CN105882648A - Hybrid power system energy management method based on fuzzy logic algorithm - Google Patents

Abstract

The invention discloses an energy management method of a hybrid power system based on a fuzzy logic algorithm, which can automatically calculate the power demand of the whole vehicle and the power distribution combination in real time, and improve the fuel economy of the whole vehicle under the premise of ensuring power and meeting the needs of different users. The technical solution is: system self-inspection; the vehicle controller sends access signals to the energy source controller and drive motor controller to obtain signal data; judges whether the signal data is complete; The required power and the accessory power of the vehicle power system are calculated in real time through the fuzzy logic algorithm to obtain the output power of each energy source; the fuzzy logic algorithm is used to adjust and correct the output power of the energy source calculated in real time to obtain the power distribution combination; Based on the power distribution combination, the vehicle controller sends the output power distribution results to each energy source controller through the CAN bus, and completes the real-time adjustment of the output power of each energy source of the power system by the vehicle controller.

Description

A Fuzzy Logic Algorithm Based Energy Management Method for Hybrid Power System

technical field

The invention relates to the technical field of hybrid electric vehicle control, in particular to an energy management method of a hybrid electric system based on a fuzzy logic algorithm.

Background technique

A range-extended electric vehicle is a special hybrid electric vehicle designed to solve the problem of short mileage of pure electric vehicles. On the basis of pure electric vehicles, a range extender is added to increase the mileage of electric vehicles. The power battery is its main energy source, and the range extender system is its backup energy source. When the power of the power battery drops to a certain level, the range extender starts to work to charge the power battery or directly drive the vehicle to increase the mileage of the vehicle. Under the coordinated control of the vehicle energy management system, the dual-energy system cooperates with other components to perform multiple optimized combinations to form different power system working modes to adapt to different driving conditions.

The goal of an energy management strategy is usually a multi-objective nonlinear optimization problem with multiple input variables and constraints, and its control strategy has a significant impact on vehicle dynamics and fuel economy. Usually, when the designed vehicle travel distance is reached, the on-board energy storage system reaches a depleted state. On the one hand, excessive power consumption of the vehicle's power battery may lead to high-voltage electrical loss in the vehicle system or energy surplus in the range extender, affecting the overall energy efficiency of the vehicle; on the other hand, insufficient vehicle power consumption may not be able to obtain advance Designed for the purpose of reducing fuel consumption, the capacity of the power battery system is far from reaching the available limit. Therefore, how to obtain the appropriate distribution of power and energy flow between different energy sources in the application of HEVs is one of the fundamental issues of energy management strategies. In practical applications, since the driving conditions cannot be accurately predicted, an appropriate energy management strategy is the key to realizing energy saving and environmental protection of hybrid electric vehicles.

The four most widely studied energy management strategies for HEVs are rule-based control strategies, instantaneous optimal control strategies, global optimal control strategies, and adaptive control strategies based on optimization algorithms.

The working mechanism of the rule-based control strategy is: set a series of expected working state values of the vehicle in advance based on theoretical analysis and working experience intuition, and divide its working area. According to the set critical operating point, it can judge the area where the vehicle is working, so as to adopt the corresponding control method. The rule-based logic threshold algorithm is relatively simple and can be applied to real vehicle controllers. Combined with the results of offline optimization, parameters can be optimized to obtain more reasonable and economical working mode switching rules. The biggest advantage of this type of strategy is that it is easy to implement in engineering. However, the rule-based energy management strategy, no matter whether the control parameters have been optimized or not, still has certain limitations in improving fuel economy.

The instantaneous optimal control strategy usually adopts the algorithm of minimum equivalent fuel consumption or minimum power loss, and calculates the instantaneous minimum energy consumption of the whole vehicle by quantifying and unifying the energy consumption of the two energy sources with a specific method. This strategy is optimal in each step length, but it cannot be guaranteed to be optimal in the entire driving cycle, and requires a large number of floating-point calculations and relatively accurate vehicle and power system models, which requires a large amount of calculation and is difficult to implement. This type of energy management strategy has achieved good fuel economy results in computer simulations, but it has not been widely used in real vehicles because it has high requirements for the collection, analysis and processing of real-time driving state parameters of the vehicle. Changes in the performance of the vehicle power system have a greater impact on the real-time update of the basic database.

The global optimization control strategy can realize the global optimization of energy management under the condition of knowing all the parameters of all working conditions in the driving process of the car in advance. The global optimization mode realizes optimization in the true sense, but the algorithm to realize this strategy is often complex, the calculation amount is also large, and all road information needs to be obtained in advance, which is difficult to be applied in the real-time control of actual vehicles .

The adaptive control strategy based on the optimization algorithm can automatically predict the power and energy demand in the future according to the current vehicle driving state and road conditions, and automatically adjust the control parameters to adapt to the changes in driving conditions. The so-called self-adaptation means that at each time step, the working mode of the components is adjusted according to the current driving conditions and road conditions. Through the optimization algorithm, the energy demand is reasonably allocated to each energy source under the premise of ensuring the optimization of the objective function. Although the optimization algorithm of the objective function model of the adaptive control strategy is different, because the adaptive control needs to collect a large amount of power system operating data in real time, calculate the energy consumption, and predict the future working conditions, the optimization process is complicated and the calculation amount is large, which leads to its It cannot be applied in practice at present.

Contents of the invention

A brief summary of one or more aspects is presented below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all contemplated aspects and is intended to neither identify key or critical elements of all aspects nor attempt to delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.

The purpose of the present invention is to solve the above problems. It provides an energy management method for a hybrid power system based on a fuzzy logic algorithm, which can automatically calculate the power demand of the vehicle and the power distribution combination in real time according to the actual state of the vehicle. Under the premise of user needs, it can improve the fuel economy of the whole vehicle, and at the same time, it is easy to realize on the real vehicle.

The technical solution of the present invention is: the present invention discloses a hybrid system energy management method based on fuzzy logic algorithm, characterized in that the hybrid system includes a vehicle controller, an energy source controller, a motor controller and a CAN bus, The hybrid system energy management method includes:

Step 1: Carry out self-inspection on the vehicle controller, energy controller and motor controller, if there is no fault, go to step 2, if there is a fault, go to the fault handling mechanism;

Step 2: The vehicle controller sends access signals to the energy source controller and drive motor controller through the CAN bus and obtains the signal data required for fuzzy logic algorithm calculation;

Step 3: The vehicle controller judges whether the received signal data required for fuzzy logic algorithm calculation is complete, and if complete, proceed to step 4, and if incomplete, return to step 2;

Step 4: The vehicle controller calculates the required signal data according to the received fuzzy logic algorithm, and calculates the required power of the vehicle and/or the accessory power of the vehicle power system and/or the output power of the energy source in real time.

According to an embodiment of the fuzzy logic algorithm-based hybrid system energy management method of the present invention, it also includes:

Step 5: Use the fuzzy logic algorithm to adjust and correct the output power of the energy source to obtain the output power distribution combination;

Step 6: The vehicle controller sends the output power distribution results to each energy source controller through the CAN bus based on the power distribution combination, and completes the real-time adjustment of the output power of the energy source by the vehicle controller.

According to an embodiment of the hybrid system energy management method based on fuzzy logic algorithm of the present invention, the hybrid system further includes a power battery controller, a range extender controller, and a vehicle power accessory, and its energy source includes a power battery, The range extender and the vehicle controller are respectively connected to the power battery controller, range extender controller, drive motor controller, and vehicle power accessories through the CAN bus. Between the range extender and the range extender controller, the power battery and The power battery controllers are connected through the CAN bus, and the range extender is connected with the power battery through high-voltage wires.

According to an embodiment of the hybrid system energy management method based on fuzzy logic algorithm of the present invention, the power accessories of the whole vehicle include the heat dissipation subsystem of the whole vehicle, the air conditioning subsystem, headlights, electrical components of relays, and electrical consumers including meters.

According to an embodiment of the fuzzy logic algorithm-based hybrid power system energy management method of the present invention, the signal data in step 2 includes the current power battery SOC, the SOC variation ΔSOC within Δt time, the remaining fuel mass m _re of the range extender system, The vehicle demand power P _vehicle and the power system accessory power P _auxiliary of the vehicle.

According to an embodiment of the hybrid system energy management method based on fuzzy logic algorithm of the present invention, the required power of the whole vehicle in step 4 is calculated as follows:

The required power of the whole vehicle G=mg, m is the full load mass of the vehicle, f is the rolling resistance coefficient, α is the slope, C _D is the air resistance coefficient, A is the windward area of the car, V is the current speed of the car, η _t is the overall transmission efficiency, and δ is the vehicle Mass conversion coefficient, α is the slope angle of the driving road, when α is less than a certain value, cosα=1, α=sin α=tanα=i, i is the road slope, _Pauxiliary is the power of the vehicle power accessory system.

According to an embodiment of the hybrid power system energy management method based on fuzzy logic algorithm of the present invention, the power _Pauxiliary of the power accessory system of the whole vehicle is the electric device including the heat dissipation subsystem of the whole vehicle, the air conditioning subsystem, the headlight, the relay, and the instrument. The sum of all low-voltage electrical devices including electrical appliances when they are all working at the maximum power.

According to an embodiment of the hybrid power system energy management method based on fuzzy logic algorithm of the present invention, the real-time calculation of the output power of the range extender in step 4 is as follows:

Range extender output power Where E _re is the electrical energy that can be converted from the remaining fuel through the range extender, T is the sustainable use time estimated by the power battery based on the consumption rate of the SOC in the previous period of charge, and M is the calorific value of the fuel used by the range extender. η is the energy conversion efficiency of the range extender system for converting fuel into electric energy, SOC _t is the current state of charge of the power battery, SOC _min is the allowable cut-off value of the power battery, Δt is the sampling period, and ΔSOC is the SOC change within the sampling period amount, m _re is the remaining mass of fuel in the current range extender system.

According to an embodiment of the hybrid power system energy management method based on the fuzzy logic algorithm of the present invention, the adjustment and correction of the output power of the energy source in step 5 is as follows:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}\\ {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; max</span>}}\\ {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; max</span>}}\end{array}\right.$

Where _Pre is the adjusted output power of the range extender, P _{re_cal} is the output power of the range extender calculated in real time, P _{re_max} is the maximum sustainable output power of the range extender, and P _{re_min} is the minimum allowable output power of the range extender ;

Power battery output power P _battery =P _vehicle -P _re .

According to an embodiment of the energy management method for a hybrid power system based on a fuzzy logic algorithm of the present invention, the energy control method includes an energy management strategy suitable for a hybrid power system of an extended-range electric vehicle.

Compared with the prior art, the present invention has the following beneficial effects:

1) Based on the fuzzy logic algorithm, the present invention can provide real-time detection of the energy consumption rate of the current vehicle and the real-time status of each energy source, predict the future power request of the whole vehicle through fuzzy logic calculation, and real-time Adjust the output power of each energy source with good real-time performance;

2) The fuzzy logic calculation formula and logic rules in the present invention have the following effects: a) When the power battery consumption is too fast, the power output of the range extender can be increased in real time, and the power output of the range extender can be reduced in real time when the power consumption rate is low , to avoid the high-current charging and discharging of the power battery, taking into account the life of the power battery and the energy conversion efficiency; b) According to the corresponding fuzzy logic rules, the low-efficiency working area of the range extender system can be avoided, and the fuel economy of the whole vehicle can be improved;

3) The fuzzy logic algorithm solves the problem of excessive consumption or insufficient consumption of power battery power of hybrid electric vehicles mentioned in the background technology;

4) The fuzzy logic algorithm has low requirements on the controller hardware and is easy to implement on the whole vehicle;

5) The energy management algorithm adopted in the present invention can be applied to fuel cell-battery, internal combustion engine-battery, internal combustion engine-supercapacitor and other forms of new energy vehicle hybrid power systems, and has good scalability.

Description of drawings

Fig. 1 shows a flow chart of a preferred embodiment of the energy management method for a hybrid power system based on a fuzzy logic algorithm of the present invention.

Fig. 2 shows a schematic diagram of the topological structure of the power system of the extended-range hybrid electric vehicle applicable to the present invention.

detailed description

After reading the detailed description of the embodiments of the present disclosure in conjunction with the following drawings, the above-mentioned features and advantages of the present invention can be better understood, but the present invention is not limited in any form. In the drawings, components are not necessarily drawn to scale, and components with similar related properties or characteristics may have the same or similar reference numerals. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, all of which belong to the protection scope of the present invention.

The overall concept of the hybrid system energy management method of the present invention is described first, which is based on a fuzzy logic algorithm, which simplifies the energy management strategy of the hybrid system into a set of multi-input, single-output capacity management rules, using fuzzy logic The calculation method controls the hybrid power system through real-time calculation according to parameters such as power battery SOC, SOC variation within Δt time ΔSOC, range extender system remaining fuel mass m _re , vehicle demand power P _vehicle , vehicle power system accessory power P _auxiliary , etc. The output power of the energy source provides high fuel economy of the vehicle under the premise of meeting the needs of users.

The topology structure of the hybrid power system of the extended-range electric vehicle applying the method of this embodiment will be described in conjunction with FIG. 2 . As shown in Figure 2, the power system includes power battery 1, range extender 2, range extender system 3, drive motor 4, vehicle controller VMS 5, power battery controller BMS 6, range extender controller RES 7, Drive motor controller PEU 8, vehicle power system accessories 9, CAN bus 10.

The vehicle controller VMS 5 is connected to the range extender controller RES 7 , the power battery controller BMS 6 , the driving motor controller PEU 8 and the vehicle power system accessory 9 through the CAN bus 10 respectively. The range extender 2 is connected to the range extender controller RES 7, the power battery 1 is connected to the power battery controller BMS 6, and the range extender 2 is connected to the power battery 1 through a high-voltage wire.

The control parameters of the energy management strategy complete data interaction between the vehicle controller VMS 5 and the power battery controller 6 and the range extender controller 7 as energy sources through the CAN bus 10 . The vehicle controller VMS 5 obtains the data required for energy management strategy calculation from the CAN bus 10, provides the fuzzy logic algorithm formula to calculate the output power of the range extender, and then sends the output power combination to the controller of each energy source through the CAN bus 10 (power battery controller 6 and range extender controller 7) to complete the real-time adjustment of the power of the energy source of the power system.

Attachment 9 of the vehicle power system includes the power consumption of the vehicle cooling subsystem, air conditioning subsystem, headlights, relays and other electrical components, instruments and other electrical appliances.

Based on the extended-range electric vehicle hybrid power system shown in FIG. 2 , the flow chart of a preferred embodiment of the hybrid power system energy management method based on the fuzzy logic algorithm of the present invention is shown in FIG. 1 .

In step S201, the vehicle controller VMS, the drive motor controller PEU, the power battery controller BMS, and the range extender controller RES respectively perform self-checks on the subsystems they are responsible for to determine whether there is a fault. If the system is ready, execute step 203; if yes, execute step S202 of the fault handling mechanism.

In step 203, the vehicle controller VMS sends access signals to the power battery controller BMS, the range extender controller RES, and the drive motor controller PEU through the CAN bus to obtain signal data required for energy management strategy calculation.

The required signal data includes the current power battery SOC (State of Charge, that is, the state of charge of the power battery, which represents the remaining capacity of the battery after a period of use), the SOC change ΔSOC within Δt time, and the remaining fuel of the range extender system. The mass m _re , the required power of the whole vehicle P _vehicle , and the auxiliary power of the power system of the whole vehicle P _auxiliary .

In step 204, the vehicle controller VMS judges whether the received signal data is complete, if yes, execute step 205; if not, return to step 203.

In step 205, the vehicle controller VMS calculates the demand data according to the received energy management strategy, obtains the requested power P _vehicle of the vehicle through real-time calculation, and obtains the range extender output power P _re and The output power P _battery of the traction battery is adjusted in real time to finally obtain the power distribution combination P _vehicle =F(P _battery , _Pre , P _auxiliary ), and then go to step 206 .

The fuzzy logic algorithm involved in step 205 is as follows:

a) The requested power of the whole vehicle

G=mg, m is the full load mass of the vehicle, f is the rolling resistance coefficient, η _t is the transmission efficiency of the vehicle, α is the slope, C _D is the air resistance coefficient, A is the frontal area of the vehicle, V is the current speed of the vehicle, and δ is Vehicle mass conversion coefficient, usually when the slope angle of the driving road is not large, cosα = 1, α = sin α = tan α = i, i is the road slope, P _auxiliary is the accessory power of the vehicle power system, mainly for the vehicle radiator System, air-conditioning subsystem, headlights, electrical components of relays, and electrical appliances of instrument lights, the value of _Pauxiliary is the sum of all the above-mentioned low-voltage electrical components when they all work at the maximum power;

b) Real-time calculated value of range extender output power

Among them, E _re is the electric energy that can be converted by the remaining fuel through the range extender, T is the sustainable use time estimated by the power battery based on the consumption rate of the SOC in the previous period, and M is the calorific value of the fuel used by the range extender (xxMJ/kg ), η is the energy conversion efficiency (%) of the range extender system converting fuel into electric energy, SOC _t is the current state of charge of the power battery, SOC _min is the allowable cut-off value of the power battery, Δt is the sampling period (h), ΔSOC is the SOC variation within the sampling period, and m _re is the remaining mass of fuel in the current range extender system.

In step 206, the output power of the energy source calculated in real time is adjusted and corrected by a fuzzy logic algorithm to obtain a power distribution combination. The details are as follows: a) If P _{re_cal} is greater than the maximum sustainable power P _{re_max} of the range extender system, it can only output at P _{re_max} ; if P _{re_cal} is less than the minimum allowable output power of the range extender system, it can output at P _{re_min} . Where _{Pre_min} is the power point corresponding to the low efficiency region defined according to the characteristic curve of the range extender system. Taking the engine as an example, P _{re_min} is the power point corresponding to avoiding the engine entering the low speed region.

The summary formula is:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}\\ {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; max</span>}}\\ {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; max</span>}}\end{array}\right.<span\; class="notranslate">\; ;</span>$

Battery output power P _battery =P _vehicle -P _re .

In step 207, the vehicle controller VMS sends the power output results to the power battery controller BMS and the range extender controller RES through the CAN bus, and completes the output power distribution of the vehicle controller VMS to each energy source of the power system.

Although the methods described above are illustrated and described as a series of acts for simplicity of explanation, it is to be understood and appreciated that the methodologies are not limited by the order of the acts, as some acts may occur in a different order according to one or more embodiments And/or concurrently with other actions from those illustrated and described herein or not illustrated and described herein but can be understood by those skilled in the art.

Those of skill in the art would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The various illustrative logic blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented with a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other Implemented or performed by programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integrated into the processor. The processor and storage medium can reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and storage medium may reside as discrete components in the user terminal.

In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or other Any other medium that is suitable for program code and can be accessed by a computer. Any connection is also properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave , then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of media. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc, where disks are often reproduced magnetically. data, while a disc (disc) uses laser light to reproduce data optically. Combinations of the above should also be included within the scope of computer-readable media.

The previous description of the present disclosure is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to the present disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the present disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A hybrid system energy management method based on fuzzy logic algorithm, characterized in that, the hybrid system includes a vehicle controller, an energy source controller, a motor controller and a CAN bus, and the hybrid system energy management method includes : Step 1: Carry out self-inspection on the vehicle controller, energy controller and motor controller, if there is no fault, go to step 2, if there is a fault, go to the fault handling mechanism; Step 2: The vehicle controller sends access signals to the energy source controller and drive motor controller through the CAN bus and obtains the signal data required for fuzzy logic algorithm calculation; Step 3: The vehicle controller judges whether the received signal data required for fuzzy logic algorithm calculation is complete, and if complete, proceed to step 4, and if incomplete, return to step 2; Step 4: The vehicle controller calculates the required signal data according to the received fuzzy logic algorithm, and calculates the required power of the vehicle and/or the accessory power of the vehicle power system and/or the output power of the energy source in real time. 2. The method for energy management of hybrid power system based on fuzzy logic algorithm according to claim 1, further comprising: Step 5: Use the fuzzy logic algorithm to adjust and correct the output power of the energy source to obtain the output power distribution combination; Step 6: The vehicle controller sends the output power distribution results to each energy source controller through the CAN bus based on the power distribution combination, and completes the real-time adjustment of the output power of the energy source by the vehicle controller. 3. The energy management method of a hybrid power system based on a fuzzy logic algorithm according to claim 1, wherein the hybrid system also includes a power battery controller, a range extender controller, a vehicle power accessory, and its energy The source includes the power battery, the range extender, and the vehicle controller is respectively connected to the power battery controller, the range extender controller, the drive motor controller, and the vehicle power accessories through the CAN bus, and the distance between the range extender and the range extender controller The power battery and the power battery controller are connected through the CAN bus, and the range extender is connected to the power battery through high-voltage wires. 4. The energy management method for a hybrid power system based on a fuzzy logic algorithm according to claim 3, wherein the power accessories of the whole vehicle include the heat dissipation subsystem of the whole vehicle, the air conditioning subsystem, headlights, electric devices of relays, including instruments electrical appliances. 5. The fuzzy logic algorithm-based hybrid power system energy management method according to claim 4, wherein the signal data in step 2 includes the current power battery SOC, SOC variation ΔSOC within Δt time, range extender system remaining fuel quality m _re , vehicle required power P _vehicle , and vehicle power system accessory power P _auxiliary . 6. According to the hybrid system energy management method based on fuzzy logic algorithm according to any one of claims 1 and 5, it is characterized in that the required power of the whole vehicle in step 4 is calculated as follows: The required power of the whole vehicle G=mg, m is the full load mass of the vehicle, f is the rolling resistance coefficient, α is the slope, C _D is the air resistance coefficient, A is the windward area of the car, V is the current speed of the car, η _t is the overall transmission efficiency, and δ is the vehicle Mass conversion coefficient, α is the slope angle of the driving road, when α is less than a certain value, cos α=1, α=sin α=tan α=i, i is the road slope, _Pauxiliary is the power of the vehicle power accessory system. 7. The energy control method of a hybrid power system based on a fuzzy logic algorithm according to claim 6, wherein the vehicle power accessory system power P _auxiliary is a power supply comprising a vehicle cooling subsystem, an air conditioning subsystem, a headlight, and a relay. The sum of all low-voltage electrical devices, including electrical devices and instrument consumers, when they are all working at the maximum power. 8. The hybrid power system energy control method based on fuzzy logic algorithm according to claim 7, is characterized in that, the real-time calculation of the output power of the range extender in step 4 is as follows: Range extender output power Where E _re is the electrical energy that can be converted from the remaining fuel through the range extender, T is the sustainable use time estimated by the power battery based on the consumption rate of the SOC in the previous period of charge, and M is the calorific value of the fuel used by the range extender. η is the energy conversion efficiency of the range extender system for converting fuel into electric energy, SOC _t is the current state of charge of the power battery, SOC _min is the allowable cut-off value of the power battery, Δt is the sampling period, and ΔSOC is the SOC change within the sampling period amount, m _re is the remaining mass of fuel in the current range extender system. 9. The energy control method of a hybrid power system based on a fuzzy logic algorithm according to claim 8, wherein the adjustment and correction of the output power of the energy source in step 5 is as follows: ${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}\\ {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; max</span>}}\\ {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; max</span>}}\end{array}\right.$ Where _Pre is the adjusted output power of the range extender, P _{re_cal} is the output power of the range extender calculated in real time, P _{re_max} is the maximum sustainable output power of the range extender, and P _{re_min} is the minimum allowable output power of the range extender ; Power battery output power P _battery =P _vehicle -P _re . 10 . The fuzzy logic algorithm-based energy management method for a hybrid power system according to claim 1 , wherein the energy control method includes an energy management strategy suitable for a range-extended electric vehicle hybrid power system. 11 .