阅读说明：本技术 一种混合驱动无人驾驶矿用卡车的能量管理方法 (Energy management method for hybrid drive unmanned mining truck ) 是由 田韶鹏 赵国强 罗毅 于 2021-08-11 设计创作，主要内容包括：本发明公开了一种混合驱动无人驾驶矿用卡车的能量管理方法,所述方法包括在云端服务器上设置人工输入的装料点及卸料点；路测控制单元采集道路信息,实时上传到云端服务器；云端服务器规划矿用卡车的物料运输路径,发至无人驾驶上层；无人驾驶上层对当前矿用卡车进行速度规划得到规划车速,并将规划车速发送至无人驾驶上层的功率输出模块；车速传感器采集实际车速并反馈给功率输出模块；功率输出模块根据实际车速与规划车速的差值得到所需输出功率,并下发给整车控制器；整车控制器根据所需输出功率控制燃料电池模块和动力蓄电池模块,实行最优的功率分配。本发明更加准确的得到所需输出功率,提高矿用卡车的燃料电池和动力蓄电池使用寿命。(The invention discloses an energy management method of a hybrid drive unmanned mining truck, which comprises the steps of setting manually input charging points and discharging points on a cloud server; the road test control unit acquires road information and uploads the road information to the cloud server in real time; the cloud server plans a material transportation path of the mining truck and sends the material transportation path to an unmanned driving upper layer; the unmanned driving upper layer performs speed planning on the current mining truck to obtain a planned speed, and the planned speed is sent to a power output module of the unmanned driving upper layer; the vehicle speed sensor collects the actual vehicle speed and feeds the actual vehicle speed back to the power output module; the power output module obtains required output power according to the difference value between the actual vehicle speed and the planned vehicle speed and sends the required output power to the whole vehicle controller; and the vehicle control unit controls the fuel cell module and the power storage battery module according to the required output power to realize optimal power distribution. The invention can more accurately obtain the required output power and prolong the service life of the fuel cell and the power storage battery of the mining truck.)

1. The energy management method of the hybrid drive unmanned mining truck is characterized in that power provided by a fuel cell and a power storage battery of the hybrid drive unmanned mining truck in the driving process is distributed through an energy management optimization system, wherein the energy management optimization system comprises an unmanned driving upper layer, a whole vehicle controller, a communication unit, a vehicle speed sensor, a fuel cell module, a power storage battery module, a road test control unit and a cloud server;

the energy management method comprises the following steps:

s1: a loading point and a discharging point which are manually input are arranged on the cloud server;

s2: the road test control unit acquires road information including road gradient, curve angle and road speed limit and uploads the road information to the cloud server in real time;

s3: the cloud server calculates and analyzes the road gradient and the curve angle, plans a material transportation path of the mining truck, and sends the material transportation path to an unmanned driving upper layer through the communication unit;

s4: the unmanned driving upper layer is then in the strategy optimization module, a mining truck control strategy based on a dynamic planning algorithm is adopted, the current mining truck is subjected to speed planning to obtain a planned speed, and the planned speed is sent to a power output module of the unmanned driving upper layer;

s5: after acquiring the actual speed of the mining truck, the speed sensor feeds the actual speed back to the power output module on the upper layer of the unmanned driving;

s6: the power output module on the upper layer of the unmanned driving adopts a PID algorithm to obtain required output power according to the difference value of the actual vehicle speed and the planned vehicle speed, and the required output power is sent to the vehicle control unit II as a required power signal

S7: and after the vehicle control unit receives a required power signal issued by the upper layer of unmanned driving, the vehicle control unit controls the fuel cell module and the power storage battery module to increase or decrease the respective output power and implement optimal power distribution.

2. The method of claim 1, wherein the dynamic programming algorithm-based mining truck control strategy is: the method comprises the steps that the minimum accumulated value of equivalent hydrogen consumption of the mining truck in the running process between a loading point and a unloading point mine field is taken as an optimization objective function, the SOC value of a power storage battery is taken as a state variable, the output power of a fuel battery and the output power of the power storage battery are taken as control variables, the upper limit and the lower limit of the SOC value of the electric quantity of the power storage battery and the road speed limit collected by a road test control unit are taken as constraint conditions, and an unmanned driving upper-layer strategy optimization module carries out speed planning on the current mining truck to obtain the planned speed.

3. The energy management method of the hybrid unmanned mining truck of claim 2, wherein the calculation method of the cumulative value of equivalent hydrogen consumption is to optimize the minimum equivalent hydrogen consumption of different road sections by using the following formula:

wherein J is the cumulative value of equivalent hydrogen consumption, m_f(k) As the equivalent hydrogen consumption amount of the k-th traveling section,as a function of the hydrogen consumption of the fuel cell during a single control cycle, η is the power battery energy conversion efficiency, P_batt(k) For power output of the power accumulator, H_fuelFor the lower heating value of the hydrogen, SOC (k) represents the power battery SOC value of the k-th driving route, P_fc(k) And representing the output power of the fuel cell of the k-th driving section, k represents the k-th driving section, k is more than 0 and is an integer, and N represents the total number of the driving sections.

4. The method of energy management for a hybrid unmanned mining truck according to claim 1, wherein the PID algorithm is:

wherein e [ n ]]Is the difference between the actual speed at time n and the planned speed, e [ n-1 ]]For the actual speed and the planning vehicle at the moment of n-1Difference in speed, e n-2]Is the difference value between the actual speed and the planned speed at the moment of n-2, n is a positive integer greater than 3, k_P、k_i、k_dProportional, integral and differential coefficients, P, respectively, of a PID controller₀For the initial power required before start-up of the mining truck, Pn]For the required output power at time n, Pn-1]The required output power, V, for the time n-1_factFor the actual transport speed, V, of the mining truck_goalAnd driving the planned vehicle speed issued by the upper layer for the unmanned vehicle.

Technical Field

The invention belongs to the technical field of energy management of unmanned automobiles, and particularly relates to an energy management method of a hybrid drive unmanned mining truck.

Background

At present, most of the transportation operations of the open mines in China have the characteristics of organization, planning, sealing and the like, and the working environment of most of workers is severe. The development of the unmanned mining truck in the mining area can not only avoid and reduce the harm to the health and safety of drivers, but also greatly improve the working efficiency, reduce the cost, reduce the emission and improve the environmental protection performance.

Under the current background of the era of energy shortage and serious environmental pollution, fuel cells are used as a small number of power equipment with the advantages of low noise, less pollution, high efficiency, high reliability, continuous working capacity and the like, and besides being researched and applied to general passenger vehicles, the fuel cells are also gradually adopted as power sources on mining trucks used in certain specific occasions. However, since the fuel cell has a drawback of a relatively weak output characteristic as a power source, a hybrid drive system is generally adopted in which the fuel cell is used as a main power source and the power storage battery is used as an auxiliary power source.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: aiming at the problems in the prior art, the energy management method for the hybrid drive unmanned mining truck is provided, the required output power can be obtained more accurately, the defect of insufficient output power when a fuel cell is used as a single power source can be overcome, the problem of short endurance mileage of the hybrid power mining truck is solved, the service lives of the fuel cell and a power storage battery of the mining truck are prolonged, and the energy consumption of a system is reduced.

In order to solve the problems, the invention provides an energy management method of a hybrid drive unmanned mining truck, which distributes power provided by a fuel cell and a power storage battery of the hybrid drive unmanned mining truck in the driving process through an energy management optimization system, wherein the energy management optimization system comprises an unmanned driving upper layer, a whole vehicle controller, a communication unit, a vehicle speed sensor, a fuel cell module, a power storage battery module, a road test control unit and a cloud server;

the energy management method comprises the following steps:

s1: a loading point and a discharging point which are manually input are arranged on the cloud server;

s2: the road test control unit acquires road information including road gradient, curve angle and road speed limit and uploads the road information to the cloud server in real time;

s3: the cloud server calculates and analyzes the road gradient and the curve angle, plans a material transportation path of the mining truck, and sends the material transportation path to an unmanned driving upper layer through the communication unit;

s4, carrying out speed planning on the current mining truck to obtain a planned speed by adopting a mining truck control strategy based on a dynamic planning algorithm in a strategy optimization module after the unmanned driving upper layer, and sending the planned speed to a power output module of the unmanned driving upper layer;

s5: after acquiring the actual speed of the mining truck, the speed sensor feeds the actual speed back to the power output module on the upper layer of the unmanned driving;

s6: the power output module on the upper layer of the unmanned driving adopts a PID algorithm to obtain required output power according to the difference value between the actual vehicle speed and the planned vehicle speed, and the required output power is used as a required power signal and is issued to the whole vehicle controller;

s7: and after the vehicle control unit receives a required power signal issued by the upper layer of unmanned driving, the vehicle control unit controls the fuel cell module and the power storage battery module to increase or decrease the respective output power and implement optimal power distribution.

Further, the mining truck control strategy based on the dynamic programming algorithm is as follows: the method comprises the steps that the minimum accumulated value of equivalent hydrogen consumption of the mining truck in the running process between a loading point and a unloading point mine field is taken as an optimization objective function, the SOC value of a power storage battery is taken as a state variable, the output power of a fuel battery and the output power of the power storage battery are taken as control variables, the upper limit and the lower limit of the SOC value of the electric quantity of the power storage battery and the road speed limit collected by a road test control unit are taken as constraint conditions, and an unmanned driving upper-layer strategy optimization module carries out speed planning on the current mining truck to obtain the planned speed.

Further, the calculation method of the cumulative value of the equivalent hydrogen consumption refers to that the following formula is adopted to optimize and obtain the minimum equivalent hydrogen consumption of different road sections:

wherein J is the cumulative value of equivalent hydrogen consumption, m_f(k) As the equivalent hydrogen consumption amount of the k-th traveling section,as a function of the hydrogen consumption of the fuel cell during a single control cycle, η is the power battery energy conversion efficiency, P_batt(k) For power output of the power accumulator, H_fuelFor the lower heating value of the hydrogen, SOC (k) represents the power battery SOC value of the k-th driving route, P_fc(k) And representing the output power of the fuel cell of the k-th driving section, k represents the k-th driving section, k is more than 0 and is an integer, and N represents the total number of the driving sections.

Further, the PID algorithm is:

wherein e [ n ]]Is the difference between the actual speed at time n and the planned speed, e [ n-1 ]]Is the difference between the actual vehicle speed at the moment n-1 and the planned vehicle speed, e [ n-2 ]]Is the difference value between the actual speed and the planned speed at the moment of n-2, n is a positive integer greater than 3, k_P、k_i、k_dProportional, integral and differential coefficients, P, respectively, of a PID controller₀For the initial power required before start-up of the mining truck, Pn]For the power required at the nth moment, Pn-1]The required output power, V, for the time n-1_factFor the actual transport speed, V, of the mining truck_goalAnd driving the planned vehicle speed issued by the upper layer for the unmanned vehicle.

Compared with the prior art, the invention has the advantages that:

1) the unmanned driving upper-layer strategy optimization module corrects a controlled object (vehicle speed) by using a PID algorithm based on a difference value between a planned vehicle speed instruction issued based on a dynamic planning algorithm and an actual vehicle speed fed back by a vehicle-mounted vehicle speed sensor and the like, and finally determines the required power of each of the fuel battery and the power storage battery, so that the required power can be obtained more accurately, and the energy management is optimized.

2) The invention is based on a dynamic programming algorithm, takes the SOC value of the power storage battery as a state variable, and takes the output power distribution of the fuel battery and the power storage battery as a control variable, so as to realize the minimum equivalent hydrogen consumption of different sections of the whole material transportation section, thus being beneficial to reasonably distributing the required power, being beneficial to increasing the endurance mileage of the unmanned mining truck, prolonging the service lives of the fuel battery and the power storage battery, and reducing the maintenance cost of the mining truck in the use process.

Drawings

Fig. 1 is a block diagram of an energy management optimization system for a hybrid drive unmanned mining truck according to an embodiment of the present invention.

Fig. 2 is a control schematic diagram of power distribution for a hybrid drive unmanned mining truck according to an embodiment of the present invention.

Detailed Description

The invention will be further described with reference to the following examples and FIG. 1, but the invention is not limited thereto.

The energy management method of the hybrid drive unmanned mining truck provided by the invention can overcome the defect of insufficient output power when a fuel cell is used as a single power source, solve the problem of short endurance mileage of the hybrid power mining truck, prolong the service lives of the fuel cell and a power storage battery of the mining truck and reduce the energy consumption of a system.

The invention provides an energy management method of a hybrid drive unmanned mining truck, which is characterized in that power provided by a fuel cell and a power storage battery of the hybrid drive unmanned mining truck in the driving process is distributed through an energy management optimization system, referring to fig. 1, the energy management optimization system comprises an unmanned driving upper layer 3, a whole vehicle controller 4, a communication unit (not shown in the figure), a vehicle speed sensor 7, a fuel cell module 5, a power storage battery module 6, a road test control unit 1 and a cloud server 2, the unmanned driving upper layer 3 comprises a strategy optimization module 301 and a power output module 302, and the unmanned driving upper layer 3, the communication unit, the vehicle speed sensor 7, the whole vehicle controller 4, the fuel cell module 5 and the power storage battery module 6 are located at the end of the unmanned mining truck. The unmanned driving upper layer 3 is respectively communicated with the cloud server and the vehicle speed sensor 7 through the communication unit.

The main implementation steps and principles are as follows:

s1, setting manually input charging points and discharging points on the cloud server;

the position information of a material loading and unloading point of a mine field is introduced into the cloud server by a worker, and the mine truck is guaranteed to run on a fixed transportation road section by combining a GPS positioning device on the mine truck.

S2, the road test control unit collects road information including road gradient, curve angle and road speed limit and uploads the road information to the cloud server in real time;

during driving, the road test control unit uploads real-time road information to the cloud server by collecting road information (including road gradient, curve angle and road speed limit) along the road.

S3, the cloud server calculates and analyzes the road gradient and the curve angle, plans a material transportation path of the mining truck, and sends the material transportation path to an unmanned driving upper layer through the communication unit;

the cloud server analyzes the road gradient and the curve angle, ensures that a steering system turns timely by combining the real-time curve angle on the premise that the mining truck runs on the road with the minimum road gradient, plans the most suitable material transportation path and sends the material transportation path to an unmanned driving upper layer through the communication unit.

S4, carrying out speed planning on the current mining truck to obtain a planned speed by adopting a mining truck control strategy based on a dynamic planning algorithm in a strategy optimization module after the unmanned driving upper layer, and sending the planned speed to a power output module of the unmanned driving upper layer;

after the unmanned driving upper layer receives the planned material transportation path signal, in a strategy optimization module, a mining truck control strategy based on a dynamic planning algorithm is adopted, the minimum accumulated value of equivalent hydrogen consumption of the mining truck in the driving process between a loading point and a unloading point mine field is taken as an optimization objective function, the SOC value of a power storage battery is taken as a state variable, the output power of a fuel battery and the output power of the power storage battery are taken as control variables, the upper limit and the lower limit of the SOC value of the power storage battery and the road speed limit collected by a road test control unit are taken as constraint conditions, the current mining truck is subjected to speed planning to obtain a planned vehicle speed, and the planned vehicle speed is sent to a power output module of the unmanned driving upper layer;

wherein, the minimum cumulative value of the equivalent hydrogen consumption can be calculated by the following formula:

wherein J is the cumulative value of equivalent hydrogen consumption, m_f(k) As the equivalent hydrogen consumption amount of the k-th traveling section,as a function of the hydrogen consumption of the fuel cell during a single control cycle, η is the power battery energy conversion efficiency, P_batt(k) For power output of the power accumulator, H_fuelFor the lower heating value of the hydrogen, SOC (k) represents the power battery SOC value of the k-th driving route, P_fc(k) And representing the output power of the fuel cell of the k-th driving section, k represents the k-th driving section, k is more than 0 and is an integer, and N represents the total number of the driving sections.

S5, after acquiring the actual speed of the mining truck, the speed sensor feeds the actual speed back to the power output module on the upper layer of the unmanned driving;

meanwhile, the vehicle-mounted vehicle speed sensor monitors actual wheel speed information of the mining truck, converts the actual wheel speed information into actual vehicle speed, and feeds the actual vehicle speed information back to the power output module on the upper layer of the unmanned driving through the communication unit in the form of electric signals.

And S6, the power output module on the upper layer of the unmanned driving obtains the required output power by adopting a PID algorithm according to the difference value between the actual vehicle speed and the planned vehicle speed, and the required output power is used as a required power signal and issued to the whole vehicle controller.

The principle of the PID algorithm is as follows, the actual speed of the mining truck is obtained according to a vehicle-mounted speed sensor, a speed error value e [ n ] is obtained by comparing the actual speed with a planned speed issued by an upper layer of unmanned driving, and a controlled object (namely the speed) is corrected by using the PID control algorithm, so that the speed is kept in a good dynamic stable state. The required output power is determined by the upper layer of unmanned driving according to the speed error value e [ n ], and the calculation method for obtaining the required output power is as follows:

1) and acquiring a fuzzy control rule, and presetting and storing the fuzzy control rule in an upper layer of the unmanned driving.

2) Fuzzification processing is carried out on the e [ n ] (vehicle speed error) by utilizing a fuzzy rule table to obtain the corresponding membership degree.

3) The obtained membership degree and the abscissa (PB, PS) of the membership degree are used to obtain the initial value Kp of the proportionality coefficient₀Initial value of integral coefficient Ki₀Initial value of differential coefficient Kd₀The respective increments Δ Kp, Δ Ki, Δ Kd.

4) From Kp to Kp₀+ΔKp，Ki＝Ki₀+ΔKi，Kd＝Kd₀And obtaining the coefficients Kp, Ki and Kd after setting by the + delta Kd, and substituting the coefficients into a PID controller for operation to obtain a calculation formula of the required output power, wherein the calculation formula is as follows:

wherein e [ n ]]Is the difference between the actual speed at time n and the planned speed, e [ n-1 ]]Is the difference between the actual vehicle speed at the moment n-1 and the planned vehicle speed, e [ n-2 ]]Is the difference value between the actual speed and the planned speed at the moment of n-2, n is a positive integer greater than 3, k_P、k_I、k_DProportional, integral and differential coefficients, P, respectively, of a PID controller₀For the initial power required before start-up of the mining truck, Pn]For the output power required at time n, Pn-1]Output power required for time n-1, V_factFor the actual transport speed, V, of the mining truck_goalAnd (4) driving the planned speed issued by the upper layer for the unmanned vehicle, wherein t is the time corresponding to different driving moments.

And S7, after receiving the power signal issued by the upper layer of unmanned driving, the vehicle control unit controls the fuel cell module and the power storage battery module to execute corresponding actions and implement power distribution.

Referring to fig. 2, fig. 2 is a control schematic diagram of power distribution of a hybrid drive unmanned mining truck, where a vehicle controller controls a fuel cell module and a power storage battery module to perform corresponding actions, and corresponding logics are as follows:

when the required power Pr does not exceed 20% of the rated power Pe of the power storage battery, the power storage battery independently provides the required power in a starting state;

when the required power Pr exceeds 20% of the rated power Pe of the power storage battery but does not exceed 80% of the rated power Pe of the power storage battery, the fuel cell is in an acceleration state at the moment, the fuel cell is in a preparation state and is ready to start, and the power storage battery provides the required power;

when the required power Pr exceeds 80% of the rated power Pe of the power storage battery, the fuel cell is started, and is in a full-load running state at the moment, the fuel cell is mainly driven by the fuel cell to provide the required power, the power storage battery is used for assisting, and the redundant power is provided by the power storage battery or charges the power storage battery;

when braking, the fuel cell is closed, and is in a regenerative braking mode, and redundant braking energy is used for charging the power storage battery.

The energy management method for hybrid driving the mining truck is described in detail, and the implementation description is only used for helping understanding the method and the core idea of the method; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.