CN107878445A - A kind of energy-optimised management method of hybrid vehicle for considering cell performance decay - Google Patents

Abstract

The invention discloses a hybrid electric vehicle energy optimization management method considering battery performance attenuation. The control strategy takes the battery power as the control variable, the battery state of charge (SOC) as the state variable, introduces a weight factor, and takes the sum of fuel consumption cost, battery performance attenuation cost, and power maintenance cost at each stage as the objective function to form a global optimal control The discrete dynamic programming algorithm is used to solve the problem; finally, the control rules that can be used in real vehicles are extracted according to the optimization results. Compared with the prior art, on the basis of ensuring fuel economy, the present invention can effectively reduce the increase of internal resistance of the battery, increase the peak power and life of the battery, and is beneficial to improving the power of the vehicle and reducing the total cost of the vehicle.

Description

A hybrid electric vehicle energy optimization management method considering battery performance degradation

technical field

The invention belongs to the technical field of energy management of hybrid electric vehicles, and in particular relates to an energy optimization management method of hybrid electric vehicles considering battery performance attenuation.

Background technique

In the era of global energy shortage and increasing awareness of environmental protection, coupled with the development of the automobile industry, traditional internal combustion engine vehicles can no longer meet the requirements of the current era due to their high pollution and high energy consumption. The development of energy-saving and new energy vehicles is imminent. Due to the technical limitations of pure electric vehicle battery mileage and energy density in new energy vehicles, the current hybrid vehicles are gradually being favored by vehicle developers and consumers. A hybrid vehicle is a vehicle that combines the engine, motor and battery through a control system. It combines the advantages of electric vehicles and traditional internal combustion engine vehicles, and has the advantages of low energy consumption and good power.

The hybrid power system is composed of multiple energy sources. Multiple energy sources and components of the power system cooperate with each other to achieve different working modes, so as to achieve the best overall performance of the vehicle. Energy management strategy is the core content of hybrid vehicle control and the key to realize vehicle performance. The current energy optimization management control strategy mainly focuses on how to achieve the minimum fuel consumption through the selection of performance indicators and optimization strategies, and rarely considers the impact of the control strategy on the performance degradation of the vehicle battery. When a hybrid electric vehicle is working, the power battery is mainly to make up for the lack of engine power relative to the vehicle's demanded power, that is, to play the role of "shaving peaks and filling valleys". Frequent work of the battery in the state of charging and discharging will lead to an increase in its internal resistance and a decrease in power , affecting the power and economy of the vehicle. Studies have shown that there is a contradictory relationship between fuel consumption and battery performance decay speed, so coordinating the relationship between fuel consumption and battery performance decay in the energy management optimization control strategy is crucial to improving the power and fuel economy during vehicle operation and reducing the total cost of vehicle use. is very necessary. The performance attenuation of the battery is mainly manifested as capacity attenuation and internal resistance increase. For pure electric and plug-in hybrid electric vehicles, the actual capacity of the battery directly affects the driving range, so the battery performance model is mostly a capacity attenuation model. For example, the Chinese patent publication number is CN104627167A, and the publication date is 2015-5-20, which discloses a hybrid electric vehicle energy management method considering battery life, which uses the sum of fuel consumption cost and battery capacity decay cost as the control strategy cost function, It can effectively improve the battery life, but does not consider that because the hybrid electric vehicle works in the power maintenance mode, its battery is a power battery, and the battery capacity decay model is not suitable for the situation of the hybrid electric vehicle.

In addition, hybrid control strategies can generally be divided into rule control strategies and optimal control strategies. Although rule control strategies are often used in real-time control, the determination of the rules often requires repeated debugging based on experience or intuitive observations due to the lack of accurate mathematical models. Therefore, its performance level is very dependent on the adjustment of rules, and the dynamic programming global optimization management strategy with excellent performance in domestic research cannot be directly applied to real-time control because it performs offline optimization.

Contents of the invention

In order to solve the above-mentioned deficiencies in the prior art, the present invention provides a hybrid electric vehicle energy optimization management method that considers battery performance attenuation, and balances consideration of fuel consumption and battery performance attenuation for battery-maintenance hybrid electric vehicles. While saving fuel, the power of the vehicle is guaranteed, the performance and life of the battery are effectively improved, and the total cost of the vehicle is reduced.

In order to achieve the above object, a kind of hybrid electric vehicle energy optimization management method that considers battery performance attenuation that the present invention proposes mainly comprises the following steps:

(1) Carry out global optimization according to the typical cycle conditions of Chinese cities to obtain the results of changes in the working state of the engine and battery of the hybrid system with the speed of the vehicle and the required power of the vehicle, including:

①Divide the typical cycle conditions of Chinese cities into different stages, usually in seconds, and calculate the sum of fuel consumption cost, battery performance attenuation cost and power maintenance cost of each stage according to the speed and vehicle power demand of each stage ;

② establish a multi-objective control model, the multi-objective control model includes objective functions and constraints, and adopts a dynamic programming algorithm to obtain the optimal control quantity that satisfies the optimization target;

The objective function is:

In the formula, α is a weight factor, with a value of 0 to 1. When it is 1, it is a control strategy that only considers fuel economy, and when it is 0, it is a control strategy that only considers battery performance attenuation; M is a measurement constant, which ensures that fuel consumption costs It has the same magnitude as the cost of battery performance decay; x _k is the state variable; u _k is the control variable;

x _k ＝g[x _k-1 ， u _k-1 ]

g is the state transition function, specifically:

SOC _k is the state of charge of the battery at time k, I _k+1 is the current of the battery at time k, C _E (x _k , u _k ) is the fuel consumption cost at time k; _CH (x _k , u _k ) is the Battery performance attenuation cost; C _SOC (x _k ) is the power maintenance cost at time k;

The fuel consumption cost C _E (x _k , u _k ) at time k is calculated by the following formula:

C _E (x _k ，u _k )＝f _eng (k)+K _eq f _batt (k)

In the formula, f _eng (k) is the engine fuel consumption at time k, which is determined by the engine operating point. The engine operating point is obtained by checking the optimal operating curve of the engine after determining the engine demand power. The relationship between the engine demand power and the battery power in each stage The relationship is:

P _req (k) = P _eng (k) + P _batt (k)

In the formula, P _req (k) is the vehicle demand power at time k, P _eng (k) is the engine power at time k, P _batt (k) is the battery power at time k; f _batt (k) is the battery power at time k The energy provided, K _eq is the equivalent fuel oil coefficient;

The battery performance attenuation cost _CH (x _k , u _k ) at time k is calculated by the following formula:

_CH (x _k , u _k )=ΔR(k)=a(I)SOC(k) ² +b(I)SOC(k)+c(I)

a(I)＝A ₁ I ² +B ₁ I+C ₁

b(I)＝A ₂ I+B ₂

c(I)＝A ₃ I ³ +B ₃ I ² +C ₃ I+D ₃

In the formula, ΔR(k) is the battery internal resistance growth value at time k, SOC is the state of charge of the battery at time k, A ₁ , B ₁ , C ₁ , A ₂ , B ₂ , A ₃ , B ₃ , C ₃ , D ₃ is the various coefficients obtained by fitting the experimental data of constant current charging and discharging conditions corresponding to different currents of a specific battery by using the least square method;

The power maintenance cost C _SOC (x _k ) at time k is obtained by the following formula:

C _SOC (x _k )＝α _soc (SOC(k)-SOC _cs ) ²

In the formula, α _soc is a constant, which represents the penalty coefficient, SOC is the state of charge of the battery at time k, and SOC _cs is the control threshold of the state of charge of the battery;

The constraints are:

SOC _min ≤ SOC(k) ≤ SOC _max

The optimal control amount is the power that the battery should provide;

Use dynamic programming to solve the optimal control amount of each step in the entire comprehensive test condition. The step size is generally taken as 1s, which is the power that the battery should provide at each moment;

(2) Extract control rules from the optimization results based on dynamic programming, specifically:

① Extract the mode switching rules, and determine the switching rules between the pure electric mode and the hybrid mode of the hybrid power system;

② In the hybrid power mode, according to the vehicle speed, vehicle power demand, and battery charge state in the global optimization results, the battery power demand in the hybrid power mode is fitted, and the relationship between the engine operating point, the battery demand power and the vehicle operating state is obtained. The relationship between them can determine the throttle control and motor control in the real vehicle.

Compared with the prior art, the present invention has the following advantages:

(1) The present invention balances fuel economy and battery performance attenuation, adds the cost of battery performance attenuation to the objective function, and introduces a weight factor to coordinate and control the two costs, thereby improving battery performance while ensuring little change in fuel consumption .

(2) The present invention uses the battery internal resistance increase value to represent the battery performance attenuation degree, considering the situation that the battery internal resistance increases and causes the peak power of the battery to decrease thereby reducing the dynamic performance of the hybrid electric vehicle, the energy management optimization control strategy of the design increases the cost of fuel consumption On a small basis, the increase in internal resistance is minimized, avoiding excessive reduction in battery power that affects vehicle dynamics, improving battery life and reducing the total cost of vehicle use.

(3) The present invention extracts control rules based on optimization results, which can be applied to real vehicles.

Description of drawings

Fig. 1 is a schematic flow chart of the present invention.

Fig. 2 is a flow chart of extracting control rules according to the optimization results of the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

(1) Based on the overall optimization of the typical cycle conditions in Chinese cities, the results of the changes in the working state of the engine and battery of the hybrid system with the speed of the vehicle and the required power of the vehicle are obtained, including:

①Divide the typical cycle conditions of Chinese cities into different stages, usually in seconds, and calculate the sum of fuel consumption cost, battery performance attenuation cost and power maintenance cost of each stage according to the speed and vehicle power demand of each stage ;

2. Establish a multi-objective control model, the multi-objective control model includes an objective function and a constraint condition, and adopts a dynamic programming algorithm to obtain the optimal control quantity satisfying the optimization objective;

The objective function is:

In the formula, α is a weight factor, with a value of 0 to 1. When it is 1, it is a control strategy that only considers fuel economy, and when it is 0, it is a control strategy that only considers battery performance attenuation; M is a measurement constant, which ensures that fuel consumption costs It has the same magnitude as the cost of battery performance decay; x _k is the state variable; u _k is the control variable;

x _k ＝g[x _k-1 ， u _k-1 ]

g is the state transition function, specifically:

SOC _k is the state of charge of the battery at time k, I _k+1 is the current of the battery at time k, C _E (x _k , u _k ) is the fuel consumption cost at time k; _CH (x _k , u _k ) is the Battery performance attenuation cost; C _SOC (x _k ) is the power maintenance cost at time k;

The fuel consumption cost C _E (x _k , u _k ) at time k is calculated by the following formula:

C _E (x _k ，u _k )＝f _eng (k)+K _eq f _batt (k)

In the formula, f _eng (k) is the fuel consumption of the engine at time k, which is determined by the engine operating point. The engine operating point is obtained by checking the optimal operating curve of the engine after determining the required power of the engine. The required power of the engine and the power of the battery in each stage The relationship is:

P _req (k) = P _eng (k) + P _batt (k)

In the formula, P _req (k) is the required power of the vehicle at time k, P _eng (k) is the power provided by the engine at time k, P _batt (k) is the power provided by the battery at time k; f _batt (k) is k The energy provided by the battery at all times, K _eq is the equivalent fuel coefficient;

The battery performance attenuation cost _CH (x _k , u _k ) at time k is calculated by the following formula:

_CH (x _k , u _k )=ΔR(k)=a(I)SOC ² +b(I)SIC+c(I)

a(I)＝A ₁ I ² +B ₁ I+C ₁

b(I)＝A ₂ I+B ₂

c(I)＝A ₃ I ³ +B ₃ I ² +G ₃ I+D ₃

In the formula, ΔR(k) is the battery internal resistance growth value at time k, SOC is the state of charge of the battery at time k, A ₁ , B ₁ , C ₁ , A ₂ , B ₂ , A ₃ , B ₃ , C ₃ , D ₃ is the various coefficients obtained by fitting the experimental data of constant current charging and discharging conditions corresponding to different currents of a specific battery by using the least square method;

The power maintenance cost C _SOC (x _k ) at time k is obtained by the following formula:

C _SOC (x _k )＝α _soc (SOC-SOC _cs ) ²

In the formula, α _soc is a constant, which represents the penalty coefficient, SOC is the state of charge of the battery at time k, and SOC _cs is the control threshold of the state of charge of the battery;

The constraints are:

SOC _min ≤ SOC(k) ≤ SOC _max

In the formula, SOC(k) is the state of charge of the battery at time k;

The optimal control amount is the power that the battery should provide;

The control problem of global optimization is transformed into solving a series of minimum value problems under certain constraints, and the recurrence relation is written according to the Bellman optimization principle:

Use the dynamic programming method to solve the problem, control the solution process within the constraint range of the control variable and the state variable, and define these two ranges as: allowable control set and reachable state set, as follows:

P _{eng_min} (k)≤P _eng (k)≤P _{eng_max} (k)

P _{batt_min} (k)≤P _batt (k)≤P _{batt_max} (k)

SOC _min ≤ SOC(k) ≤ SOC _max

In the formula, P _eng (k) is the power provided by the engine at time k, P _batt (k) is the power provided by the battery at time k, and SOC(k) is the state of charge of the battery at time k;

The first is the reverse calculation, that is, from the time N to the end of the first time. Discretize the control variables equally in the allowable state set, taking u(k) and u _i (k) at time k as an example, according to the transfer equation of the state variables:

SOC(k+1), SOC _i (k+1) can be calculated, interpolated to obtain performance indicators J(k+1), J _i corresponding to SOC(k+1), SOC _i (k+1) (k+1). Among them u(k), C(k) and C _i (k) of stage k of u _i (k) can be obtained by calculation, and then take C(k)+J(k+1), C _i (k) +J _i (k+1), and obtain the control variable corresponding to the minimum value, which is the optimal control variable at this stage. The rest of the stages are the same, according to the recursive equation of the performance objective function, iterate the above content in reverse until the point in the allowable control set ends.

Secondly, forward calculation is adopted, that is, from the first moment to the end of N moment. The initial value SOC ₀ is known, and the result of the reverse end is known data, and the optimal control quantity at each time is obtained through interpolation respectively, so far the energy management optimization strategy under this working condition is obtained.

(2) Extract control rules from the optimization results based on dynamic programming, specifically:

① Extract the mode switching rules, and determine the switching rules between the pure electric mode and the hybrid mode of the hybrid power system;

② In the hybrid power mode, according to the vehicle speed, vehicle power demand, and battery charge state in the global optimization results, the battery power demand in the hybrid power mode is fitted, and the relationship between the engine operating point, the battery demand power and the vehicle operating state is obtained. The relationship between them can determine the throttle control and motor control in the real vehicle.

Claims (2)

1. a kind of energy-optimised management method of hybrid vehicle for considering cell performance decay, comprises the following steps：

(1) based on Typical Cities in China operating mode carry out global optimization obtain hybrid power system engine and cell operating status with The result of variations of speed and vehicle demand power, is specifically included：

1. Typical Cities in China operating mode is divided into the different stages, generally in seconds, according to the speed in each stage and Vehicle power demand, the fuel consumption cost, cell performance decay cost and electricity for calculating each stage maintain control threshold Cost summation；

2. establishing multi objective control model, the multi objective control model includes object function and constraints, is advised using dynamic The method of calculating obtains the optimum control amount for meeting optimization aim；

The object function is：

<mrow> <mi>J</mi> <mo>=</mo> <munderover> <mi>&amp;Sigma;</mi> <mrow> <mi>k</mi> <mo>=</mo> <mn>0</mn> </mrow> <mrow> <mi>N</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <mo>&amp;lsqb;</mo> <mi>&amp;alpha;</mi> <mo>&amp;CenterDot;</mo> <mi>M</mi> <mo>&amp;CenterDot;</mo> <msub> <mi>C</mi> <mi>E</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>k</mi> </msub> <mo>,</mo> <msub> <mi>u</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>&amp;alpha;</mi> <mo>)</mo> </mrow> <mo>&amp;CenterDot;</mo> <msub> <mi>C</mi> <mi>H</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>k</mi> </msub> <mo>,</mo> <msub> <mi>u</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>S</mi> <mi>O</mi> <mi>C</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow>

In formula, α is weight factor, and value is 0~1, is only to examine only to consider the control strategy of fuel economy when taking 1, when taking 0 Consider the control strategy of cell performance decay；M is scaling constant, and it is identical to ensure that fuel consumption cost and cell performance decay cost have Magnitude；x_kFor state variable；u_kTo control variable；

x_k=g [x_k-1, u_k-1]

G is state transition function, is specially：

<mrow> <mi>g</mi> <mo>&amp;lsqb;</mo> <msub> <mi>x</mi> <mi>k</mi> </msub> <mo>,</mo> <msub> <mi>u</mi> <mi>k</mi> </msub> <mo>&amp;rsqb;</mo> <mo>=</mo> <msub> <mi>SOC</mi> <mi>k</mi> </msub> <mo>-</mo> <mfrac> <msub> <mi>I</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mrow> <mn>3600</mn> <mi>C</mi> </mrow> </mfrac> </mrow>

SOC_kFor the state-of-charge of k moment batteries, I_k+1For the electric current of k moment batteries, C is battery rated capacity, C_E(x_k, u_k) it is k Moment fuel consumption cost, C_H(x_k, u_k) it is k moment cell performance decay costs, C_SOC(x_k) it is that k moment electricity maintains cost；

The k moment fuel consumption cost C_E(x_k, u_k) asked for by following formula：

C_E(x_k, u_k)=f_eng(k)+K_eqf_batt(k)

In formula, f_eng(k) it is k moment engine fuel consumption quantities, is determined by engine working point, f_batt(k) carried for k moment batteries The energy of confession, K_eqFor equivalent fuel oil coefficient；

The k moment cell performance decay cost C_H(x_k, u_k) asked for by following formula：

C_H(x_k, u_k)=Δ R (k)=a (I) SOC (k)²+b(I)SOC(k)+c(I)

A (I)=A₁I²+B₁I+C₁

B (I)=A₂I+B₂

C (I)=A₃I³+B₃I²+C₃I+D₃

In formula, Δ R (k) be the k moment internal resistance of cell increasing value, SOC (k) be the battery k moment state-of-charge, A₁、B₁、C₁、A₂、 B₂、A₃、B₃、C₃、D₃To utilize constant current charge-discharge working condition experimenting number corresponding to the different electric currents of least square method combination particular battery Each term coefficient obtained according to fitting；

The k moment electricity maintains cost C_SOC(x_k) asked for by following formula：

C_SOC(x_k)=α_soc(SOC(k)-SOC_cs)²

In formula, α_socFor constant, penalty coefficient is represented, SOC is the state-of-charge at battery k moment, SOC_csFor the state-of-charge of battery Control threshold；

The constraints is：

SOC_min≤SOC(k)≤SOC_max

The optimum control amount is the power that battery should provide；

The optimum control amount of each step in whole integration test operating mode is solved using Dynamic Programming, step-length is typically taken as 1s, The power that i.e. each moment battery should provide；

(2) Rule Extraction is controlled to the optimum results based on Dynamic Programming, is specially：

1. extracting pattern switching rule, hybrid power system electric-only mode and the switching law of hybrid mode are determined；

2. under hybrid mode, the speed, vehicle demand power, battery charge state in global optimization result are to mixing Battery requirements power under dynamic mode is fitted, and obtains engine working point, battery requirements power and vehicle running status Between relation, so that it is determined that throttle control and motor control in real vehicle.

2. a kind of energy-optimised management method of hybrid vehicle for considering cell performance decay according to claim 1, Characterized in that, engine working point takes engine optimum working curve to obtain by being looked into after determination engine demand power, it is each The relation of stage engine demand power and the power of battery is：

P_req(k)=P_eng(k)+P_batt(k)

In formula, P_req(k) it is the vehicle demand power at k moment, P_eng(k) it is the engine power at k moment, P_batt(k) it is the k moment The power of battery.