A power battery energy consumption optimization method and system based on navigation congestion data

Abstract

The application discloses a power battery energy consumption optimization method and system based on navigation congestion data, relates to the field of battery energy consumption optimization, and comprises the following steps: S1, collecting multi-source operation data, wherein the multi-source operation data at least comprises vehicle-mounted navigation road condition data, vehicle state data and environment data; S2, determining a congestion state according to the collected multi-source operation data; S3, triggering a corresponding energy consumption optimization strategy according to the determined congestion state; and S4, cooperatively regulating the power battery, the driving system and the high-voltage accessory system according to the energy consumption optimization strategy, so as to realize energy consumption optimization. Through the above scheme, the hierarchical strategy is refined according to different congestion levels, efficient transmission of instructions is realized in combination with a closed-loop system architecture, fine and self-adaptive regulation and control of battery energy consumption are realized, and the vehicle endurance under the congestion working condition can be improved by 5% to 15% through actual measurement, and the energy-saving effect is remarkable.

Description

Technical Field

[0001] This application relates to the field of battery energy consumption optimization, and in particular to a method for optimizing the energy consumption of power batteries based on navigation congestion data, a system for optimizing the energy consumption of power batteries based on navigation congestion data, electronic devices, storage media, and vehicles. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the range and lifespan of power batteries have become core indicators of concern for users. Vehicle driving conditions directly determine battery energy consumption and lifespan. In congested areas such as urban roads and highway entrances / exits, new energy vehicles generally face frequent start-stop cycles, low-speed crawling, and frequent acceleration and deceleration. These conditions present numerous energy consumption and battery usage pain points:

[0003] Firstly, the motor frequently operates in a low-efficiency range, with large fluctuations in instantaneous charge and discharge rates. This not only significantly increases the vehicle's energy consumption and shortens the actual driving range, but also exacerbates battery polarization losses and affects battery cycle life.

[0004] Secondly, high-voltage accessories such as air conditioners, vehicle systems, and thermal management systems continue to operate, resulting in a high proportion of static power consumption, which further exacerbates power consumption.

[0005] Third, existing vehicle energy management systems are mostly based on real-time vehicle condition control, lacking the ability to predict road conditions ahead, unable to adapt to congested conditions in advance, resulting in limited energy-saving effects, and are prone to problems such as power output jerking and lagging energy consumption control.

[0006] In existing technologies, some new energy vehicles achieve energy saving simply by switching basic driving modes, without deeply integrating navigation congestion data for accurate prediction. A few energy consumption management solutions that incorporate road conditions lack detailed tiered control strategies for battery power, thermal management, energy recovery, and high-voltage accessories based on congestion levels, resulting in poor adaptability, inadequate energy-saving effects, and negative impacts on the driving experience. Therefore, how to leverage navigation congestion data to achieve forward-looking and refined optimization of power battery energy consumption, balancing range improvement, battery life assurance, and driving comfort, has become a pressing technical problem to be solved in this field. Summary of the Invention

[0007] The purpose of this invention is to provide a power battery energy consumption optimization method based on navigation congestion data, a power battery energy consumption optimization system based on navigation congestion data, an electronic device, a storage medium, and a vehicle, thereby solving at least one of a number of technical problems.

[0008] Core technical issues: Existing new energy vehicles suffer from high battery energy consumption, lagging management and control, and poor energy-saving effects under congested conditions. They also face key problems such as high misjudgment rate in congested scenarios, strong dependence on navigation data, obvious jerking during mode switching, and imperfect safety interlocking mechanisms.

[0009] This invention provides the following solution:

[0010] According to a first aspect of the present invention, a method for optimizing the energy consumption of a power battery based on navigation congestion data is provided, comprising:

[0011] Collect multi-source operational data, which includes at least vehicle navigation traffic data, vehicle status data, and environmental data;

[0012] The congestion status is determined based on the collected multi-source operational data;

[0013] Based on the determined congestion status, the corresponding energy consumption optimization strategy is triggered;

[0014] Based on the energy consumption optimization strategy, the power battery, drive system, and high-voltage accessory system are coordinated and regulated to achieve energy consumption optimization.

[0015] Furthermore, determining congestion status includes: eliminating misjudgments of non-congestion scenarios through road condition type exclusion rules;

[0016] The road condition type exclusion rules include: underground parking garage scenario, reversing / moving scenario, waiting at a red light scenario, and crawling into a parking space scenario. If any of these conditions are met, the congestion judgment will be blocked.

[0017] Furthermore, the determination of congestion status also includes classifying congestion status into four levels: smooth traffic, light congestion, moderate congestion, and severe congestion.

[0018] The congestion classification standards are as follows: smooth traffic level corresponds to a vehicle speed ≥ the first vehicle speed threshold; light congestion level corresponds to a second vehicle speed threshold ≤ vehicle speed < the first vehicle speed threshold; moderate congestion level corresponds to a third vehicle speed threshold ≤ vehicle speed < the second vehicle speed threshold; severe congestion level corresponds to a vehicle speed < the third vehicle speed threshold and an expected standby time ≥ the first duration threshold.

[0019] It also includes setting a speed hysteresis range for a third speed threshold and a preset minimum duration to prevent boundary oscillations.

[0020] Furthermore, determining congestion status also includes: performing filtering and anti-shaking processing on the collected multi-source operational data;

[0021] The filtering and anti-shake processing includes: weighted moving average of vehicle speed data, dual threshold filtering of congestion confidence, outlier removal, and suppression of repeated triggering on the same road segment.

[0022] Furthermore, it also includes: establishing a fallback mechanism for navigation data failure, which switches to the corresponding fallback strategy to maintain energy-saving functions when navigation data fails;

[0023] The fallback mechanism includes: based on the validity of the navigation data, implementing fallback strategies in sequence, such as full-function energy saving, conservative energy saving, historical data matching, and pure vehicle sensor recognition.

[0024] Furthermore, it also includes: adaptively adjusting the dynamic prediction time and space window based on the current vehicle speed and road type, and triggering energy consumption optimization strategies in advance;

[0025] The dynamic prediction time and space window is: the first time and space window for highways, the second time and space window for urban expressways, the third time and space window for urban main roads, the third time and space window for urban branch roads, or the prediction is completely shielded in the reversing / R gear scenario.

[0026] Furthermore, it also includes: energy consumption optimization strategies, implementing differentiated power battery charging and discharging control, energy recovery regulation, thermal management optimization, and high-voltage accessory load reduction operations for different congestion levels;

[0027] Among them, the charging and discharging management of the power battery adopts slope control: when entering the power limit, the power drop per second is ≤ the first floating threshold; when the power limit is released, the power increase per second is ≤ the second floating threshold, so as to avoid sudden power changes;

[0028] Among them, the energy recovery regulation is set with a safety interlock: when ESP / ABS / TCS is activated, or when there is a low-friction surface, the intensity of energy recovery is limited to ensure driving safety;

[0029] Among them, thermal management optimization sets safety boundaries: adjusts thermal management power according to battery temperature to avoid battery temperature exceeding limits;

[0030] Among them, the high-voltage accessory load reduction adopts a priority list, and the load reduction is carried out in stages according to the safety priority of the accessory;

[0031] It also includes determining when congestion is alleviated and gradually exiting the energy consumption optimization strategy at a preset slope for a smooth transition.

[0032] Furthermore, it also includes: a safety-first interlock mechanism: when a safety trigger signal is detected, the energy consumption optimization strategy is immediately suspended to prioritize driving safety;

[0033] Safety trigger signals include: rapid acceleration, emergency braking, safety system activation, battery failure, high voltage failure, charging connection, and non-driving gear position;

[0034] When a safety trigger signal is received, the energy-saving strategy is suspended within the second duration threshold.

[0035] According to a second aspect of the present invention, a power battery energy consumption optimization system based on navigation congestion data is provided, for implementing a power battery energy consumption optimization method based on navigation congestion data. The power battery energy consumption optimization system based on navigation congestion data includes:

[0036] The data acquisition module is used to collect operational data from multiple sources.

[0037] The congestion determination module is used to determine the congestion status based on the collected multi-source operational data;

[0038] The central decision-making module is used to generate corresponding energy consumption optimization instructions based on the congestion status;

[0039] The execution control module is used to coordinate and regulate the power battery, drive system, and high-voltage accessory system according to optimization instructions.

[0040] Furthermore, it also includes: a data fusion and failure management module, used to realize multi-source data fusion, navigation failure degradation, driving mode management, and filtering and anti-shake processing;

[0041] It also includes: a safety interlock module, used to monitor safety signals in real time and trigger the pause and resumption of energy consumption optimization strategies;

[0042] It also includes a human-computer interaction module, which displays the status to the user and provides mode selection and manual triggering functions.

[0043] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0044] The memory stores a computer program that, when executed by the processor, causes the processor to perform steps such as a power battery energy consumption optimization method based on navigation congestion data.

[0045] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, comprising: storing a computer program executable by an electronic device, wherein when the computer program is run on the electronic device, the electronic device performs steps such as a power battery energy consumption optimization method based on navigation congestion data.

[0046] According to a fifth aspect of the present invention, a vehicle is provided, comprising:

[0047] Electronic devices for implementing steps such as power battery energy consumption optimization methods based on navigation congestion data;

[0048] The processor runs programs, and when the programs are running, they execute steps such as power battery energy consumption optimization methods based on navigation congestion data, based on data output from electronic devices.

[0049] Storage medium for storing programs that, when running, execute steps such as power battery energy consumption optimization methods based on navigation congestion data, based on data output from electronic devices.

[0050] The above solution achieves the following beneficial technical effects:

[0051] This application relies on navigation data to predict traffic congestion ahead in advance, eliminating the lag of traditional real-time control. It refines the classification strategy for different levels of congestion and combines a closed-loop system architecture to achieve efficient command transmission, enabling refined and adaptive control of battery energy consumption. According to actual tests, the vehicle's range can be increased by 5%-15% under congested conditions, with significant energy-saving effects, achieving the effect of forward-looking prediction and more precise energy-saving control.

[0052] This application effectively distinguishes between real congestion and non-congestion scenarios by using multi-dimensional road condition type identification and exclusion rules (underground parking garage, reversing, waiting at red lights, crawling into parking spaces, etc.), eliminating inappropriate restrictions when users are using their cars normally. The system's misjudgment rate is reduced by more than 90%, greatly improving the user experience, achieving accurate scene identification, and avoiding misjudgment interference.

[0053] This application establishes a four-level degradation mechanism for navigation data failure (confidence level + historical data assistance + V2X fusion + sensor replacement), and defines a pure vehicle sensor fallback mode to ensure that energy-saving functions are not interrupted in extreme situations such as no navigation, no GPS, or navigation crash. The system availability is improved from "navigation-dependent" to "full-scenario coverage", achieving multiple data fallbacks and strong system robustness.

[0054] This application achieves the effect of protecting the battery and extending its lifespan by limiting the peak charging and discharging power of the power battery, reducing fluctuations during high-rate charging and discharging, optimizing battery thermal management strategies, reducing battery polarization loss and aging rate, effectively extending the cycle life of the power battery, reducing user vehicle operating costs, and thus protecting the battery and extending its lifespan.

[0055] This application employs a triple design of vehicle speed lag range (5km / h buffer zone), time anti-shake mechanism (minimum duration + cooling period), and power slope control (entry at 10% / second, exit at 15% / second) to completely eliminate mode oscillation and power sudden changes. The driver is unaware of the switching process, which balances energy saving and comfort, achieving a smooth and jerk-free transition and an excellent driving experience.

[0056] This application establishes multiple mechanisms, including driver intent P0 priority (immediate release during rapid acceleration), ESP / ABS / TCS safety interlock (return to zero upon activation), temperature safety boundary (mandatory protection from -20℃ to 45℃), and high-voltage accessory priority list (P0 safety key never shut off), to ensure that safety functions are not affected by energy-saving strategies under any circumstances, achieving the effect of highest safety priority and comprehensive risk control.

[0057] The prediction window of this application is dynamically adjusted according to vehicle speed and road type (2-5km / 2-5min on highways, 0.5-2km / 5-10min in urban areas), and responds in conjunction with driving modes such as Sport / Economy / Snow, to achieve personalized energy saving with "one vehicle, one condition, one strategy", adapting to all scenarios such as highways, urban areas, and extreme weather, achieving strong dynamic adaptive capability and adapting to complex working conditions.

[0058] This application requires no new hardware equipment. It can build the architecture and realize the functions by relying on existing in-vehicle navigation, VCU, BMS and other systems. Upgrades can be completed through software algorithm optimization. It is compatible with various pure electric passenger vehicles and commercial vehicles, and has strong engineering feasibility and market adaptability, achieving the effect of strong adaptability and easy implementation and promotion. Attached Figure Description

[0059] Figure 1 This is a flowchart of a power battery energy consumption optimization method based on navigation congestion data provided by one or more embodiments of the present invention.

[0060] Figure 2 This is a structural diagram of a power battery energy consumption optimization system based on navigation congestion data provided by one or more embodiments of the present invention.

[0061] Figure 3 This is a schematic diagram of the power battery energy consumption optimization scheme architecture based on navigation congestion data provided in a specific embodiment of the present invention.

[0062] Figure 4 This is a block diagram of an electronic device structure for a power battery energy consumption optimization method based on navigation congestion data, provided by one or more embodiments of the present invention. Detailed Implementation

[0063] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0064] Figure 1 This is a flowchart of a power battery energy consumption optimization method based on navigation congestion data provided by one or more embodiments of the present invention.

[0065] like Figure 1 The power battery energy consumption optimization method based on navigation congestion data shown includes:

[0066] Step S1: Collect multi-source operational data, which includes at least vehicle navigation traffic data, vehicle status data, and environmental data.

[0067] Step S2: Determine the congestion status based on the collected multi-source operational data;

[0068] Step S3: Based on the determined congestion status, trigger the corresponding energy consumption optimization strategy;

[0069] Step S4: Based on the energy consumption optimization strategy, the power battery, drive system, and high-voltage accessory system are coordinated and regulated to achieve energy consumption optimization.

[0070] In this embodiment, determining the congestion status includes: eliminating misjudgments of non-congestion scenarios through road condition type exclusion rules;

[0071] The road condition type exclusion rules include: underground parking garage scenario, reversing / moving scenario, waiting at a red light scenario, and crawling into a parking space scenario. If any of these conditions are met, the congestion judgment will be blocked.

[0072] In this embodiment, determining the congestion status also includes: classifying the congestion status into four levels: smooth traffic, light congestion, moderate congestion, and severe congestion;

[0073] The congestion classification standards are as follows: smooth traffic level corresponds to a vehicle speed of ≥40km / h; light congestion level corresponds to a vehicle speed of 20km / h≤<40km / h; moderate congestion level corresponds to a vehicle speed of 5km / h≤<20km / h; and severe congestion level corresponds to a vehicle speed of <5km / h and an expected standby time of ≥5min.

[0074] It also includes setting a speed hysteresis range of 5 km / h and a minimum duration of 10-30 seconds to prevent boundary oscillations.

[0075] In this embodiment, determining the congestion status further includes: performing filtering and anti-shaking processing on the collected multi-source operating data;

[0076] The filtering and anti-shake processing includes: weighted moving average of vehicle speed data, dual threshold filtering of congestion confidence, outlier removal, and suppression of repeated triggering on the same road segment.

[0077] In this embodiment, it also includes: establishing a fallback mechanism for navigation data failure, which switches to the corresponding fallback strategy when navigation data fails to maintain energy-saving function;

[0078] The fallback mechanism includes: based on the validity of the navigation data, implementing fallback strategies in sequence, such as full-function energy saving, conservative energy saving, historical data matching, and pure vehicle sensor recognition.

[0079] In this embodiment, it also includes: adaptively adjusting the dynamic prediction spatiotemporal window based on the current vehicle speed and road type to trigger the energy consumption optimization strategy in advance;

[0080] The dynamic prediction time and space window is: 2-5km / 2-5min for highways, 1-3km / 3-8min for urban expressways, 0.5-2km / 5-10min for urban arterial roads, and 0.2-1km / 3-5min for urban secondary roads, or the prediction is completely shielded in the reverse / R gear scenario.

[0081] In this embodiment, it also includes: an energy consumption optimization strategy, which implements differentiated power battery charging and discharging control, energy recovery regulation, thermal management optimization, and high-voltage accessory load reduction operation for different congestion levels;

[0082] Among them, the charging and discharging management of the power battery adopts slope control: when entering the power limit, the power drop per second is ≤10%; when the power limit is released, the power increase per second is ≤15% to avoid sudden power changes;

[0083] Among them, the energy recovery regulation is set with a safety interlock: when ESP / ABS / TCS is activated, or when there is a low-friction surface, the intensity of energy recovery is limited to ensure driving safety;

[0084] Among them, thermal management optimization sets safety boundaries: adjusts thermal management power according to battery temperature to avoid battery temperature exceeding limits;

[0085] Among them, the high-voltage accessory load reduction adopts a priority list, and the load reduction is carried out in stages according to the safety priority of the accessory;

[0086] It also includes determining when congestion is alleviated and gradually exiting the energy consumption optimization strategy at a preset slope for a smooth transition.

[0087] In this embodiment, a safety-first interlocking mechanism is also included: when a safety trigger signal is detected, the energy consumption optimization strategy is immediately suspended to prioritize driving safety;

[0088] Safety trigger signals include: rapid acceleration, emergency braking, safety system activation, battery failure, high voltage failure, charging connection, and non-driving gear position;

[0089] When a safety trigger signal is received, the energy-saving strategy will be paused within 100ms.

[0090] Specifically, the power battery energy consumption optimization method based on navigation congestion data in this application can be understood as follows:

[0091] Step 1: Multi-source data collection and accurate congestion status determination. Real-time collection of in-vehicle navigation traffic data, vehicle status data, and environmental data. By using traffic condition type exclusion rules (underground parking garage, reversing, waiting at a red light, creeping into a parking space) to eliminate misjudgments in non-congested scenarios, filtering and anti-shake of navigation data and classifying it into four congestion levels. At the same time, a four-level degradation mechanism for navigation data failure is established (confidence level + historical data + V2X + sensor backup).

[0092] Step 2: Congestion prediction and energy-saving mode triggering. Set a dynamic prediction time and space window (adaptively adjusted according to vehicle speed and road type: 2-5km / 2-5min for highways, 0.5-2km / 5-10min for urban areas, and prediction shielding for reversing). Combined with the hysteresis anti-shake mechanism (5km / h hysteresis range + minimum duration + cooling period), the vehicle controller triggers the corresponding graded energy-saving mode.

[0093] Step 3: Implement a graded energy consumption optimization strategy. For different levels of congestion, coordinate the vehicle controller, battery management system, drive system, and high-voltage accessory system to implement differentiated power battery charging and discharging control (with slope control: enter 10% / second, deactivate 15% / second), energy recovery adjustment (interlocked with ESP / ABS / TCS), thermal management optimization (-20℃~45℃ safety boundary), high-voltage accessory load reduction (P0-P5 priority list), and establish a driver intention P0 level priority (immediate deactivation for rapid acceleration) and safety interlock mechanism.

[0094] Step 4: Smooth exit of the strategy and safety interlock. After the congestion is relieved, exit gradually according to the slope (severe → moderate → mild → smooth, slope 10%-20% / second). If a safety trigger signal is encountered (rapid acceleration / ABS / ESP / temperature over-limit / leakage / fast charging / R gear), the energy-saving strategy will be immediately suspended (<100ms response) to prioritize driving safety.

[0095] The congestion classification standards in Step 1 are as follows: smooth traffic (Level 0) with a vehicle speed ≥ 40 km / h; light congestion (Level 1) with a vehicle speed ≤ 20 km / h and < 40 km / h; moderate congestion (Level 2) with a vehicle speed ≤ 5 km / h and < 20 km / h; and severe congestion (Level 3) with a vehicle speed < 5 km / h and an expected standby time ≥ 5 min. A 5 km / h lag interval and a minimum duration of 10-30 seconds are set to prevent boundary oscillations.

[0096] The road condition type exclusion rules in step 1 include: underground parking garage (GPS lost + vehicle speed <10km / h for more than 30 seconds), reversing / moving (R gear or steering wheel angle >180°), waiting at a red light (intersection + stopping <120 seconds), crawling into the parking space (end point <100m + vehicle speed <5km / h). If any of these conditions are met, the congestion judgment will be blocked.

[0097] The navigation data failure degradation mechanism in step 1 includes: fully effective (confidence ≥ 70%), full-function energy saving; mild degradation (50%-70%), conservative energy saving; moderate degradation (no route / weak GPS), historical congestion database prediction; and complete failure (offline / crash > 30 seconds), switching to pure vehicle sensor mode (camera + radar to identify congestion) to ensure that the energy saving function is not interrupted.

[0098] In step 2, the dynamic prediction window is as follows: highway 2-5km / 2-5min, urban expressway 1-3km / 3-8min, urban main road 0.5-2km / 5-10min, secondary road 0.2-1km / 3-5min, reverse / R gear completely blocked, prediction distance = min(vehicle speed × time coefficient, maximum distance).

[0099] In step 3, the power limit slope control is as follows: when entering the limit, the decrease per second is ≤10%; when releasing the limit, the increase per second is ≤15%; when a rapid acceleration request (throttle >80%) is made, the P0 level is prioritized and immediately released (<100ms).

[0100] The energy recovery safety interlock in step 3 is as follows: when ESP / ABS is activated, the recovery intensity is zero; when TCS is activated, it is <20%; in rain and snow mode, it is <50%; and on low-adhesion road surfaces (adhesion coefficient <0.3), it is <30%.

[0101] In step 3, the thermal management safety boundaries are: when the temperature is >45℃ or <-20℃, the energy-saving mode is forcibly exited and the protection mode is entered; when the temperature is 35-45℃, the heat dissipation power is ≥50%; when there is severe congestion, the fan / water pump maintains the minimum safe speed to ensure that the battery surface temperature is <40℃ and the cell temperature difference is <5℃.

[0102] In step 3, the priority for reducing the load on high-voltage accessories is as follows: P0 (brake vacuum pump, EPS, BMS, VCU) should never be shut down; P1 (battery / motor water pump, heater) should be reduced to ≤50% when the temperature allows; P2 (air conditioner) should be kept at 10% when there is severe congestion; P3-P5 should be shut down step by step, and P1 should be automatically restored when the temperature is >40℃.

[0103] In step 3, the driving mode linkage is as follows: Sport mode only performs mild energy saving and the power limit is ≤20%; Snow mode disables enhanced energy recovery; Eco mode relaxes the trigger threshold; and Custom mode supports intensity settings of 1-5 levels.

[0104] The smooth exit mechanism in step 4 is as follows: severe → moderate (vehicle speed > 10km / h + 15 seconds, slope 10% / second, 3-5 seconds), moderate → mild (vehicle speed > 25km / h + 10 seconds, slope 15% / second, 2-3 seconds), mild → smooth (vehicle speed > 45km / h + 10 seconds, slope 20% / second, 1-2 seconds), and predictive exit (early recovery when congestion ends within 500m).

[0105] The safety interlock conditions in step 4 include: rapid acceleration (throttle > 80%), emergency braking (braking > 90%), ABS / ESP / TCS activation, battery temperature > 45℃ or < -20℃ or voltage difference > 300mV, high voltage insulation fault / interlock disconnection / leakage alarm, fast charging / slow charging connection, power system / battery fault light illumination, R gear / N gear, and exiting energy-saving mode within < 100ms when triggered.

[0106] The system comprises seven modules: a data acquisition module, a data fusion and failure management module (including data validity assessment, multi-source fusion, failure degradation, driving mode management, and anti-shake control unit), a congestion determination module, a central decision-making module, an execution control module, a safety interlock module, and a human-machine interaction module. These seven modules form a closed-loop architecture, supporting full-scenario coverage and failure degradation.

[0107] The data fusion and failure management module achieves filtering and anti-shake through weighted moving average (vehicle speed weighted over 5 seconds, current 0.4 / historical 0.6), dual threshold filtering (trigger >75% / cancel <60%), 3σ outlier removal, and 5-minute repetition suppression on the same road segment.

[0108] In another embodiment, it further includes automatically switching to a basic energy-saving mode when the on-board sensors also fail, limiting only the power consumption of non-essential high-voltage accessories without limiting power output, thus avoiding a complete interruption of the energy-saving function.

[0109] Furthermore, when there is no matching historical congestion data for an unfamiliar road segment, a basic congestion status determination is made based on the current vehicle speed and acceleration changes, triggering the corresponding energy-saving strategy.

[0110] Furthermore, in response to prolonged severe congestion, the thermal management power is dynamically adjusted based on the battery temperature change trend to avoid repeated start-stop of thermal management and ensure stable battery temperature.

[0111] Furthermore, it includes adaptively adjusting the thresholds for determining vehicle speed and throttle based on the user's historical driving habits, thereby avoiding frequent mode switching caused by special driving habits.

[0112] Furthermore, when the battery temperature is below 0°C, the heating power of the thermal management system should not be reduced, and priority should be given to ensuring the battery's operating temperature to avoid low-temperature losses.

[0113] Furthermore, when a green light countdown or a vehicle in front starts at an intersection is detected, the power limit is lifted in advance to eliminate the power response delay for running the light / emergency overtaking.

[0114] Furthermore, it integrates current real-time vehicle speed data to correct navigation congestion data, avoiding strategy errors caused by navigation update delays and misjudgments of special road sections.

[0115] Furthermore, when it is detected that a user has activated camping or rest mode, the high-voltage accessories should not be deloaded to ensure the user's comfort needs.

[0116] Furthermore, when the SOC of the power battery is below 15%, the power limit is automatically lifted to ensure the user's ability to get out of trouble and to prioritize the user's journey to a charging station.

[0117] Furthermore, for hybrid vehicles, the start-stop control logic of the engine is linked to adapt the energy recovery strategy of the hybrid system to avoid power limitation affecting the normal intervention of the engine.

[0118] Furthermore, on highways, it increases the ability to anticipate sudden changes in the speed of vehicles ahead, triggering energy-saving strategies in advance to cope with sudden congestion caused by outdated navigation systems.

[0119] Furthermore, it includes adaptively adjusting the thresholds for determining rapid acceleration and emergency braking based on the user's historical driving habits to avoid misjudgment of intent recognition.

[0120] Further features include synchronizing and backing up local historical traffic congestion data to the cloud, automatically restoring it after OTA upgrades and factory resets to avoid data loss.

[0121] Further features include differentiating between different drivers, storing personalized historical congestion data and driving habits, and adapting to multi-driver usage scenarios.

[0122] Figure 2 This is a structural diagram of a power battery energy consumption optimization system based on navigation congestion data provided by one or more embodiments of the present invention.

[0123] like Figure 2 The power battery energy consumption optimization system shown is used to implement the power battery energy consumption optimization method based on navigation congestion data. The power battery energy consumption optimization system based on navigation congestion data includes:

[0124] The data acquisition module is used to collect operational data from multiple sources.

[0125] The congestion determination module is used to determine the congestion status based on the collected multi-source operational data;

[0126] The central decision-making module is used to generate corresponding energy consumption optimization instructions based on the congestion status;

[0127] The execution control module is used to coordinate and regulate the power battery, drive system, and high-voltage accessory system according to optimization instructions.

[0128] In this embodiment, it also includes: a data fusion and failure management module, used to realize multi-source data fusion, navigation failure degradation, driving mode management, and filtering and anti-shake processing;

[0129] It also includes: a safety interlock module, used to monitor safety signals in real time and trigger the pause and resumption of energy consumption optimization strategies;

[0130] It also includes a human-computer interaction module, which displays the status to the user and provides mode selection and manual triggering functions.

[0131] It is worth noting that although this system / device only discloses the above-mentioned modules / units, it does not mean that this system / device is limited to the above-mentioned basic functional modules. On the contrary, what this invention intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can add one or more functional modules in combination with the prior art to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. It cannot be assumed that the scope of protection of the claims of this invention is limited to the above-disclosed basic functional modules just because this embodiment only discloses a few basic functional modules.

[0132] In one specific embodiment, addressing the technical shortcomings of existing new energy vehicles in congested conditions, such as high battery energy consumption, lagging management, and poor energy-saving effects, as well as key issues in existing technologies such as high misjudgment rate in congested scenarios, strong dependence on navigation data, significant jerking during mode switching, and imperfect safety interlocking mechanisms, this embodiment discloses a method such as... Figure 3 The power battery energy consumption optimization scheme shown is based on navigation congestion data. It predicts the congestion status ahead by using navigation data and combines multi-dimensional road condition recognition and data fusion technology to adapt the vehicle's energy consumption management strategy in a hierarchical manner. This enables adaptive regulation of power battery charging and discharging, thermal management, energy recovery, and high-voltage accessory load. It also establishes a backup mechanism for navigation failure and a safety-first interlocking system. This not only improves the vehicle's range under congested conditions but also reduces battery losses due to high rate fluctuations and extends battery life. At the same time, it eliminates scene misjudgments, ensures availability in all scenarios, and achieves smooth and seamless switching, comprehensively improving driving safety and ride comfort.

[0133] The power battery energy consumption optimization solution based on navigation congestion data relies on the collaborative efforts of the in-vehicle navigation system, vehicle control unit (VCU), battery management system (BMS), drive system, high-voltage accessory system, ESP / ABS safety system, and data fusion module. The specific steps are as follows:

[0134] Step 1: Multi-source data collection and accurate congestion status determination

[0135] Real-time traffic data transmitted by the vehicle navigation system is collected, including the length of the congestion ahead, the estimated duration of congestion, the average vehicle speed, and the confidence level of the traffic conditions. At the same time, vehicle status data (power battery SOC, battery temperature, motor speed, vehicle speed, pedal signal, gear signal, steering wheel angle), driver assistance status data (ACC / NOA activation status), and environmental data (outdoor temperature and humidity, rainfall) are collected. The system uses a multi-dimensional fusion judgment logic to classify the congestion status into four levels: smooth traffic, light congestion, moderate congestion, and severe congestion. A fallback mechanism is also established to prevent navigation data failure.

[0136] 1.1 Multi-dimensional judgment logic for congestion classification

[0137] To avoid misjudgments caused by a single vehicle speed threshold, congestion classification needs to be comprehensively determined by combining road condition type identification and vehicle operating status (as shown in Table 1):

[0138] Table 1

[0139] Base speed Navigation data / wheel speed sensor As the primary reference indicator Road condition type Navigation map attributes + vehicle gear position Excluding non-congestion scenarios Vehicle Intent Gear position signal + steering wheel angle + accelerator pedal Identify special working conditions Duration of time Duration timer Avoid instantaneous misjudgment

[0140] Road condition type exclusion rules to prevent misjudgment (as shown in Table 2):

[0141] Table 2

[0142] underground parking garage Navigation recognizes "indoor parking lot" attribute, or GPS is lost + vehicle speed <10km / h for >30 seconds. Non-congestion scenarios Do not trigger power saving mode Reversing / Moving Gear position signal = Reverse (R) gear, or steering wheel angle >180° + vehicle speed <5km / h maneuver working condition Blocking congestion determination waiting at a red light Navigation recognizes "intersection" + parking time <120 seconds Signal waiting Maintain the original mode and do not switch. creeping into storage Navigation destination distance <100m + vehicle speed <5km / h Destination reached Do not trigger severe congestion mode

[0143] Priority of judgment: Road condition type exclusion rules > Vehicle speed threshold judgment. If any exclusion condition is met, the corresponding energy-saving mode will not be triggered even if the vehicle speed meets the congestion standard.

[0144] The congestion classification criteria (optimized) are shown in Table 3:

[0145] Table 3

[0146] Smooth flow (Level 0) ≥40km / h No exclusion scenarios - Light congestion (Level 1) 20km / h≤vehicle speed<40km / h No exclusion scenarios >10 seconds Moderate congestion (Level 2) 5km / h≤vehicle speed<20km / h No exclusion scenarios + navigation confidence ≥ 60% >15 seconds Severe congestion (Level 3) <5km / h Expected pause duration ≥ 5 minutes + non-excluded scenarios >30 seconds

[0147] 1.2 Degradation and fallback mechanisms for navigation data failure

[0148] Define navigation data validity grading and system response strategies (as shown in Table 4):

[0149] Table 4

[0150] Fully effective Online navigation + reliable GPS + confidence level ≥ 70% Normal execution Full-function graded energy saving Mild downgrade Confidence level 50%-70% or signal fluctuation Instrument warning Conservative energy-saving mode (mild congestion strategy only) Moderate downgrade No navigation route or weak GPS signal Historical data assistance Predictive energy saving based on historical congestion database Complete failure Navigation offline / crashed / no GPS for more than 30 seconds Pure vehicle sensor mode Basic energy-saving mode (limited to static high load only)

[0151] Specific implementation of the fallback strategy:

[0152] Historical congestion database: Vehicles locally store the historical congestion time periods and locations of frequently used routes (home-work, frequently visited locations). When navigation fails, the system matches the historical pattern based on the current location and timestamp, and automatically triggers the corresponding energy-saving strategy for the corresponding time period.

[0153] V2X vehicle-to-infrastructure cooperation: receiving congestion information broadcast by roadside units (RSUs) via C-V2X as a supplement to navigation data;

[0154] Vehicle sensor fusion: Using a forward-facing camera to identify the density of vehicles ahead and using millimeter-wave radar to monitor the relative speed of surrounding vehicles to help determine the degree of congestion;

[0155] Driver manual trigger: A "forced energy saving" button is provided, allowing users to manually enter energy saving mode. The system provides an input interface for "estimated congestion duration", supporting three settings: 15min / 30min / 60min.

[0156] 1.3 Data Filtering Anti-shake Algorithm

[0157] A weighted moving average + double threshold filtering combined algorithm is used:

[0158] Vehicle speed data: weighted average of the last 5 seconds (current weight 0.4, historical weight 0.6), sampling frequency 10Hz;

[0159] Congestion confidence level: dual threshold filtering (>75% triggers energy saving, <60% cancels energy saving, 60%-75% maintains the original state);

[0160] Outlier removal: The 3σ criterion is used to remove data points that exceed the mean ± 3 standard deviations;

[0161] Repeated triggering on the same road segment: Record the coordinates (±100m range) of road segments that have triggered the energy-saving mode in the last 5 minutes to avoid repeated triggering.

[0162] Step 2: Congestion prediction and energy-saving mode triggering

[0163] A dynamic prediction time-space window is set, based on the vehicle's current position, and the prediction range is adaptively adjusted according to the current vehicle speed and road type. Combined with the road condition confidence level (≥70% is considered a valid prediction, 50%-70% is a conservative prediction, and <50% is supplemented by historical data), the vehicle controller triggers the corresponding graded energy-saving mode according to the determined congestion level. The mode switching follows the principle of hysteresis anti-shake + smooth transition to avoid jerking and frequent vibration of the vehicle's power output.

[0164] 2.1 Dynamic Prediction Window Algorithm

[0165] Predicted distance = min(current vehicle speed × prediction time coefficient, maximum predicted distance)

[0166] Predicted time = Basic predicted time × Road type coefficient; (as shown in Table 5)

[0167] Table 5

[0168] highway >80km / h 2-5km 2-5 min Longer reaction distance reserved for high-speed scenarios Urban expressway 40-80km / h 1-3km 3-8min Taking into account both speed and road conditions Urban main roads 20-40km / h 0.5-2km 5-10 min Standard prediction window Side road / neighborhood <20km / h 0.2-1km 3-5 min Accurate prediction at short distances Reverse / R gear - 0km 0min Complete shielding prediction

[0169] 2.2 Mode Switching Hysteresis and Anti-shake Design

[0170] Vehicle speed hysteresis range (to prevent boundary oscillations) (as shown in Table 6):

[0171] Table 6

[0172] Congestion level switching Entry threshold Exit threshold Hysteresis interval Minimum duration Unobstructed → Mild <40km / h ≥45km / h 5km / h 10 seconds Mild to moderate <20km / h ≥25km / h 5km / h 15 seconds Moderate to severe <5km / h ≥10km / h 5km / h 30 seconds Any → Exit - Meets the exit threshold - +10-second cooldown

[0173] Image stabilization mechanism:

[0174] Mode switching must meet a minimum duration to avoid frequent switching due to instantaneous fluctuations;

[0175] Set a mode switching cooldown period (10 seconds), and implement a policy that will not be repeatedly executed when the same level is repeatedly triggered.

[0176] Suppressing repeated triggering on the same road segment: Record the coordinates (±100m range) of road segments that have triggered the energy-saving mode in the last 5 minutes to avoid repeated triggering.

[0177] Step 3: Tiered Energy Consumption Optimization Strategy

[0178] For different levels of congestion, the system coordinates the VCU, BMS, drive system, and high-voltage accessory system to implement differentiated battery energy consumption optimization strategies. The core focuses on the control of power battery charging and discharging, energy recovery, thermal management, and high-voltage accessory load reduction, and establishes a safety-first interlocking mechanism and driving mode linkage strategy.

[0179] 3.1 Smooth Traffic Mode (Level 0)

[0180] The default vehicle control strategy is implemented, with the power battery charging and discharging power, motor torque, energy recovery intensity, and high-voltage accessory load all using factory default parameters, balancing power performance and basic energy consumption control to meet normal driving needs.

[0181] 3.2 Light Congestion Energy-Saving Mode (Level 1)

[0182] Slightly increase the intensity of energy recovery, improving the motor braking recovery efficiency by 10%-15%; optimize the motor operating point, forcing the motor to operate in a high-efficiency range; slightly reduce the load on high-voltage accessories such as air conditioners and heat pumps, reduce the fan speed and compressor operating frequency, reduce static power consumption, and at the same time, the BMS maintains the conventional thermal management strategy to ensure stable battery operating temperature.

[0183] 3.3 Moderate Congestion Energy-Saving Mode (Level 2)

[0184] Reduce low-speed creep torque to minimize unnecessary power output and limit peak battery discharge power to 15%-25% (entry slope ≤10% / second) to avoid high-rate discharge losses; further enhance energy recovery intensity by adopting a medium-to-high-level recovery mode to maximize the recovery of braking energy; the BMS flexibly adjusts the thermal management strategy to reduce the operating power consumption of cooling fans and water pumps, and relaxes the temperature control range within the battery's safe temperature range; high-voltage accessories enter energy-saving mode, retaining only basic cooling / heating functions to significantly reduce load power consumption.

[0185] 3.4 Severe Congestion Energy-Saving Mode (Level 3)

[0186] The highest level of energy recovery is activated to achieve full recovery of braking energy; the power battery enters a low-power management state, limiting instantaneous charge and discharge peaks to 30%-40%, retaining only basic driving power needs; high-voltage accessories are reduced to the minimum safe load, and unnecessary electrical equipment is shut down; the BMS enters an ultra-low power thermal management mode, activating temperature control only when the battery temperature exceeds the safe threshold, while the vehicle controller, in conjunction with the driving assistance system, optimizes the follow-stop logic to avoid frequent rapid acceleration and braking, further reducing energy consumption fluctuations.

[0187] 3.5 Resolution of Conflicts Between Power Limitations and Safety Requirements (Core Safety Mechanisms)

[0188] Establish a driver intent priority matrix (as shown in Table 7):

[0189] Table 7

[0190] Driver operation signal source Priority System Response Request for acceleration Accelerator pedal opening >80% or rate of change >50% / s P0 (highest) Immediately remove all power limitations and output full power. Overtaking request Turn signal + accelerator pedal opening > 60% P1 Temporarily increase the power limit threshold by 50% for 10 seconds. Emergency braking Brake pedal opening >90% or ABS triggered P0 Immediately exit energy-saving mode and apply full braking. Normal driving Standard pedal operation P2 Implement the current energy-saving strategy.

[0191] Power limiting slope control (to prevent jerking):

[0192] Entry restrictions: Power descent per second shall not exceed 10% of the maximum permissible power;

[0193] Removal of restrictions: Power increase per second shall not exceed 15% of the maximum permissible power;

[0194] Avoid power interruptions caused by sudden power surges.

[0195] 3.6 Energy recovery and ESP / ABS / TCS safety interlocks (as shown in Table 8)

[0196] Table 8

[0197] Vehicle status signal source Energy recovery strategy Safety interlock action ESP activation ESP status bit = 1 Recycling intensity reduced to level 0 (completely shut down) Prohibit any energy recovery to ensure ESP has complete control over wheel speed. ABS activation ABS status bit = 1 Recycling intensity reduced to level 0 As above, to prevent regenerative torque from interfering with ABS modulation. TCS / ASR activation Traction control state = 1 The recycling intensity has decreased to a low level (<20%). To avoid drive wheel slippage caused by recycling Rain and snow mode Driving Mode = Snow / Slippery Recycling intensity limit 50% Allow more room for wheel speed adjustment Abnormal yaw rate Yaw rate Threshold

[0198] Low-adhesion road surface recognition: The road surface adhesion coefficient is identified by monitoring the speed difference between each wheel through the ESP wheel speed sensor; when the road surface adhesion coefficient is <0.3, the energy recovery intensity is automatically limited to below 30%.

[0199] 3.7 Thermal management strategy safety boundary (to prevent battery overheating / low temperature failure) (as shown in Table 9).

[0200] Table 9

[0201] <-20℃ Forced heating, energy saving and load reduction are prohibited. Exit Severe / Medium Energy Saving Mode Low temperature protection, limiting charge and discharge -20℃~0℃ Normal heating, slight load reduction is permissible. Only mild energy saving is implemented Monitoring temperature rise 0℃~15℃ Conventional thermal management All energy efficiency levels available - 15℃~35℃ Within the optimal efficiency range, control can be relaxed. All energy efficiency levels available - 35℃~45℃ Improve heat dissipation and limit the descent rate. In severe energy-saving mode, heat dissipation power is ≥50%. Instrument warning prompts >45℃ Forced maximum heat dissipation, prohibiting energy-saving load reduction. Exit all energy-saving modes Over-temperature protection, power limiting

[0202] Detailed rules for heat management in severe congestion mode:

[0203] Minimum fan speed: Minimum airflow required to maintain battery surface temperature <40℃;

[0204] Minimum water pump circulation flow rate: The minimum flow rate to ensure a battery cell temperature difference of <5℃;

[0205] Temperature sampling frequency: increased from 1Hz to 2Hz to ensure timely response;

[0206] Emergency heat dissipation trigger: When the temperature of any cell exceeds 42℃, the heat dissipation power is automatically restored to 100%.

[0207] 3.8 High-voltage accessory load reduction priority list (to ensure the strategy can be implemented) (as shown in Table 10)

[0208] Table 10

[0209] Priority Attachment type Mild congestion Moderate congestion Severe congestion Absolutely prohibited from being closed P0 (Safety Critical) Brake vacuum pump, EPS power steering, BMS main controller, vehicle controller No derating No derating No derating ✓ P1 (Thermal Management) Battery cooling water pump, battery heater, motor cooling water pump No derating Reduce load by 30% Reduce load by 50% (if temperature permits). ✓ (Temperature exceeding limit recovery) P2 (Comfort Core) Air conditioning compressor (cooling / heating), PTC heater Reduce load by 20% Reduce load by 50% Only basic air supply (10%) is maintained. - P3 (Comfort Assist) Seat heating / ventilation, steering wheel heating, ambient lighting Reduce load by 50% closure closure - P4 (Infotainment) Central control screen, audio system, car refrigerator, wireless charging Reduce load by 30% 70% load reduction closure - P5 (Not required) Heated exterior mirrors, heated wipers, cigarette lighter closure closure closure -

[0210] Load reduction implementation principles:

[0211] Gradual load reduction: P5 → P4 → P3 → P2, P0 / P1 are never completely shut down;

[0212] Temperature linkage: When the battery temperature is >40℃, P1 type accessories will automatically resume without slashing load;

[0213] Driver Coverage: Provides a "Comfort Priority" button, which can temporarily restore P2 class accessories to 50% power.

[0214] 3.9 Driving mode linkage strategy (to improve user experience), as shown in Table 11.

[0215] Table 11

[0216] User selection mode Energy-saving strategy response Policy restrictions Sports Mode Only mild energy saving (Level 1) is allowed; moderate / severe energy saving is prohibited. Power limit capped at 20% Standard / Comfort Mode All energy efficiency levels available Normal execution economic model All energy-saving levels are available, and the trigger threshold has been relaxed (earlier triggering). Power limit range +10% Snow / Off-road mode Enhanced energy recovery is prohibited, and thermal management does not degrade load. Only perform attachment unloading Custom mode Users can set the energy efficiency level (1-5). Subject to safety interlock constraints

[0217] Step 4: Policy Exit and Security Interlock

[0218] 4.1 Smooth exit mechanism (to prevent sudden stops / jerks), as shown in Table 12.

[0219] Table 12

[0220] Current mode Exit trigger condition Exit slope Exit duration intermediate state Severe congestion → Moderate Vehicle speed > 10km / h for 15 seconds Power limit +10% / second 3-5 seconds First enter medium mode Moderate → Mild Vehicle speed >25km / h for 10 seconds Power limit +15% / second 2-3 seconds First enter light mode Mild → Unobstructed Vehicle speed > 45km / h for 10 seconds Power limit +20% / second 1-2 seconds Restore default settings directly Force quit (safe) Rapid acceleration / ABS / ESP trigger Immediately lift <100ms Restores 100% directly

[0221] Exit process control:

[0222] Smooth torque output transition: Employing a ramp function instead of a step change;

[0223] Predictive exit: Navigation indicates that the congestion will end within 500m, and power is gradually restored in advance;

[0224] Driver perception optimization: If a change in the accelerator pedal is detected during the exit process, the exit will be paused and the current state will be maintained.

[0225] 4.2 Complete List of Safety Interlock Conditions

[0226] The trigger conditions for forcibly exiting the energy-saving mode are shown in Table 13:

[0227] Table 13

[0228] Condition Category Specific trigger signal System Response Recovery conditions Driver's emergency intent Accelerator pedal >80% or brake pedal >90% Immediately disengage, full power / braking Pedal release + 3-second stabilization Active safety system ABS / ESP / TCS / EBD activation Exit immediately, security system priority Security system off +5 seconds Navigation data anomaly Confidence level < 50% or data loss > 10 seconds Downgraded to basic energy efficiency Data recovery + confidence level > 70% Battery safety Temperature >45℃ or <-20℃, or pressure difference >300mV Exit and enter battery protection Temperature recovery + 2 minutes stabilization High-voltage system failure Insulation fault, high voltage interlock disconnection, leakage alarm Exit immediately and enter fault mode. Troubleshooting + Power On Charging status Fast charging / slow charging connection signal = 1 Disable energy saving mode Charging disconnected Fault light status Powertrain fault light / battery fault light illuminated Disable energy saving mode Troubleshooting Special gear R or N gear Blocking congestion determination Switch to D gear

[0229] II. Power Battery Energy Consumption Optimization System Based on Navigation Congestion Data

[0230] The system includes a data acquisition module, a congestion determination module, a central decision-making module, an execution control module, a safety interlock module, and a human-machine interaction module, with a newly added data fusion and failure management module. All modules are electrically connected and work collaboratively. This invention adopts a closed-loop hierarchical architecture + failure degradation architecture. Data interaction and command transmission between modules are achieved through the vehicle-mounted CAN bus / SOA service. The architecture diagram and data flow are shown below:

[0231] Architecture Description: Solid arrows indicate the direction of data / command transmission. Modules are interconnected via the vehicle communication bus. The data fusion and failure management module serves as the data hub, enabling multi-source data arbitration, failure degradation, and driving mode management. The data acquisition module aggregates information from multiple sources, the congestion determination module verifies road conditions, the central decision-making module issues energy-saving commands, the execution control module implements energy consumption optimization operations, the safety interlock module provides full-process driving safety protection, and the human-machine interaction module provides status feedback and user-defined controls. These seven modules work together to form a complete closed loop for battery energy consumption optimization under congested conditions.

[0232] 1. Data Acquisition Module

[0233] This system is used to collect multi-dimensional operational data, including a navigation data acquisition unit, a vehicle condition data acquisition unit, and an environmental data acquisition unit. The navigation data acquisition unit communicates with the in-vehicle navigation system to obtain data such as the length and duration of the traffic jam ahead, vehicle speed, and confidence level. The vehicle condition data acquisition unit collects data such as the power battery SOC, temperature, motor status, vehicle speed, driver assistance status, gear position signal, and steering wheel angle via the CAN bus. The environmental data acquisition unit collects environmental parameters such as outdoor temperature, humidity, and rainfall to provide data support for subsequent congestion assessment and strategy formulation.

[0234] 2. Data Fusion and Failure Management Module (New Core Module)

[0235] Used to implement multi-source data arbitration, failure scenario degradation, driving mode linkage, and anti-shake control, including:

[0236] Multi-source data fusion unit: integrates navigation data, historical congestion database, V2X signals, and vehicle sensor data to generate a comprehensive congestion assessment result;

[0237] Failure Degradation Control Unit: Automatically switches to the corresponding degradation strategy (conservative energy saving / historical prediction / sensor mode / basic energy saving) based on data status.

[0238] Driving mode management unit: Receives user selections for Sport / Eco / Snow modes and dynamically adjusts the available range of energy-saving strategies;

[0239] Anti-shake control unit: realizes vehicle speed lag, time-based anti-shake, and road segment repetition suppression functions.

[0240] 3. Congestion Determination Module

[0241] The system integrates with the data fusion and failure management module to perform filtering and anti-shaking processing on the fused traffic data (weighted moving average + dual threshold filtering). Based on preset grading thresholds and traffic condition type exclusion rules, it determines the congestion level and classifies it into four levels: smooth, light congestion, moderate congestion, and severe congestion. At the same time, it outputs the congestion prediction results within the predicted time and space range to ensure accurate, distortion-free, and error-free congestion determination.

[0242] 4. Central Decision-Making Module

[0243] With the vehicle control unit (VCU) as the core, it connects to the congestion judgment module. Based on the congestion level and prediction results, combined with the driving mode status and safety interlock signals, it generates corresponding graded energy consumption optimization instructions and sends them to each execution unit to achieve unified scheduling and smooth switching of energy-saving strategies, ensuring the coordination of vehicle energy consumption management.

[0244] 5. Execution Control Module

[0245] The system is used to execute optimization instructions issued by the central decision-making module, including the battery management unit, drive control unit, and accessory energy-saving unit. The battery management unit interfaces with the BMS to limit the charging and discharging power of the power battery (with slope control) and adjust the thermal management strategy (with temperature safety boundary). The drive control unit interfaces with the motor control system to optimize creep torque and adaptively adjust the energy recovery intensity (interlocked with ESP / ABS). The accessory energy-saving unit interfaces with high-voltage accessories such as air conditioners, heat pumps, and vehicle infotainment systems, and implements load grading and load reduction according to the P0-P5 priority list to comprehensively optimize battery energy consumption.

[0246] 6. Safety interlock module

[0247] To ensure driving safety, it monitors driving intentions (rapid acceleration / overtaking / braking), vehicle safety system status (ABS / ESP / TCS / malfunction indicator lamp), battery operating status (temperature / differential pressure / insulation), charging status (fast charging / slow charging), and gear status (R / N gear) in real time. When a safety trigger signal is detected, it immediately blocks the execution of the energy-saving strategy (<100ms response) and forces a switch to the safety control mode to prevent the energy-saving strategy from affecting core safety functions such as braking, steering, and power output.

[0248] 7. Human-Computer Interaction Module

[0249] It includes an instrument panel display unit and a central control settings unit. The instrument panel display unit shows the congestion level, energy-saving mode status, corrected subsequent mileage, and data validity status (normal / downgraded / invalid) in real time. The central control settings unit provides users with a mode selection function, supports switching between three modes: automatic energy saving, energy saving priority, and comfort priority, and supports manual triggering of "forced energy saving" and temporary overriding of "comfort priority" to take into account users' personalized needs.

[0250] This embodiment relies on navigation data to predict traffic congestion ahead in advance, eliminating the lag of traditional real-time control. It refines the grading strategy for different congestion levels and combines a closed-loop system architecture to achieve efficient command transmission, enabling refined and adaptive control of battery energy consumption. According to actual tests, the vehicle's range can be increased by 5%-15% under congested conditions, with significant energy-saving effects.

[0251] This embodiment limits the peak charging and discharging power of the power battery, reduces fluctuations during high-rate charging and discharging, optimizes battery thermal management strategies, reduces battery polarization loss and aging rate, effectively extends the cycle life of the power battery, and reduces the user's vehicle operating costs.

[0252] This embodiment features a multi-level safety interlock mechanism, prioritizing driving safety and ensuring smooth mode switching without any jerks. It also supports users in selecting their own control modes, balancing energy-saving needs with driving comfort and addressing the drawbacks of traditional energy-saving solutions that sacrifice power and comfort.

[0253] This embodiment requires no new hardware equipment. The architecture and functions can be built based on existing in-vehicle navigation, VCU, BMS and other systems. Upgrades can be completed through software algorithm optimization. It is compatible with various pure electric passenger vehicles and commercial vehicles, and has strong engineering feasibility and market adaptability.

[0254] In another specific embodiment, this application will be described in further detail.

[0255] Example 1: Energy consumption optimization under moderate urban traffic congestion (including misjudgment elimination)

[0256] The vehicle is traveling on a main urban road. The onboard navigation system collects data for the road section 1km ahead, showing an average speed of 12km / h, an estimated congestion duration of 8 minutes, and a road condition confidence level of 85%. The data fusion module identifies that the vehicle is currently in Drive mode, with a steering wheel angle of <30° and is not at an intersection, excluding scenarios such as waiting at a red light or reversing. The data collection module transmits the above data to the congestion determination module, which determines it as moderate congestion (level 2). The central decision-making module triggers the moderate congestion energy-saving mode and executes the corresponding strategies: limiting the peak discharge power of the power battery by 20% (entry slope 10% / second), reducing low-speed creep torque by 30%, adjusting the energy recovery intensity to a medium-high level (confirmed not activated by ESP status), reducing the operating frequency of the air conditioning compressor by 40%, and reducing the power consumption of the cooling fan by 50% (battery temperature 28℃, within the safe range).

[0257] During driving, if the driver suddenly presses the accelerator pedal deeply (85% opening) to request overtaking, the safety interlock module recognizes the P0-level priority signal, immediately releases the power limit, and outputs full power. After overtaking, the accelerator pedal is released for 3 seconds to stabilize, and the system automatically resumes the medium energy-saving mode. When the vehicle leaves the congested area and the navigation data shows that the speed has returned to 35 km / h, the system gradually releases the power limit at a rate of 15% per second, smoothly transitioning to the light energy-saving mode within 2 seconds without any power jerkiness. Under this condition, the vehicle's energy consumption is reduced by 11% compared to the traditional mode, the battery temperature fluctuation is controlled within 5℃, and there is no sense of jerkiness during driving.

[0258] Example 2: Energy consumption optimization under severe congestion conditions at highway entrances and exits (including fallback for navigation failure)

[0259] When the vehicle reached the highway toll station, the navigation data showed a speed of 3 km / h, an estimated dwell time of 10 minutes, and a road condition confidence level of 90%, indicating severe congestion (level 3). The system triggered the severe congestion energy-saving mode, activating the highest level of energy recovery (ESP not activated), limiting the peak discharge power of the power battery to 40% (entry slope 10% / second, limit completed in 4 seconds), the air conditioner only retained basic ventilation function (P2 level reduced to 10%), the BMS entered ultra-low power thermal management mode (battery temperature 32℃, allowing for relaxed control), and the brake vacuum pump and EPS maintained P0 level without reducing load.

[0260] If the navigation system suddenly malfunctions while driving (data loss > 10 seconds), the data fusion module identifies a complete failure and automatically switches to a pure vehicle sensor mode: the forward-facing camera detects high vehicle density ahead and the radar monitors a relative speed < 5 km / h, determining that congestion is ongoing and maintaining a severe energy-saving mode; simultaneously, the instrument panel displays "Navigation signal lost, sensor-assisted energy saving activated"; once the congestion eases and the vehicle speed gradually increases, the system sequentially exits the severe congestion → moderate congestion → light congestion modes at a rate of 10% / second, restoring normal driving. Under this condition, the vehicle's static energy consumption is reduced by more than 60%, and even in the event of navigation failure, the energy-saving function is not interrupted, effectively avoiding unnecessary power consumption.

[0261] Example 3: Verification of Misjudgment Elimination in Underground Parking Garage Scene

[0262] The vehicle enters the underground parking garage at a speed of 5 km / h, with a steering wheel angle greater than 200° (reversing into a parking space), and the GPS signal is lost. The data fusion module identifies the scenario as follows: GPS loss > 30 seconds + vehicle speed < 10 km / h, classifying it as an underground parking garage scenario; gear = R + steering wheel angle > 180°, classifying it as a reversing situation; triggering the exclusion rule, congestion detection is disabled, no energy-saving mode is activated, and default power output is maintained. The driver successfully reverses into the parking space without power limitations, energy recovery enhancement, or accessory unloading, providing a user experience completely consistent with regular driving, thus verifying the effectiveness of the false alarm exclusion mechanism.

[0263] Figure 4 This is a block diagram of an electronic device structure for a power battery energy consumption optimization method based on navigation congestion data, provided by one or more embodiments of the present invention.

[0264] like Figure 4 As shown, this application provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0265] The memory stores a computer program that, when executed by the processor, causes the processor to perform steps of a power battery energy consumption optimization method based on navigation congestion data.

[0266] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform steps of a power battery energy consumption optimization method based on navigation congestion data.

[0267] This application also provides a vehicle, including:

[0268] Electronic devices, steps for implementing a power battery energy consumption optimization method based on navigation congestion data;

[0269] The processor runs a program that, when running, executes steps of a power battery energy consumption optimization method based on navigation congestion data, taking data output from the electronic device.

[0270] Storage medium for storing programs that, when running, execute steps of a power battery energy consumption optimization method based on navigation congestion data on data output from electronic devices.

[0271] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0272] The electronic device comprises a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory. The operating system can be any one or more computer operating systems that control the electronic device through processes, such as Linux, Unix, Android, iOS, or Windows. Furthermore, in this embodiment of the invention, the electronic device can be a smartphone, tablet computer, or other handheld device, or a desktop computer, portable computer, or other electronic device; there is no particular limitation in this embodiment.

[0273] In this embodiment of the invention, the executing entity for electronic device control can be an electronic device itself, or a functional module within an electronic device capable of calling and executing a program. The electronic device can obtain the firmware corresponding to the storage medium. This firmware is provided by the supplier, and different storage media may have the same or different firmware; no limitation is made here. After obtaining the firmware corresponding to the storage medium, the electronic device can write this firmware into the storage medium; specifically, it burns the firmware corresponding to the storage medium into the storage medium. The process of burning the firmware into the storage medium can be implemented using existing technology, and will not be elaborated upon in this embodiment of the invention.

[0274] Electronic devices can also obtain reset commands corresponding to the storage media. The reset commands corresponding to the storage media are provided by the supplier. The reset commands corresponding to different storage media can be the same or different, and no restrictions are imposed here.

[0275] At this time, the storage medium of the electronic device is a storage medium on which the corresponding firmware has been written. The electronic device can respond to the reset command corresponding to the storage medium on which the corresponding firmware has been written, thereby resetting the storage medium on which the corresponding firmware has been written according to the reset command. The process of resetting the storage medium according to the reset command can be implemented by existing technology and will not be described in detail in this embodiment of the invention.

[0276] For ease of description, the above devices are described separately by function as various units and modules. Of course, in implementing this application, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0277] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0278] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0279] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0280] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the energy consumption of power batteries based on navigation congestion data, characterized in that, The power battery energy consumption optimization method based on navigation congestion data includes: Collect multi-source operational data, which includes at least vehicle navigation traffic data, vehicle status data, and environmental data; The congestion status is determined based on the collected multi-source operational data; Based on the determined congestion status, the corresponding energy consumption optimization strategy is triggered; Based on the energy consumption optimization strategy, the power battery, drive system, and high-voltage accessory system are coordinated and regulated to achieve energy consumption optimization.

2. The power battery energy consumption optimization method based on navigation congestion data according to claim 1, characterized in that, The determination of congestion status includes: eliminating misjudgments of non-congestion scenarios through road condition type exclusion rules; The road condition type exclusion rules include: underground parking garage scenario, reversing / moving scenario, waiting at a red light scenario, and crawling into a parking space scenario. If any of these conditions are met, the congestion determination will be blocked.

3. The power battery energy consumption optimization method based on navigation congestion data according to claim 1, characterized in that, The method for determining congestion status also includes classifying congestion status into four levels: smooth traffic, light congestion, moderate congestion, and severe congestion. The congestion classification standards are as follows: smooth traffic level corresponds to a vehicle speed ≥ the first vehicle speed threshold; light congestion level corresponds to a second vehicle speed threshold ≤ vehicle speed < the first vehicle speed threshold; moderate congestion level corresponds to a third vehicle speed threshold ≤ vehicle speed < the second vehicle speed threshold; severe congestion level corresponds to a vehicle speed < the third vehicle speed threshold and an expected standby time ≥ the first duration threshold. It also includes setting a speed hysteresis range for a third speed threshold and a preset minimum duration to prevent boundary oscillations.

4. The power battery energy consumption optimization method based on navigation congestion data according to claim 1, characterized in that, The method for determining congestion status also includes: performing filtering and anti-shaking processing on the collected multi-source operating data; The filtering and anti-shake processing includes: weighted moving average of vehicle speed data, dual threshold filtering of congestion confidence, outlier removal, and suppression of repeated triggering on the same road segment.

5. The power battery energy consumption optimization method based on navigation congestion data according to claim 1, characterized in that, It also includes: establishing a fallback mechanism for navigation data failure, which switches to the corresponding fallback strategy to maintain energy-saving function when navigation data fails; The downgrade fallback mechanism includes: based on the validity of the navigation data, implementing downgrade strategies in sequence: full-function energy saving, conservative energy saving, historical data matching, and pure vehicle sensor identification.

6. The power battery energy consumption optimization method based on navigation congestion data according to claim 1, characterized in that, Also includes: Based on the current vehicle speed and road type, the dynamic prediction time and space window is adaptively adjusted to trigger energy consumption optimization strategies in advance. The dynamic prediction spatiotemporal window is: the first spatiotemporal window for highways, the second spatiotemporal window for urban expressways, the third spatiotemporal window for urban main roads, the third spatiotemporal window for urban branch roads, or the prediction is completely shielded in the reversing / R gear scenario.

7. The power battery energy consumption optimization method based on navigation congestion data according to claim 1, characterized in that, Also includes: The energy consumption optimization strategy implements differentiated power battery charging and discharging control, energy recovery regulation, thermal management optimization, and high-voltage accessory load reduction operations for different congestion levels. The power battery charging and discharging management adopts slope control: when entering the power limit, the power drop per second is ≤ the first floating threshold; when the power limit is released, the power increase per second is ≤ the second floating threshold, to avoid sudden power changes; The energy recovery regulation is equipped with a safety interlock: when ESP / ABS / TCS is activated, or when there is low road surface, the intensity of energy recovery is limited to ensure driving safety. The thermal management optimization setting safety boundary is as follows: the thermal management power is adjusted according to the battery temperature to avoid the battery temperature exceeding the limit; The high-voltage accessory load reduction adopts a priority list, and performs graded load reduction according to the safety priority of the accessory; It also includes determining when congestion is alleviated and gradually exiting the energy consumption optimization strategy at a preset slope for a smooth transition.

8. The power battery energy consumption optimization method based on navigation congestion data according to claim 1, characterized in that, Also includes: Safety-first interlocking mechanism: When a safety trigger signal is detected, the energy consumption optimization strategy is immediately suspended to prioritize driving safety; The safety trigger signals include: rapid acceleration, emergency braking, safety system activation, battery failure, high voltage failure, charging connection, and non-driving gear. When a safety trigger signal is received, the energy-saving strategy is suspended within the second duration threshold.

9. A power battery energy consumption optimization system based on navigation congestion data, used to implement the power battery energy consumption optimization method based on navigation congestion data as described in any one of claims 1 to 8, characterized in that, The power battery energy consumption optimization system based on navigation congestion data includes: The data acquisition module is used to collect operational data from multiple sources. The congestion determination module is used to determine the congestion status based on the collected multi-source operational data; The central decision-making module is used to generate corresponding energy consumption optimization instructions based on the congestion status; The execution control module is used to coordinate and regulate the power battery, drive system, and high-voltage accessory system according to optimization instructions.

10. The power battery energy consumption optimization system based on navigation congestion data according to claim 9, characterized in that, Also includes: The data fusion and failure management module is used to realize multi-source data fusion, navigation failure degradation, driving mode management, and filtering and anti-shake processing; It also includes: a safety interlock module, used to monitor safety signals in real time and trigger the pause and resumption of energy consumption optimization strategies; It also includes a human-computer interaction module, which displays the status to the user and provides mode selection and manual triggering functions.

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