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CN-122009131-A - Energy optimization scheduling control method for plug-in hybrid electric vehicle

CN122009131ACN 122009131 ACN122009131 ACN 122009131ACN-122009131-A

Abstract

The invention relates to the technical field of hybrid power vehicle control, in particular to an energy optimization scheduling control method of a plug-in hybrid power vehicle, which comprises the following steps of 1, collecting real-time data, 2, preprocessing the data, extracting features, cleaning, filtering and normalizing the data collected in the step 1, extracting feature parameters, wherein the feature parameters comprise average speed, speed change rate, acceleration, deceleration and road gradient change trend, 3, predicting driving conditions in a future period based on the feature parameters extracted in the step 2, wherein the driving conditions comprise future speed, future road gradient and future traffic conditions, 4, establishing an energy management optimization model, 5, optimizing model prediction control, and 6, controlling and executing. Dynamic optimization scheduling of the energy of the plug-in hybrid electric vehicle is realized through data acquisition, preprocessing, feature extraction and future driving condition prediction.

Inventors

  • Tong Qianyi

Assignees

  • 武汉理工大学

Dates

Publication Date
20260512
Application Date
20260206

Claims (10)

  1. 1. The energy optimization scheduling control method for the plug-in hybrid electric vehicle is characterized by comprising the following steps of: The method comprises the steps of 1, collecting real-time data, and collecting vehicle state parameters, environment parameters and external information, wherein the vehicle state parameters comprise vehicle speed, engine rotating speed, motor rotating speed, battery state of charge, battery temperature and battery health state, the environment parameters comprise environment temperature and road gradient, and the external information comprises real-time traffic flow, traffic signal lamp state and road type; Step 2, data preprocessing and feature extraction, namely performing data cleaning, filtering and normalization on the data acquired in the step 1, and extracting feature parameters, wherein the feature parameters comprise average vehicle speed, vehicle speed change rate, acceleration, deceleration and road gradient change trend; predicting future driving conditions, wherein the driving conditions comprise future vehicle speed, future road gradient and future traffic conditions, and predicting the driving conditions in a future period of time based on the characteristic parameters extracted in the step 2; Step 4, an energy management optimization model is established, a dynamics model and an energy flow model of the plug-in hybrid electric vehicle are established, the dynamics model comprises an engine model, a motor model, a battery model and a transmission system model, the energy flow model describes a power distribution relation among the engine, the motor and the battery, the optimization target is to minimize total equivalent fuel consumption, meanwhile, battery life factors are considered, optimization variables comprise engine output power, motor output power and battery charge and discharge power, constraint conditions comprise vehicle power requirements, upper and lower limits of battery charge states, a battery temperature range, an engine efficiency range and a motor efficiency range; 5, model predictive control optimization, namely solving the energy management optimization model in the step 4 by using a model predictive control algorithm, rolling and optimizing an energy distribution sequence in a future time domain based on the current vehicle state and the future driving condition predicted in the step 3 in each control period, converting the optimization problem into a quadratic programming problem, and solving by using a numerical optimization algorithm; And 6, controlling and executing, namely sending the first element of the optimal power distribution sequence obtained in the step 5 to an engine control unit, a motor controller and a battery management system as a current control instruction, wherein the engine control unit adjusts the opening degree of an engine throttle valve and the fuel injection quantity, the motor controller adjusts the motor torque, the battery management system controls the charge and discharge current of a battery, and meanwhile, monitors the state change of a vehicle, and the step 1 is returned to perform the next period of data acquisition and optimization.
  2. 2. The method for optimizing and dispatching the energy of the plug-in hybrid electric vehicle according to claim 1, wherein in the step 1, the vehicle speed is acquired by a vehicle speed sensor arranged on wheels, the vehicle speed sensor outputs pulse signals, and the pulse signals are converted into digital signals through a signal conditioning circuit; the engine speed is acquired through an engine control unit, and the engine control unit acquires a speed signal through a crankshaft position sensor; the motor rotating speed is acquired through a motor controller, and the motor controller acquires a rotating speed signal through an encoder; The battery state of charge and the battery temperature are collected through a battery management system, the battery management system directly measures the battery voltage, the battery current and the battery temperature through a voltage sensor, a current sensor and a temperature sensor, and the battery state of charge is calculated by utilizing an ampere-hour integration method; The battery health state is obtained through calculation through battery history use data, the battery history use data comprises battery cycle times and capacity fading histories, the battery cycle times are recorded by a battery management system each time of charge and discharge cycles, the capacity fading histories are obtained through comparison of the current battery capacity and the initial battery capacity, and the battery capacity is obtained through discharge tests; the environment temperature is acquired through a temperature sensor arranged outside the vehicle, and the temperature sensor outputs an analog signal and is converted into a digital signal through an analog-to-digital converter; The road gradient is acquired through an inertial measurement unit, the inertial measurement unit comprises an accelerometer and a gyroscope, the longitudinal acceleration and the pitch angle of the vehicle are measured, and the road gradient is calculated through a data fusion algorithm by combining elevation data provided by a global positioning system; The real-time traffic flow and the traffic signal lamp state are acquired from a traffic management center through a vehicle networking communication module, and the vehicle networking communication module receives traffic data by using a special short-range communication protocol; The road type is acquired through a navigation system, the navigation system provides road type information based on global positioning system coordinates and electronic map data, and the road type comprises expressways, urban roads and rural roads.
  3. 3. The method for energy optimized dispatch control of a plug-in hybrid vehicle of claim 1, wherein the data cleansing in step 2 comprises checking data integrity, rejecting missing values and values outside a reasonable range; The filtering applies a low-pass filter to the speed, the engine speed and the motor speed signals to remove high-frequency noise, and the cut-off frequency of the low-pass filter is dynamically set according to the signal characteristics; the normalization scales parameters of different dimensions to the same numerical range, and a minimum-maximum scaling method is adopted; calculating an arithmetic average value by taking vehicle speed data in the last period of time, and dynamically adjusting the period of time according to driving conditions; Calculating the speed change rate of the vehicle, and dividing the speed difference value of two continuous sampling points by the sampling time interval; the acceleration and deceleration calculation is defined according to the positive and negative of the speed change rate; and the road gradient change trend extraction is used for carrying out time sequence analysis on the road gradient data, calculating the average change rate of the gradient by using a sliding window, and adjusting the size of the sliding window according to the speed of the vehicle and the road type.
  4. 4. The method for optimizing and scheduling the energy of the plug-in hybrid vehicle according to claim 1, wherein in the step 3, the future vehicle speed prediction uses a time sequence prediction model, the future vehicle speed sequence is predicted based on a historical vehicle speed sequence, the time sequence prediction model adopts an autoregressive integral sliding average model, model parameters are updated through an online learning algorithm, and the online learning algorithm uses a recursive least square method; the future road gradient prediction extracts elevation information of a future path according to the current position of the vehicle and elevation data in a navigation map, and a future road gradient sequence is obtained through differential calculation; The future traffic condition prediction is based on real-time traffic flow data, a macroscopic traffic flow model is used for predicting future traffic density and average speed, a parking event and a starting event are predicted by combining traffic signal lamp states, the macroscopic traffic flow model adopts a green Berger model, and model parameters are obtained from a traffic management center in real time; The prediction time domain is dynamically adjusted according to the current speed of the vehicle and the road condition, and is shorter under the congested road condition and longer under the unblocked road condition.
  5. 5. The method for optimizing and scheduling energy for a plug-in hybrid vehicle according to claim 1, wherein the engine model in step 4 describes a relation between engine output power and fuel consumption rate, is modeled by an engine universal characteristic map provided by an engine manufacturer and is corrected on line based on real-time data; The motor model describes the relation between the output power and the efficiency of the motor, the motor efficiency map is modeled, and the motor efficiency map is provided by a motor manufacturer and is corrected on line based on real-time data; the battery model describes the relationship between the change of the state of charge of the battery and the charge and discharge power, an internal resistance model is adopted, and parameters of the internal resistance model are calibrated through battery test data; The transmission system model describes the power flow of the power distribution device, and establishes a mathematical relationship based on an energy conservation law; The energy flow model defines the total power demand as the sum of the output power of the engine and the output power of the motor, the total power demand is determined by the running resistance of the vehicle, and the running resistance of the vehicle comprises air resistance, rolling resistance and gradient resistance; The total equivalent fuel consumption in the optimization target comprises equivalent fuel consumption corresponding to actual fuel consumption and battery discharge, an equivalent fuel consumption coefficient is adjusted along with the state of charge and the state of health of the battery, and a battery state of health influence coefficient is determined through experimental data; The upper limit and the lower limit of the battery state of charge in the constraint condition are dynamically set according to the battery type and the battery state of health, the upper limit and the lower limit of the battery temperature are determined by a battery safety working range, the upper limit and the lower limit of the engine output power are determined by an engine characteristic curve, and the upper limit and the lower limit of the motor output power are determined by a motor characteristic curve.
  6. 6. The method for energy-optimized dispatching control of plug-in hybrid vehicle according to claim 1, wherein the control cycle length in step 5 is set according to the system response speed; the prediction time domain length is determined according to the prediction precision of driving conditions and the calculation load balance; The rolling optimization takes the current vehicle state as an initial condition and predicted future driving conditions as input in each control period, and solves an optimization problem to obtain an optimal engine output power sequence, a motor output power sequence and a battery charge and discharge power sequence; The quadratic programming problem construction is used for linearizing an optimization target and constraint conditions and converting the optimization target and constraint conditions into a quadratic programming standard form, wherein the quadratic programming problem comprises an objective function and a linear constraint matrix; And solving by using an interior point method by the numerical optimization algorithm, obtaining an optimal solution by iterative computation by the interior point method, and setting an iteration termination condition to be that the change of the objective function is smaller than a threshold value or the maximum iteration times are reached.
  7. 7. The method for optimizing and scheduling energy of plug-in hybrid vehicle according to claim 1, wherein in step 6, the engine control unit receives an engine output power command, determines the throttle opening and the fuel injection amount by querying an engine map, and the engine map is obtained by calibration experiments; The motor controller receives a motor output power instruction, determines motor torque and rotational speed by querying a motor characteristic curve, and the motor characteristic curve is provided by a motor manufacturer; The battery management system receives a battery charge and discharge power instruction, and controls the power electronic device to adjust charge and discharge current so as to ensure that the battery works in a safe range; the monitoring of the vehicle state change includes monitoring the battery state of charge and the battery temperature in real time, and triggering re-optimization or safety protection measures if the parameters are outside the expected range.
  8. 8. The method for optimizing and scheduling the energy of the plug-in hybrid vehicle according to claim 2, wherein the signal conditioning circuit in the step 1 comprises an amplifier and a filter, and amplifies and filters out noise of a pulse signal output by a vehicle speed sensor; The crank shaft position sensor adopts a magneto-electric sensor, and the encoder or the resolver is an incremental encoder or an absolute encoder; the voltage sensor, the current sensor and the temperature sensor respectively adopt a voltage dividing circuit, a Hall effect sensor and a thermistor; when the ampere-hour integration method calculates the state of charge of the battery, integrating the current of the battery and correcting the efficiency of the battery; the battery cycle number record comprises weighted counts of deep discharge cycles and shallow discharge cycles, and the capacity fading history calculation uses a Kalman filter to perform state estimation; The resolution and sampling rate of the analog-to-digital converter of the temperature sensor are set according to the change rate of the ambient temperature; the data fusion algorithm of the inertial measurement unit adopts an extended Kalman filter to fuse the data of the accelerometer, the gyroscope and the global positioning system; the special short-range communication protocol of the Internet of vehicles communication module comprises DSRC or C-V2X, and the electronic map data of the navigation system comprises a high-precision digital elevation model.
  9. 9. The method for optimizing and scheduling energy of a plug-in hybrid vehicle according to claim 3, wherein the reasonable range value of the data cleaning in the step 2 is set according to the sensor range and the physical limit of the vehicle; the cut-off frequency of the low-pass filter is dynamically adjusted according to signal spectrum analysis, and the dominant frequency is calculated by using fast Fourier transform; in the minimum-maximum scaling method, the scaling range is adaptively updated according to the parameter history statistic value; the period of time for which the average vehicle speed is calculated is set to 10 seconds in an urban road and 30 seconds in an expressway; The sampling time interval is determined by a control system clock, and the control system clock precision is in the millisecond level; In the acceleration and deceleration calculation, the speed change rate is calculated by a difference method, and the difference order is selected according to the signal smoothness; the sliding window size of the road slope trend is associated with a predicted horizon.
  10. 10. The method for energy-optimized dispatching control of plug-in hybrid vehicle according to claim 4, wherein the order of the autoregressive integral moving average model in step 3 is determined by Akaike information criterion; When the model parameters are updated by the recursive least square method, the forgetting factor is adaptively adjusted according to the prediction error; the difference calculation of the future road gradient prediction adopts a center difference method, so that edge errors are reduced; parameters of the green-Berger model comprise traffic flow density and speed relation, and the real-time traffic data is used for online calibration; in the dynamic adjustment of the prediction time domain, the prediction time domain is set to 10-20 seconds under the congested road condition, and the prediction time domain is set to 30-60 seconds under the unimpeded road condition; The parking event and starting event predictions are based on traffic light periods and a vehicle queuing model that uses a following model to calculate vehicle interactions.

Description

Energy optimization scheduling control method for plug-in hybrid electric vehicle Technical Field The invention relates to the technical field of hybrid power vehicle control, in particular to an energy optimization scheduling control method for a plug-in hybrid power vehicle. Background As a vehicle type combining an internal combustion engine and an electric motor, a plug-in hybrid vehicle can recover energy through an external power grid charging and regenerative braking technology, thereby improving energy utilization efficiency and reducing exhaust emissions. However, the energy management strategy of PHEVs has been a key challenge for technological development. In the prior art, energy management methods are mainly classified into rule-based policies and optimization algorithm-based policies. Rule-based strategies rely on preset thresholds and logic conditions, such as starting the engine when the battery state of charge is below a certain fixed value, and prioritizing the use of electric mode when the battery state of charge is above a certain fixed value. Although the method is simple to realize and has small calculation burden, the method lacks adaptability to real-time driving conditions, and cannot be optimally adjusted according to dynamically-changed road gradient, traffic flow, driver behaviors and environmental temperature. For example, on congested urban roads, a rule-based strategy may lead to frequent engine start-up and shut-down, increased fuel consumption and component wear, while on highways, battery energy may be depleted prematurely, failing to fully utilize the efficient range of electric modes, resulting in overall energy efficiency degradation. Strategies based on optimization algorithms, such as global optimization methods (e.g., dynamic planning) or real-time optimization methods (e.g., equivalent fuel consumption minimization strategies), attempt to improve energy distribution efficiency through mathematical optimization. The global optimization method requires that complete driving cycle information is known before driving, which is not feasible in practical applications because driving conditions have uncertainty. Although the real-time optimization method can be adjusted on line, the real-time optimization method generally depends on fixed parameters and a simplified model, and cannot accurately reflect dynamic factors such as battery health status, real-time traffic conditions, road gradient and the like. In addition, the existing optimization method often ignores the influence of the service life of the battery, for example, the battery is overdischarged or charged, so that the aging of the battery is accelerated, and the service life of the battery is shortened. Another significant problem is that the prior art rarely integrates external sources of information, such as real-time traffic data provided by the internet of vehicles, road slope prediction or weather forecast data provided by navigation systems, which are critical to predicting future driving conditions and optimizing energy distribution. Therefore, the existing energy management strategies have the problems of low energy efficiency, poor fuel economy, shortened battery life, insufficient real-time adaptability and the like. The limitations of the prior art include, firstly, that the energy management strategy cannot respond to dynamic driving conditions in real time, resulting in non-optimal energy distribution, secondly, that the optimization algorithm is complex in calculation and difficult to execute in real time on limited computing resources of the vehicle-mounted controller, thirdly, that long-term influences of battery health states are not considered, and short-term efficiency is possibly sacrificed, and thirdly, that multi-source information fusion, such as traffic flow and road gradient prediction, is lacking, and prediction accuracy and optimization effects are limited. Aiming at the problems, the invention provides an energy optimization scheduling control method of a plug-in hybrid power vehicle based on real-time data acquisition and model prediction control, which predicts future driving conditions by integrating vehicle state parameters, environment parameters and external information and optimizes energy distribution in a rolling way, thereby realizing efficient, self-adaptive and sustainable energy management. Disclosure of Invention Based on the above purpose, the invention provides a plug-in hybrid vehicle energy optimization scheduling control method, which comprises the following steps: The method comprises the steps of 1, collecting real-time data, and collecting vehicle state parameters, environment parameters and external information, wherein the vehicle state parameters comprise vehicle speed, engine rotating speed, motor rotating speed, battery state of charge, battery temperature and battery health state, the environment parameters comprise environment temperature and road gradient,