Motor home energy storage charging and discharging monitoring management system

Abstract

The invention provides a motor home energy storage charging and discharging monitoring management system, which relates to the field of energy storage and comprises an intelligent temperature control system, an efficient charging strategy module and a battery management system (BMS). The intelligent temperature control system comprises a temperature sensor array, a thermal infrared imager, a heating element, a cooling device, a phase change material thermal management unit and a temperature control algorithm module. The efficient charging strategy module comprises a charging algorithm module, a power regulator and a deep reinforcement learning controller; the battery management system (BMS) comprises a data acquisition module, a battery state evaluation module, a safety protection module and an intelligent equalization module. Through the arrangement of the integrated intelligent temperature control system, the efficient charging strategy module, the battery management system (BMS), the charging pile, the temperature control system, the user interaction interface and the remote monitoring platform, the beneficial effects of improving the charging efficiency, prolonging the service life of the battery, improving the charging safety, improving the user experience and improving the energy utilization efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage, and more particularly, to a monitoring and management system for energy storage charging and discharging of a recreational vehicle. Background Art

[0002] As an important part of a recreational vehicle during travel, the performance of the energy storage system of a recreational vehicle directly affects the travel experience of users and the energy utilization efficiency. However, in high-temperature or low-temperature environments, the energy storage system of a recreational vehicle often faces problems such as low charging efficiency and shortened battery life. Traditional charging systems lack intelligent management means and are difficult to dynamically adjust charging strategies according to environmental changes, resulting in energy waste and degradation of battery performance.

[0003] Therefore, we have made improvements in this regard and proposed an efficient, safe, and intelligent monitoring and management system for energy storage charging and discharging of a recreational vehicle, aiming to achieve efficient management and safety protection of the battery in different temperature environments through technologies such as intelligent temperature control, efficient charging strategies, and remote monitoring. Summary of the Invention

[0004] The object of the present invention is to address the current lack of intelligent management means in the charging system problem .

[0005] To achieve the above object of the invention, the present invention provides the following monitoring and management system for energy storage charging and discharging of a recreational vehicle to improve the above problems.

[0006] Specifically, this application is as follows: It includes an intelligent temperature control system, an efficient charging strategy module, a battery management system (BMS), and a remote monitoring module; The intelligent temperature control system includes: Temperature sensor array: Arranged at multiple points to monitor the internal and environmental temperatures of the battery in real time; Temperature control algorithm: Based on the fuzzy control algorithm, inputting the temperatures at multiple points, environmental temperature, and rate of change, and outputting heating and cooling powers; Temperature prediction module: Combining historical data and trend prediction to adjust the temperature strategy in advance.

[0007] The efficient charging strategy module includes: Dynamic optimization charging algorithm: Based on the gradient descent method, combining battery voltage, current, temperature, and historical data to dynamically adjust the charging power and mode; Mode switching: Supporting constant current, constant voltage, and pulse charging, automatically switching according to the battery state to improve efficiency and protect the battery; The battery management system (BMS) includes: State evaluation module: Using a deep neural network model to accurately predict the SOC, SOH, and remaining life of the battery; Safety protection module: Multi-level protection (such as overvoltage, over-temperature, short-circuit protection, etc.), and provides battery balancing function to improve overall performance.

[0008] Remote monitoring module, including: User interface: Touch screen displays battery status, fault information and setting functions; Remote monitoring platform: Real-time data transmission, supports fault warning, data analysis and OTA upgrade.

[0009] Compared with the prior art, the beneficial effects of the present invention are: In the solution of this application: By setting the integrated intelligent temperature control system, efficient charging strategy module, battery management system (BMS), charging pile and temperature control system, as well as user interface and remote monitoring platform, the following beneficial effects are achieved: Improve charging efficiency: In traditional charging systems, the charging efficiency drops significantly in extreme temperature environments. However, in the present invention, the intelligent temperature control system maintains the battery pack within the optimal operating temperature range, significantly improving the charging acceptance ability and charging efficiency of the battery at various ambient temperatures. The efficient charging strategy module dynamically adjusts the charging power and charging mode according to the battery status and charging target, further optimizing the charging process, shortening the charging time and reducing energy loss. For example, through simulation or experimental data, it can be demonstrated that the charging efficiency of the present invention is increased by X% in low-temperature environments and the energy loss is reduced by Y% in high-temperature environments.

[0010] Prolong battery life: Extreme temperature is one of the important factors leading to shortened battery life. The present invention effectively controls the temperature of the battery pack through the intelligent temperature control system, avoiding the battery from operating at too high or too low temperatures, thus slowing down the attenuation rate of the battery and prolonging the service life of the battery. In addition, the battery balancing function of the BMS also eliminates the differences between individual batteries, further improving the overall life of the battery pack. For example, through long-term experimental or simulation data, it can be demonstrated that the present invention can extend the battery cycle life by Z%.

[0011] Improve charging safety: The present invention considers charging safety at multiple levels. The intelligent temperature control system avoids battery overheating or overcooling, the efficient charging strategy module sets charging safety thresholds, the BMS implements multi-level protection strategies (overvoltage, undervoltage, overcurrent, over-temperature, short-circuit, etc.), and the charging pile also has corresponding safety protection measures. These measures jointly ensure the safety of the charging process and reduce the risk of battery damage and safety accidents.

[0012] Improving User Experience: The user interface provides intuitive display and operation functions, facilitating users to understand the battery status and charging information. The remote monitoring platform offers functions such as remote monitoring, fault diagnosis, and data analysis, enabling users to grasp the battery and charging conditions anytime and anywhere and receive fault warning information, enhancing the convenience and safety of user use. The big data analysis module helps users optimize their charging behaviors and improve energy utilization efficiency through group optimization strategy recommendations, further enhancing the user experience.

[0013] Improving Energy Utilization Efficiency: Efficient charging strategies and optimized battery management reduce energy losses during the charging process, improve energy utilization efficiency, and lower users' energy costs. By analyzing the operation data of multiple RVs, the big data analysis module can discover group charging patterns and energy-saving driving habits, providing users with better charging and usage suggestions and further improving energy utilization efficiency. Description of the Drawings

[0014] Figure 1 It is the overall system framework diagram of the RV energy storage charge and discharge monitoring and management system provided by this application; Figure 2 It is the structure diagram of the intelligent temperature control system of the RV energy storage charge and discharge monitoring and management system provided by this application; Figure 3 It is the logic diagram of the efficient charging strategy module of the RV energy storage charge and discharge monitoring and management system provided by this application; Figure 4 It is the flowchart of the battery management system in the RV energy storage charge and discharge monitoring and management system provided by this application. Detailed Embodiments

[0015] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0016] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0017] It should be noted that, without conflict, the embodiments in the present invention and the features and technical solutions in the embodiments can be combined with each other.

[0018] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0019] Embodiment 1 Please refer to Figure 1 、 Figure 2 、 Figure 3 and Figure 4 , a monitoring and management system for energy storage charging and discharging of a recreational vehicle, including its intelligent temperature control system, efficient charging strategy module, and battery management system (BMS); The intelligent temperature control system is used to monitor the temperature of the energy storage battery pack and its charging environment in real time. The intelligent temperature control system includes a temperature sensor array, a heating element, a cooling device, and a temperature control algorithm module for controlling heating or cooling. The temperature control algorithm module automatically adjusts the heating element or cooling device to maintain the optimal operating temperature range of the battery through real-time data analysis and historical data prediction. It also includes an infrared thermal imager and a phase change material thermal management unit; The temperature sensor array and the infrared thermal imager monitor the temperature distribution at multiple positions inside the energy storage battery pack and the charging environment in a multi-point layout manner; The phase change material thermal management unit uses PCM materials with specific phase change temperatures to wrap the battery pack, improving the temperature regulation efficiency through latent heat of phase change and heat pipe networks; The temperature control algorithm module uses a fuzzy-PID hybrid control algorithm, combines real-time temperature data, historical temperature data, and an environmental temperature change trend prediction model, and outputs a control signal to the heating element and the cooling device; The fuzzy-PID hybrid control algorithm adaptively adjusts the PID parameters according to the temperature deviation to achieve precise control of the temperature of the energy storage battery pack. The core formula of the fuzzy-PID hybrid control algorithm is: , where O is the control output, wi is the weight, and μi(xi) is the membership function, ensuring efficient charging of the battery under different environmental temperatures; The algorithm is as follows: class FuzzyPIDController: def __init__(self): self.Kp = 0 self.Ki = 0 self.Kd = 0 self.error_prev = 0 self.integral = 0 def fuzzy_rules(self, error, error_change): # Fuzzy rule base rules = { ('NH', 'NH'): {'Kp': 'PB', 'Ki': 'NB', 'Kd': 'PS'}, ('NM', 'NM'): {'Kp': 'PM', 'Ki': 'NM', 'Kd': 'PS'}, #... More rules } return self.defuzzification(rules[self.fuzzification(error,error_change)]) def control(self, target_temp, current_temp): error = target_temp - current_temp error_change = error - self.error_prev # Adjust PID parameters through fuzzy rules self.update_pid_parameters(error, error_change) # PID control calculation self.integral += error control_output = (self.Kp * error + self.Ki * self.integral + self.Kd * error_change) self.error_prev = error return control_output。

[0020] Temperature monitoring network: - Temperature sensor array: Using PT100 platinum resistance (accuracy ±0.1°C) - Infrared thermal imager: Resolution 640×480, temperature resolution 0.05°C - Sensor layout optimization algorithm: ```python def sensor_placement_optimization(battery_dimensions, n_sensors): # Optimize sensor positions using genetic algorithm def fitness_function(positions): coverage = calculate_coverage(positions) cost = calculate_installation_cost(positions) return w1 * coverage - w2 * cost population = initialize_population(battery_dimensions, n_sensors) best_positions = genetic_algorithm(population, fitness_function) return best_positions The efficient charging strategy module, connected to the intelligent temperature control system, is used to dynamically adjust the charging power and strategy according to the battery state and user requirements. The efficient charging strategy module includes a charging algorithm module, a power regulator, and a communication interface, including: A charging algorithm module, a power regulator, and a deep reinforcement learning controller; The charging algorithm module adopts an adaptive optimization algorithm based on deep reinforcement learning, combines the current state of the battery, historical charging data, and grid load conditions, and dynamically adjusts the charging strategy. This module uses a dynamic optimization algorithm based on the gradient descent method, and the formula is: where Pt+1 is the charging power at the next moment, Pt is the charging power at the current moment, η is the learning rate, is the derivative of the loss function with respect to the charging power. The algorithm dynamically updates the charging strategy according to real-time data to minimize the charging time and maximize the charging efficiency. At the same time, the efficient charging strategy module also supports the intelligent switching of different charging modes (such as fast charging, economy charging) to meet the diverse needs of users; The deep reinforcement learning controller continuously optimizes the charging strategy by interacting with the environment to achieve a multi-objective balance of charging time, battery life, and charging cost; The algorithm of the deep reinforcement learning controller is as follows: class DRLChargeController: def __init__(self): self.model = self.build_model() def build_model(self): # Build a deep neural network model = Sequential( Dense(128, activation='relu', input_shape=(state_dim,)), Dense(64, activation='relu'), Dense(action_dim, activation='softmax') ) return model def get_action(self, state): # The state includes: SOC, temperature, voltage, current, etc. state_tensor = tf.convert_to_tensor([state]) action_probs = self.model(state_tensor) return tf.argmax(action_probs[0]).numpy() def train(self, replay_buffer): # Train the network using experience replay states, actions, rewards, next_states = replay_buffer.sample() #... Implement the training logic of PPO or SAC algorithm The power regulator is equipped with a battery impedance spectrum analysis function to monitor the battery impedance change during charging in real time and protect the battery safety.

[0021] The battery management system (BMS) is responsible for monitoring the voltage, current, temperature, and health status of the battery, and provides charging optimization suggestions and safety protection mechanisms through data analysis algorithms. The BMS uses neural network algorithms to evaluate the health status of the battery. The loss function of the neural network model is: , where yj is the actual output and ŷj is the predicted output. The weight parameters are optimized through multiple rounds of training to improve the evaluation accuracy. In addition, the BMS also has a multi-level safety protection mechanism, which can detect battery abnormal states (such as overcharging, over-discharging, short circuit, etc.) in real time and take timely measures to prevent battery damage.

[0022] The battery management system (BMS) includes: a data acquisition module, a battery state evaluation module, a safety protection module, and an intelligent balancing module; The data acquisition module uses a high-precision Hall sensor array to collect battery parameters in real time; The battery state evaluation module integrates an electrochemical model and a deep neural network to achieve high-precision prediction of SOC, SOH, and remaining life; The safety protection module adopts a multi-level protection strategy and a thermal runaway warning mechanism; Thermal runaway warning algorithm: def thermal_runaway_detection(temperature_data, voltage_data,current_data): # Calculate the temperature change rate dT_dt = np.gradient(temperature_data, time_data) # Calculate the entropy change entropy_change = calculate_entropy(temperature_data, current_data) # Threshold-based warning risk_level = 0 if dT_dt>TEMP_RATE_THRESHOLD: risk_level += 1 if entropy_change>ENTROPY_THRESHOLD: risk_level += 1 return risk_level The intelligent balancing module adaptively selects the active or passive balancing method according to the battery state.

[0023] Embodiment 2 The monitoring and management system for the energy storage charging and discharging of the RV provided in Embodiment 1 is further optimized. Specifically, as Figure 1 、 Figure 2 、 Figure 3 and Figure 4 shown, the temperature prediction module of the intelligent temperature control system adopts: Time series analysis (ARIMA model); Kalman filter algorithm; Deep learning prediction model; A method of fusing three algorithms to improve the temperature prediction accuracy.

[0024] Furthermore, as Figure 1 、 Figure 2 、 Figure 3 and Figure 4 shown, the efficient charging strategy module further includes: Intelligent peak shaving charging mode based on load prediction; Adaptive fast charging mode considering battery temperature and health status; User-customizable economic charging mode.

[0025] Furthermore, as Figure 1 、 Figure 2 、 Figure 3 and Figure 4 shown, the BMS further includes: SOC estimation module based on quantum-behaved particle swarm optimization; Health diagnosis module for electrochemical impedance spectroscopy analysis; The impedance spectroscopy analysis algorithm is as follows: def impedance_spectroscopy_analysis(voltage, current, frequency): # Calculate complex impedance Z = np.fft.fft(voltage) / np.fft.fft(current) # Extract characteristic parameters R_ohmic = np.real(Z[0]) R_ct = np.real(Z) - R_ohmic C_dl = -1 / (2 * np.pi * frequency * np.imag(Z)) return { 'R_ohmic': R_ohmic, 'R_ct': R_ct, 'C_dl': C_dl } Fault prediction module based on deep learning.

[0026] Furthermore, as shown in Figure 1 、 Figure 2 、 Figure 3 and Figure 4 shown, it further includes an edge computing architecture, adopting a dual-processor architecture of DSP + FPGA; realizing data local processing and real-time response; supporting distributed computing and load balancing.

[0027] Furthermore, as shown in Figure 1 、 Figure 2 、 Figure 3 and Figure 4 shown, it further includes a remote monitoring platform, characterized in that: providing Web and mobile monitoring interfaces; supporting OTA remote upgrade; including a data security storage module based on blockchain; having big data analysis and intelligent decision-making functions.

[0028] Furthermore, as shown in Figure 1 、 Figure 2 、 Figure 3 and Figure 4 shown, the big data analysis function includes: Analysis of group charging behavior; Battery life prediction; Recommendation for charging station location; Energy optimization suggestions.

[0029] Furthermore, as shown in Figure 1 、 Figure 2 、 Figure 3 and Figure 4 shown, it further includes an artificial intelligence-assisted decision-making system: Supporting multi-objective optimization decision-making; Providing predictive maintenance suggestions; Realizing adaptive optimization of charging strategies.

[0030] Embodiment 3 The RV energy storage charge and discharge monitoring and management system provided in Embodiment 1 and Embodiment 2 is further optimized. As shown in Figure 1 、 Figure 2 、 Figure 3 andFigure 4 As shown, the phase change material thermal management unit has: A replaceable PCM material compartment; An intelligent temperature field control algorithm; An efficient heat pipe network structure; Phase change material thermal management unit: PCM material selection: n-octadecane (phase change temperature 28°C, phase change latent heat 244 kJ / kg); Heat pipe network design: Using a multi-stage heat pipe series-parallel structure; Algorithm for calculating heat transfer efficiency: python def calculate_heat_transfer(T_battery, T_pcm, pcm_properties): # Calculate the phase change heat transfer of the PCM if T_pcm < pcm_properties['melting_point']: Q = m_pcm * c_solid * (T_pcm - T_initial) elif T_pcm == pcm_properties['melting_point']: Q = m_pcm * latent_heat * phase_fraction else: Q = m_pcm * c_liquid * (T_pcm - T_melting) return Q.

[0031] Furthermore, as shown in Figure 1 , Figure 2 , Figure 3 and Figure 4 , it also includes a grid interaction module: Supporting V2G (vehicle-to-grid) function; Having demand response capabilities; Providing auxiliary service functions.

[0032] SOC estimation algorithm: class SOCEstimator: def __init__(self): self.ekf = ExtendedKalmanFilter() self.coulomb = CoulombCounting() self.nn_model = NeuralNetwork() def estimate_soc(self, voltage, current, temperature): # EKF estimation soc_ekf = self.ekf.estimate(voltage, current) # Coulomb counting soc_coulomb = self.coulomb.count(current) # Neural network estimation soc_nn = self.nn_model.predict([voltage, current, temperature]) # Weighted fusion weights = self.calculate_weights() soc = (weights[0] * soc_ekf + weights[1] * soc_coulomb + weights[2] * soc_nn) return soc 。

[0033] In the present invention, unless otherwise clearly defined and limited, terms such as "installation", "connection", "connection", "fixation" and the like shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection, an electrical connection or communication with each other; it may be directly connected, or indirectly connected through an intermediate medium, and may be the communication inside two components or the interaction relationship between two components, unless otherwise clearly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0034] Obviously, the embodiments described above are only a part of the embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are shown in the drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure made by using the content of the specification and drawings of the present invention, directly or indirectly applied in other related technical fields, is similarly within the scope of the patent protection of the present invention.

Claims

1. A monitoring and management system for energy storage charging and discharging of a recreational vehicle, It is characterized in that, comprising: Intelligent temperature control system, efficient charging strategy module and battery management system (BMS); The intelligent temperature control system includes: Temperature sensor array, infrared thermal imager, heating element, cooling device, phase change material thermal management unit and temperature control algorithm module; The temperature sensor array and the infrared thermal imager monitor the temperature distribution at multiple positions inside the energy storage battery pack and the charging environment in a multi-point layout manner in real time; The phase change material thermal management unit uses PCM materials with specific phase change temperatures to wrap the battery pack, and improves the temperature regulation efficiency through latent heat of phase change and heat pipe network; The temperature control algorithm module uses a fuzzy-PID hybrid control algorithm, combines real-time temperature data, historical temperature data and environmental temperature change trend prediction model, and outputs control signals to the heating element and the cooling device; The fuzzy-PID hybrid control algorithm adaptively adjusts the PID parameters according to the temperature deviation to achieve precise control of the temperature of the energy storage battery pack; The efficient charging strategy module includes: Charging algorithm module, power regulator and deep reinforcement learning controller; The charging algorithm module uses an adaptive optimization algorithm based on deep reinforcement learning, combines the current state of the battery, historical charging data and grid load conditions, and dynamically adjusts the charging strategy; The deep reinforcement learning controller continuously optimizes the charging strategy by interacting with the environment to achieve multi-objective balance of charging time, battery life and charging cost; The power regulator is equipped with a battery impedance spectrum analysis function to monitor the battery impedance change during the charging process in real time and protect the battery safety; The battery management system (BMS) includes: Data acquisition module, battery state evaluation module, safety protection module and intelligent equalization module; The data acquisition module uses a high-precision Hall sensor array to collect battery parameters in real time; The battery state evaluation module integrates an electrochemical model and a deep neural network to achieve high-precision prediction of SOC, SOH and remaining life; The safety protection module adopts a multi-level protection strategy and a thermal runaway early warning mechanism; The intelligent equalization module adaptively selects the active or passive equalization method according to the battery state.

2. The energy storage charging and discharging monitoring and management system for a recreational vehicle according to claim 1, characterized in that, The temperature prediction module of the intelligent temperature control system adopts: Time series analysis (ARIMA model); Kalman filter algorithm; Deep learning prediction model; A method of fusing the three algorithms to improve the temperature prediction accuracy.

3. A monitoring and management system for energy storage charging and discharging of a motorhome according to claim 1, characterized in that, The efficient charging strategy module further includes: Intelligent peak shaving charging mode based on load prediction; Adaptive fast charging mode considering battery temperature and health status; User-customizable economic charging mode.

4. A monitoring and management system for energy storage charging and discharging of a motorhome according to claim 1, characterized in that, The BMS further includes: SOC estimation module based on quantum-behaved particle swarm optimization; Health diagnosis module for electrochemical impedance spectroscopy analysis; Fault prediction module based on deep learning.

5. A monitoring and management system for energy storage charging and discharging of a recreational vehicle according to claim 1, further comprising an edge computing architecture, characterized in that: Adopting a DSP+FPGA dual-processor architecture; Realizing local data processing and real-time response; Supporting distributed computing and load balancing.

6. A monitoring and management system for energy storage charging and discharging of a recreational vehicle according to claim 1, further comprising a remote monitoring platform, characterized in that: Provide Web and mobile monitoring interfaces; Support OTA remote upgrade; Include a data security storage module based on blockchain; Have big data analysis and intelligent decision-making functions.

7. The energy storage charge and discharge monitoring and management system for a recreational vehicle according to claim 6, characterized in that, The big data analysis function includes: Analysis of group charging behavior; Battery life prediction; Recommendation for charging station location; Energy optimization suggestions.

8. A monitoring and management system for energy storage charging and discharging of a motorhome according to claim 1, characterized in that, It also includes an artificial intelligence-assisted decision-making system: Support multi-objective optimization decision-making; Provide predictive maintenance suggestions; Achieve adaptive optimization of charging strategies.

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