A ramp prediction adaptive ECMS hybrid electric vehicle energy management method

Abstract

The invention discloses a ramp prediction adaptive ECMS hybrid vehicle energy management method, which relates to the technical field of hybrid vehicle. First, according to the working condition characteristics of hybrid heavy trucks traveling in hilly areas, an AG-ECMS strategy is established; based on the AG-ECMS strategy, ramp prediction is extended, and the long and short time downhill working conditions are judged by the slope of the road ahead and the corresponding discharge coefficient is calculated, and the equivalent fuel consumption factor is corrected to obtain the SP-AG-ECMS strategy; then, the umbrella lizard algorithm is used to iteratively optimize the parameters to be optimized with fuel economy as the goal; finally, the SP-AG-ECMS strategy formulated with the optimal parameters is used to perform online power distribution of the engine and the drive motor through multiple parameters. The invention has the characteristics of maximizing the braking energy recovery potential of hybrid heavy trucks in mountainous areas and improving the fuel economy of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid electric vehicles, and more particularly to an energy management method for a ramp prediction adaptive ECMS hybrid electric vehicle. Background Art

[0002] As an important technical route for the low carbonization of commercial vehicles, hybrid technology has pure electric and series fuel-saving modes compared to traditional fuel heavy trucks, which can greatly improve the fuel economy of vehicles; compared to pure electric heavy trucks, hybrid heavy trucks have a higher cruising range and are not limited to fixed scenes such as docks and ports, but have a wider range of applicable scenes and working conditions. Therefore, hybrid heavy trucks are also the mainstream technical direction for the new energy of commercial vehicles.

[0003] The energy management strategies of hybrid heavy-duty trucks on the market are generally formulated for vehicles traveling in cities. Their slow and slow driving conditions are more conducive to the pure electric driving characteristics of hybrid vehicles and improve fuel saving. At the same time, in order to apply the energy management strategy online to hybrid heavy-duty trucks, most manufacturers use the ECMS equivalent energy consumption minimum strategy; however, in the face of high-speed, high-altitude mountain conditions, general energy management strategies cannot play their greatest role. For example, the driving time in pure electric mode is shorter and the engine works longer. At the same time, the energy recovery efficiency is low in the face of long-term downhill conditions. And under high-altitude conditions, the gravitational potential energy of hybrid heavy-duty trucks will also be a key factor affecting economy. Summary of the invention

[0004] The purpose of the present invention is to design and develop a slope prediction adaptive ECMS hybrid electric vehicle energy management method, which adjusts the battery discharge power according to long-term or short-term downhill descent to improve the braking recovery efficiency and the fuel economy of the vehicle.

[0005] The technical solution provided by the present invention is:

[0006] A ramp prediction adaptive ECMS hybrid electric vehicle energy management method comprises the following steps:

[0007] Step 1: Collect the battery SOC value, vehicle mass, vehicle altitude and road slope information of the vehicle at the current moment, and calculate the required power of the vehicle at the current moment;

[0008] Step 2: Establish and optimize the slope prediction adaptive equivalent energy consumption minimum strategy;

[0009] Among them, the optimized ramp prediction adaptive equivalent energy consumption minimum strategy satisfies:

[0010] ;

[0011] In the formula, is the Hamiltonian function, for Fuel consumption at the time, for The control variables at time , and , for The engine power distribution at the moment, for The drive motor distributes power at each moment. is the initial value of the optimal equivalent fuel consumption factor, is the optimal proportional adjustment coefficient, for The optimal slope prediction discharge coefficient at the moment, for The battery SOC reference value at the moment, for The battery SOC value at the moment, for The amount of electricity consumed at any given moment;

[0012] Step 3: Solve the problem with the goal of minimizing the Hamiltonian function to obtain the optimal control variable , that is, obtain The engine power distribution at the moment and The drive motor distributes power at each moment.

[0013] Preferably, the step 2 specifically includes:

[0014] Step 1: Establish AG-ECMS, specifically meeting the following requirements:

[0015] ;

[0016] In the formula, is the Hamiltonian function, for Fuel consumption at the time, for The equivalent fuel consumption of electrical energy at the moment, for The equivalent fuel consumption factor at time, for Moment The amount of electricity consumed at any given moment, for Control variables at time;

[0017] Among them, the The equivalent fuel consumption factor at the moment satisfies:

[0018] ;

[0019] In the formula, is the initial value of equivalent fuel consumption factor, is the proportional adjustment coefficient, for The battery SOC reference value at the moment, for Battery SOC value at the moment;

[0020] Step 2: The equivalent fuel consumption factor at the time is corrected:

[0021] ;

[0022] In the formula, is the equivalent fuel consumption factor after slope prediction correction, for The slope at the moment predicts the discharge coefficient;

[0023] Step 3: Establish a ramp prediction adaptive equivalent energy consumption minimum strategy:

[0024] ;

[0025] Step 4: Optimize the parameters to be optimized by using the umbrella lizard algorithm to obtain the optimal initial value of the equivalent fuel consumption factor, the optimal proportional adjustment coefficient, the optimal short-time downhill discharge coefficient and the optimal long-time downhill discharge coefficient, and obtain the optimized ramp prediction adaptive equivalent energy consumption minimum strategy;

[0026] The parameters to be optimized include an initial value of an equivalent fuel consumption factor, a proportional adjustment coefficient, a discharge coefficient for a short downhill period, and a discharge coefficient for a long downhill period.

[0027] Preferably, the The battery SOC reference value at the moment satisfies:

[0028] ;

[0029] In the formula, is the battery SOC reference initial value, is the gravitational potential energy adjustment coefficient, is the vehicle mass, is the acceleration due to gravity, is the vehicle altitude.

[0030] Preferably, the The slope prediction discharge coefficient at the moment satisfies:

[0031]

[0032] In the formula, for The slope prediction discharge coefficient at the moment, is the discharge coefficient for a short downhill period, is the discharge coefficient for long downhill periods, for The vehicle is traveling at the current speed The average slope of the road over this distance is It is the standard value for slope determination.

[0033] Preferably, the The vehicle is traveling at the current speed The average slope of the road for this distance satisfies:

[0034] ;

[0035] In the formula, To predict the total duration, for The future corresponding to each moment The slope of the moment.

[0036] Preferably, the slope determination standard value is -2.5%.

[0037] Preferably, the step 4 specifically includes:

[0038] Step a: Establish parameter optimization objective function:

[0039] ;

[0040] In the formula, For the The parameter optimization objective function of the frilled lizard is: is the number of iterations of the frill lizard algorithm, For the Frilled Lizard The fuel consumption corresponding to the iteration result is: For the Frilled Lizard The equivalent fuel consumption corresponding to the iteration result;

[0041] The constraints of the parameters to be optimized are:

[0042] ;

[0043] Step b: Generate the initial population according to the constraints, and calculate the corresponding parameter optimization objective function for each frilled lizard individual ;

[0044] Among them, the initial population satisfies;

[0045] ;

[0046] In the formula, is the element in the population matrix of frilled lizards, is the number of frilled lizards, is the parameter to be optimized, For the The lower limit of the parameter to be optimized, for The random numbers in For the The upper limit of the parameter to be optimized;

[0047] Step c: Create a prey set:

[0048] ;

[0049] In the formula, For the A collection of prey for a frilled lizard. is the first After the first iteration Individuals with good objective function values ​​for frilled lizards, For the The iteration The objective function value of each individual;

[0050] Step d: Initial update of the position of the frilled lizard:

[0051] ;

[0052] ;

[0053] In the formula, For the proposed new features for frilled lizards, for the characteristics of a random frilled lizard in the prey set, for A random number in For the The iteration The objective function value corresponding to the proposed new features of the frilled lizard, For the The new individuals obtained by transformation of the frilled lizards, For the The first transformation of a frilled lizard suggested a new individual;

[0054] Step e: Perform a secondary update on the new features of the frilled lizard:

[0055] ;

[0056] ;

[0057] In the formula, For the A new feature of the frilled lizard returning to the treetops, For the The iteration The objective function value corresponding to the new feature at the top of the tree is returned by the frill lizard. For the The second transformation of the frilled lizard into a new individual was proposed;

[0058] Step f: Iterate according to the above steps until the number of iterations is reached to obtain the parameters of the optimal individual. , , and ;

[0059] in, is the initial value of the optimal equivalent fuel consumption factor, is the optimal proportional adjustment coefficient, is the optimal short-time downhill discharge coefficient, is the optimal discharge coefficient for long downhill periods;

[0060] Step g: Substitute the parameters of the optimal individual into SP-AG-ECMS to obtain the optimized ramp prediction adaptive equivalent energy consumption minimum strategy.

[0061] Preferably, the Frilled Lizard The equivalent fuel consumption corresponding to the iteration result satisfies:

[0062] ;

[0063] In the formula, For the Frilled Lizard The equivalent fuel consumption factor corresponding to the iteration result.

[0064] The beneficial effects of the present invention are:

[0065] (1) The present invention designs and develops a slope prediction adaptive ECMS hybrid vehicle energy management method, which can predict whether the road ahead is a long downhill or a short downhill according to the collected road information, thereby adjusting the battery discharge. When encountering a long downhill, rapid discharge allows the battery to fully absorb the energy generated by the downhill, giving full play to the efficiency of braking recovery.

[0066] (2) The ramp prediction adaptive ECMS hybrid electric vehicle energy management method designed and developed by the present invention uses the umbrella lizard algorithm to iteratively optimize the parameters in the SP-AG-ECMS strategy, which greatly improves the performance of the optimized control strategy and improves the fuel economy of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic flow chart of the ramp prediction adaptive ECMS hybrid electric vehicle energy management method of the present invention.

[0068] Figure 2 It is a schematic diagram of the curve of slope prediction discharge according to the present invention.

[0069] Figure 3 It is a schematic diagram of the flow chart of the umbrella lizard algorithm described in the present invention. DETAILED DESCRIPTION

[0070] The present invention is further described in detail below so that those skilled in the art can implement it according to the description.

[0071] The present invention provides a ramp prediction adaptive ECMS hybrid vehicle energy management method. First, according to the working condition characteristics of hybrid heavy trucks traveling in hilly areas, an adaptive equivalent energy consumption minimum strategy (AG-ECMS) considering gravitational potential energy is formulated; on this basis, the AG-ECMS strategy is extended by ramp prediction, and the long and short time downhill working conditions are judged and the corresponding discharge coefficient is calculated through the slope information of the road ahead obtained from the ADAS map, and the equivalent fuel consumption factor is corrected to obtain the ramp prediction adaptive equivalent energy consumption minimum strategy (SP-AG-ECMS); then, the umbrella lizard algorithm is used to iteratively optimize the parameters to be optimized with fuel economy as the goal; finally, the SP-AG-ECMS strategy formulated with the optimal parameters is used to perform online power allocation for the engine and the drive motor through the real-time required power and battery SOC, and specifically includes the following steps:

[0072] like Figure 1 As shown, step 1, collecting the current vehicle's fuel consumption, power consumption, battery SOC value, vehicle mass, vehicle altitude and road slope information;

[0073] In this embodiment, the slope information of the road ahead is obtained according to the standardized ADASIS data format and protocol.

[0074] Step 2: Establish SP-AG-ECMS, including:

[0075] Step 1: Establish AG-ECMS, specifically meeting the following requirements:

[0076]

[0077] In the formula, for The prediction function at time, for Fuel consumption at the time, mL; for Equivalent fuel consumption of electrical energy at the moment, mL; for The equivalent fuel consumption factor at time, for Moment The amount of electricity consumed at any given moment, for The control variables at time , and The control variables at time satisfy:

[0078] ;

[0079] In the formula, for The engine power distribution at the moment, kW; for The driving motor power distribution at the moment, kW;

[0080] Among them, the The equivalent fuel consumption factor at the moment satisfies:

[0081] ;

[0082] In the formula, is the initial value of equivalent fuel consumption factor, is the proportional adjustment coefficient, for The battery SOC reference value at the moment, for Battery SOC value at the moment;

[0083] In this embodiment, considering the operating characteristics of hybrid heavy trucks in hilly areas that often encounter long downhill roads, the gravitational potential energy factor is added to the battery SOC reference value, so that a lower battery SOC reference value is beneficial to energy recovery. The battery SOC reference value at the moment satisfies:

[0084] ;

[0085] In the formula, is the battery SOC reference initial value, is the gravitational potential energy adjustment coefficient, is the vehicle mass, kg; is the acceleration due to gravity, m / s 2 ; is the vehicle altitude, m;

[0086] In this embodiment, the , , .

[0087] Step 2: Based on AG-ECMS, add slope prediction discharge function expansion, such as Figure 2 As shown, the future is drawn based on the slope information collected ahead of the road. The slope of the moment curve, and then establish the slope prediction equation:

[0088] ;

[0089] In the formula, for The vehicle is traveling at the current speed The average slope of the road over this distance is To predict the total duration, for The future corresponding to each moment The slope of the moment;

[0090] Solve the future corresponding to each moment The average slope of the road ahead in seconds, and Figure 2 Draw the corresponding curve;

[0091] In this embodiment, the total duration of the predicted future is 150 seconds.

[0092] Predicted slope calculated using the slope prediction equation Slope judgment standard value For comparison, the long / short downhill is judged according to the change of slope, and then it is predicted whether the road ahead is a long downhill or a short downhill, and different discharge coefficients are selected:

[0093] ;

[0094] In the formula, for The slope prediction discharge coefficient at the moment, is the discharge coefficient for a short downhill period, is the discharge coefficient for long downhill periods;

[0095] When the predicted slope The value is lower than the slope judgment standard value When the discharge coefficient is selected for a long downhill period Discharge the battery quickly; when the slope is predicted The value is higher than the slope judgment standard value When the discharge coefficient is selected for a short downhill Let the battery discharge slowly, and the specific SOC value curve is drawn on Figure 2 middle;

[0096] In this embodiment, ;

[0097] The equivalent fuel consumption factor is then corrected for the slope prediction:

[0098] ;

[0099] In the formula, Equivalent fuel consumption factor corrected for slope prediction.

[0100] Step 3: Create SP-AG-ECMS based on the above steps:

[0101] ;

[0102] Step 4: Using the umbrella lizard algorithm, the high-altitude mountainous conditions collected from the actual vehicle are selected as the optimization conditions, and the parameters to be optimized are optimized;

[0103] The parameters to be optimized include the initial value of the equivalent fuel consumption factor , proportional adjustment coefficient , discharge coefficient for short downhill and the discharge coefficient for long downhill periods ;

[0104] Specific:

[0105] Step a: The parameter optimization objective function of the lizard algorithm for reducing fuel consumption is:

[0106] ;

[0107] In the formula, For the The parameter optimization objective function of the frilled lizard is: is the number of iterations of the frill lizard algorithm, For the Frilled Lizard The fuel consumption corresponding to the iteration result is: For the Frilled Lizard The equivalent fuel consumption corresponding to the iteration result satisfies:

[0108] ;

[0109] In the formula, For the Frilled Lizard The power consumption corresponding to the iteration result is For the Frilled Lizard The equivalent fuel consumption factor corresponding to the iteration result;

[0110] The constraints of the parameters to be optimized are:

[0111] ;

[0112] Step b: Generate the initial population according to the constraints, and calculate the corresponding parameter optimization objective function for each frilled lizard individual ;

[0113] Among them, the initial population satisfies;

[0114] ;

[0115] In the formula, is the element in the population matrix of frilled lizards, is the number of frilled lizards, is the parameter to be optimized, For the The lower limit of the parameter to be optimized, for The random numbers in For the The upper limit of the parameter to be optimized;

[0116] In this embodiment, the initial population (N) is 50 first-generation fringed lizards, each individual contains four parameters to be optimized, and the total number of iterations (Y) is 80 times.

[0117] Step c: Create a prey set:

[0118]

[0119] In the formula, For the A collection of prey for a frilled lizard. is the first After the iteration A frilled lizard, also Bidi Individuals with good objective function values ​​for frilled lizards, For the The iteration The objective function value of each individual;

[0120] Step d: Initially update the position of the frilled lizard, i.e. update the Parameters to be optimized for the frilled lizard:

[0121] ;

[0122] ;

[0123] In the formula, For the proposed new features for frilled lizards, for the characteristics of a random frilled lizard in the prey set, for A random number in For the The iteration The objective function value corresponding to the proposed new features of the frilled lizard, For the The new individuals obtained by transformation of the frilled lizards, For the The first transformation of a frilled lizard suggested a new individual;

[0124] That is when The objective function value of the new feature suggested by the frill lizard is better than that of the original When the objective function value of a frilled lizard is reached, the position of the frilled lizard is replaced, otherwise, it remains unchanged;

[0125] Step e: imitate the habit of the frilled lizard to return to the tree top after hunting to make a secondary adjustment to the new features of the frilled lizard. The equation is as follows:

[0126] ;

[0127] ;

[0128] In the formula, For the A new feature of the frilled lizard returning to the treetops, For the The iteration The objective function value corresponding to the new feature at the top of the tree is returned by the frill lizard. For the The second transformation of the frilled lizard into a new individual was proposed;

[0129] That is when The objective function value of the new feature returned by the frilled lizard at the top of the tree is better than the original When the objective function value of a frilled lizard is reached, the position of the frilled lizard is replaced, otherwise, it remains unchanged;

[0130] Step f: Iterate according to the above steps until the number of iterations is reached to obtain the parameters of the optimal individual. , , and ;

[0131] in, is the initial value of the optimal equivalent fuel consumption factor, is the optimal proportional adjustment coefficient, is the optimal short-time downhill discharge coefficient, is the optimal discharge coefficient for long downhill periods;

[0132] Step g: Substitute the parameters of the optimal individual into SP-AG-ECMS to obtain:

[0133] ;

[0134] In the formula, for The optimal slope prediction discharge coefficient at the moment;

[0135] The constraints are:

[0136] ;

[0137] In the formula, is the minimum SOC value; is the maximum SOC value; is the minimum power of the engine, kW; is the maximum power of the engine, kW; is the minimum power of the drive motor, kW; is the maximum power of the driving motor, kW;

[0138] Step 3: Minimize the prediction function based on the constraints to obtain the control variables , that is, obtaining the engine distribution power at the moment and the drive motor distribution power at the moment.

[0139] The present invention designs and develops a slope prediction adaptive ECMS hybrid vehicle energy management method, which can predict whether the road ahead is a long or short downhill slope based on the collected road information, thereby adjusting the battery discharge. When encountering a long downhill slope, rapid discharge allows the battery to fully absorb the energy generated by the downhill slope and give full play to the efficiency of braking recovery; and uses the umbrella lizard algorithm to iteratively optimize the parameters in the SP-AG-ECMS strategy, which greatly improves the performance of the optimized control strategy and improves the fuel economy of the vehicle.

[0140] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the embodiments shown and described herein.

Claims

1. A ramp prediction adaptive ECMS hybrid electric vehicle energy management method, characterized in that: The steps include: Step 1: Collect the battery SOC value, vehicle mass, vehicle altitude and road slope information of the vehicle at the current moment, and calculate the required power of the vehicle at the current moment; Step 2: Establish and optimize the slope prediction adaptive equivalent energy consumption minimum strategy; The step 2 specifically includes: Step 1: Establish AG-ECMS, specifically meeting the following requirements: ； In the formula, is the Hamiltonian function, for Fuel consumption at the time, for The equivalent fuel consumption of electrical energy at the moment, for The equivalent fuel consumption factor at time, for Moment The amount of electricity consumed at any given moment, for Control variables at time; Among them, the The equivalent fuel consumption factor at the moment satisfies: ； In the formula, is the initial value of equivalent fuel consumption factor, is the proportional adjustment coefficient, for The battery SOC reference value at the moment, for Battery SOC value at the moment; Step 2: The equivalent fuel consumption factor at the time is corrected: ； In the formula, is the equivalent fuel consumption factor after slope prediction correction, for The slope at the moment predicts the discharge coefficient; Step 3: Establish a ramp prediction adaptive equivalent energy consumption minimum strategy: ； Step 4: Optimize the parameters to be optimized by using the umbrella lizard algorithm to obtain the optimal initial value of the equivalent fuel consumption factor, the optimal proportional adjustment coefficient, the optimal short-time downhill discharge coefficient and the optimal long-time downhill discharge coefficient, and obtain the optimized ramp prediction adaptive equivalent energy consumption minimum strategy; The parameters to be optimized include the initial value of the equivalent fuel consumption factor, the proportional adjustment coefficient, the discharge coefficient for a short downhill run and the discharge coefficient for a long downhill run; The optimized ramp prediction adaptive equivalent energy consumption minimum strategy satisfies: ； In the formula, is the Hamiltonian function, for Fuel consumption at the time, for The control variables at time , and , for The engine power distribution at the moment, for The drive motor distributes power at each moment. is the initial value of the optimal equivalent fuel consumption factor, is the optimal proportional adjustment coefficient, for The optimal slope prediction discharge coefficient at the moment, for The battery SOC reference value at the moment, for The battery SOC value at the moment, for The amount of electricity consumed at any given moment; Step 3: Solve the problem with the goal of minimizing the Hamiltonian function to obtain the optimal control variable , that is, obtain The engine power distribution at the moment and The drive motor distributes power at each moment.

2. The ramp prediction adaptive ECMS hybrid electric vehicle energy management method according to claim 1, characterized in that: Said The battery SOC reference value at the moment satisfies: ； In the formula, is the battery SOC reference initial value, is the gravitational potential energy adjustment coefficient, is the vehicle mass, is the acceleration due to gravity, is the vehicle altitude.

3. The ramp prediction adaptive ECMS hybrid electric vehicle energy management method according to claim 2, characterized in that: Said The slope prediction discharge coefficient at the moment satisfies: ； In the formula, for The slope prediction discharge coefficient at the moment, is the discharge coefficient for a short downhill period, is the discharge coefficient for long downhill periods, for The vehicle is traveling at the current speed The average slope of the road over this distance is It is the standard value for slope determination.

4. The ramp prediction adaptive ECMS hybrid electric vehicle energy management method according to claim 3, characterized in that: Said The vehicle is traveling at the current speed The average slope of the road for this distance satisfies: ； In the formula, To predict the total duration, for The future corresponding to each moment The slope of the moment.

5. The ramp prediction adaptive ECMS hybrid electric vehicle energy management method as claimed in claim 4, characterized in that: The slope determination standard value is -2.5%.

6. The ramp prediction adaptive ECMS hybrid electric vehicle energy management method according to claim 5, characterized in that: The step 4 specifically includes: Step a: Establish parameter optimization objective function: ； In the formula, For the The parameter optimization objective function of the frilled lizard is: is the number of iterations of the frill lizard algorithm, For the Frilled Lizard The fuel consumption corresponding to the iteration result is: For the Frilled Lizard The equivalent fuel consumption corresponding to the iteration result; The constraints of the parameters to be optimized are: ； Step b: Generate the initial population according to the constraints, and calculate the corresponding parameter optimization objective function for each frilled lizard individual ; Among them, the initial population satisfies: ； In the formula, is the element in the population matrix of frilled lizards, is the number of frilled lizards, is the parameter to be optimized, For the The lower limit of the parameter to be optimized, for The random numbers in For the The upper limit of the parameter to be optimized; Step c: Create a prey set: ； In the formula, For the A collection of prey for a frilled lizard. is the first After the first iteration Individuals with good objective function values ​​for frilled lizards, For the The iteration The objective function value of each individual; Step d: Initial update of the position of the frilled lizard: ； ； In the formula, For the proposed new features for frilled lizards, for the characteristics of a random frilled lizard in the prey set, for A random number in For the The iteration The objective function value corresponding to the proposed new features of the frilled lizard, For the The new individuals obtained by transformation of the frilled lizards, For the The first transformation of a frilled lizard suggested a new individual; Step e: Perform a secondary update on the new features of the frilled lizard: ； ； In the formula, For the A new feature of the frilled lizard returning to the treetops, For the The iteration The objective function value corresponding to the new feature at the top of the tree is returned by the frill lizard. For the The second transformation of the frilled lizard into a new individual was proposed; Step f: Iterate according to the above steps until the number of iterations is reached to obtain the parameters of the optimal individual. , , and ; in, is the initial value of the optimal equivalent fuel consumption factor, is the optimal proportional adjustment coefficient, is the optimal short-time downhill discharge coefficient, is the optimal discharge coefficient for long downhill periods; Step g: Substitute the parameters of the optimal individual into SP-AG-ECMS to obtain the optimized ramp prediction adaptive equivalent energy consumption minimum strategy.

7. The ramp prediction adaptive ECMS hybrid electric vehicle energy management method according to claim 6, characterized in that: The said Frilled Lizard The equivalent fuel consumption corresponding to the iteration result satisfies: ； In the formula, For the Frilled Lizard The equivalent fuel consumption factor corresponding to the iteration result.

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