braking needs to dynamically coordinate the motor regenerative force and the mechanical braking force, but the absence of the electro-hydraulic braking system makes the mechanical braking passively respond to the pedal stroke and cannot be actively controlled. Under this condition, the existing scheme is forced to limit the recovery strength to a conservative interval of less than 0.1g to avoid the jerk caused by the accidental intervention of the mechanical braking, thereby sacrificing more than 30% of the recoverable energy potential. At the same time, the current application of the resistance model in the industry is still limited to range estimation, completely ignoring the reconstruction ability of the model to the vehicle dynamics equation. This makes the target deceleration control strategy be regarded as an unachievable control target under the condition of limited hardware resources.

[0055] The present scheme realizes "precise control of deceleration under hardware absence" through theoretical reconstruction, constructs a dynamics equation based on the resistance model and the vehicle speed differential, and regenerates the acceleration signal by software algorithm to realize closed-loop control instead of physical sensors. Moreover, the static economy model is converted into a real-time control hub to directly output the optimal recovery torque, so that the target deceleration becomes the core control variable of the unified driving intention and economic optimization. This method realizes precise tracking of the target deceleration on a low-cost hardware platform, breaking through the technical limitations of previous systems with low configuration that cannot simultaneously consider high energy recovery strength and good driving experience, and providing a feasible path for electric vehicles to improve comprehensive performance through software algorithms.

[0056] Embodiment 2

[0057] In this embodiment, the model parameters are also automatically calibrated with driving data to solve the problem of model drift caused by battery attenuation. Specifically, during vehicle coasting or braking, the vehicle speed decay curve and the actual power generation of the motor are captured in real time, specifically through a wheel speed sensor and a motor controller without additional hardware. In this embodiment, the vehicle control unit (VCU) collects the driving motor torque signal (close to 0Nm), the brake pedal stroke signal (0%), the brake force signal (0MPa) of the electronic stability program (ESP), and the slope estimation value (<2%) in real time through the CAN bus.

[0058] When all the following conditions are met:

[0059] The motor torque ∈ [-5Nm, 5Nm]; the brake pedal opening = 0%; the ESP hydraulic pressure = 0MPa; | estimated slope | < 2%; vehicle speed > 30km / h;

[0060] The pure coasting condition is determined to be entered, and data recording is started.

[0061] In this embodiment, the system records the current vehicle speed (v) and the vehicle speed change rate (dv / dt) at a period of 100ms. ​). The vehicle speed change rate is calculated by the difference of high-precision wheel speed sensor signals, and the instantaneous deceleration is -( ). According to Newton's second law, the actual total resistance F r is calculated by the vehicle speed attenuation rate, which is represented as F r =m*a, m is the vehicle mass. The actual total resistance is calculated. The average actual resistance is calculated by collecting multiple data points, i.e. vehicle speed and actual total resistance, in each vehicle speed interval. At the same time, the interval median vehicle speed is substituted into the current resistance model to obtain the model prediction value.

[0062] The measured total resistance F r is compared with the predicted value of the pre-calibration model , and the resistance error offset of the vehicle speed interval is calculated to generate a dynamic error compensation table.

[0063] Every 100Km of effective "pure sliding" data is accumulated, and a parameter update is automatically performed. In this embodiment, the weighted least squares method is used to fuse and fit the new and old data (new data points and original calibration data), and a new set of optimal parameters, i.e. A, B, C coefficients, are recalculated, which preferentially corrects low-frequency offset, such as rolling resistance drift caused by tire pressure changes, and is smoothly updated to the control model to avoid parameter jumps, thereby improving the accuracy and stability of the curve model. The static resistance model is updated to a self-evolving model, which overcomes the long-term interference caused by battery aging or seasonal temperature or tire pressure fluctuations, etc., so that the accuracy of the recovered torque calculation is higher, and the cost is reduced, and the development and maintenance cycle is shortened.

[0064] Embodiment 3

[0065] In this embodiment, the slope is calculated by GPS elevation data to adapt to the slope changes in different regions and reduce errors. In this embodiment, the wheel speed signal, motor torque, brake master cylinder pressure and GPS altitude are obtained through CAN bus. The wheel speed signal is calculated by difference, and the theoretical longitudinal acceleration value of the vehicle, i.e. the vehicle speed change rate, is obtained. The vehicle speed ( ) and the altitude change rate ( ) in the vehicle-mounted GPS module are fused with the longitudinal acceleration value to estimate the slope in real time, which is represented as:

[0066] ;

[0067] In the formula, is the motor recovered force; is the friction braking force (obtained by querying the pressure-braking force calibration table through ESP); is the output of the updated resistance model; is the longitudinal acceleration.

[0068] Based on the ESP system wheel speed difference signal of CAN bus data, the coaxial wheel speed difference and drive wheel slip rate are monitored, and low adhesion road surface such as ice and snow road surface is identified. When the wheel speed difference is greater than the threshold value, the target deceleration upper limit is automatically reduced, and the mechanical brake is optimized to avoid the regenerative torque triggering slip.

[0069] In the embodiment, when the coaxial non-drive wheel speed difference is monitored, when the left and right wheel speed difference of the rear axle is greater than the threshold value, such as 2km / h, and lasts for a certain time (such as 200ms), it is determined that one of the rear wheels may slip, and the road adhesion coefficient is low. At this time, the low adhesion mode is triggered, the target deceleration upper limit is immediately reduced from-0.3g to-0.1g, the motor recovery torque is linearly faded to the new upper limit within 100ms, and the user is prompted through the instrument panel related icon that the current road surface is wet and the recovery function is limited.

[0070] When the wheel speed difference or slip rate returns to normal and lasts for a period of time (such as 3 seconds), the system slowly recovers to the normal recovery mode.

[0071] In the embodiment, multi-source signal fusion is used to realize slope / adhesion compensation. Without inclination sensor and additional adhesion sensor, early identification of slope driving and low adhesion road surface and adaptive adjustment of energy recovery strategy are realized, the recovery of slope and low adhesion road surface is more accurate, the misalignment problem is avoided, the safety is improved, and the recovery coverage is expanded.

[0072] The above is only an embodiment of the present application, and the specific technical solutions and / or common knowledge of the scheme are not described in detail. It should be noted that for those skilled in the art, without departing from the technical solutions of the present application, some modifications and improvements can be made, which should be regarded as the protection scope of the present application, and these will not affect the effect and practicality of the present application. The protection scope of the present application should be subject to the content of its claims, and the specific implementation mode and the like in the specification can be used to explain the content of the claims.

Claims

1. A method for quickly formulating an energy recovery strategy, characterized by, The method comprises the following steps: Step 1, obtain the coasting resistance curve of the vehicle of the base mass through the calculation of the power economy performance parameters in the vehicle development process, and generate the approximate coasting resistance curves of different mass vehicles of the same type from the base mass vehicle; The coasting resistance curve of the base mass vehicle is obtained through the actual coasting measurement fitting, and the approximate coasting resistance curve of the different mass vehicles is represented as ； In the formula, is the Nth vehicle mass in the same vehicle model; is the reference mass of the vehicle; is the constant resistance; is the vehicle speed; is the transmission loss; is the wind resistance loss; , are coefficients, respectively; Step 2, based on the coasting resistance curves, the coasting deceleration target values of each operating mode of the vehicle are set through the shallow throttle coasting resistance test; The shallow recovery strategy parameters are set based on the target values; Step 3, taking the maximum difference of the throttle opening degree within 2s < 5% and the maximum difference of the slope < 1% as the effective observation interval, the throttle state, the output torque of the drive motor, the vehicle speed and the slope value in the interval are recorded in real time, the running mass of the vehicle during the operation of the new vehicle is predicted, and the real-time zero-torque coasting deceleration is obtained; Step 4, according to the real-time zero-torque coasting deceleration and the operating mode set for the current vehicle, the compensation amount of the electric brake deceleration is calculated, the coasting recovery torque is dynamically adjusted according to the compensation amount, and the shallow recovery strategy is matched based on the adjusted recovery torque.

2. The method of claim 1, wherein: In step 2, the operating mode includes a high comfort mode and a strong economy mode, and a corresponding coasting deceleration target value is set; the parameters of the shallow recovery strategy include a throttle depth threshold value and a shallow electric brake torque value .

3. The method of claim 1, wherein: In step 3, the running mass of the vehicle during the operation of the current vehicle is represented as ； wherein is the current vehicle mass; is the average torque; is the transmission ratio; is the tire radius; is the transmission efficiency; is the vehicle reference mass; is the gravitational acceleration; is the ramp angle.

4. The method of claim 3, wherein: In step 3, the real-time zero-torque coasting deceleration is expressed as ; wherein, is the vehicle zero-torque flat road coasting self-deceleration, ; is the vehicle weight ramp component acceleration, , A positive value represents that the vehicle is in a climbing state identified by the ramp, and a negative value represents that the vehicle is in a downhill state identified by the ramp.

5. The method of claim 4, wherein, In step 4, according to the operating mode set for the current vehicle, the compensation amount of the electric brake deceleration is calculated: When in high comfort mode, the compensation amount is then expressed as ; When the strong economic mode is selected, the compensation amount is expressed as ; In the formula, is the glide deceleration target value for the high comfort mode; is the glide deceleration target value for the strong economy mode.

6. The method of claim 5, wherein, In step 4, the coasting recovery torque is dynamically adjusted according to the compensation amount: When ≥ 0, the coasting recovery torque is set to the torque value in the shallow recovery strategy parameter, i.e. ; When If < 0, then the coast-down recuperation torque is 。 7. The method of claim 1, wherein: It also includes that when the vehicle is running, if the effective estimated mass data cannot be obtained within a certain time, the running mass of the vehicle is calculated according to the base mass.

8. The method of claim 3, wherein: During the operation of the vehicle, the throttle state, the output torque of the drive motor and the feedback value of the slope sensor are detected through the CAN bus.

9. The method of claim 1, wherein: It also includes that during the vehicle coasting or braking process, the real-time speed attenuation curve and the actual power generation of the motor are captured, and the model parameters are automatically calibrated with the driving data.

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