Real-time battery power estimation method and instrumentation device for electric forklifts

Abstract

This invention discloses a real-time battery power estimation method and an instrumentation device for an electric forklift, relating to the field of energy storage systems. The key technical points are: the method includes generating an initial voltage threshold array, acquiring real-time voltage, comparing and determining the current power level stage, and determining whether to jump to the next stage based on continuous voltage trends. The voltage threshold array is segmented according to high-voltage, stable-voltage, and low-voltage zones, and combined with a dynamic jump control mechanism, it achieves precise judgment of power changes. This method does not rely on current sensors, estimating entirely based on voltage signals, and has advantages such as low hardware cost, low energy consumption, and small error. It is particularly suitable for lead-acid batteries without BMS systems and cost-sensitive industrial equipment, such as electric forklifts and warehouse handling equipment.

Description

Technical Field

[0001] This invention relates to the field of energy storage systems, and in particular to a method for real-time battery power estimation and an instrument device for an electric forklift. Background Technology

[0002] With the development of new energy technologies, electric forklifts, as an efficient and environmentally friendly industrial transportation tool, have been widely used in manufacturing, warehousing, logistics and other fields. Electric forklifts mainly rely on lead-acid batteries or lithium batteries for power, and the remaining charge (SOC, State of Charge) of the battery is a key parameter affecting the operating efficiency and safety of the forklift.

[0003] In the electric forklift industry, power batteries are mainly divided into two categories: lead-acid batteries and lithium batteries. For forklifts using lithium batteries, their battery systems typically integrate a Battery Management System (BMS). This system can accurately calculate the battery capacity and send the State of Charge (SOC) data to the forklift's instrument panel for display via a CAN bus. However, this approach requires the instrument panel to have a CAN communication interface, along with related CAN transceivers and controllers. In particular, the main control chip integrating the CAN controller is very expensive, increasing the overall vehicle manufacturing cost.

[0004] For the more common lead-acid batteries, which do not have a built-in BMS (Battery Management System), the power estimation function must be handled by the forklift's instrument panel. Traditional methods for estimating the power of lead-acid batteries mainly include the following:

[0005] Open-circuit voltage method: This method estimates the battery capacity by monitoring its static voltage. However, battery voltage varies greatly depending on factors such as type, age, and temperature, and it fluctuates drastically under load. Therefore, this method has very low accuracy and can usually only be used as a rough voltage reference.

[0006] Coulomb counting (ampere-hour integration method): This method calculates the remaining charge by accurately measuring the current charged and discharged. It offers high accuracy but requires initial capacity calibration. Furthermore, to measure the large currents during forklift operation, expensive external devices such as shunts or Hall effect sensors must be installed, resulting in high cost and complex structure.

[0007] Impedance tracking method: This method estimates battery capacity by measuring changes in the battery's internal resistance. However, it also requires sophisticated measurement equipment, is costly to implement, and is unsuitable for cost-sensitive forklift applications.

[0008] In summary, there is a contradiction between cost and accuracy in existing technologies: high-precision solutions (such as CAN communication or coulomb counting) are too expensive, while low-cost solutions (such as open-circuit voltage method) are not accurate enough and have poor reliability, and cannot meet the real needs of engineering machinery for power display under harsh working conditions. Summary of the Invention

[0009] The purpose of this invention is to provide a real-time battery power estimation method, which has the advantages of not requiring a current sensor, low cost, strong adaptability, and applicability to various battery types and load conditions. It is particularly suitable for industrial vehicles and embedded devices without BMS functionality, such as electric forklifts and warehouse handling platforms, enabling accurate and stable estimation and display of remaining battery power.

[0010] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0011] A method for real-time battery power estimation includes the following steps:

[0012] S01. Based on the current operating conditions, generate an initial voltage threshold array to indicate the remaining battery power level; wherein, the threshold points of the initial voltage threshold array cover the entire voltage operating range from the fully charged static voltage value to the discharge cutoff voltage value, and the threshold points within the array are divided into three intervals corresponding to the high voltage region, the stable voltage region, and the low voltage region, respectively, according to their voltage values.

[0013] S02. Obtain the real-time voltage of the battery;

[0014] S03. Compare the real-time voltage with the initial voltage threshold array to determine the battery's remaining power stage;

[0015] S04. Continuously acquire real-time voltage, and based on these continuous real-time voltages, determine whether the preset jump judgment conditions are met. If so, determine that the current battery level has entered the next stage and update the battery level display. The preset jump judgment conditions include the real-time voltage change trend, the voltage threshold corresponding to the current battery remaining power stage, and a preset calibration coefficient.

[0016] Further settings: The steps in S04 to determine whether the preset jump condition is met include:

[0017] S041. Within a continuously updated time window T, calculate the cumulative difference between the real-time voltage and the voltage threshold corresponding to the current battery remaining power stage;

[0018] S042. Compare the accumulated difference with a dynamic judgment threshold, wherein the dynamic judgment threshold is determined based on the duration of the time window T and a preset calibration coefficient α;

[0019] S043. When the cumulative difference is greater than the dynamic judgment threshold, it is determined that the current power level has entered the next stage.

[0020] Further configuration: In the high voltage region, stable voltage region, and low voltage region, different voltage ranges are divided into several voltage sub-segments based on different rules. The voltage sub-segments within the same voltage range are fixed and divided at equal intervals. The number of voltage sub-segments in the stable voltage region is X, and the number of voltage sub-segments in the high voltage region and low voltage region is N, where X is greater than N.

[0021] Further settings: The initial voltage threshold array mentioned in S01 is generated by weighted fusion of multiple preset basic threshold voltage arrays.

[0022] Further settings: The operating conditions include full load condition, half load condition, light load condition, alternating full load and no load condition, alternating half load and no load condition, and alternating light load and no load condition;

[0023] Multiple basic threshold voltage arrays include:

[0024] An array of alternating full-load and no-load threshold voltages corresponding to alternating full-load and no-load conditions;

[0025] An array of alternating half-load and no-load threshold voltages corresponding to alternating half-load and no-load conditions;

[0026] And an array of light-load alternating threshold voltages corresponding to light-load and no-load alternating operating conditions.

[0027] Further settings: the number N of voltage sub-segments in the high-voltage and low-voltage regions of the full-load alternating threshold voltage array, the half-load alternating threshold voltage array, and the light-load alternating threshold voltage array are different, and the value of the number N is inversely proportional to the load size of the corresponding working condition; and the total number of voltage sub-segments contained in each of the three basic threshold voltage arrays remains the same.

[0028] Further settings: The weighted fusion adopts multiple weight coefficients, where the weight coefficient corresponding to the full-load alternating threshold voltage array is k0, the weight coefficient corresponding to the half-load alternating threshold voltage array is k1, and the weight coefficient corresponding to the light-load alternating threshold voltage array is k2; the weight coefficients satisfy k0+k1+k2=3, and the weight coefficients are dynamically adjusted according to the real-time monitored battery discharge current.

[0029] Further settings: The weighting coefficients are dynamically adjusted based on the real-time monitored battery discharge current. The steps include:

[0030] The determination is made based on the ratio of the battery discharge current to a preset reference current;

[0031] When the ratio is greater than 1, increase the weight coefficient k0 corresponding to the full-load alternating threshold voltage array, and decrease the weight coefficient k1 corresponding to the half-load alternating threshold voltage array and the weight coefficient k2 corresponding to the light-load alternating threshold voltage array.

[0032] When the ratio is less than 1 and greater than 0.3, increase the weight coefficient k1 corresponding to the half-load alternating threshold voltage array, and decrease the weight coefficient k0 corresponding to the full-load alternating threshold voltage array and the weight coefficient k2 corresponding to the light-load alternating threshold voltage array.

[0033] When the ratio is less than 0.3, increase the weight coefficient k2 corresponding to the light load alternating threshold voltage array and decrease the weight coefficient k0 corresponding to the full load alternating threshold voltage array and the weight coefficient k1 corresponding to the half load alternating threshold voltage array.

[0034] Further setting: The preset reference current is the smaller of the battery's maximum operating current and the maximum allowable operating current.

[0035] Another object of the present invention is to provide an instrumentation device for an electric forklift, comprising:

[0036] The voltage sampling module is used to collect the real-time voltage of the connected battery;

[0037] LED display module is used to display the remaining battery power status;

[0038] The main control chip is electrically connected to the voltage sampling module and the LED display module, and the main control chip is configured to execute the above-described real-time battery power estimation method.

[0039] In summary, the present invention has the following beneficial effects:

[0040] First, this invention introduces a strategy combining voltage segmentation, continuous trend judgment, and dynamic jump control, which differs from the static point value judgment of the existing open-circuit voltage method, the current integration method of the coulomb meter method, or the model fitting logic of the impedance tracking method. Through the structured interpretation and dynamic mapping of real-time voltage change characteristics, it constructs a completely new estimation path. It does not rely on current acquisition components such as Hall sensors or shunts, and estimates the power consumption entirely based on the voltage signal change trend and historical segment threshold judgment. This not only reduces the system hardware cost and power consumption, but also avoids the dependence on initial capacity calibration and error accumulation problems of the traditional coulomb meter method. It is particularly suitable for lead-acid battery systems without BMS function and cost-sensitive embedded scenarios, such as electric forklifts, warehousing equipment and other industrial products.

[0041] Secondly, through a dynamic judgment mechanism, it achieves extremely high display stability and anti-interference capability, solving the defect of the traditional voltage method which changes immediately upon voltage fluctuation. The drawback of the traditional voltage method is that it cannot distinguish between a sharp voltage drop caused by a momentary change in load and a continuous decrease in the actual battery capacity. For example, when a forklift lifts a heavy object, the sudden increase in load will cause the battery voltage to drop sharply and instantaneously. Traditional instruments will immediately and incorrectly display a sudden decrease in battery capacity, causing confusion for the operator.

[0042] This invention continuously acquires voltage data multiple times during device operation and dynamically judges the battery's remaining charge stage by combining stage voltage thresholds and calibration coefficients. It updates the remaining charge stage only when specific transition conditions are met, avoiding erroneous charge level jumps caused by occasional fluctuations or sampling noise. It does not rely on any single instantaneous voltage value but continuously acquires real-time voltage and makes a comprehensive dynamic judgment based on the overall trend of real-time voltage changes. This mechanism gives charge estimation adaptive dynamic characteristics, enabling a reasonable and smooth judgment of changes in the remaining battery charge stage based on voltage trends, making it more reliable than existing methods that rely solely on single-point voltage judgments.

[0043] Third, furthermore, traditional methods typically only determine whether the voltage is below a threshold at a single moment. This single-point judgment is easily misled by a single voltage spike or dip. The method of this invention provides a highly interference-resistant, non-obvious jump judgment method. This is achieved by calculating the cumulative difference over a time window T. This cumulative difference is not a simple voltage value, but a comprehensive energy index encompassing both the depth and duration of the voltage drop. A brief but deep voltage drop and a persistent but shallow voltage drop can both result in similar cumulative values. This step, shifting from instantaneous value judgment to cumulative value judgment, elevates the basis of judgment from a single voltage value to a more physically meaningful voltage deviation energy.

[0044] Even when assessing cumulative amounts, traditional approaches often compare them to a fixed threshold. This method cannot effectively eliminate interference that lasts for a long time but has a very small magnitude. This invention introduces a dynamic threshold, which increases linearly with the time window T. This means that the threshold for assessment is not static but continuously rises, placing demands on the rate of increase of the cumulative difference.

[0045] Combining the two principles above, i.e., triggering an update only when the accumulated difference is greater than the dynamic judgment threshold, is equivalent to constructing a filter:

[0046] For instantaneous and severe load surges, although the voltage drop is deep, the duration T is extremely short, and the cumulative difference does not have time to become large enough to exceed the dynamic threshold, thus it is effectively filtered out.

[0047] For small, continuous system noise, although its duration T is long, causing the dynamic threshold to rise continuously, the growth rate of the accumulated difference cannot keep up with the rise rate of the threshold because its voltage deviation is small, and therefore it cannot cross the threshold and is effectively filtered out.

[0048] Only in the case of actual battery discharge, where the voltage drop is continuous and significant, can the cumulative difference grow faster than the dynamic threshold, eventually successfully exceeding the threshold and being recognized as a valid stage transition. This solves the technical problem in existing technologies where battery level displays are easily affected by instantaneous load interference and frequently fluctuate.

[0049] Third, by dividing the voltage operating range into a high voltage region, a stable voltage region, and a low voltage region, and further adopting differentiated segmentation rules based on the voltage characteristics of each region, this invention achieves a more refined and practical electrochemical characteristic-based method for dividing the charge stages.

[0050] Specifically, voltage changes in the stable voltage region are least sensitive to changes in power status, but correspond to the main power consumption segment in usage scenarios. Therefore, this invention divides the stable voltage region into a larger number of voltage sub-segments. Essentially, in the stable voltage region where voltage signals are least sensitive and information density is lowest, the density of the measurement scale is artificially increased to improve the resolution and tracking accuracy of power estimation in this region. In contrast, although the high-voltage and low-voltage regions experience drastic voltage changes, the energy release is relatively small. Therefore, they are divided into only a smaller number of voltage sub-segments to reduce the possibility of frequent display jumps, thereby improving system stability and user experience.

[0051] Fourth, this invention solves the technical problems of poor adaptability and large estimation errors of traditional single voltage threshold configuration methods under different load conditions by introducing a weighted fusion strategy of multi-condition basic threshold voltage arrays. In practical applications, the operating status of electric forklifts is complex and variable, and battery voltage is significantly affected by the combined effects of factors such as operating current, load changes, and temperature. When only the voltage threshold array obtained under a certain operating condition is used for power estimation, problems such as discontinuous power display, frequent jumps, or lag and inaccuracy can easily occur, seriously affecting the user's judgment of the vehicle's remaining range.

[0052] This invention weights and fuses voltage threshold arrays statistically obtained from multiple typical operating conditions. The resulting comprehensive array is no longer a dedicated model for a specific operating condition, but a general model that incorporates battery behavior characteristics under various conditions. This allows the model to make more reasonable and accurate judgments than any single model when facing ambiguous or unknown operating conditions that fall between several typical conditions or have never appeared in calibration. Compared to abrupt switching between different models, this invention ensures the smoothness and continuity of estimation results. When operating conditions change, the estimation model adapts to the change through a smooth transition of weights, rather than a sudden model switch. This avoids sudden changes in power display that may be caused by model switching, making the entire estimation system more stable and reliable.

[0053] Fifth, existing technologies typically employ constant load models under laboratory conditions. However, the actual working scenario of a forklift involves a dynamic cycle of acceleration, lifting, deceleration, and idle states, rather than a constant static load. The dynamic characteristics of the battery during this cycle, such as voltage sags and voltage rebound after load removal, are completely uncaptured by static models, which is one of the core reasons for inaccurate estimations. This invention introduces six typical operating conditions and configures an independent basic threshold voltage array for each condition, breaking through the simplistic assumption of setting thresholds solely based on static conditions in traditional methods. This significantly improves the adaptability to voltage variation patterns under complex operating conditions. Essentially, it replaces the isolated, static operating points in traditional technologies with a dynamic curve that reflects work, rest, and rework. By using alternating operating conditions to construct the basic array, the threshold points within it embed the voltage sag and rebound recovery characteristics of the battery in real dynamic cycles, more closely resembling the actual on-site simulation of forklift operation.

[0054] Sixth, the present invention further improves the estimation sensitivity and adaptability of the system under different load conditions by setting the following: in the full-load alternating threshold voltage array, half-load alternating threshold voltage array and light-load alternating threshold voltage array, the number N of the sub-segments dividing the high voltage area and the low voltage area increases as the load decreases, and the total number of voltage sub-segments in each array remains the same.

[0055] The discharge curves of batteries exhibit significant differences under different loads. Higher loads result in steeper discharge curves and faster voltage drops, requiring less stringent response in the high / low voltage range for estimation; the stable voltage region becomes crucial for accuracy. However, under light loads, voltage drops are slow, particularly exhibiting a longer transition plateau between high and low voltage ranges. Without refined threshold divisions for these two ranges, misjudgments or segment skipping lags may occur. Therefore, this invention divides sub-segments based on the inverse relationship between load and N value. Under light loads, this increases the accuracy of characterizing the high and low voltage regions, while under heavy loads, it concentrates limited segment resources in the more volatile stable region. This dynamically allocates discrimination accuracy resources, ensuring that the internal data point distribution of each basic array is tailored to its specific operating condition. This avoids wasting data points in steep curve regions while ensuring sufficient resolution in gentle curve regions, thus optimizing the accuracy of each basic model. At the same time, by keeping the total number of voltage sub-segments in each array consistent, not only is the uniformity of the overall algorithm framework and data structure ensured and the complexity of software processing reduced, but also a basic structural compatibility is provided for the subsequent fusion of multiple arrays.

[0056] Seventh, the voltage response curves of batteries exhibit significant differences under different operating conditions. This invention, by fusing multiple preset arrays and dynamically adjusting their contribution weights, allows the system to adaptively infer which typical operating condition is closer to the current based on the ratio of the current current to the preset reference current, thereby dynamically adjusting the emphasis of the fusion result. The dynamic adjustment of the weight coefficients means that the dynamic array can be reshaped in real time according to the actual load state of the forklift at every moment. When the forklift is heavily loaded, the weight of the fully loaded model automatically increases; when the forklift is idle, the weight of the lightly loaded model increases accordingly. This ensures that the estimation model is always in an optimal state that most closely reflects the current actual operating condition, thus maintaining the highest estimation accuracy throughout the entire dynamic operation.

[0057] Eighth, by acquiring the discharge current in real time to determine the current load status of the forklift, the weight coefficients k0, k1, and k2 of the model are dynamically adjusted to make the current model more suitable for the current operating conditions and to make the display of the remaining power more accurate.

[0058] Ninth. The present invention further provides that: the preset reference current is the smaller of the battery's maximum operating current and the maximum allowable operating current, which is used as the benchmark comparison value when dynamically adjusting the weighting coefficient.

[0059] The upper limit of safe operation of a complete system consisting of a power source and a load depends on the weakest link in the chain, following the "weakest link" principle. No matter how strong the battery's power supply capacity, it cannot exceed the forklift's carrying capacity; conversely, no matter how high the forklift's power, it cannot exceed the battery's safe discharge limit. Therefore, this invention compares the battery's maximum operating current with the electric forklift's maximum permissible operating current and takes the smaller value to obtain a correct reference benchmark representing the true upper limit of the entire system's capacity. This ensures that the algorithm's judgment benchmark will not exceed the actual safe operating boundary of the entire system. This fundamentally avoids the risk of incorrectly judging a dangerous overload condition for a component as a normal heavy load due to improper reference value setting. It guarantees that the safety protection logic in the algorithm can be accurately triggered when most needed.

[0060] Secondly, this setting, based on a reference current derived from physical boundary conditions, gives the instrument a stronger plug-and-play capability. Especially when there are performance differences between different battery types or products from different manufacturers, this method avoids misjudgments caused by improper human settings, providing a more universal and robust current reference selection logic. Attached Figure Description

[0061] Figure 1 This is a flowchart illustrating the steps of a real-time battery power estimation method.

[0062] Figure 2This is a power supply circuit diagram for the instrumentation device used in electric forklifts;

[0063] Figure 3 This is a circuit diagram for the instrumentation device used in electric forklifts. Detailed Implementation

[0064] The present application will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the application and are not intended to limit the scope of the application.

[0065] In the following description, numerous specific details are set forth for purposes of explanation in order to provide a thorough understanding of the inventive concept. As part of this specification, some of the accompanying drawings of this disclosure are block diagrams illustrating structures and devices to avoid complicating the disclosed principles. For clarity, not all features of the actual embodiment need to be described. Furthermore, the language used in this disclosure has been primarily chosen for readability and instructional purposes and may not have been chosen to define or limit the subject matter of the invention, thus requiring the necessary claims to determine such inventive subject matter. References to “an embodiment” or “an embodiment” in this disclosure mean that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment, and multiple references to “an embodiment” or “an embodiment” should not be construed as necessarily referring to the same embodiment.

[0066] Unless explicitly defined, the terms “a,” “an,” and “the” are not intended to refer to a singular entity, but rather to include a general category whose specific examples can be used for illustration. Therefore, the use of the terms “a” or “an” can mean any number of at least one, including “a,” “one or more,” “at least one,” and “one or more.” The term “or” means any of the options and any combination of the options, including all options unless explicitly indicated that the options are mutually exclusive. The phrase “at least one of” when combined with a list of items refers to a single item in the list or any combination of items in the list. The phrase does not require all items listed unless explicitly defined as such.

[0067] like Figure 1 As shown, this application discloses a method for real-time battery power estimation, including the following steps:

[0068] S01. Based on the battery's fully charged static voltage (which can be denoted as V1), generate an initial voltage threshold array to indicate the battery's remaining charge level, and embed this initial voltage threshold array into the device via software. The threshold points of this initial voltage threshold array numerically cover the entire effective operating range from the battery's fully charged static voltage (which can be denoted as V1) to the discharge cutoff voltage (which can be denoted as V2) defined in its specifications.

[0069] To improve estimation accuracy and response stability, this voltage range is further divided into three parts: a high-voltage region, a stable voltage region, and a low-voltage region. The principles for dividing the voltage range are as follows:

[0070] ;

[0071] The high voltage region is defined as extending n voltage units downward from the fully charged static voltage value V1 to (V1-n); the low voltage region is defined as extending n voltage units upward from the discharge cutoff voltage value V2 to (V2+n); and the stable voltage region is the segment between (V1-n) and (V2+n).

[0072] The parameter n is a voltage value in volts. Its function is to serve as a macroscopic scale, narrowing from the full-charge voltage and cutoff voltage towards the middle to define the overall boundary between high and low voltage regions. Its value is proportional to the rated voltage of the battery system, aiming to enable the algorithm to adapt to battery platforms with different voltage levels, such as 12V, 24V, and 48V.

[0073] Where n is an adjustable value. Taking a 24V lead-acid battery as a reference, n is 2. When applied to batteries of different capacities, n is proportional to the voltage. For example, n is 4 for a 48V battery and 1 for a 12V battery.

[0074] Different segmentation strategies are used for different voltage zones:

[0075] The high-voltage region and the low-voltage region are each divided into N segments with equal spacing, and the stable voltage region is divided into X segments. After each segment is divided, (N-1) high-voltage region threshold points, (X-1) stable voltage region threshold points, and (N-1) low-voltage region threshold points can be formed, totaling (N-1)² + (X-1) voltage threshold points.

[0076] The parameter N is a dimensionless quantity representing the number of sub-segments within the high-voltage and low-voltage zones. Its value is inversely proportional to the load size under specific operating conditions, aiming to optimize the stability of the power display by adjusting the grid density. This represents a micro-adaptation strategy for different load characteristics within a specific battery platform.

[0077] In a preferred embodiment, the number N of sub-segments in the high and low voltage regions can be 3, while the number X of sub-segments in the stable voltage region can be 19. With this setting, the entire voltage operating range is precisely divided into 25 (i.e., N+X+N) consecutive voltage sub-segments. This is equivalent to dividing the battery's 100% charge range into 25 stages, meaning that the voltage decreases by approximately 4% with each subsequent voltage sub-segment.

[0078] For example, in a typical 48V lead-acid battery system, its effective operating voltage range may be between approximately 54V (fully charged) and 42V (discharge cutoff). According to the principles of this invention, this range can be divided into: a high-voltage region of 54V to 50V, a low-voltage region of 46V to 42V, and a stable voltage region between the two, from 50V to 46V.

[0079] To implement the non-uniform resolution partitioning strategy described in this invention, the high-voltage region and the low-voltage region with more drastic voltage changes can be divided into sub-segments of every (4 / 3) V; at the same time, the stable voltage region with the most gradual voltage changes, but corresponding to the core charge range, can be divided into sub-segments of every (4 / 19) V.

[0080] With this setup: by using a denser division (i.e., a smaller voltage interval) in the stable voltage region (gradually changing section) where the voltage signal is weakest but the energy content is most critical, the detection accuracy and estimation resolution of this critical region can be significantly improved; at the same time, by using a relatively coarser division in the high and low voltage regions (sensitive sections) where the voltage signal itself is already clear enough and changes drastically, it helps to improve the overall stability of the estimation and effectively suppress unnecessary power display jumps caused by small fluctuations.

[0081] Based on the above division rules, the voltage threshold points for each interval can be generated in the following way:

[0082] The threshold array for the high voltage region is: V1, V1-(n / N), ..., V1-(n / N)×(N-1), V1-n;

[0083] The threshold array for the low voltage region is: V2+n, V2+(n / N)×(N-1), ..., V2+(n / N), V2;

[0084] The threshold array for the stable voltage region is: (V1-n)-[(V1-V2-2n) / X], (V1-n)-2×[(V1-V2-2n) / X], ..., (V1-n)-(X-1)×[(V1-V2-2n) / X].

[0085] In this embodiment, to adapt to the impact of battery voltage variation characteristics under different operating loads on the accuracy of power estimation, the operating conditions can be further divided into: full load condition, half load condition, light load condition, alternating full load and no load condition, alternating half load and no load condition, and alternating light load and no load condition, based on the actual working state of the electric forklift. For each of these three alternating operating conditions, three basic threshold voltage arrays are set accordingly:

[0086] Full load alternating threshold voltage array (SOT-FA) corresponding to the alternating full load and no load conditions.

[0087] Half-load alternating threshold voltage array (SOT-HA) corresponding to half-load and no-load alternating operating conditions.

[0088] Light load alternating threshold voltage array (SOT-SA) corresponding to light load and no load alternating conditions.

[0089] All the aforementioned basic arrays employ a unified voltage segmentation strategy, dividing the battery's complete operating voltage range, from the fully charged static voltage to the discharge cutoff voltage, into multiple consecutive voltage sub-segments. The number of sub-segments N in the high-voltage and low-voltage regions differs among the three basic threshold voltage arrays, and this value of N is inversely proportional to the load size of the corresponding operating condition.

[0090] For example, for the full-load alternating load condition with the largest load, the corresponding full-load alternating threshold voltage array (SOT-FA) can use the smallest N value, such as N=1; for the half-load condition, the corresponding half-load alternating threshold voltage array (SOT-HA) can use a moderate N value, such as N=2; while for the lightest load condition, the corresponding light-load alternating threshold voltage array (SOT-SA) uses the largest N value, such as N=3.

[0091] It should be noted that although the number of sub-segments N in the high-voltage and low-voltage regions differs under different operating conditions, the total number of voltage segments in each basic array remains consistent, i.e., N+X+N is always the same. This ensures that the battery voltage is divided into the same number of equal parts in each model.

[0092] In this embodiment, to further improve the stability and accuracy of the estimation results, the initial voltage threshold array is generated by weighted fusion of multiple preset basic threshold voltage arrays. Specifically, the basic threshold voltage array includes the aforementioned full-load alternating threshold voltage array (SOT-FA), half-load alternating threshold voltage array (SOT-HA), and light-load alternating threshold voltage array (SOT-SA).

[0093] The above fusion process uses multiple weighting coefficients for weighted calculation to form an array of initial voltage thresholds applicable to the current operating state. The weighted fusion formula is:

[0094] SOT (threshold voltage array) = [(k0×SOT-FA+k1×SOT-HA+=k2×SOT-SA)] / 3;

[0095] Wherein, k0, k1, and k2 correspond to the weighting coefficients of the base arrays for alternating full-load, half-load, and light-load operating conditions, respectively, satisfying the relationship: k0 + k1 + k2 = 3; under normal conditions, the weighting coefficients k0, k1, and k2 can be uniformly set to 1, that is, the proportions of the base arrays for the three operating conditions participating in the weighted fusion are equal; alternatively, a configuration such as k0 = 0.9, k1 = 1.2, and k2 = 0.9 can be used to moderately enhance the response accuracy for medium-load operating conditions.

[0096] Based on the above-mentioned setting of weighting coefficients by fixed empirical values, the present invention also provides another optional scheme to enable the estimation method to adapt to batteries of different capacities and more extensive and varied actual operating conditions.

[0097] In this alternative scheme, a dynamic correction logic for the weighting coefficients k0, k1, and k2 is introduced. The core of this logic is to compare the real-time monitored actual discharge current of the forklift with a preset reference current that reflects the upper limit of the system's capacity, and dynamically adjust the influence of each basic array in the fusion process based on the comparison result. Therefore, the specific values ​​of the weighting coefficients are not fixed, but dynamically adjusted based on the real-time discharge current of the battery, thereby achieving an adaptive response to the dynamic characteristics of different operating conditions. Its adjustment mechanism includes the following steps:

[0098] Real-time acquisition of current battery discharge current;

[0099] The current value is compared with a preset reference current to reflect the current load level;

[0100] The preset reference current is crucial for ensuring the scientific validity and effectiveness of the aforementioned logical judgment benchmark. Its value is the smaller of two upper limits: the battery's maximum operating current (usually related to battery capacity) and the electric forklift's actual maximum permissible operating current (usually related to the forklift's motor power). This ensures that the reference benchmark always remains within the actual safe operating boundaries of the entire system.

[0101] Here, the battery discharge current is the actual operating current, serving as the input parameter for adaptive response to operating conditions. It should be noted that this invention does not impose specific limitations on the method of obtaining this actual operating current; it can be obtained in various ways, aiming to provide a reliable basis for load level judgment for the algorithm with minimal additional cost. In a preferred embodiment, the instrument device of this invention can communicate with the existing motor controller on the electric forklift via its main control chip through a communication interface (e.g., a UART serial port or a CAN bus interface).

[0102] In another preferred embodiment, the actual operating current is obtained through indirect estimation. The main control chip estimates the magnitude of the current causing the instantaneous voltage change by frequently monitoring the instantaneous rate of change of the voltage, thereby determining the current load level.

[0103] It should be noted that the actual operating current value is used to determine the approximate load range of the forklift (e.g., light load, half load, or heavy load). The ultimate goal is to select the most suitable set of weighting coefficients (k0, k1, k2) for the weighted fusion algorithm. The determination of the load level is a qualitative or semi-quantitative identification; it is not sensitive to the instantaneous measurement accuracy of the current, allowing for a certain degree of measurement error, and this error does not accumulate over time. A slight deviation in the current reading may only have a negligible, temporary effect on the weighting coefficients, unlike the irreversible cumulative error caused by coulomb counting. Because the requirement for measurement accuracy is significantly reduced, this invention completely eliminates the need for expensive high-precision measurement schemes. Therefore, current information meeting the algorithm's requirements can be obtained from data acquired from the motor controller or indirectly estimated through the voltage change rate.

[0104] Furthermore, based on this ratio, the following judgments and adjustments are made:

[0105] When the ratio is greater than 1, it indicates that the current condition is under heavy load. The value of the full load alternation weight k0 should be increased, while the values ​​of the half load and light load alternation weights k1 and k2 should be decreased accordingly.

[0106] When the ratio is less than 1 and greater than 0.3, it indicates that the current condition is half-load. The value of the half-load alternation weight k1 should be increased, while the values ​​of the full-load and light-load alternation weights k0 and k2 should be decreased accordingly.

[0107] When the ratio is less than 0.3, it indicates that the current condition is light load. The value of the light load alternation weight k2 should be increased, and the values ​​of k1 and k0 should be decreased accordingly.

[0108] The above dynamic adjustment method can effectively correct the applicability deviation of the static basic array under different battery capacities or different electric forklift power platforms, improve the accuracy of SOT division of different battery platforms in actual operation, and thus improve the stability and reliability of remaining power estimation.

[0109] Further, after generating the initial voltage threshold array, step S02 is executed: obtaining the real-time voltage of the battery. Specifically, the real-time voltage is the instantaneous operating voltage collected from the two terminals of the battery at the current moment, and this voltage value directly reflects the output characteristics of the battery under the current load condition.

[0110] Next, step S03 is executed: the real-time voltage obtained above is compared with each threshold point in the initial voltage threshold array, and the corresponding battery remaining capacity stage is determined based on the interval position of the real-time voltage. For example, if the real-time voltage is in the third sub-segment of the stable voltage zone, it can be inferred that the current SOC is approximately between 80% and 76%.

[0111] In this embodiment, the real-time battery power estimation method further includes the following steps:

[0112] S04: During forklift operation, continuously acquire the real-time battery voltage and, based on this continuous voltage data, determine whether the battery has entered the next remaining power stage; if so, update the power display.

[0113] The logic for the jump determination is mainly based on preset jump determination conditions. These conditions comprehensively consider the real-time voltage change trend, the voltage threshold corresponding to the current battery remaining power level, and a calibration coefficient. The specific determination process includes the following sub-steps:

[0114] S041: Within a continuously updated time window T, collect T sets of real-time voltage data, and calculate the real-time voltage for each set sequentially. Threshold voltage corresponding to the current energy level The difference between them, if If the voltage is below this threshold, the difference is accumulated to obtain the total accumulated low-voltage deviation.

[0115] S042: Compare the accumulated difference with a dynamic judgment threshold. This dynamic judgment threshold is calculated based on the duration of the time window T and the calibration coefficient P1 of the model, and is used to adjust the sensitivity of the jump judgment and adapt to the operating characteristics of different types of batteries.

[0116] S043: When the cumulative difference is greater than the dynamic judgment threshold, it is determined that the current power consumption has entered the next stage.

[0117] The dynamic judgment in S043 is made using the following mathematical expression:

[0118] ;

[0119] in:

[0120] Y2 represents the final stage transition judgment value, in volts (V), which measures the cumulative deviation of the voltage within the current time window T from the current charge stage voltage threshold.

[0121] The real-time voltage value collected during the i-th time window T is expressed in volts (V).

[0122] X SOT The threshold voltage corresponding to the current remaining battery charge stage, in volts (V), is preferably the lower boundary value or the center value of the voltage at this stage;

[0123] T represents the number of samplings, which is the total number of continuous real-time voltage data points contained within a time window that constitutes a complete judgment period, preferably ranging from 50 to 500.

[0124] P1 is the model calibration coefficient, a dimensionless parameter set according to the inherent discharge characteristics of battery type (such as lead-acid battery, lithium iron phosphate battery, ternary lithium battery, etc.). Its preferred range is 1 / 3 to 1 / 9, which is used to finely adjust the sensitivity of the jump judgment.

[0125] In the dynamic jump judgment model described above, the calibration coefficient P1 is an adjustable parameter that enables broad adaptability to different types of batteries. Its value is primarily determined based on the inherent electrochemical characteristics of the applied battery, such as the flatness of the discharge plateau, internal resistance structure, and voltage response sensitivity.

[0126] In the dynamic jump judgment model described above, the calibration coefficient P1 is an adjustable parameter that enables broad adaptability to different types of batteries. Its value is primarily determined by the inherent electrochemical characteristics of the applied battery, such as the flatness of the discharge plateau, internal resistance structure, and voltage response sensitivity. The underlying principle is that for batteries with a flatter discharge plateau, even small voltage changes can correspond to significant actual energy consumption; therefore, a smaller P1 value is needed to improve the sensitivity of the jump judgment and promptly capture effective stage changes. Conversely, for batteries with relatively steep discharge curves, a larger P1 value can be used to make the judgment more conservative, thereby enhancing system stability and filtering out unnecessary fluctuations.

[0127] In a preferred embodiment of the present invention, recommended values ​​for the calibration coefficient P1 are given for several mainstream battery types to achieve optimal estimation results:

[0128] For lead-acid batteries with relatively steep discharge curves and direct voltage responses, the calibration coefficient P1 is preferably 1 / 3;

[0129] For lithium iron phosphate batteries with an extremely flat discharge platform and extremely low voltage sensitivity, the calibration coefficient P1 is preferably 1 / 7.

[0130] For ternary lithium batteries with relatively linear discharge curves and characteristics between the former two, the calibration coefficient P1 is preferably 1 / 5.

[0131] In the nested Max(0,…) function structure in the above expression:

[0132] Inner layer This is used to determine whether the current sampling voltage is lower than the threshold of this stage. If it is, the deviation is included; otherwise, it is not included to prevent high-voltage samples from interfering with the judgment.

[0133] The outer Max(0,…) ensures that the calculation result is non-negative, that is, Y2 is at least 0, to avoid misjudgment of the stage.

[0134] During continuous operation, this formula will be executed periodically after every T real-time samplings. If the calculation result Y2>0, it means that the voltage has been continuously lower than the stage threshold within the current time window, and the cumulative deviation has exceeded the dynamic judgment threshold (P1×T×0.1). In this case, it is determined that the current SOC stage has been completed and the battery capacity has entered the next stage; otherwise, the current SOC stage remains unchanged.

[0135] This invention constructs a dynamic jump strategy with strong adaptability and high judgment accuracy by setting an adjustable time window T and a calibration coefficient P1.

[0136] When the T value is small (e.g., 50), the system responds quickly to voltage fluctuations;

[0137] When the T value is large (e.g., 500), the system can effectively filter out transient voltage interference and improve the stability of judgment.

[0138] The smaller the calibration coefficient P1, the more sensitive the jump; the larger the P1, the more conservative the judgment, which is suitable for battery systems with high voltage accuracy requirements.

[0139] In summary, this formula not only enables quantitative judgment of battery charge stage transitions, but also has good parameter adjustability and model universality, and can adapt to the voltage change characteristics of different types of battery systems (such as lead-acid batteries, lithium iron phosphate batteries, ternary lithium batteries, etc.), thereby significantly improving the accuracy and robustness of charge estimation.

[0140] Furthermore, the optimal selection of the T value is also related to the battery capacity. For battery systems with larger capacities, their voltage response typically exhibits greater inertia, meaning the voltage drop is more gradual under varying loads. In this case, choosing a larger T value helps the algorithm observe over a longer timescale, thereby accurately identifying the true long-term discharge trend and avoiding interference from instantaneous fluctuations. Conversely, for batteries with smaller capacities, their voltage is more sensitive to load changes and responds faster. In this case, choosing a smaller T value ensures the algorithm's sensitivity, enabling it to promptly capture rapid changes in state of charge and preventing display lag due to excessive smoothing. Therefore, by matching the T value to the battery capacity, the adaptability and accuracy of the estimation strategy across different hardware platforms can be further improved.

[0141] This embodiment also provides an instrumentation device for an electric forklift, including a voltage sampling module, an LED display module, and a main control chip. The voltage sampling module is used to collect the real-time voltage signal of the connected battery; the LED display module is used to display the remaining battery power status; the main control chip is electrically connected to the voltage sampling module and the LED display module, and the main control chip is configured to execute the above-described real-time battery power estimation method.

[0142] The instrument device in this embodiment includes two parts: a power supply circuit and a main control circuit.

[0143] Reference Figure 2 The main function of the power supply circuit is to convert the high voltage of the forklift battery into a stable low-voltage operating voltage required by the device. In this power supply circuit, after the battery positive voltage B+ is input, it first passes through a protection and pre-regulation circuit composed of a transient voltage suppressor diode (TVS) D1 and a first-stage voltage regulator module U1. This power supply circuit can effectively protect the subsequent circuits from damage caused by transient voltage spikes. The first-stage voltage regulator module U1 mainly consists of a Zener diode, a freewheeling transistor, and a current-limiting resistor, achieving initial voltage reduction and current limiting. Subsequently, it passes through a filter network composed of capacitors C2 (1uF), C3 (10uF), and C4 (100nF) for low-frequency, medium-frequency, and high-frequency filtering to ensure smooth and stable DC power. Finally, the current is fed into a second-stage fine voltage regulator module composed of an adjustable voltage regulator U2 (such as LM317-G). By adjusting the ratio of resistors R6 and R7, a stable operating voltage is precisely set and output to supply the main control chip and other core components. A diode D3 (such as 1N4148W) is also included in the power supply circuit to protect the regulator from reverse voltage surges.

[0144] Reference Figure 3The main control circuit is the core of the entire device. It mainly includes the main control chip circuit (MCU), the battery level indicator module, and the programming interface. The battery level indicator module consists of four LEDs (LED1, LED2, LED3, LED4) and their respective current-limiting resistors (R3, R4, R10, R11), directly driven by the I / O port of the main control chip U1, used to visually display the battery status. The programming interface leads out the I2C bus (SCL, SDA) and related pins through connector J1, and is configured with pull-up resistors R1 and R2 for firmware burning, updating, or debugging. The main control chip U1, as the carrier of the software algorithm, has external filter capacitors C6 and C7 connected to its power supply pins to ensure stable operation. In addition, the main control circuit also includes protective components such as diode D5 to prevent reverse current from damaging the circuit.

[0145] In one specific embodiment of the present invention, the LED display module is used to indicate the current estimated remaining battery power stage by segmented illumination, and its display rules are as follows:

[0146] When the main control chip estimates that the remaining battery power is between 76% and 100%, it controls the LED display module to light up all four LEDs, indicating that the battery is fully charged.

[0147] When the SOC is between 51% and 75%, control the lighting of three LEDs;

[0148] When the SOC is between 26% and 50%, control the lighting of two LEDs to indicate low battery level;

[0149] When the SOC is equal to or lower than 25%, only one LED will be lit as a low battery warning to remind the operator to charge the battery as soon as possible.

[0150] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for real-time battery power estimation, characterized in that, Includes the following steps: S01. Based on the current operating conditions, generate an initial voltage threshold array to indicate the remaining battery power level. The threshold points in this initial voltage threshold array cover the entire operating voltage range from the fully charged static voltage to the discharge cutoff voltage. Furthermore, the threshold points within this array are divided into three intervals corresponding to a high-voltage region, a stable voltage region, and a low-voltage region, based on their voltage values. Within the high-voltage region, stable voltage region, and low-voltage region, different voltage intervals are further divided into several voltage sub-segments according to different rules. The voltage sub-segments within the same voltage interval are fixed and equally spaced. The number of voltage sub-segments in the stable voltage region is X, and the number of voltage sub-segments in the high-voltage region and the low-voltage region is N, where X is greater than N. S02. Obtain the real-time voltage of the battery; S03. Compare the real-time voltage with the initial voltage threshold array to determine the battery's remaining power stage; S04. Continuously acquire real-time voltage, and determine whether the preset jump judgment conditions are met based on these continuous real-time voltages. If so, determine that the current battery level has entered the next stage and update the battery level display. The preset jump judgment conditions include the real-time voltage change trend, the voltage threshold corresponding to the current battery remaining power stage, and a preset calibration coefficient. The steps in S04 to determine whether the preset jump condition is met include: S041. Within a continuously updated time window T, calculate the cumulative difference between the real-time voltage and the voltage threshold corresponding to the current battery remaining power stage; S042. Compare the accumulated difference with a dynamic judgment threshold, wherein the dynamic judgment threshold is determined based on the duration of the time window T and a preset calibration coefficient α; S043. When the cumulative difference is greater than the dynamic judgment threshold, it is determined that the current power level has entered the next stage.

2. The real-time battery power estimation method according to claim 1, characterized in that: The initial voltage threshold array mentioned in S01 is generated by weighted fusion of multiple preset basic threshold voltage arrays.

3. The real-time battery power estimation method according to claim 2, characterized in that: The operating conditions include full load, half load, light load, alternating full load and no load, alternating half load and no load, and alternating light load and no load. Multiple basic threshold voltage arrays include: An array of alternating full-load and no-load threshold voltages corresponding to alternating full-load and no-load conditions; An array of alternating half-load and no-load threshold voltages corresponding to alternating half-load and no-load conditions; And an array of light-load alternating threshold voltages corresponding to light-load and no-load alternating operating conditions.

4. The real-time battery power estimation method according to claim 3, characterized in that: The number N of voltage sub-segments in the high-voltage and low-voltage regions of the full-load alternating threshold voltage array, the half-load alternating threshold voltage array, and the light-load alternating threshold voltage array are different, and the value of the number N is inversely proportional to the load size of the corresponding operating condition; in addition, the total number of voltage sub-segments contained in each of the three basic threshold voltage arrays remains the same.

5. The real-time battery power estimation method according to claim 3 or 4, characterized in that: The weighted fusion uses multiple weight coefficients, where the weight coefficient corresponding to the full-load alternating threshold voltage array is k0, the weight coefficient corresponding to the half-load alternating threshold voltage array is k1, and the weight coefficient corresponding to the light-load alternating threshold voltage array is k2; the weight coefficients satisfy k0+k1+k2=3, and the weight coefficients are dynamically adjusted according to the real-time monitored battery discharge current.

6. The real-time battery power estimation method according to claim 5, characterized in that: The weighting coefficients are dynamically adjusted based on the real-time monitored battery discharge current. This process includes: The determination is made based on the ratio of the battery discharge current to a preset reference current; When the ratio is greater than 1, increase the weight coefficient k0 corresponding to the full-load alternating threshold voltage array, and decrease the weight coefficient k1 corresponding to the half-load alternating threshold voltage array and the weight coefficient k2 corresponding to the light-load alternating threshold voltage array. When the ratio is less than 1 and greater than 0.3, increase the weight coefficient k1 corresponding to the half-load alternating threshold voltage array, and decrease the weight coefficient k0 corresponding to the full-load alternating threshold voltage array and the weight coefficient k2 corresponding to the light-load alternating threshold voltage array. When the ratio is less than 0.3, increase the weight coefficient k2 corresponding to the light load alternating threshold voltage array and decrease the weight coefficient k0 corresponding to the full load alternating threshold voltage array and the weight coefficient k1 corresponding to the half load alternating threshold voltage array.

7. The real-time battery power estimation method according to claim 6, characterized in that: The preset reference current is the smaller of the battery's maximum operating current and the maximum allowable operating current.

8. An instrument device for an electric forklift, characterized in that, include: The voltage sampling module is used to collect the real-time voltage of the connected battery; LED display module is used to display the remaining battery power status; The main control chip is electrically connected to the voltage sampling module and the LED display module, and the main control chip is configured to perform the real-time battery power estimation method according to any one of claims 1 to 7.

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