Intelligent control system for air conditioner of golf vehicle

Abstract

The invention relates to the technical field of air conditioner power supply control, in particular to an intelligent control system for an air conditioner of a golf vehicle, and adopts the technical scheme that a self-adaptive Kalman filtering algorithm is adopted to process a voltage signal so as to realize real-time monitoring of battery parameters, and air conditioner operation parameters are adjusted according to a voltage attenuation prediction model so as to realize dynamic distribution of power. Multi-stage safety protection is achieved through self-adaptive threshold control based on the state of health of the battery, a user is allowed to set an endurance priority mode to achieve man-machine collaborative optimization, the safety performance is enhanced through prediction of low-voltage risks, a charging and discharging curve is optimized, the attenuation rate is reduced, the service life of the battery is prolonged, and the endurance capacity of the battery is improved. Therefore, the effective service time is prolonged, the accuracy of monitoring and attenuation of the power supply voltage of the air conditioner of the golf vehicle is improved, and the intelligence of power distribution and power supply control is also improved.

Description

Technical Field

[0001] The present invention relates to the technical field of air - conditioner power control, and particularly to an intelligent control system for a golf vehicle air - conditioner. Background Art

[0002] Golf carts are environment - friendly passenger vehicles designed and developed specifically for golf courses. They can also be used in resorts, villa areas, garden - style hotels, tourist attractions, etc. From golf courses, villas, hotels, schools to private users, they will be short - distance transportation tools. Nowadays, golf vehicles are equipped with air - conditioning systems, which can provide a comfortable driving and riding environment and offer appropriate temperature adjustment in different seasons and environments. However, too low voltage of the golf vehicle air - conditioner power supply will affect the working state of the air - conditioner. The lack of monitoring of the golf vehicle air - conditioner power supply voltage will increase the attenuation rate of charge and discharge, thus affecting the battery life and further affecting the battery endurance.

[0003] In view of this, we propose an intelligent control system for a golf vehicle air - conditioner to solve the existing problems. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent control system for a golf vehicle air - conditioner to solve the problems raised in the above - mentioned background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An intelligent control system for a golf vehicle air - conditioner, comprising: a real - time battery parameter monitoring module, a dynamic power distribution unit, a multi - level safety protection system, and a human - machine collaborative optimization interface; wherein, the real - time battery parameter monitoring module processes voltage signals using an adaptive Kalman filtering algorithm, the dynamic power distribution unit adjusts the air - conditioner operation parameters according to a voltage attenuation prediction model, the multi - level safety protection system includes an adaptive threshold control based on the battery health state, and the human - machine collaborative optimization interface allows users to set a range - priority or comfort - priority mode.

[0006] Further, in the real - time battery parameter monitoring module, the voltage sampling circuit adopts a dual - redundant design, including a master and a slave ADC chip for cross - verification.

[0007] Further, in the dynamic power distribution unit, the voltage attenuation prediction model adopts an LSTM neural network, and the input parameters include a historical voltage sequence, ambient temperature, compressor working cycle, and battery charge - discharge cycle times.

[0008] Further, in the dynamic power distribution unit, a dynamic voltage compensation technology is adopted, and when an accelerator pedal signal is detected, the air - conditioner power is automatically reduced for 0.5 - 1.5 seconds.

[0009] Furthermore, the dynamic power distribution unit includes a fuzzy control algorithm, and the membership function is dynamically adjusted according to the voltage stability coefficient calculated in real time.

[0010] Furthermore, the fuzzy control algorithm includes a self-learning module that optimizes historical operation strategies through reinforcement learning to generate a personalized control parameter set.

[0011] Furthermore, the multi-level security protection system includes a three-level response mechanism, namely, primary warning, secondary protection, and tertiary power-off; among them, the primary warning includes sound and light prompts and automatic switching to the energy-saving mode, the secondary protection includes limiting the maximum power and starting the standby fan, and the tertiary power-off includes cutting off the air conditioner power supply and activating the emergency ventilation.

[0012] Furthermore, in the multi-level security protection system, the battery health state assessment adopts the impedance spectroscopy analysis method, and the battery internal resistance change rate is calculated by injecting a test signal with a specific frequency.

[0013] Furthermore, in the human-machine collaborative optimization interface, the user interface provides an intelligent suggestion function, which recommends the optimal temperature setting according to the remaining power, the slope information of the driving route, and the weather forecast data.

[0014] Furthermore, it also includes a vehicle networking communication module, which can receive the charging pile location information and dynamically adjust the air conditioner energy consumption strategy accordingly.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0016] The present invention uses an adaptive Kalman filter algorithm to process voltage signals, thereby realizing real-time monitoring of battery parameters, adjusting the air conditioner operation parameters according to the voltage decay prediction model, thereby realizing dynamic power distribution, using an adaptive threshold control based on the battery health state to achieve multi-level security protection, allowing users to set the endurance priority mode to achieve human-machine collaborative optimization, enhancing the safety performance by predicting low voltage risks, optimizing the charge and discharge curve to reduce the decay rate, thereby extending the battery life and improving the battery endurance ability, and further extending the effective usage time. It not only improves the accuracy of monitoring and attenuation of the air conditioner power supply voltage of golf vehicles, but also improves the intelligence of power distribution and power supply control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a working flow chart of an intelligent control system for an air conditioner of a golf vehicle according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0019] Embodiment 1

[0020] As Figure 1As shown in the figure, an intelligent control system for the air conditioner of a golf vehicle includes: a real-time battery parameter monitoring module, a dynamic power distribution unit, a multi-level safety protection system, and a human-machine collaborative optimization interface. Among them, the real-time battery parameter monitoring module processes voltage signals using an adaptive Kalman filter algorithm. The dynamic power distribution unit adjusts the air conditioner operation parameters according to the voltage decay prediction model. The multi-level safety protection system includes an adaptive threshold control based on the battery health state. The human-machine collaborative optimization interface allows users to set the range priority or comfort priority mode.

[0021] The system architecture includes a data acquisition layer, an intelligent decision-making layer, and an execution control layer. The data acquisition layer includes a high-precision voltage sampling module, a temperature sensing array, and a user interaction unit. The intelligent decision-making layer includes a digital twin battery model, a dynamic energy efficiency optimization engine, and a safety protection decision tree. The execution control layer includes a variable frequency compressor drive unit, a stepless air volume regulation system, and an emergency backup power supply switch.

[0022] The working principle of an intelligent control system for the air conditioner of a golf vehicle based on Embodiment 1 is as follows:

[0023] In the real-time battery parameter monitoring module, a second-order RC equivalent circuit model is used to characterize the battery dynamic characteristics. The battery terminal voltage equation is: V t = V(SOC) - I·(R0 + R1 + R2) - V1 - V2, where V(SOC) is the open-circuit voltage of the battery, R0 is the internal resistance of the battery, R1 and R2 are the resistance values of the two RC circuits, V1 and V2 are the voltages of the two RC circuits, and I is the current; the differential equation of the RC network is: And The state variables are The observed variable is z k = V t . [[ID=2,3]]

[0024] In the adaptive Kalman filter algorithm, the prediction stage of the standard Kalman filter process is: The update stage of the standard Kalman filter process is: In the online adjustment of the noise covariance matrix, Q adaptively reduces the interference of the process noise and is dynamically adjusted according to the SOC change rate: Q k = Q0·(1 + α|ΔSOC|), α = 0.5, and is activated when the SOC change rate > 2% / s; R adaptively reduces the interference of the observation noise and is estimated based on the residual variance of the sliding window (N = 50):

[0025] In the voltage signal processing flow, after the original voltage is sampled, outlier detection is performed. If the detection result is normal, adaptive Kalman filtering is performed and then SOC / SOH estimation is carried out, and finally the state is output; if the detection result is abnormal, the backup data is started, and finally the state is output.

[0026] The voltage sampling circuit adopts a dual-redundancy design, including a master and a slave ADC chip for cross-verification. After collecting the data of the dual ADCs, difference verification is performed. If the result of the difference verification is normal, the average value is output. If the result of the difference verification is abnormal, the self-check program is started. After starting the self-check program, the ADC register is diagnosed. If a recoverable error is found, the correction instruction is executed. If no recoverable error is found, the backup channel is switched. After executing the correction instruction or switching the backup channel, resampling verification is performed. If the verification passes, the average value is output. If the verification fails, system alarm and degraded operation are performed.

[0027] In the dynamic power distribution unit, the voltage decay prediction model is constructed using the LSTM neural network architecture. The input layer has 8 nodes, and the input layer includes the historical voltage sequence, ambient temperature, cumulative working time of the compressor, number of battery charge and discharge cycles, and real-time load current. The time range of the historical voltage sequence is the past 60 seconds; the hidden layer has two layers of LSTM, with 32 units in each layer. The hidden layer uses the tanh activation function, and the time step is 60, corresponding to 60 seconds of historical data; the output layer has 3 nodes, and the output layer includes the voltage prediction values every 60 seconds within the next 180 seconds. The output layer uses the linear activation function.

[0028] For the model training strategy, in the dataset, 10,000 groups of battery discharge data under different working conditions are collected, including extreme scenarios such as sudden acceleration, climbing, and high-temperature environment; in the training parameters, the learning rate of the Adam optimizer is 0.001, the batch size is 32, and the patience of the early stopping mechanism is 10; in the verification metrics, the mean absolute error < 15 mV, and the prediction time correlation coefficient > 0.93.

[0029] In the dynamic power distribution control logic, the multi-objective optimization model is min(w1·P error +w2·T dev +w3·E cost ), where P error is the deviation between the actual power and the target power, T dev is the deviation between the actual temperature and the set value, E cost is the energy consumption cost per unit time, and the weight coefficients are dynamically adjusted as: w1 = f(V predict ), w2 = g(User priority ), w3 = h(SOC); the input variables of the fuzzy PID hybrid controller design are the voltage prediction decay rate, the current SOC, and the user-set temperature deviation, and the output variables are the compressor PWM duty cycle, the fan speed, and the opening degree of the electronic expansion valve.

[0030] In the adjustment process of air conditioner operation parameters, after sampling the voltage, the LSTM predicts the voltage in the next 3 minutes, and then evaluates the voltage decay rate. If the evaluation is normal decay, the PID control maintains the current power. If the evaluation is accelerated decay, fuzzy rule inference is performed. After fuzzy rule inference, a power adjustment scheme is generated, and then a conditional judgment is executed. If the judgment is that adjustment is allowed, a control instruction is issued. If the judgment is conflict detection, multi-objective optimization decision-making is carried out. After issuing the control instruction or multi-objective optimization decision-making, the air conditioner parameters are adjusted, and finally the effect is fed back to the prediction model.

[0031] In the multi-level security protection system, for the real-time assessment of the battery health status, Combined with capacity, internal resistance, number of cycles, and temperature history, fuzzy logic comprehensive scoring is adopted. For the SOH segmented adjustment strategy, SOH≥80% is regarded as the healthy state, using the standard threshold, allowing maximum power operation; 50%≤SOH<80% is regarded as mild aging, the voltage threshold is increased by 5%, the current limit is reduced by 10%, and the capacity compensation algorithm is enabled; SOH<50% is regarded as severe aging, the voltage threshold is increased by 10%, fast charging is prohibited, the "maintenance mode" is triggered, and the maximum power of the air conditioner is limited to 50%.

[0032] In the human-machine collaborative optimization interface, a mode switching button is set on the main air conditioner control interface. The mode selection includes range priority, comfort priority, and automatic mode. In the range mode, a dynamic range gain prediction is displayed, and a slider is supported to adjust the energy-saving intensity.

[0033] In data collection and preprocessing, for the remaining battery power, the SOC value of the battery management system is obtained in real time with an accuracy of ±1%; for the route slope information, the path elevation data is obtained through the navigation system and parsed into a segmented slope sequence, with each 100 meters as a unit, and then the equivalent energy consumption coefficient of each segment is calculated: where θ is the slope angle, m is the vehicle weight, v is the vehicle speed; for the weather forecast data, the hourly temperature, humidity, and solar radiation temperature are obtained by accessing the meteorological API to predict the heat absorption of the vehicle body: Q solar =A car ·η·I solar ·cos(φ), where A car is the vehicle surface area, η is the heat absorption rate, I solar is the solar radiation, and φ is the solar altitude angle.

[0034] In the establishment of the air conditioner energy consumption model, the empirical formula for the cooling function is P ac (T set )=P base +k·(T out -T set ), where P base is the basic power, T setis the set temperature, T out is the ambient temperature, T out > 25°C, k = 35 W / °C; the hot power model is T in is the temperature inside the vehicle, C air is the specific heat capacity of air, ρ is the air density, V is the volume of the vehicle interior, t heat is the heating time, η PTC is the heating efficiency.

[0035] In the multi-objective optimization model, the objective function is The constraint conditions are where, E drive is the driving energy consumption, E ac is the air-conditioning energy consumption, T pref is the user-preferred temperature, defaulting to 24°C, ΔT tol is the allowable temperature difference, defaulting to ±2°C.

[0036] In the recommended optimal temperature setting process, input SOC / route / weather, then calculate the basic energy consumption in segments to generate a temperature candidate set, and then traverse and calculate the total energy consumption of each temperature. If the SOC constraint is met, retain the candidate temperature; if the SOC constraint is not met, exclude the temperature. After retaining the candidate temperature, select the temperature closest to T pref and output the recommended temperature and range gain. After excluding the temperature, activate the extreme power-saving mode.

[0037] In the dynamic energy consumption strategy decision model, the charging accessibility assessment is: accessibility coefficient When γ < 0.2, the energy-saving mode is triggered; when γ > 0.5, the comfort mode is restored; the dynamic programming of the air-conditioning power is: P ac,new = P ac,base ·(1 - α·e -β·d ), d is the distance to the target charging pile, α = 0.4, representing the maximum reduction coefficient, β = 0.15, representing the attenuation rate, and the constraint condition is T cabin ∈[T set - ΔT, T set + ΔT],

[0038] In the intelligent pre-regulation technology, predict the arrival of the charging pile, combined with the navigation ETA (estimated time of arrival) and real-time road conditions In the control timing, the current time is t0. If t arrival - t0 < 15 min, then start pre-cooling or pre-heating. If t arrival- If t0 ≥ 15 min, maintain the conventional strategy; calculate the required temperature change rate after starting pre-cooling or pre-heating, and adjust the PTC / compressor power. In the battery temperature control coordination, when a fast charging pile is detected, turn on the battery cooling system in advance and optimize the cold quantity distribution ratio:

[0039] The above specific embodiments are only several preferred embodiments of the present invention. Based on the technical solution of the present invention and the relevant revelations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. An intelligent control system for a golf vehicle air conditioner, characterized in that, It includes: a real-time battery parameter monitoring module, a dynamic power distribution unit, a multi-level safety protection system, and a human-machine collaborative optimization interface; among them, the real-time battery parameter monitoring module processes voltage signals using an adaptive Kalman filtering algorithm, the dynamic power distribution unit adjusts the air conditioner operation parameters according to a voltage decay prediction model, the multi-level safety protection system includes an adaptive threshold control based on the battery health status, and the human-machine collaborative optimization interface allows users to set the range priority or comfort priority mode.

2. The intelligent control system for the air conditioner of a golf vehicle according to claim 1, wherein: In the real-time battery parameter monitoring module, the voltage sampling circuit adopts a dual-redundancy design, including a master and a slave ADC chip for cross-verification.

3. The intelligent control system for a golf vehicle air conditioner according to claim 1, wherein: In the dynamic power distribution unit, the voltage decay prediction model adopts an LSTM neural network, and the input parameters include the historical voltage sequence, ambient temperature, compressor working cycle, and battery charge-discharge cycle count.

4. The intelligent control system for the air conditioner of a golf vehicle according to claim 1, characterized in that: In the dynamic power distribution unit, a dynamic voltage compensation technology is adopted to automatically reduce the air conditioner power for 0.5 - 1.5 seconds when an accelerator pedal signal is detected.

5. The intelligent control system for the air conditioner of a golf vehicle according to claim 1, wherein: The dynamic power distribution unit includes a fuzzy control algorithm, and the membership function is dynamically adjusted according to the voltage stability coefficient calculated in real time.

6. The intelligent control system for the air conditioner of a golf vehicle according to claim 5, characterized in that: The fuzzy control algorithm includes a self-learning module that optimizes historical operation strategies through reinforcement learning to generate a personalized control parameter set.

7. An intelligent control system for a golf vehicle air conditioner according to claim 1, characterized in that: The multi-level safety protection system includes a three-level response mechanism, namely, a first-level warning, a second-level protection, and a third-level power-off; among them, the first-level warning includes an audible and visual prompt and an automatic switch to an energy-saving mode, the second-level protection includes limiting the maximum power and starting a standby fan, and the third-level power-off includes cutting off the air conditioner power supply and activating emergency ventilation.

8. The intelligent control system for the air conditioner of a golf vehicle according to claim 1, characterized in that: In the multi-level safety protection system, the battery health status assessment adopts an impedance spectroscopy analysis method to calculate the battery internal resistance change rate by injecting a test signal of a specific frequency.

9. The intelligent control system for the air conditioner of a golf vehicle according to claim 1, characterized in that: In the human-machine collaborative optimization interface, the user interface provides an intelligent suggestion function to recommend the optimal temperature setting based on the remaining battery power, the slope information of the driving route, and the weather forecast data.

10. The intelligent control system for the air conditioner of a golf vehicle according to claim 1, wherein: It also includes a vehicle-to-everything communication module that can receive the charging pile location information and dynamically adjust the air conditioner energy consumption strategy accordingly.

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