Vehicle energy management system and method based on dynamic path feedback and multi-mode optimization

Abstract

The present application relates to the technical field of new energy vehicle energy management, in particular to a vehicle energy management system and method based on dynamic path feedback and multi-mode optimization, the system comprising: a dynamic path feedback module, configured to acquire real-time road condition information, build a dynamic vehicle speed curve and reconstruct path weights; a multi-mode energy management module, configured to perform three-stage energy management based on the dynamic vehicle speed curve and the path weights, wherein the three-stage energy management comprises a preparation stage, a sharing stage and a recovery stage; and an SOC optimal distribution module, configured to dynamically adjust charging and discharging strategies according to the execution results of the three-stage energy management, so as to realize vehicle energy management. The present application realizes range-extender vehicle hybrid energy storage collaboration through the three-stage energy management mode, optimizes energy consumption by combining MPC and SDP algorithms, and improves energy efficiency and battery life.

Description

Technical Field

[0001] This invention relates to the field of energy management technology for new energy vehicles, and in particular to a vehicle energy management system and method based on dynamic path feedback and multi-mode optimization, applicable to hybrid vehicles in vehicle-to-vehicle (V2V) energy sharing scenarios. Background Technology

[0002] With the escalating global energy crisis and increasingly prominent environmental pollution problems, the development of new energy vehicles has received widespread attention. Among them, hybrid vehicles and pure electric vehicles, as important components of new energy vehicles, require optimized energy management systems to improve energy efficiency, reduce energy consumption, and decrease emissions. Currently, the following technical shortcomings exist:

[0003] (1) Single energy management: Existing V2V energy sharing technologies are mostly based on the homogeneous energy storage system of pure electric vehicles, without considering the hybrid energy storage characteristics of hybrid vehicles such as range-extended vehicles (such as the coordination of fuel and battery), resulting in low energy distribution efficiency.

[0004] (2) Static route planning: Traditional energy management relies on static route planning, which cannot respond to changes in traffic flow and dynamic energy demand in real time, and is prone to energy waste or insufficient supply.

[0005] (3) Coarse SOC control: Existing methods for optimizing battery state of charge (SOC) mostly use fixed thresholds and lack dynamic adjustment mechanisms, resulting in rapid battery life decay and poor adaptability under complex operating conditions.

[0006] (4) Low energy transmission efficiency: The existing system does not integrate multi-mode energy management strategy, making it difficult to coordinate the power distribution of the engine, battery and regenerative braking, resulting in insufficient energy recovery and utilization rate.

[0007] Therefore, this invention proposes a vehicle energy management system and method based on dynamic path feedback and multi-mode optimization to address the urgent technical challenges of how to achieve dynamic coordination between fuel power and battery power in range-extended vehicles to improve the energy utilization efficiency of hybrid energy storage systems; how to dynamically adjust route planning based on real-time traffic information and accurately match it with energy demand to reduce energy redundancy or shortage; how to dynamically optimize the SOC threshold under complex operating conditions to avoid battery overcharging / over-discharging while improving energy transmission efficiency; and how to integrate fuel economy optimization, regenerative braking energy recovery, and microgrid electricity price signals to achieve global energy management. Summary of the Invention

[0008] The purpose of this invention is to provide a vehicle energy management system and method based on dynamic path feedback and multi-mode optimization. It achieves hybrid energy storage collaboration for range-extended vehicles through a three-stage energy management mode (preparation, sharing, and recovery), and optimizes energy consumption by combining MPC and SDP algorithms to improve energy efficiency and battery life. It is suitable for intelligent transportation and vehicle-to-everything (V2X) interaction scenarios.

[0009] To achieve the above objectives, in one aspect, the present invention provides a vehicle energy management system based on dynamic path feedback and multi-mode optimization, comprising:

[0010] The dynamic path feedback module is used to acquire real-time road condition information, construct dynamic speed curves, and reconstruct path weights;

[0011] The multi-mode energy management module is used to perform three-stage energy management based on the dynamic vehicle speed curve and path weight, wherein the three-stage energy management includes a preparation stage, a sharing stage, and a recovery stage.

[0012] The SOC optimal allocation module is used to dynamically adjust the charging and discharging strategy based on the execution results of the three-stage energy management to achieve vehicle energy management.

[0013] Optionally, the dynamic path feedback module acquires road condition information in real time, constructs a dynamic vehicle speed curve, and reconstructs path weights, including:

[0014] Real-time data collection of vehicle location, energy demand, route tasks, and road network dynamics is used. By rematching and reconstructing route weights, and based on traffic light phase and time window constraints, a rolling time-domain model predictive control is employed to generate energy-saving vehicle speed curves.

[0015] Optionally, the preparation phase dynamically adjusts the SOC safety threshold through path energy consumption prediction, and the range of the SOC safety threshold is:

[0016] ;

[0017] Among them, SOC min and SOC max These are the minimum and maximum values ​​of SOC, respectively, and ΔE. pred Forecast net energy consumption for the route; E batt denoted as the total usable capacity of the battery; k1,k2∈[0.1,0.3] are safety margin coefficients.

[0018] Optionally, the sharing phase utilizes stochastic dynamic programming to optimize the power allocation between the engine and the battery, and combines this with an equivalent fuel minimization strategy to generate optimal control commands. The objective function of the sharing phase is:

[0019] ;

[0020] Where J is the comprehensive objective function, α(t), β(t), λ SOC γ(t) and m are the weighting coefficients for fuel cost, battery aging cost, SOC maintenance, and grid interaction cost, respectively. fuel (t), C batt (t), SOC(t), P grid (t) represents instantaneous fuel consumption rate, instantaneous battery aging cost, real-time battery state of charge, and power interaction with the grid, respectively.

[0021] Optionally, the recovery phase formulates a charging plan based on the microgrid time-of-use pricing signal, and the charging power satisfies:

[0022] ;

[0023] Among them, P charge (t) represents the real-time charging power of the battery, P max The maximum allowable charging power of the battery is given by π(t), where π is the real-time electricity price. peak For peak electricity price, π threshold This represents the electricity price threshold.

[0024] Optionally, the SOC optimal allocation module dynamically adjusts the charging and discharging strategy based on the execution results of the three-stage energy management, including:

[0025] Based on the execution results of the three-stage energy management, a rolling time-domain model is used to predict and control the dynamic allocation of SOC, and the charging and discharging strategy is adjusted in real time in conjunction with the battery health status.

[0026] On the other hand, the present invention also provides a vehicle energy management method based on dynamic path feedback and multi-mode optimization, including:

[0027] Real-time traffic information is acquired to construct dynamic vehicle speed curves and reconstruct path weights;

[0028] Three-stage energy management is performed based on the dynamic vehicle speed curve and path weight, wherein the three-stage energy management includes a preparation stage, a sharing stage, and a recovery stage;

[0029] The charging and discharging strategy is dynamically adjusted based on the execution results of the three-stage energy management to achieve vehicle energy management.

[0030] Optionally, the preparation phase dynamically adjusts the SOC safety threshold through path energy consumption prediction, and the range of the SOC safety threshold is:

[0031] ;

[0032] Among them, SOC min and SOC maxThese are the minimum and maximum values ​​of SOC, respectively, and ΔE. pred Forecast net energy consumption for the route; E batt denoted as the total usable capacity of the battery; k1,k2∈[0.1,0.3] are safety margin coefficients.

[0033] Optionally, the sharing phase utilizes stochastic dynamic programming to optimize the power allocation between the engine and the battery, and combines this with an equivalent fuel minimization strategy to generate optimal control commands. The objective function of the sharing phase is:

[0034] ;

[0035] Where J is the comprehensive objective function, α(t), β(t), λ SOC γ(t) and m are the weighting coefficients for fuel cost, battery aging cost, SOC maintenance, and grid interaction cost, respectively. fuel (t), C batt (t), SOC(t), P grid (t) represents instantaneous fuel consumption rate, instantaneous battery aging cost, real-time battery state of charge, and power interaction with the grid, respectively.

[0036] Optionally, the recovery phase formulates a charging plan based on the microgrid time-of-use pricing signal, and the charging power satisfies:

[0037] ;

[0038] Among them, P charge (t) represents the real-time charging power of the battery, P max The maximum allowable charging power of the battery is given by π(t), where π is the real-time electricity price. peak For peak electricity price, π threshold This represents the electricity price threshold.

[0039] The beneficial effects of this invention are as follows:

[0040] This invention achieves hybrid energy storage collaboration for range-extended vehicles through a three-stage energy management mode (preparation, sharing, and recovery), and optimizes energy consumption by combining MPC and SDP algorithms to improve energy efficiency and battery life. It is suitable for intelligent transportation and vehicle-to-everything (V2X) interaction scenarios. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1This is a schematic diagram of the vehicle energy management system based on dynamic path feedback and multi-mode optimization according to an embodiment of the present invention.

[0043] Figure 2 This is a flowchart illustrating the three-stage energy management process according to an embodiment of the present invention.

[0044] Figure 3 This is a schematic diagram of the SOC dynamic threshold adjustment curve according to an embodiment of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] On the one hand, this embodiment provides a vehicle energy management system based on dynamic path feedback and multi-mode optimization, such as Figure 1 As shown, it includes:

[0048] The dynamic path feedback module is used to acquire real-time road condition information, construct dynamic speed curves, and reconstruct path weights;

[0049] The multi-mode energy management module is used to perform three-stage energy management based on the dynamic vehicle speed curve and path weight, wherein the three-stage energy management includes a preparation stage, a sharing stage, and a recovery stage.

[0050] The SOC optimal allocation module is used to dynamically adjust the charging and discharging strategy based on the execution results of the three-stage energy management to achieve vehicle energy management.

[0051] Specifically, this embodiment achieves hybrid energy storage collaboration for range-extended vehicles through a three-stage energy management mode (preparation, sharing, and recovery), and optimizes energy consumption by combining MPC and SDP algorithms to improve energy efficiency and battery life, making it suitable for intelligent transportation and vehicle-to-everything (V2X) interaction scenarios.

[0052] The system provided in this embodiment supports the coordinated energy storage of fuel-battery hybrid systems in range-extended vehicles and achieves energy sharing through V2V communication.

[0053] Specifically, the dynamic path feedback module acquires real-time road condition information (SPAT, traffic flow) via V2X communication, generates dynamic speed curves, and reconstructs path weights, including:

[0054] Real-time data collection of vehicle location, energy demand, route tasks, and road network dynamics is used. By rematching and reconstructing route weights, and based on traffic light phase and time window constraints, a rolling time-domain model predictive control is employed to generate energy-saving vehicle speed curves.

[0055] The vehicle obtains SPAT information of the traffic lights ahead via V2I and calculates the target speed range; it uses the MPC algorithm to generate the optimal speed sequence and inputs it into the powertrain controller to ensure that the vehicle passes through the intersection within the green light window and reduces idling energy consumption.

[0056] The multi-mode energy management module adopts a three-stage energy management mode, such as... Figure 2 As shown, it includes:

[0057] (1) Preparation phase: Dynamically adjust the SOC safety threshold based on path energy consumption prediction (SOC) min SOC max ),like Figure 3 As shown. The SOC security threshold range satisfies:

[0058] ;

[0059] Among them, SOC min and SOC max These are the minimum and maximum values ​​of SOC, respectively, and ΔE. pred Forecast net energy consumption for the route; E batt denoted as the total usable battery capacity; k1,k2∈[0.1,0.3] are safety margin coefficients, obtained through training with historical road spectrum data.

[0060] (2) Sharing phase: Stochastic dynamic programming (SDP) is applied to optimize the power allocation between the engine and the battery, and the optimal control command is generated by combining the equivalent fuel minimum strategy (A-ECMS).

[0061] When the SOC is between 20% and 80%, the SDP algorithm is activated to optimize engine and battery output. The objective function for the sharing phase is:

[0062] ;

[0063] Where J is the comprehensive objective function, α(t), β(t), λ SOC γ(t) and β(t) are the weighting coefficients for fuel cost, battery aging cost, SOC maintenance, and grid interaction cost, respectively. The weights α and β are dynamically adjusted based on real-time electricity prices and fuel costs. fuel (t), C batt (t), SOC(t), P grid(t) represents instantaneous fuel consumption rate, instantaneous battery aging cost, real-time battery state of charge, and power interaction with the grid, respectively.

[0064] (3) Recovery phase: A battery charging plan is developed based on the microgrid electricity price signal, prioritizing charging during off-peak electricity price periods, with charging power meeting the following requirements:

[0065] ;

[0066] Among them, P charge (t) represents the real-time charging power of the battery, P max The maximum allowable charging power of the battery is given by π(t), where π is the real-time electricity price. peak For peak electricity price, π threshold This represents the electricity price threshold.

[0067] Maximum allowable charging power P of the battery max From battery temperature T batt Dynamic adjustment:

[0068] .

[0069] in, This is the rated maximum charging power. This is the temperature derating factor.

[0070] The SOC optimal allocation module dynamically allocates SOC based on the execution results of the three-stage energy management through rolling time-domain model predictive control (MPC), and adjusts the charging and discharging strategy in real time in conjunction with the battery state of health (SOH).

[0071] On the other hand, this embodiment also provides a vehicle energy management method based on dynamic path feedback and multi-mode optimization, including:

[0072] Real-time traffic information is acquired to construct dynamic vehicle speed curves and reconstruct path weights;

[0073] Three-stage energy management is performed based on the dynamic vehicle speed curve and path weight, wherein the three-stage energy management includes a preparation stage, a sharing stage, and a recovery stage;

[0074] The charging and discharging strategy is dynamically adjusted based on the execution results of the three-stage energy management to achieve vehicle energy management.

[0075] Specifically, real-time acquisition of road condition information, construction of dynamic vehicle speed curves, and reconstruction of path weights include:

[0076] Real-time data collection of vehicle location, energy demand, route tasks, and road network dynamics is used. By rematching and reconstructing route weights, and based on traffic light phase and time window constraints, a rolling time-domain model predictive control is employed to generate energy-saving vehicle speed curves.

[0077] Three-stage energy management includes:

[0078] (1) Preparation phase: Dynamically adjust the SOC safety threshold based on path energy consumption prediction (SOC) min SOC max The SOC security threshold range meets the following requirements:

[0079] ;

[0080] Among them, SOC min and SOC max These are the minimum and maximum values ​​of SOC, respectively, and ΔE. pred Forecast net energy consumption for the route; E batt denoted as the total usable battery capacity; k1,k2∈[0.1,0.3] are safety margin coefficients, obtained through training with historical road spectrum data.

[0081] (2) Sharing phase: Stochastic dynamic programming (SDP) is applied to optimize the power allocation between the engine and the battery, and the optimal control command is generated by combining the equivalent fuel minimum strategy (A-ECMS).

[0082] When the SOC is between 20% and 80%, the SDP algorithm is activated to optimize engine and battery output. The objective function for the sharing phase is:

[0083] ;

[0084] Where J is the comprehensive objective function, α(t), β(t), λ SOC γ(t) and β(t) are the weighting coefficients for fuel cost, battery aging cost, SOC maintenance, and grid interaction cost, respectively. The weights α and β are dynamically adjusted based on real-time electricity prices and fuel costs. fuel (t), C batt (t), SOC(t), P grid (t) represents instantaneous fuel consumption rate, instantaneous battery aging cost, real-time battery state of charge, and power interaction with the grid, respectively.

[0085] (3) Recovery phase: A battery charging plan is developed based on the microgrid electricity price signal, prioritizing charging during off-peak electricity price periods, with charging power meeting the following requirements:

[0086] ;

[0087] Among them, P charge (t) represents the real-time charging power of the battery, P max The maximum allowable charging power of the battery is given by π(t), where π is the real-time electricity price. peak For peak electricity price, π threshold This represents the electricity price threshold.

[0088] Maximum allowable charging power P of the battery max From battery temperature T batt Dynamic adjustment:

[0089] .

[0090] in, This is the rated maximum charging power. This is the temperature derating factor.

[0091] Dynamically adjusting the charging and discharging strategy based on the results of the three-stage energy management includes:

[0092] Based on the execution results of the three-stage energy management, the SOC is dynamically allocated through rolling time-domain model predictive control (MPC), and the charging and discharging strategy is adjusted in real time in conjunction with the battery state of health (SOH).

[0093] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A vehicle energy management system based on dynamic path feedback and multi-mode optimization, characterized in that, include: The dynamic path feedback module is used to acquire real-time road condition information, construct dynamic speed curves, and reconstruct path weights; The multi-mode energy management module is used to perform three-stage energy management based on the dynamic vehicle speed curve and path weight, wherein the three-stage energy management includes a preparation stage, a sharing stage, and a recovery stage. The SOC optimal allocation module is used to dynamically adjust the charging and discharging strategy based on the execution results of the three-stage energy management to realize vehicle energy management. The preparation phase dynamically adjusts the SOC safety threshold through path energy consumption prediction, and the range of the SOC safety threshold is: ； Among them, SOC min and SOC max These are the minimum and maximum values ​​of SOC, respectively, Δ E pred Predict net energy consumption for the route; E batt This represents the total usable capacity of the battery. k 1, k 2∈[0.1,0.3] represents the safety margin coefficient; The sharing phase utilizes stochastic dynamic programming to optimize engine and battery power allocation, and combines an equivalent fuel minimization strategy to generate optimal control commands. The objective function of the sharing phase is: ； in, J For the comprehensive objective function, α ( t ), β ( t ), λ SOC , γ ( t These are the weighting coefficients for fuel cost, battery aging cost, SOC maintenance, and grid interaction cost, respectively. m fuel ( t ), C batt ( t ), SOC ( t ), P grid ( t These are, respectively, instantaneous fuel consumption rate, instantaneous battery aging cost, real-time battery state of charge, and power interaction with the power grid; The recovery phase formulates a charging plan based on the microgrid time-of-use pricing signal, and the charging power meets the following requirements: ； in, P charge ( t This refers to the real-time charging power of the battery. P max The maximum allowable charging power of the battery. π ( t (This refers to the real-time electricity price.) π peak Peak electricity price, π threshold This represents the electricity price threshold.

2. The vehicle energy management system based on dynamic path feedback and multi-mode optimization according to claim 1, characterized in that, The dynamic path feedback module acquires road condition information in real time, constructs a dynamic vehicle speed curve, and reconstructs path weights, including: Real-time data collection of vehicle location, energy demand, route tasks, and road network dynamics is used. By rematching and reconstructing route weights, and based on traffic light phase and time window constraints, a rolling time-domain model predictive control is employed to generate energy-saving vehicle speed curves.

3. The vehicle energy management system based on dynamic path feedback and multi-mode optimization according to claim 1, characterized in that, The SOC optimal allocation module dynamically adjusts the charging and discharging strategy based on the execution results of the three-stage energy management, including: Based on the execution results of the three-stage energy management, a rolling time-domain model is used to predict and control the dynamic allocation of SOC, and the charging and discharging strategy is adjusted in real time in conjunction with the battery health status.

4. A vehicle energy management method based on dynamic path feedback and multi-mode optimization, characterized in that, include: Real-time traffic information is acquired to construct dynamic vehicle speed curves and reconstruct path weights; Three-stage energy management is performed based on the dynamic vehicle speed curve and path weight, wherein the three-stage energy management includes a preparation stage, a sharing stage, and a recovery stage; The charging and discharging strategy is dynamically adjusted based on the execution results of the three-stage energy management to achieve vehicle energy management; The preparation phase dynamically adjusts the SOC safety threshold through path energy consumption prediction, and the range of the SOC safety threshold is: ； Among them, SOC min and SOC max These are the minimum and maximum values ​​of SOC, respectively, Δ E pred Predict net energy consumption for the route; E batt This represents the total usable capacity of the battery. k 1, k 2∈[0.1,0.3] represents the safety margin coefficient; The sharing phase utilizes stochastic dynamic programming to optimize engine and battery power allocation, and combines an equivalent fuel minimization strategy to generate optimal control commands. The objective function of the sharing phase is: ； in, J For the comprehensive objective function, α ( t ), β ( t ), λ SOC , γ ( t These are the weighting coefficients for fuel cost, battery aging cost, SOC maintenance, and grid interaction cost, respectively. m fuel ( t ), C batt ( t ), SOC ( t ), P grid ( t These are, respectively, instantaneous fuel consumption rate, instantaneous battery aging cost, real-time battery state of charge, and power interaction with the power grid; The recovery phase formulates a charging plan based on the microgrid time-of-use pricing signal, and the charging power meets the following requirements: ； in, P charge ( t This refers to the real-time charging power of the battery. P max The maximum allowable charging power of the battery. π ( t (This refers to the real-time electricity price.) π peak Peak electricity price, π threshold This represents the electricity price threshold.

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