Commercial vehicle multi-source integrated electric driving system and long-time-domain energy management method

Abstract

The invention discloses a multi-source integrated electric driving system of a commercial vehicle and a long-time-domain energy management method, and the system introduces a super capacitor as a short-time high-power output and rapid energy recovery device, and combines an intelligent energy management method. The method can comprehensively consider the dynamic power demand of a driving system, the power loss of the composite energy storage system, the life loss of an energy storage element and other factors, distributes the output power of a power battery and a super capacitor in the composite energy storage system, and gives full play to the respective advantages of the power battery and the super capacitor in the aspects of energy storage and supply. And the system not only can realize efficient scheduling of energy under different working conditions, but also can adjust the energy management method according to the performance attenuation of the energy storage element of the system in a long time domain, so that the comprehensive service life of the system is prolonged, and the long-term working stability of the system is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobile drive systems, and relates to the management of an integrated power battery-supercapacitor composite energy storage subsystem and a regenerative braking subsystem, and specifically to a commercial vehicle multi-source integrated electric drive system and a long-time domain energy management method. Background Art

[0002] The national "dual carbon" goal and the rapid development of new energy technologies have greatly promoted the transformation of commercial vehicles to electrification. As the core of pure electric commercial vehicles, the electric drive system directly affects its economy, safety and reliability. However, the current single power battery energy storage electric drive system still faces many challenges in energy management, such as the rapid life decay of power batteries under frequent high-load charging and discharging conditions, the large power demand gap during the heavy-load starting phase of the vehicle, and the low energy transfer efficiency between regenerative braking and the energy storage system. This series of problems not only leads to a reduction in system efficiency and cruising range, but also further increases the cost of using the system.

[0003] The power battery-supercapacitor composite energy storage system is an ideal alternative to the single power battery energy storage system. On the one hand, the supercapacitor can provide instantaneous large current to meet the high power demand of the vehicle's starting and acceleration stages; on the other hand, the power battery can provide a long-term small current power output to meet the stable power demand of the vehicle's uniform speed driving stage; in addition, the supercapacitor can also cooperate with the regenerative braking system to quickly store the recovered energy, avoid frequent charging and discharging of the power battery, and extend the life of the power battery. The Chinese invention patent application number is CN202410298529.X, and the patent name is "A composite energy storage electric vehicle energy management optimization method". It proposes an energy management method that considers the loss of the battery and DC / DC converter circuit, and uses DDPG network training to optimize the DC bus voltage and the expected voltage difference, which reduces the system power loss and ensures the stability of the bus voltage. However, the patent lacks consideration of the attenuation of the energy storage model parameters in the long time domain, so its reliability will deteriorate with the long-term use of the system. Compared with the traditional single energy storage system, the composite energy storage system has certain advantages in dynamic power, response speed and system life. However, the composite energy storage system contains multiple energy sources and multiple energy storage devices, which may cause certain conflicts and affect the overall performance of the system when considering multiple optimization issues such as power and life. Therefore, introducing a composite energy storage system into the electric drive system of commercial vehicles and considering the long-term energy management of the composite energy storage system are of great significance to improving the performance of the electric drive system and the life of the energy storage system. Summary of the invention

[0004] In view of the above problems, the main purpose of the present invention is to design a multi-source integrated electric drive system for commercial vehicles and a long-term energy management method. By introducing supercapacitors as short-term high-power output and rapid energy recovery devices, and combining them with intelligent energy management strategies, the problem of energy storage model parameter attenuation in the long time domain caused by the single energy storage system is solved.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A multi-source integrated electric drive system for commercial vehicles, the system includes an energy management main control unit, a vehicle information acquisition unit, an energy storage system power monitoring unit, a power battery assembly, a supercapacitor assembly, a DC / DC converter, a DC bus, an electric drive actuator assembly, and a regenerative braking assembly;

[0007] The energy management main control unit communicates data with the vehicle information acquisition unit, the energy storage system power monitoring unit, the power battery assembly, the supercapacitor assembly, the regenerative braking assembly, and the electric drive actuator assembly respectively;

[0008] The power battery assembly and the supercapacitor assembly are respectively connected to the DC / DC converter circuit;

[0009] The DC / DC converter is connected to the electric drive actuator assembly and the regenerative brake assembly through a DC bus.

[0010] As a further description of the present invention, the power battery assembly includes: a power battery pack, a power battery SOC observation module, and a power battery SOH estimation module;

[0011] The supercapacitor assembly includes: a power battery pack, a power battery SOC observation module, and a power battery SOH estimation module;

[0012] The electric drive actuator assembly includes: a drive motor, a DC / AC converter, a reducer, a central differential, and a half shaft.

[0013] As a further description of the present invention, the vehicle information acquisition unit includes: a vehicle status information storage module, a vehicle sensor module and a road condition module;

[0014] The vehicle status information storage module is used to receive and cache information from the vehicle sensor module and forward it to the energy management main control unit;

[0015] The vehicle sensor module is used to obtain vehicle status data and send it to the vehicle status information storage module;

[0016] The road condition module is used to obtain road condition information and send it to the vehicle status information storage module.

[0017] As a further description of the present invention, the energy management main control unit includes: a data management module, a power demand calculation module, an objective function calculation module, and a nonlinear model predictive control calculation module;

[0018] The data management module is used to process information obtained from the vehicle information acquisition unit, the energy storage system power monitoring unit, the power battery assembly, the supercapacitor assembly, the regenerative braking assembly, and the electric drive actuator assembly, and store data;

[0019] The power demand calculation module calculates the real-time power demand of the energy storage system according to the vehicle driving and braking control signals and road condition information, and provides it to the objective function calculation module;

[0020] The objective function calculation module calculates the energy management objective function taking into account power demand, system energy loss, and maximizing system life according to the actual output power, SOC, and SOH of the power battery assembly and the supercapacitor assembly, and provides it to the nonlinear model predictive control calculation module;

[0021] The nonlinear model predictive control calculation module is used to calculate the optimal power distribution coefficient based on the system model and objective function of the power battery assembly and the supercapacitor assembly to guide the power output of the power battery assembly and the supercapacitor assembly.

[0022] As a further description of the present invention, the energy storage system power monitoring unit includes: a DC bus current and voltage sensor module, a power battery side current and voltage sensor module, a supercapacitor side current and voltage sensor module, and a data storage module;

[0023] The DC bus current and voltage sensor module is used to measure the current and voltage signals on the DC bus and calculate the real-time power, and the obtained data is sent to the data storage module;

[0024] The power battery side current and voltage sensor module is used to measure the current and voltage signals of the power battery assembly side and calculate the real-time power, and the obtained data is sent to the data storage module;

[0025] The supercapacitor side current and voltage sensor module is used to measure the current and voltage signals on the supercapacitor assembly side and calculate the real-time power, and the obtained data is sent to the data storage module;

[0026] The data storage module receives and caches the information from the current and voltage sensor modules, and packages and sends the information to the energy management main control unit.

[0027] A method for long-term energy management of a commercial vehicle electric drive system, the method is an energy management method for a commercial vehicle electric drive system integrating a power battery-supercapacitor composite energy storage subsystem and a regenerative braking subsystem, and comprises the following steps:

[0028] S1: Collect vehicle status data, energy storage system power data, power battery and supercapacitor SOC and SOH data;

[0029] S2: Calculate the power demand of the energy storage system according to the vehicle status data collected in step S1;

[0030] S3: According to the energy storage system power data, power battery and supercapacitor SOC, SOH data collected in step S1, and the energy storage system power demand calculated in step S2, the power battery and supercapacitor charging and discharging model states are updated to form a system state sequence;

[0031] S4: Based on the data collected in step S1 and the power demand of the energy storage system calculated in step S2, an energy management optimization objective function is constructed;

[0032] S5: using the system state sequence formed in step S3 and the energy management optimization objective function constructed in step S4, performing nonlinear model predictive control optimization solution to calculate the power distribution coefficient between the power battery and the supercapacitor;

[0033] S6: completing energy distribution control of the composite energy storage system according to the control instruction obtained in step S5;

[0034] Among them, the energy management main control unit obtains the power requirements of the power battery and the supercapacitor according to the power requirement calculated in step S2 and the power allocation coefficient calculated in step S5, sends a control instruction to the DC / DC converter, adjusts the output voltage of the power battery and the supercapacitor, and realizes power control.

[0035] As a further description of the present invention, in step S2, the expression for calculating the power demand of the energy storage system is:

[0036] (1) The power requirement for pure driving is expressed as:

[0037]

[0038] (2) The power requirement during regenerative braking is expressed as:

[0039]

[0040] Where g is the acceleration due to gravity; v is the vehicle speed; α is the grade; ρ is the air density; M is the total mass of the vehicle, including the sprung mass m0 and the unsprung mass m1; η Tis the mechanical transmission efficiency; η m is the motor drive efficiency; η r is the regenerative braking efficiency.

[0041] As a further description of the present invention, in step S3, the power battery and supercapacitor charging and discharging model update expression is:

[0042] (1) Power battery state of charge (SOC) B The state equation is expressed as:

[0043]

[0044] (2) Supercapacitor voltage SOC SC The state equation is expressed as:

[0045]

[0046] Among them, U OC is the open circuit voltage of the power battery, which is a function of the state of charge of the power battery and is used to measure and draw a relationship curve between the two; Q B is the battery ampere-hour capacity; is the power distribution coefficient of the power battery; P demand is the total power demand of the system; η DC is the DC / DC converter efficiency; U Cut is the cut-off voltage of the supercapacitor; U NoC is the difference between the nominal voltage and the cut-off voltage; C SC is the supercapacitor capacitance;

[0047] Forming the system state sequence, the expression is:

[0048]

[0049] As a further description of the present invention, in step S4, the construction of the energy management optimization objective function of the composite energy storage system includes:

[0050] (1) According to the difference between the actual power and the required power of the power battery and the supercapacitor, the objective function of the dynamic power demand satisfaction of the composite energy storage system is expressed as 1, and the expression is:

[0051]

[0052] Among them, P B is the actual power of the power battery, P B =I B U OC ;P SC is the actual power of the supercapacitor, P SC =I SC USC ;

[0053] (2) The energy dissipation power of supercapacitor during operation is divided into energy supply and energy storage, and the expression is:

[0054]

[0055]

[0056] Among them, R B is the internal resistance of the power battery; R SC is the internal resistance of the supercapacitor;

[0057] (3) Based on the energy dissipation power of the power battery and supercapacitor during operation, the objective function of the energy consumption of the composite energy storage system is expressed as 2, and the expression is:

[0058]

[0059] (4) Based on the calculation of the equivalent impairment coefficient, the objective function of the equivalent life loss of the composite energy storage system is expressed as 3, and the expression is:

[0060]

[0061] Among them, SOH B SOH is the health status of the power battery; SC is the health state of the supercapacitor; κ is the equivalent loss factor;

[0062] The equivalent loss system κ is expressed as:

[0063]

[0064] Where s is the equivalent factor. When the value is 1, it means that the power battery and the supercapacitor have the same life loss rate. Under this condition, if the initial health status is the same, the power battery and the supercapacitor can reach the end of their life together; M SC is the total energy of the supercapacitor, M B A is the total energy of the power battery; h,tot E is the total ampere-hour capacity of the power battery in a new state; h,tot is the potential of the supercapacitor in a new state.

[0065] As a further description of the present invention, in step S5, the nonlinear model predictive control optimization solution process includes the following steps:

[0066] S51: The energy management problem of the composite energy storage system is expressed as a planning problem:

[0067]

[0068] Among them, λ1, λ2, λ3 are weight coefficients of the objective function, λ1+λ2+λ3=1, and the importance of each objective in the energy management strategy of the composite energy storage system is adjusted by changing the size of the weight coefficient;

[0069] S52: According to the prediction time domain t set in the nonlinear model predictive control problem p With control time domain t c , randomly generate the control sequence u(k), the expression is:

[0070]

[0071] S53: According to the randomly generated control sequence, the state is updated according to step S3, and whether the constraint conditions of the nonlinear model predictive control are met is checked. If so, the next step is performed; if not, the process returns to step S52;

[0072] S54: Calculate the objective function according to step S4, and calculate the direction of the control amount change that can make the objective function gradient decrease, and optimize the control sequence in this direction, and repeat step S53 until the gradient stops decreasing or the maximum number of iterations is reached;

[0073] S55: output the optimal control sequence U(k);

[0074] S56: Output the first element of the optimal control sequence as the optimal system control instruction.

[0075] Compared with the prior art, the technical effects of the present invention are:

[0076] The present invention provides a multi-source integrated electric drive system for commercial vehicles and a long-term energy management method. The system introduces a supercapacitor as a short-term high-power output and rapid energy recovery device, and is combined with an intelligent energy management method. It can comprehensively consider factors such as the dynamic power demand of the drive system, the power loss of the composite energy storage system, and the life loss of the energy storage element, and distribute the output power of the power battery and the supercapacitor in the composite energy storage system, give full play to the respective advantages of the power battery and the supercapacitor in energy storage and supply, improve the performance of the electric drive system and the life of the energy storage system, and the system can not only realize efficient energy scheduling under different working conditions, but also adjust the energy management method according to the performance attenuation of the system energy storage element in the long-term domain, extend the comprehensive life of the system, and ensure the stability of the long-term operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a schematic diagram of the system structure of the present invention;

[0078] Figure 2 It is a schematic diagram of the method flow of the present invention.

[0079] In the figure, 1. Vehicle information acquisition unit, 2. Energy management main control unit (EMCU), 3. Supercapacitor assembly, 4. Power battery assembly, 5. DC / DC converter, 6. Energy storage system power monitoring unit, 7. DC bus, 8. DC / AC converter, 9. Drive motor, 10. Reducer, 11. Central differential, 12. Half shaft, 13. Regenerative brake assembly;

[0080] AA'-Power battery state of charge (SOC B ) and health status (SOH B ) signal, BB'-power battery output current I B And voltage U OC , CC'-supercapacitor voltage (SOC SC ) and health status (SOH SC ) signal, DD'-supercapacitor output current I SC And voltage U SC Signal, EE'-total output current I of the composite energy storage system HSS And voltage U HSS Signal, FF'-the vehicle information acquisition unit output signal, including vehicle status signal, road condition signal, and vehicle driving and braking control signal. DETAILED DESCRIPTION

[0081] The present invention is described in detail below in conjunction with the accompanying drawings:

[0082] In one embodiment of the present invention, a multi-source integrated electric drive system for a commercial vehicle is disclosed, such as Figure 1 As shown, the system includes: an energy management main control unit 2, a vehicle information acquisition unit 1, an energy storage system power monitoring unit 6, a power battery assembly 4, a super capacitor assembly 3, a DC / DC converter 5, a DC bus 7, an electric drive actuator assembly, and a regenerative braking assembly 13; wherein the energy management main control unit 2 communicates data with the vehicle information acquisition unit 1, the energy storage system power monitoring unit 6, the power battery assembly 4, the super capacitor assembly 3, the regenerative braking assembly 13, and the electric drive actuator assembly respectively; the power battery assembly 4 and the super capacitor assembly 3 are respectively connected to the DC / DC converter 5 circuit; the DC / DC converter 5 is connected to the electric drive actuator assembly and the regenerative braking assembly 13 through the DC bus 7.

[0083] Specifically, in this embodiment, the power battery assembly 4 includes: a power battery pack, a power battery SOC observation module, and a power battery SOH estimation module; the supercapacitor assembly 3 includes: a power battery pack, a power battery SOC observation module, and a power battery SOH estimation module; the electric drive actuator assembly includes: a drive motor 9, a DC / AC converter 8, a reducer 10, a central differential 11, and a half-axle 12.

[0084] In this embodiment, the above-mentioned vehicle information acquisition unit 1 includes: a vehicle status information storage module, a vehicle sensor module and a road condition module; wherein the vehicle status information storage module is used to receive and cache information from the vehicle sensor module, and forward it to the energy management main control unit 2; the vehicle sensor module is used to obtain vehicle status data and send it to the vehicle status information storage module; the road condition module is used to obtain road condition information and send it to the vehicle status information storage module.

[0085] In this embodiment, the energy management main control unit 2 includes: a data management module, a power demand calculation module, an objective function calculation module, and a nonlinear model predictive control calculation module; wherein the data management module is used to process information obtained from the vehicle information acquisition unit 1, the energy storage system power monitoring unit 6, the power battery assembly 4, the supercapacitor assembly 3, the regenerative braking assembly 13, and the electric drive actuator assembly, and store the data; the power demand calculation module calculates the real-time power demand of the energy storage system based on the calculated vehicle drive and braking control signals and road condition information, and provides it to the objective function calculation module; the objective function calculation module calculates the energy management objective function that takes into account power demand, system energy loss, and maximizes system life according to the actual output power, SOC, and SOH of the power battery assembly 4 and the supercapacitor assembly 3, and provides it to the nonlinear model predictive control calculation module; the nonlinear model predictive control calculation module is used to calculate the optimal power distribution coefficient based on the system model and objective function of the power battery assembly 4 and the supercapacitor assembly 3, and guide the power output of the power battery assembly 4 and the supercapacitor assembly 3.

[0086] In this embodiment, the energy storage system power monitoring unit 6 includes: a DC bus current and voltage sensor module, a power battery side current and voltage sensor module, a supercapacitor side current and voltage sensor module, and a data storage module; wherein the DC bus current and voltage sensor module is used to measure the current and voltage signals on the DC bus and calculate the real-time power, and the obtained data is sent to the data storage module; the power battery side current and voltage sensor module is used to measure the current and voltage signals on the power battery assembly side and calculate the real-time power, and the obtained data is sent to the data storage module; the supercapacitor side current and voltage sensor module is used to measure the current and voltage signals on the supercapacitor assembly side and calculate the real-time power, and the obtained data is sent to the data storage module; the data storage module receives and caches the information from the current and voltage sensor module, and packages it and sends it to the energy management main control unit 2.

[0087] In another embodiment of the present invention, a method for long-term energy management of an electric drive system of a commercial vehicle is disclosed, such as Figure 2As shown, the method is an energy management method based on the above system. Specifically, the method is an energy management method for a commercial vehicle electric drive system integrating a power battery-supercapacitor composite energy storage subsystem and a regenerative braking subsystem, and includes the following steps:

[0088] S1: Collect vehicle status data, energy storage system power data, power battery and supercapacitor SOC and SOH data;

[0089] S2: Calculate the power demand of the energy storage system according to the vehicle status data collected in step S1;

[0090] S3: According to the energy storage system power data, power battery and supercapacitor SOC, SOH data collected in step S1, and the energy storage system power demand calculated in step S2, the power battery and supercapacitor charging and discharging model states are updated to form a system state sequence;

[0091] S4: Based on the data collected in step S1 and the power demand of the energy storage system calculated in step S2, an energy management optimization objective function is constructed;

[0092] S5: using the system state sequence formed in step S3 and the energy management optimization objective function constructed in step S4, performing nonlinear model predictive control optimization solution to calculate the power distribution coefficient between the power battery and the supercapacitor;

[0093] S6: Complete energy distribution control of the composite energy storage system according to the control instruction obtained in step S5; wherein the energy management main control unit obtains the power requirements of the power battery and the supercapacitor according to the power requirement calculated in step S2 and the power distribution coefficient calculated in step S5, sends a control instruction to the DC / DC converter, adjusts the output voltage of the power battery and the supercapacitor, and realizes power control.

[0094] Specifically, this embodiment describes the above method in detail, and the content is as follows:

[0095] In this embodiment, in the above step S1, vehicle status data is collected based on the vehicle information acquisition unit in the above system, energy storage system power data is acquired based on the energy storage system power detection unit in the above system, and power battery and supercapacitor SOC and SOH data are acquired based on the power battery assembly and supercapacitor assembly in the above system.

[0096] In this embodiment, in the above step S2, the power demand of the energy storage system is calculated based on the vehicle status data collected in step S1 and the energy management main control unit in the above system. Specifically, the expression for calculating the power demand of the energy storage system is:

[0097] (1) The power requirement for pure driving is expressed as:

[0098]

[0099] (2) The power requirement during regenerative braking is expressed as:

[0100]

[0101] Where g is the acceleration due to gravity; v is the vehicle speed; α is the grade; ρ is the air density; M is the total mass of the vehicle, including the sprung mass m0 and the unsprung mass m1; η T is the mechanical transmission efficiency; η m is the motor drive efficiency; η r is the regenerative braking efficiency.

[0102] In this embodiment, in the above step S3, the power battery and supercapacitor charging and discharging model update expression is:

[0103] (1) Power battery state of charge (SOC) B The state equation is expressed as:

[0104]

[0105] (2) Supercapacitor voltage SOC SC The state equation is expressed as:

[0106]

[0107] Among them, U OC is the open circuit voltage of the power battery, which is a function of the state of charge of the power battery and is used to measure and draw a relationship curve between the two; Q B is the battery ampere-hour capacity; is the power distribution coefficient of the power battery; P demand is the total power demand of the system; η DC is the DC / DC converter efficiency; U Cut is the cut-off voltage of the supercapacitor; U NoC is the difference between the nominal voltage and the cut-off voltage; C SC is the supercapacitor capacitance;

[0108] (3) Form a system state sequence, expressed as:

[0109]

[0110] In this embodiment, in the above step S4, the construction of the energy management optimization objective function of the composite energy storage system includes:

[0111] (1) By calculating the difference between the actual power and the required power of the power battery and the supercapacitor respectively, the objective function of the dynamic power demand satisfaction of the composite energy storage system is expressed as 1, and the expression is:

[0112]

[0113] Among them, P B is the actual power of the power battery, P B =I B U OC ;P SC is the actual power of the supercapacitor, P SC =I SC U SC ;

[0114] (2) Calculate the energy dissipation power of the power battery and the supercapacitor during operation respectively. Considering that in the above system, all the energy recovered by regenerative braking is stored in the supercapacitor, the energy dissipation power of the supercapacitor during operation is calculated in two processes: energy supply and energy storage. The expression is:

[0115]

[0116] Among them, R B is the internal resistance of the power battery; R SC is the internal resistance of the supercapacitor;

[0117] (3) Based on the energy dissipation power of the power battery and supercapacitor during operation, the objective function of the energy consumption of the composite energy storage system is expressed as 2, and the expression is:

[0118]

[0119] (4) Based on the calculation of the equivalent impairment coefficient, the objective function of the equivalent life loss of the composite energy storage system is expressed as 3, and the expression is:

[0120]

[0121] Among them, SOH B SOH is the health status of the power battery; SC is the health state of the supercapacitor; κ is the equivalent loss factor;

[0122] The above equivalent loss system κ is expressed as:

[0123]

[0124] Where s is the equivalent factor. When the value is 1, it means that the power battery and the supercapacitor have the same life loss rate. Under this condition, if the initial health status is the same, the power battery and the supercapacitor can reach the end of their life together; MSC is the total energy of the supercapacitor, M B A is the total energy of the power battery; h,tot E is the total ampere-hour capacity of the power battery in a new state; h,tot is the potential of the supercapacitor in a new state.

[0125] In this embodiment, in the above step S5, the nonlinear model predictive control optimization solution process includes the following steps:

[0126] S51: The energy management problem of the composite energy storage system is expressed as a planning problem:

[0127]

[0128] Among them, λ1, λ2, λ3 are weight coefficients of the objective function, λ1+λ2+λ3=1, and the importance of each objective in the energy management strategy of the composite energy storage system is adjusted by changing the size of the weight coefficient;

[0129] S52: According to the prediction time domain t set in the nonlinear model predictive control problem p With control time domain t c , randomly generate the control sequence u(k), the expression is:

[0130]

[0131] S53: According to the randomly generated control sequence, the state is updated according to step S3, and whether the constraint conditions of the nonlinear model predictive control are met is checked. If so, the next step is performed; if not, the process returns to step S52;

[0132] S54: Calculate the objective function according to step S4, and calculate the direction of the control amount change that can make the objective function gradient decrease, and optimize the control sequence in this direction, and repeat step S53 until the gradient stops decreasing or the maximum number of iterations is reached;

[0133] S55: output the optimal control sequence U(k);

[0134] S56: Output the first element of the optimal control sequence as the optimal system control instruction, and the calculation is completed.

[0135] Through the above embodiments, the technical solution of the present invention is disclosed. Compared with the prior art, the present invention has the following advantages:

[0136] 1. The energy management method of the present invention can comprehensively consider factors such as the dynamic power demand of the drive system, the power loss of the composite energy storage system, and the life loss of the energy storage element, and allocate the output power of the power battery and the supercapacitor in the composite energy storage system, giving full play to the advantages of the power battery and the supercapacitor in energy storage and supply, and improving energy utilization efficiency;

[0137] 2. The present invention introduces supercapacitors as parallel energy storage sources in a single source (power battery) energy storage system, which can effectively improve the working environment of the power battery, reduce the workload, and extend the working life.

[0138] 3. The supercapacitor introduced in the present invention can, on the one hand, quickly store the energy generated by regenerative braking to avoid frequent charging and discharging of the power battery; on the other hand, it can quickly respond to short-term high-power demands such as heavy-load starting, output high power, and avoid overloading the power battery.

[0139] The above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in this field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. A multi-source integrated electric drive system for a commercial vehicle, characterized in that: The system includes an energy management main control unit, a vehicle information acquisition unit, an energy storage system power monitoring unit, a power battery assembly, a supercapacitor assembly, a DC / DC converter, a DC bus, an electric drive actuator assembly, and a regenerative braking assembly; The energy management main control unit communicates data with the vehicle information acquisition unit, the energy storage system power monitoring unit, the power battery assembly, the supercapacitor assembly, the regenerative braking assembly, and the electric drive actuator assembly respectively; The power battery assembly and the supercapacitor assembly are respectively connected to the DC / DC converter circuit; The DC / DC converter is connected to the electric drive actuator assembly and the regenerative brake assembly through a DC bus.

2. A multi-source integrated electric drive system for commercial vehicles according to claim 1, characterized in that: The power battery assembly comprises: a power battery pack, a power battery SOC observation module, and a power battery SOH estimation module; The supercapacitor assembly includes: a power battery pack, a power battery SOC observation module, and a power battery SOH estimation module; The electric drive actuator assembly includes: a drive motor, a DC / AC converter, a reducer, a central differential, and a half shaft.

3. A multi-source integrated electric drive system for commercial vehicles according to claim 2, characterized in that: The vehicle information acquisition unit includes: a vehicle status information storage module, a vehicle sensor module and a road condition module; The vehicle status information storage module is used to receive and cache information from the vehicle sensor module and forward it to the energy management main control unit; The vehicle sensor module is used to obtain vehicle status data and send it to the vehicle status information storage module; The road condition module is used to obtain road condition information and send it to the vehicle status information storage module.

4. A multi-source integrated electric drive system for commercial vehicles according to claim 3, characterized in that: The energy management main control unit includes: a data management module, a power demand calculation module, an objective function calculation module, and a nonlinear model predictive control calculation module; The data management module is used to process information obtained from the vehicle information acquisition unit, the energy storage system power monitoring unit, the power battery assembly, the supercapacitor assembly, the regenerative braking assembly, and the electric drive actuator assembly, and store data; The power demand calculation module calculates the real-time power demand of the energy storage system according to the vehicle driving and braking control signals and road condition information, and provides it to the objective function calculation module; The objective function calculation module calculates the energy management objective function taking into account power demand, system energy loss, and maximizing system life according to the actual output power, SOC, and SOH of the power battery assembly and the supercapacitor assembly, and provides it to the nonlinear model predictive control calculation module; The nonlinear model predictive control calculation module is used to calculate the optimal power distribution coefficient based on the system model and objective function of the power battery assembly and the supercapacitor assembly to guide the power output of the power battery assembly and the supercapacitor assembly.

5. The multi-source integrated electric drive system for commercial vehicles according to claim 1, characterized in that: The energy storage system power monitoring unit includes: a DC bus current and voltage sensor module, a power battery side current and voltage sensor module, a supercapacitor side current and voltage sensor module, and a data storage module; The DC bus current and voltage sensor module is used to measure the current and voltage signals on the DC bus and calculate the real-time power, and the obtained data is sent to the data storage module; The power battery side current and voltage sensor module is used to measure the current and voltage signals of the power battery assembly side and calculate the real-time power, and the obtained data is sent to the data storage module; The supercapacitor side current and voltage sensor module is used to measure the current and voltage signals on the supercapacitor assembly side and calculate the real-time power, and the obtained data is sent to the data storage module; The data storage module receives and caches the information from the current and voltage sensor modules, and packages and sends the information to the energy management main control unit.

6. A method for long-term energy management of a commercial vehicle electric drive system according to any one of claims 1 to 5, characterized in that: The method is an energy management method for a commercial vehicle electric drive system that integrates a power battery-supercapacitor composite energy storage subsystem and a regenerative braking subsystem, and includes the following steps: S1: Collect vehicle status data, energy storage system power data, power battery and supercapacitor SOC and SOH data; S2: Calculate the power demand of the energy storage system according to the vehicle status data collected in step S1; S3: According to the energy storage system power data, power battery and supercapacitor SOC, SOH data collected in step S1, and the energy storage system power demand calculated in step S2, the power battery and supercapacitor charging and discharging model states are updated to form a system state sequence; S4: Based on the data collected in step S1 and the power demand of the energy storage system calculated in step S2, an energy management optimization objective function is constructed; S5: using the system state sequence formed in step S3 and the energy management optimization objective function constructed in step S4, performing nonlinear model predictive control optimization solution to calculate the power distribution coefficient between the power battery and the supercapacitor; S6: completing energy distribution control of the composite energy storage system according to the control instruction obtained in step S5; Among them, the energy management main control unit obtains the power requirements of the power battery and the supercapacitor according to the power requirement calculated in step S2 and the power allocation coefficient calculated in step S5, sends a control instruction to the DC / DC converter, adjusts the output voltage of the power battery and the supercapacitor, and realizes power control.

7. A method for long-term energy management of a commercial vehicle electric drive system according to claim 6, characterized in that: In step S2, the expression for calculating the power demand of the energy storage system is: (1) The power requirement for pure driving is expressed as: (2) The power requirement during regenerative braking is expressed as: Where g is the acceleration due to gravity; v is the vehicle speed; α is the grade; ρ is the air density; M is the total mass of the vehicle, including the sprung mass m0 and the unsprung mass m1; η T is the mechanical transmission efficiency; η m is the motor drive efficiency; η r is the regenerative braking efficiency.

8. The long-term energy management method for a commercial vehicle electric drive system according to claim 6, characterized in that: In step S3, the power battery and supercapacitor charging and discharging model update expression is: (1) Power battery state of charge (SOC) B The state equation is expressed as: (2) Supercapacitor voltage SOC SC The state equation is expressed as: Among them, U OC is the open circuit voltage of the power battery, which is a function of the state of charge of the power battery and is used to measure and draw a relationship curve between the two; Q B is the battery ampere-hour capacity; is the power distribution coefficient of the power battery; P demand is the total power demand of the system; η DC is the DC / DC converter efficiency; U Cut is the cut-off voltage of the supercapacitor; U NoC is the difference between the nominal voltage and the cut-off voltage; C SC is the supercapacitor capacitance; Forming the system state sequence, the expression is:

9. A method for long-term energy management of a commercial vehicle electric drive system according to claim 8, characterized in that: In step S4, the construction of the energy management optimization objective function of the composite energy storage system includes: (1) According to the difference between the actual power and the required power of the power battery and the supercapacitor, the objective function of the dynamic power demand satisfaction of the composite energy storage system is expressed as 1, and the expression is: Among them, P B is the actual power of the power battery, P B =I B U OC ;P SC is the actual power of the supercapacitor, P SC =I SC U SC ; (2) The energy dissipation power of supercapacitor during operation is divided into energy supply and energy storage, and the expression is: Among them, R B is the internal resistance of the power battery; R SC is the internal resistance of the supercapacitor; (3) Based on the energy dissipation power of the power battery and supercapacitor during operation, the objective function of the energy consumption of the composite energy storage system is expressed as 2, and the expression is: (4) Based on the calculation of the equivalent impairment coefficient, the objective function of the equivalent life loss of the composite energy storage system is expressed as 3, and the expression is: Among them, SOH B SOH is the health status of the power battery; SC is the health state of the supercapacitor; κ is the equivalent loss factor; The equivalent loss system κ is expressed as: Where s is the equivalent factor. When the value is 1, it means that the power battery and the supercapacitor have the same life loss rate. Under this condition, if the initial health status is the same, the power battery and the supercapacitor can reach the end of their life together; M SC is the total energy of the supercapacitor, M B A is the total energy of the power battery; h,tot E is the total ampere-hour capacity of the power battery in a new state; h,tot is the potential of the supercapacitor in a new state.

10. A method for long-term energy management of a commercial vehicle electric drive system according to claim 9, characterized in that: In step S5, the nonlinear model predictive control optimization solution process includes the following steps: S51: The energy management problem of the composite energy storage system is expressed as a planning problem: Among them, λ1, λ2, λ3 are weight coefficients of the objective function, λ1+λ2+λ3=1, and the importance of each objective in the energy management strategy of the composite energy storage system is adjusted by changing the size of the weight coefficient; S52: According to the prediction time domain t set in the nonlinear model predictive control problem p With control time domain t c , randomly generate the control sequence u(k), the expression is: S53: According to the randomly generated control sequence, the state is updated according to step S3, and whether the constraint conditions of the nonlinear model predictive control are met is checked. If so, the next step is performed; if not, the process returns to step S52; S54: Calculate the objective function according to step S4, and calculate the direction of the control amount change that can make the objective function gradient decrease, and optimize the control sequence in this direction, and repeat step S53 until the gradient stops decreasing or the maximum number of iterations is reached; S55: output the optimal control sequence U(k); S56: Output the first element of the optimal control sequence as the optimal system control instruction.

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