CN104627167B - Hybrid vehicle energy managing method and system considering service life of battery - Google Patents

Abstract

The invention relates to an energy management method of a hybrid electric vehicle considering battery life, comprising the following steps: 1) collecting current vehicle operating state data and battery operating state data; 2) establishing a vehicle model, and predicting a period of time in the future according to the vehicle model 3) Calculate the sum of the battery capacity decay cost and the sum of the fuel consumption cost in the future; 4) Establish a multi-objective control model, and use a multi-objective coordinated control algorithm to obtain the optimal control amount that meets the optimization objective. The multi-objective control model includes an objective function J ^* and a constraint condition C; 5) forming a control signal according to the optimal control quantity to control the running state of the vehicle. Compared with the prior art, the invention has the advantages of good control effect, effectively improving battery life, reducing the total cost of vehicle use and the like.

Description

A hybrid electric vehicle energy management method and system considering battery life

technical field

The invention relates to the technical field of hybrid electric vehicle energy management, in particular to an energy management method and system for a hybrid electric vehicle considering battery life.

Background technique

The rise in the price of fossil fuels and the emphasis on environmental issues have promoted the development of new green vehicles that are environmentally friendly and energy-saving. Pure electric vehicles, hybrid vehicles and fuel cell vehicles can all be used as new green vehicles. Compared with traditional vehicles, they have high efficiency and low emissions, and have become a new trend in the development of the automotive industry. Due to the current immaturity of power battery technology for pure electric vehicles, such as insufficient battery life, poor battery safety, and battery life, they cannot be widely applied; fuel cell vehicles are due to technical obstacles such as fuel storage and energy conversion. And related infrastructure facilities are not perfect and other reasons seriously hinder its development. Hybrid vehicles are currently the mainstream new energy vehicles.

On the one hand, the energy management strategy of a hybrid vehicle is the brain of the vehicle control, which coordinates the working status of each power source, and seeks the highest efficiency and the least emission under the premise of ensuring the power, safety and comfort of the vehicle. Domestic research on the energy management strategy of hybrid electric vehicles is mainly aimed at the energy management of series and parallel hybrid electric vehicles, and the research on energy management of hybrid electric vehicles is still blank. Moreover, the control algorithms studied in China mainly use control strategies based on rules or intelligent algorithms. The research results of the control strategy based on the optimization method are still immature, and there is a big gap with foreign countries.

On the other hand, since the power battery of the hybrid electric vehicle is in the state of charging and discharging for a long time, the length of its life has also become a problem that needs to be considered in the energy management of the hybrid electric vehicle. Moreover, the usual research believes that there is a contradiction between the life of the power battery and the fuel consumption. How to weigh the relationship between the two is also a new issue worth studying. Traditional energy management strategies only care about fuel consumption indicators, but do not consider much about the operating status of the battery. However, studies have shown that fuel economy and battery life are indeed a contradictory relationship. If you only care about fuel consumption in energy management, it will make the battery run in an unhealthy state and affect its normal use time.

The document Battery State-of-Health Perceptive Energy Management for Hybrid Electric Vehicles (S.Ebbesen, P.Elbert and L.Guzzella,.IEEE Trans.on Vehicular Technology, 2012.61(7):p.2893-2900) proposes to consider the state of battery health Hybrid vehicle energy management strategy, but because it does not consider the impact of battery life cost, it cannot be coordinated with fuel consumption cost, and the control effect is not ideal.

Contents of the invention

The object of the present invention is to provide a hybrid electric vehicle energy management method and system considering the battery life with good control effect, effectively improving the battery life, and reducing the total cost of the vehicle in order to overcome the above-mentioned defects in the prior art.

The purpose of the present invention can be achieved through the following technical solutions:

A hybrid electric vehicle energy management method considering battery life, comprising the following steps:

1) Collect current vehicle operating status data and battery operating status data;

2) Establish a vehicle model, and predict the vehicle operating state and battery operating state in the future according to the vehicle model;

3) Calculate the sum of battery capacity decay costs and fuel consumption costs in the future;

4) Establishing a multi-objective control model, adopting a multi-objective coordinated control algorithm to obtain the optimal control quantity that satisfies the optimization objective, the multi-objective control model includes an objective function J ^* and constraint conditions C,

The objective function J ^* is: J ^* =min(W _f J _f +W _b J _b );

The constraints C include: x _min ≤ x _k ≤ x _max , y _min ≤ y _k ≤ y _max and u _min ≤ u _k ≤ u _max ;

Among them, J _f is the sum of fuel consumption cost, J _b is the sum of battery attenuation cost, W _f is the weight of fuel consumption cost, W _b is the weight of battery life cost, x _k is the state quantity of the vehicle model at time k, x _min , x _max is the minimum and maximum value of the state quantity respectively, y _k is the output quantity of the vehicle model at time k, y _min and y _max are the minimum and maximum value of the output quantity respectively, u _k is the control quantity of the vehicle model at time k , u _min and u _max are the minimum and maximum values of the control quantity respectively;

5) Form a control signal according to the optimal control amount to control the running state of the vehicle.

The vehicle running state data includes vehicle speed, engine speed and motor speed;

The battery operating state data includes battery remaining power, battery capacity decay, battery current and battery voltage.

The vehicle model is specifically

Among them, x is the state quantity of the vehicle model, u is the control quantity of the vehicle model, v is the known quantity of the vehicle model, y represents the output quantity of the vehicle model, f( ) represents the state transition equation of the vehicle model, and represents the current state Transfer to the process function of the next state, g( ) represents the output equation of the vehicle model, and represents the functional relationship between the output quantity and the control quantity, the state quantity and the known quantity.

The state quantities include the engine speed, the motor speed and the remaining battery power;

The output quantity includes the current speed of the vehicle, the current fuel consumption and the current battery capacity attenuation value;

The control quantity includes engine throttle opening, brake torque and motor torque;

The known quantities include the vehicle's current target speed and current demanded power.

The battery capacity decay cost is obtained by the following formula:

Q _loss = b(x,u)

Among them, Q _loss represents the battery capacity attenuation value, b( ) represents the functional relationship between the capacity attenuation value and the state quantity x and control quantity u of the vehicle model;

The fuel consumption cost is obtained by the following formula:

in, represents the fuel consumption value, and m(·) represents the functional relationship between the fuel consumption value and the state quantity x and control quantity u of the vehicle model.

A hybrid electric vehicle energy management system considering battery life, including:

The data collection module is used to collect the current vehicle running state data and battery running state data;

The upper controller is used to receive the data collected by the data acquisition module, predict the state quantity in the future and the battery attenuation cost and fuel consumption cost in the future according to the vehicle model, and then calculate the optimal control quantity through the multi-objective coordinated control algorithm;

The lower controller group is used to receive the optimal control quantity calculated by the upper controller to control the running state of the vehicle.

The lower controller group includes accelerator controller, brake controller and motor controller.

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

(1) The present invention considers the battery life, directly adds the battery life cost into the objective function, and uses the model predictive control algorithm to obtain the optimal control amount, which can optimize the distribution of energy, effectively improve the battery life, and reduce the total cost of vehicle use.

(2) The control effect is good, the vehicle dynamics characteristics are considered, the control model is more accurate, and the real-time optimal control algorithm (model predictive control algorithm) is adopted to improve the control performance by considering the constraints of vehicle driving under the premise of ensuring real-time performance.

(3) The fuel consumption cost and the battery life cost of the vehicle are considered in the energy control method of the hybrid electric vehicle of the present invention, and the two costs are optimized and coordinated. In the case of ensuring that the cost of fuel consumption is similar, the cost of battery life is reduced.

(4) It is optimized and practical at the same time, and improves the fuel economy and battery life of the vehicle on the premise of ensuring vehicle speed tracking.

(5) The calculation time of the algorithm of the present invention is high in real time, and can be applied to actual vehicles.

Description of drawings

Fig. 1 is a schematic flow sheet of the present invention;

Fig. 2 is a functional block diagram of the system of the present invention.

detailed description

The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

As shown in Figure 1, this embodiment provides a hybrid electric vehicle energy management method considering the battery life, according to the current vehicle driving information and battery state information, using the model predictive control method to coordinate the control of the fuel consumption of the vehicle and the battery capacity decay cost function, To obtain the optimal amount of control, the specific steps are as follows:

In step S1, current vehicle running state data and battery running state data are collected. The vehicle running state data includes vehicle speed, engine speed, motor speed, etc.; the battery running state data includes battery remaining power, battery capacity decay, battery current, battery voltage, and the like.

In step S2, a vehicle model is established, and the running state of the vehicle and the battery running state are predicted for a period of time in the future according to the vehicle model.

The vehicle model is specifically:

Among them, x is the state quantity of the vehicle model, including but not limited to engine speed, motor speed and remaining battery power, etc., u is the control quantity of the vehicle model, including but not limited to engine throttle opening, brake torque and motor torque, etc. , v is the known quantity of the vehicle model, including but not limited to the vehicle's current target speed and current demand power, etc., y represents the output of the vehicle model, including but not limited to the vehicle's current speed, current fuel consumption and current battery capacity decay value, etc. f(·) represents the state transition equation of the vehicle model, representing the process function of the current state transition to the next state, g(·) represents the output equation of the vehicle model, representing the relationship between the output quantity and the control quantity, the state quantity and the known quantity functional relationship.

Assume that the current state quantity is x ₀ and the control quantity of N _c time in the future Using the above vehicle model, the state quantity of N _p time in the future can be obtained and output

In step S3, the sum of battery capacity decay costs and the sum of fuel consumption costs in a period of time in the future are calculated.

The battery capacity decay cost is obtained by the following formula:

Q _loss = b(x,u)

Among them, Q _loss represents the battery capacity attenuation value, b(·) represents the functional relationship between the capacity attenuation value and the state quantity x and control quantity u of the vehicle model, which is related to the remaining power, current, voltage and battery cell temperature of the battery etc. are directly related;

in, Represents the fuel consumption value, m(·) represents the functional relationship between the fuel consumption value and the state quantity x and control quantity u of the vehicle model, which is directly related to the torque and speed of the engine.

Then, the sum of fuel consumption cost J _f and battery attenuation cost J _b can be calculated by the following formula within a period of N _p in the future:

In step S4, a multi-objective control model is established, and a multi-objective coordinated control algorithm is used to obtain the optimal control quantity satisfying the optimization objective. The multi-objective control model includes an objective function J ^* and constraint conditions C,

The objective function J ^* is: J ^* =min(W _f J _f +W _b J _b );

The constraints C include: x _min ≤ x _k ≤ x _max , y _min ≤ y _k ≤ y _max and u _min ≤ u _k ≤ u _max ;

Among them, J _f is the sum of fuel consumption cost, J _b is the sum of battery attenuation cost, W _f is the weight of fuel consumption cost, W _b is the weight of battery life cost, x _k is the state quantity of the vehicle model at time k, x _min , x _max is the minimum and maximum value of the state quantity respectively, y _k is the output quantity of the vehicle model at time k, y _min and y _max are the minimum and maximum value of the output quantity respectively, u _k is the control quantity of the vehicle model at time k , u _min and u _max are the minimum and maximum values of the control quantity respectively.

After establishing the multi-objective control model, the optimization problem is transformed into a quadratic programming problem with the objective function J and the constraint condition C, and the optimal solution is obtained by using the active-set method, that is, the optimal control increment Δu, then the current The optimal control quantity u(k)=u(k-1)+Δu required at time k.

In step S5, the control signals of each controller are formed according to the optimal control amount to control the running state of the vehicle.

As shown in Figure 2, the present embodiment also provides a hybrid vehicle energy management system considering battery life, including a data acquisition module 1, an upper controller 2 and a lower controller group 3, and the data acquisition module 1 is used to collect the current vehicle Running state data and battery running state data; the upper controller 2 is used to receive the data collected by the data acquisition module 1, predict the state quantity in the future and the battery attenuation cost and fuel consumption cost in the future according to the vehicle model, and then pass the multi-objective The coordinated control algorithm calculates the optimal control quantity; the lower controller group 3 is used to receive the optimal control quantity calculated by the upper controller 2, and control the running state of the vehicle. The lower controller group includes throttle controller, brake controller and motor controller.

Claims (7)

1. a hybrid electric vehicle energy management method considering battery life, is characterized in that, comprises the following steps: 1) Collect current vehicle operating status data and battery operating status data; 2) Establish a vehicle model, and predict the vehicle operating state and battery operating state in the future according to the vehicle model; 3) Calculate the sum of battery capacity decay costs and fuel consumption costs in the future; 4) Establishing a multi-objective control model, adopting a multi-objective coordinated control algorithm to obtain the optimal control quantity that satisfies the optimization objective, the multi-objective control model includes an objective function J ^* and constraint conditions C, The objective function J ^* is: J ^* =min(W _f J _f +W _b J _b ); The constraints C include: x _min ≤ x _k ≤ x _max , y _min ≤ y _k ≤ y _max and u _min ≤ u _k ≤ u _max ; Among them, J _f is the sum of fuel consumption cost, J _b is the sum of battery capacity decay cost, W _f is the weight of fuel consumption cost, W _b is the weight of battery life cost, x _k is the state quantity of the vehicle model at time k, x _min , x _max are the minimum and maximum value of the state quantity respectively, y _k is the output quantity of the vehicle model at time k, y _min , y _max are the minimum value and maximum value of the output quantity respectively, u _k is the control of the vehicle model at time k amount, u _min and u _max are the minimum and maximum value of the control amount respectively; 5) Form a control signal according to the optimal control amount to control the running state of the vehicle. 2. The hybrid electric vehicle energy management method considering battery life according to claim 1, wherein the vehicle operating state data includes vehicle speed, engine speed and motor speed; The battery operating state data includes battery remaining power, battery capacity decay, battery current and battery voltage. 3. the hybrid electric vehicle energy management method considering battery life according to claim 1, is characterized in that, described vehicle model is specifically: $\left\{\begin{array}{c}\stackrel{<span\; class="notranslate">\; \&Center\; Dot;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span>\end{array}\right.$ Among them, x is the state quantity of the vehicle model, u is the control quantity of the vehicle model, v is the known quantity of the vehicle model, y represents the output quantity of the vehicle model, f( ) represents the state transition equation of the vehicle model, and represents the current state The process function of transferring to the next state, g( ) represents the output equation of the vehicle model, and represents the functional relationship between the output quantity of the vehicle model and the control quantity, state quantity and known quantity. 4. the hybrid electric vehicle energy management method considering battery life according to claim 3, is characterized in that, described state quantity comprises engine speed, motor speed and battery residual power; The output quantity includes the current speed of the vehicle, the current fuel consumption and the current battery capacity attenuation value; The control quantity includes engine throttle opening, brake torque and motor torque; The known quantities include the vehicle's current target speed and current demanded power. 5. the hybrid electric vehicle energy management method considering battery life according to claim 1, is characterized in that, described battery capacity attenuation cost obtains by following formula: Q _loss = b(x,u) Among them, Q _loss represents the battery capacity attenuation value, b( ) represents the functional relationship between the capacity attenuation value and the state quantity x and control quantity u of the vehicle model; The fuel consumption cost is obtained by the following formula: ${\stackrel{<span\; class="notranslate">\; \&Center\; Dot;</span>}{\mathrm{<span\; class="notranslate">\; m</span>}}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; )</span>$ in, represents the fuel consumption value, and m(·) represents the functional relationship between the fuel consumption value and the state quantity x and control quantity u of the vehicle model. 6. A system that realizes the hybrid electric vehicle energy management method considering battery life as claimed in claim 1, is characterized in that, comprising: The data collection module is used to collect the current vehicle running state data and battery running state data; The upper controller is used to receive the data collected by the data acquisition module, predict the state quantity in the future and the sum of the battery capacity decay cost and the fuel consumption cost in the future according to the vehicle model, and then calculate the optimal control through the multi-objective coordinated control algorithm quantity; The lower controller group is used to receive the optimal control quantity calculated by the upper controller to control the running state of the vehicle. 7. The system according to claim 6, wherein the lower controller group includes an accelerator controller, a brake controller and a motor controller.