Multi-working-condition energy management strategy for extended-range hybrid electric vehicle

Abstract

The invention discloses a multi-working-condition energy management strategy for an extended-range hybrid electric vehicle. The multi-working-condition energy management strategy comprises a control mode switching module, a power prediction switching module and an optimization control module. The control mode switching module performs switching according to the SOC value of the power battery, the single voltage and the temperature; the power prediction module predicts the vehicle speed and acceleration by adopting Bayesian estimation; and the optimization control module performs optimization according to the predicted power value by taking the minimum equivalent fuel consumption and the local balance of the state of charge of the power battery as targets so as to realize a global optimization control strategy of the extended-range vehicle for switching between a thermostat type control scheme and a power following type control scheme. According to the strategy, on the basis of global optimization control, a mode that thermostat control and power following type control can be switched is achieved, and a future power prediction value is obtained by establishing multi-step prediction calculation; power is distributed to an engine, and output and input of a power battery are kept in a balanced state.

Description

Technical Field

[0001] The present invention relates to the technical field of extended-range hybrid vehicles, and in particular to a multi-operating-condition energy management strategy for extended-range hybrid vehicles. Background Art

[0002] The full name of hybrid electric vehicle is hybrid vehicle. As the name implies, it has more than one power source and different types. Hybrid electric vehicles have at least two independent drive systems, and the energy in at least one drive system is reversible. Currently, most of the well-known hybrid electric vehicles are narrowly defined, that is, vehicles with both thermal power source and electric power source. Thermal power source refers to traditional internal combustion engines. Both diesel engines and gasoline engines can be used as thermal power sources for hybrid electric vehicles; electric power sources mainly include batteries and motors.

[0003] The extended-range electric vehicle is named after the range extender device that comes with it. The range extender will not operate when the power battery is fully charged. When the power is too low or the power demand is too high, the range extender will start to supply energy to the car. The installation of the range extender can effectively enhance the endurance of pure electric vehicles.

[0004] The extended-range hybrid vehicle discussed in this article has a simplified structural diagram as follows: Figure 1 As shown:

[0005] In a series hybrid vehicle, the engine is no longer directly connected to a mechanical transmission, but generates electrical energy by connecting a generator. Its main operating modes include:

[0006] 1) When the car is driving, the engine and the power battery drive the car together, and the engine replenishes the power consumption of the power battery in real time;

[0007] 2) When the power demand is not high, the energy required for the vehicle to operate is only provided by the power battery;

[0008] 3) When the car is stopped, the engine runs to charge the power battery.

[0009] As environmental and energy-saving issues become increasingly prominent, hybrid electric vehicles have become the most practical new energy vehicles to be developed today because of their unique structure that can achieve energy saving and low emissions. Energy management control strategy is a key technology that determines the fuel economy and emissions of hybrid electric vehicles. It mainly includes rule-based and optimization-based (instantaneous optimization, global optimization) strategies. Among them, the rule-based control strategy is simple and easy to implement, but does not consider the dynamic changes of operating conditions. The instantaneous optimization control strategy has high execution efficiency and can ensure that it is optimal in each step, but it cannot ensure the optimality in the entire driving condition. Summary of the invention

[0010] In response to the above technical problems, the present invention proposes a multi-operating-condition energy management strategy for extended-range hybrid vehicles. The strategy is based on global optimization control to achieve a switchable mode between thermostat control and power-following control. The future power forecast value is obtained by establishing a multi-step prediction calculation; the power is distributed to the engine, so that the output and input of the power battery are maintained in a balanced state.

[0011] A multi-operating-condition energy management strategy for a range-extended hybrid vehicle, comprising a control mode switching module, a power prediction switching module, and an optimization control module;

[0012] The control mode switching module switches according to the power battery SOC value, single cell voltage and temperature, including a thermostat type control scheme and a power follower type control scheme;

[0013] The power prediction module uses Bayesian estimation to predict vehicle speed and acceleration;

[0014] The optimization control module optimizes according to the predicted power value with the goal of minimizing equivalent fuel consumption and locally balancing the power battery state of charge, so as to realize a global optimization control strategy for switching between a thermostat control scheme and a power following control scheme for the extended-range vehicle.

[0015] As a preferred embodiment of the above technical solution, when the SOC value of the battery is lower than 0.8, the single cell voltage is lower than 2.6V and the temperature is lower than 55°C, the engine operates under a power following control scheme, and in other cases, the engine operates under a thermostat control scheme.

[0016] As a preferred embodiment of the above technical solution, when the control mode switching module adopts a thermostat type control solution, when the power battery power is lower than a critical limit, the range extender system will automatically start to charge the power battery or directly drive the motor.

[0017] As a preferred embodiment of the above technical solution, when the control mode switching module adopts a follow-up control scheme, the power generated by the engine is mainly utilized under the condition of efficient vehicle operation, so as to reduce the frequency of charging and discharging of the power battery.

[0018] As a preferred embodiment of the above technical solution, Bayesian estimation is used to establish a multi-step prediction calculation to obtain the future vehicle speed and acceleration, thereby obtaining a power prediction value; the power is distributed to the engine so that the output and input of the power battery are maintained in a balanced state.

[0019] The beneficial effects of the present invention are:

[0020] This strategy is based on global optimization control, achieving a switchable mode between thermostat control and power follower control. By establishing a multi-step prediction calculation to obtain the future power prediction value; distributing the power to the engine, so that the output and input of the power battery are maintained in a balanced state, it can reduce the number of charge and discharge times of the power battery and increase the service life of the power battery. The use of this multi-operating condition energy management strategy can improve the endurance of the extended-range vehicle and further enhance its market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Extended-range hybrid vehicle structural block diagram;

[0022] Figure 2 Technical solution block diagram;

[0023] Figure 3 Prediction algorithm block diagram;

[0024] Figure 4 Software mode switching flow chart;

[0025] Figure 5 Power follower control scheme flow chart;

[0026] Figure 6 Flow chart of thermostat type control scheme; DETAILED DESCRIPTION

[0027] The technical solution of the present invention is described clearly and completely below in conjunction with the accompanying drawings of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] An energy management strategy is proposed for the multi-operating environment of extended-range hybrid vehicles. The strategy is based on global optimization control and achieves a switchable mode between thermostat control and power following control. The future power forecast value is obtained by establishing a multi-step prediction calculation; the power is distributed to the engine to keep the output and input of the power battery in a balanced state.

[0029] The multi-operating-condition energy management strategy includes a control mode switching module, a power prediction switching module and an optimization control module;

[0030] The control mode switching module switches according to the power battery SOC value, single cell voltage and temperature, including a thermostat type control scheme and a power follower type control scheme;

[0031] The power prediction module uses Bayesian estimation to predict vehicle speed and acceleration;

[0032] The optimization control module optimizes the predicted power value with the goal of minimizing equivalent fuel consumption and balancing the local state of charge of the power battery, so as to realize the global optimization control strategy of the extended-range vehicle switching between the thermostat control scheme and the power following control scheme.

[0033] The technical solution block diagram adopted by the present invention to solve the technical problem is as follows Figure 2 As shown:

[0034] Electric vehicle power demand P c It can be calculated by the following formula:

[0035]

[0036] Where m is the full load mass, kg; g is the acceleration due to gravity, m / s 2 ; v is vehicle speed, km / h; η is mechanical efficiency; f is rolling resistance coefficient, C D is the air resistance coefficient, A is the windward area, m 2 .

[0037] The control mode switching module switches according to the power battery SOC value, single cell voltage and temperature. When the battery SOC value is lower than 0.8, the single cell voltage is lower than 2.6V and the temperature is lower than 55°C, the engine operates in power following mode, and also in thermostat mode.

[0038] The power prediction module uses Bayesian estimation to predict vehicle speed and acceleration. In Bayesian estimation theory, x is first considered as a random variable, and its prior distribution is assumed to be p(x). If the measured data is obtained, p(x) can be revised according to the Bayesian formula to obtain the posterior distribution. The posterior distribution combines the sample knowledge obtained from the measurement and the prior knowledge, so the estimation of the unknown parameters is more reasonable and the error is smaller.

[0039] Consider a nonlinear non-Gaussian dynamic system whose model is:

[0040]

[0041] Among them, x k ∈R n is the state vector of the system at time k, z k ∈R p is the measured value of the system at time k, v k ∈R m and n k ∈R r are the process noise and measurement noise of the system respectively. n ×R m →R nis a nonlinear system transfer function, h:R n ×R r →R p is a nonlinear observation equation.

[0042] (1) Prediction

[0043] Before obtaining the measured value at time k, first use the known posterior probability density function p(x k |z k-1 ), the prior probability density function at time k can be predicted through the transfer function of the system:

[0044] p(x k |z k-1 )=∫p(x k |x k-1 )p(x k-1 |z k-1 )dx k-1

[0045] (2) Update

[0046]

[0047] Update block diagram see Figure 3 shown.

[0048] For the prediction of the extended-range hybrid vehicle, the vehicle speed and acceleration are taken as state variables, and the required power is taken as the observed quantity. The state equation of the system is as follows:

[0049]

[0050] Where V is the vehicle speed, α is the acceleration, and P z,i,j is the probability of acceleration transferring from i to j, S z,i,j is the number of times the acceleration is transferred from i to j.

[0051] The measurement equation is: P(k)=H j (k)V j (k)+n j (k)

[0052] According to the above method, the transfer probability matrix is ​​updated by measuring the equation, combining the current vehicle speed V(k) and acceleration α(k), and the acceleration value α(k+1) at the next moment is obtained, thereby obtaining the vehicle speed value V(k+1) and power P(k+1) at the next moment. Repeating this process at the k+1 moment and the future finite moment can obtain all the vehicle speed, acceleration and power information in the prediction time domain.

[0053] The optimization control module realizes the calculated demand power with the goal of minimizing equivalent fuel consumption and locally balancing the power battery state of charge (SOC). The calculated value is optimized and output according to the maximum and minimum output values ​​of the engine, as well as water temperature, battery SOC and other parameters.

[0054] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-operating-condition energy management strategy for a range-extended hybrid vehicle, characterized in that: It includes a control mode switching module, a power prediction switching module and an optimization control module; The control mode switching module switches according to the power battery SOC value, single cell voltage and temperature, including a thermostat type control scheme and a power follower type control scheme; The power prediction module uses Bayesian estimation to predict vehicle speed and acceleration; The optimization control module optimizes according to the predicted power value with the goal of minimizing equivalent fuel consumption and locally balancing the power battery state of charge, so as to realize a global optimization control strategy for switching between a thermostat control scheme and a power following control scheme for the extended-range vehicle.

2. The multi-operating-condition energy management strategy according to claim 2 is characterized in that: When the battery's SOC value is lower than 0.8, the single cell voltage is lower than 2.6V and the temperature is lower than 55°C, the engine operates under a power follower control scheme. In other cases, the engine operates under a thermostat control scheme.

3. The multi-operating-condition energy management strategy according to claim 2 is characterized in that: When the control mode switching module adopts a thermostat type control scheme, when the power battery power is lower than the critical limit, the range extender system will automatically start to charge the power battery or directly drive the motor.

4. The multi-operating-condition energy management strategy according to claim 1 is characterized in that: When the control mode switching module adopts a follow-up control scheme, the power generated by the engine is mainly utilized under the condition of efficient vehicle operation, so as to reduce the frequency of charging and discharging of the power battery.

5. The multi-operating-condition energy management strategy according to claim 1 is characterized in that: Bayesian estimation is used to establish a multi-step prediction calculation to obtain the future vehicle speed and acceleration, thereby obtaining the power prediction value; the power is distributed to the engine to maintain the output and input of the power battery in a balanced state.

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