I present a compact ultra-high-frequency radio-frequency sensor and an online monitoring strategy for partial discharge in high-voltage systems of an electric vehicle. My work is driven by a practical observation: as the electric vehicle evolves toward higher bus voltages, faster switching devices, denser packaging, and more integrated thermal and electrical architectures, the insulation system becomes exposed to combined electrical, thermal, and environmental stresses that are difficult to reproduce with conventional test methods. I therefore treat partial discharge not as a secondary laboratory phenomenon but as an actionable state variable that should be monitored inside the electric vehicle during operation. The sensor I designed is intended to extract weak radio-frequency emissions from partial discharge events while rejecting the dense electromagnetic background produced by traction inverters, electric machines, connectors, wireless modules, and power electronic assemblies. I use the 300 MHz to 1.5 GHz band as the main detection window, and I combine a small planar antenna, a low-noise front end, differential output, shielding, adaptive noise cancellation, multi-position placement, and time-frequency feature extraction into one coherent monitoring concept for the electric vehicle.
The motivation for my design comes from the specific failure modes of an electric vehicle high-voltage system. Partial discharge can appear in stator windings, slot liners, end-winding insulation, high-voltage connectors, busbar interfaces, terminal blocks, and inverter output assemblies. In an electric vehicle, these components are compactly arranged and often placed close to fast-switching silicon carbide or gallium nitride devices. The resulting voltage waveform is not a simple sinusoidal stress. It contains steep edges, overshoot, ringing, and repetitive impulses. I express the edge stress as
$$ \left(\frac{dv}{dt}\right)_{\max} \approx \frac{V_{dc}}{\tau_r} $$
where \(V_{dc}\) is the direct-current bus voltage and \(\tau_r\) is the rise time of the switching transition. When \(\tau_r\) falls into the nanosecond range, the local electric field inside a small void or along an interface can exceed the inception threshold even when the average field appears moderate. This is one reason why an electric vehicle insulation diagnosis cannot rely only on power-frequency partial discharge measurements. I also consider the non-stationary operating profile of an electric vehicle: motor speed, torque, coolant temperature, bus voltage, and switching frequency change continuously. The partial discharge signal is therefore intermittent, amplitude-modulated, and easily confused with switching noise. Table 1 summarizes the main differences I account for when I transfer partial discharge detection from conventional power equipment to an electric vehicle.
| Aspect | Electric vehicle high-voltage system | Conventional power equipment | Implication for my sensor |
|---|---|---|---|
| Voltage waveform | Pulse-width-modulated, steep edges, overshoot, ringing | Mainly sinusoidal or slowly varying | I must capture broadband impulsive radiation |
| Switching devices | Silicon carbide and gallium nitride with high \(dv/dt\) | Lower switching speed in many cases | I must separate partial discharge from repetitive switching noise |
| Packaging | Compact, metal-enclosed, close coupling between components | Larger clearances and more diagnostic access | I need a small sensor and multi-position placement |
| Operating conditions | Fast changes in speed, torque, temperature, and bus voltage | Relatively stable operating states | I need adaptive thresholding and time-frequency features |
| Electromagnetic environment | Inverter noise, common-mode current, wireless communication, relays | Lower density of mixed interference sources | I need filtering, shielding, differential output, and noise cancellation |
| Discharge energy | Often weak at an early stage | May be larger and more accessible | I need low-noise amplification and high sensitivity |
I treat partial discharge as a localized charge migration process. In an electric vehicle, a small air gap, a delamination, a pinhole, or a contaminated interface can concentrate the electric field. When the local field exceeds the breakdown threshold of the defect, a short current pulse is generated. The pulse duration can be extremely short, and its rise time can reach the sub-nanosecond range. I model the discharge current pulse as
$$ i_{pd}(t)=I_0 \exp\left(-\frac{t-t_0}{\tau_d}\right) \left[u(t-t_0)-u(t-t_0-T_p)\right] $$
where \(I_0\) is the peak current, \(t_0\) is the onset time, \(\tau_d\) is the decay constant, \(T_p\) is the pulse width, and \(u(t)\) is the unit step function. The corresponding spectrum has a broad envelope. For a simplified rectangular pulse approximation, I write
$$ I_{pd}(f)=I_0 T_p \frac{\sin(\pi f T_p)}{\pi f T_p} e^{-j2\pi f t_0} $$
This expression explains why partial discharge in an electric vehicle can excite a wide frequency range, from tens of megahertz to several gigahertz. The practical detection band is not determined only by the source spectrum. It is also shaped by propagation paths, metal enclosures, cable harnesses, connector geometry, and the input impedance of the sensor. I therefore examine the radio-frequency radiation characteristics before I fix the sensor band.
My measurements and design analysis indicate that the 300 MHz to 1.5 GHz range is a strong compromise for an electric vehicle. Below roughly 150 MHz, the traction inverter switching noise and common-mode current radiation can be strong. Above 2.4 GHz, wireless communication and other high-frequency services become more prominent, and the path loss in a compact metal-rich electric vehicle environment increases. In the 300 MHz to 1.5 GHz window, I obtain a favorable balance among source energy, antenna compactness, background noise, and propagation loss. Table 2 gives the frequency-band reasoning I use for the electric vehicle sensor.
| Frequency range | Typical content in an electric vehicle | Advantage | Limitation | My design decision |
|---|---|---|---|---|
| Below 150 MHz | Inverter switching harmonics, common-mode currents, power converter noise | Strong partial discharge energy in some defects | High conducted and radiated interference | Use as an auxiliary reference band only |
| 300 MHz to 1.5 GHz | Partial discharge impulses, connector sparks, some relay transients | Good source energy, compact antenna, manageable path loss | Still affected by switching edges and wireless signals | Select as the main electric vehicle detection band |
| Above 2.4 GHz | Bluetooth, Wi-Fi, high-frequency digital radiation | Low power-converter noise in some cases | Higher path loss and stronger communication interference | Avoid as the primary band |
I designed the sensor as a planar compact ultra-high-frequency antenna with a microstrip patch structure. To reduce the footprint inside an electric vehicle, I use meander-line loading and slot coupling. The dielectric substrate is polytetrafluoroethylene because it has stable permittivity and low high-frequency loss. The overall length and width are kept within 50 mm by 50 mm so that the sensor can be embedded near a motor housing, a high-voltage connector, or an inverter terminal box. The fundamental resonant behavior of the patch can be approximated by
$$ f_{mn}=\frac{c}{2\sqrt{\varepsilon_r}}\sqrt{\left(\frac{m}{L}\right)^2+\left(\frac{n}{W}\right)^2} $$
where \(c\) is the speed of light, \(\varepsilon_r\) is the relative permittivity of the substrate, \(L\) is the effective patch length, \(W\) is the effective patch width, and \(m\) and \(n\) are mode indices. I use this relation as an initial design guide and then optimize the geometry with full-wave simulation. The feed is a tapered microstrip line, and multiple resonant structures are combined to broaden the impedance bandwidth. I optimize the patch dimensions, feed position, slot length, and slot width to obtain a voltage standing wave ratio below 2.0 and a return loss above 10 dB across the working band. These conditions are expressed as
$$ \mathrm{VSWR}=\frac{1+|\Gamma|}{1-|\Gamma|}\lt 2.0 $$
$$ \mathrm{RL}=-20\log_{10}|\Gamma|\gt 10\,\mathrm{dB} $$
where \(\Gamma\) is the input reflection coefficient. I also integrate a low-noise amplifier at the back end of the sensor. The noise figure of the first stage dominates the cascaded noise performance, so I keep the amplifier noise figure below 2 dB. The cascaded noise factor is
$$ F_{total}=F_1+\frac{F_2-1}{G_1}+\frac{F_3-1}{G_1G_2}+\cdots $$
where \(F_1\), \(F_2\), and \(F_3\) are the noise factors of the successive stages and \(G_1\), \(G_2\), and \(G_3\) are their available gains. This formula is important for my electric vehicle sensor because the partial discharge signal at an early stage can be very weak, and any loss before the first amplifier directly reduces the achievable signal-to-noise ratio. Table 3 lists the main design targets and the corresponding implementation choices.
| Parameter | Symbol | Target | Implementation in my design |
|---|---|---|---|
| Working band | \(f\) | 300 MHz to 1.5 GHz | Multi-resonance patch with meander-line loading and slot coupling |
| Footprint | \(L_s \times W_s\) | At most 50 mm by 50 mm | Planar compact antenna on polytetrafluoroethylene substrate |
| Voltage standing wave ratio | \(\mathrm{VSWR}\) | Below 2.0 | Tapered feed and optimized feed point |
| Return loss | \(\mathrm{RL}\) | Above 10 dB | Slot and patch parameter optimization |
| Amplifier noise figure | \(NF_1\) | Below 2 dB | Low-noise front-end amplifier close to the antenna |
| Output form | \(v_{out}\) | Differential | Differential output stage for common-mode rejection |
| Integration | \(-\) | Embedded or clamped | Small housing compatible with motor, connector, and inverter locations |
I pay particular attention to the anti-interference design because the electric vehicle is a hostile electromagnetic environment. A partial discharge pulse may be only a few millivolts at the sensor output, while inverter switching and common-mode radiation can be much larger. I use a layered approach. In the radio-frequency front end, I integrate a band-pass filter whose passband matches the 300 MHz to 1.5 GHz working band. This filter suppresses out-of-band interference before it saturates the amplifier. In the output structure, I use a differential form. The common-mode rejection ratio is
$$ \mathrm{CMRR}=20\log_{10}\left|\frac{A_d}{A_c}\right| $$
where \(A_d\) is the differential gain and \(A_c\) is the common-mode gain. A high common-mode rejection ratio reduces the influence of conducted coupling and ground-potential fluctuations inside the electric vehicle. In the physical package, I use a metal shield with conductive treatment at the seams to reduce direct field coupling. In the signal-processing layer, I use adaptive noise cancellation. A reference channel estimates the background noise, and an adaptive filter subtracts the correlated part from the main channel. I write the error signal as
$$ e(t)=d(t)-\mathbf{w}^{T}\mathbf{n}(t) $$
where \(d(t)\) is the main channel signal, \(\mathbf{n}(t)\) is the reference vector, and \(\mathbf{w}\) is the adaptive weight vector. The weights are updated by
$$ \mathbf{w}_{k+1}=\mathbf{w}_{k}+\mu e_k \mathbf{n}_k $$
where \(\mu\) is the step size. This structure is suitable for an electric vehicle because the dominant interference is often correlated with the inverter switching state, the motor speed, or the common-mode current. Table 4 summarizes the anti-interference layers I use.
| Layer | Method | Main interference addressed | Expected effect |
|---|---|---|---|
| Radio-frequency front end | Band-pass filter matched to 300 MHz to 1.5 GHz | Out-of-band switching harmonics and wireless signals | Prevents amplifier saturation and reduces broadband noise |
| Output structure | Differential output | Common-mode current, ground bounce, conducted coupling | Improves common-mode rejection |
| Physical package | Metal shield with conductive seams | Direct spatial field coupling | Reduces unwanted electromagnetic pickup |
| Signal processing | Adaptive noise cancellation with reference channel | Correlated inverter and motor-drive noise | Dynamic suppression of background interference |
| Detection logic | Threshold and time-frequency classification | Relay transients, electrostatic discharge, switching pulses | Improves event discrimination in the electric vehicle |
I also quantify the suppression performance. If \(V_{int,ref}\) is the interference amplitude in the reference path and \(V_{int,out}\) is the residual interference after suppression, I define the suppression ratio as
$$ S_{out}=20\log_{10}\left(\frac{V_{int,ref}}{V_{int,out}}\right) $$
A larger \(S_{out}\) means stronger suppression. In my design, the band-pass filter, differential stage, shielding, and adaptive cancellation work together rather than independently. The filter removes out-of-band energy, the differential stage reduces common-mode coupling, the shield reduces direct field pickup, and the adaptive filter handles the remaining correlated noise. This layered logic is essential for an electric vehicle because no single method can suppress all interference sources in such a compact and dynamic system.
Sensor placement is another critical part of my electric vehicle monitoring concept. A single sensor at an arbitrary location is not sufficient because the metal structures of the electric vehicle can shield, reflect, and resonate with electromagnetic waves. The motor housing and battery pack enclosure can behave like a lossy cavity. Some frequencies may be enhanced by resonance, while others may be attenuated. I therefore propose a multi-position placement strategy. The first position is near the motor end winding, where partial discharge often starts because of winding insulation defects. The second position is near the high-voltage connector interface, where air gaps and interface defects can generate discharge. The third position is near the inverter direct-current bus entry, where bus insulation and terminal defects can be monitored. I define a placement score
$$ Q_i=w_1 S_i+w_2 P_i-w_3 I_i-w_4 D_i $$
where \(S_i\) is the expected signal strength at position \(i\), \(P_i\) is the propagation suitability, \(I_i\) is the interference level, \(D_i\) is the installation difficulty, and \(w_1\) to \(w_4\) are positive weights. I use this score to compare candidate positions before I finalize the electric vehicle layout. Table 5 summarizes the positions I consider.
| Position | Likely partial discharge source | Signal path | Main risk | Placement role |
|---|---|---|---|---|
| Motor end-winding region | Winding insulation, slot liner, phase-to-phase defect | Short path through housing and air gap | Strong switching noise and vibration | Primary monitoring point for traction motor insulation |
| High-voltage connector interface | Air void, contaminated interface, poor contact | Direct radiation and surface path | Mechanical tolerance and maintenance access | Primary monitoring point for connector defects |
| Inverter direct-current bus entry | Busbar insulation, terminal block, capacitor interface | Mixed radiation and conduction path | High \(dv/dt\) and strong common-mode current | Monitoring point for bus and inverter insulation |
| Battery pack service port | High-voltage feedthrough and cable termination | Longer and more shielded path | Heavy shielding and limited access | Auxiliary monitoring point |
During installation, I keep a safety distance of at least 20 mm between the sensor and high-voltage parts. I also avoid placing the sensor at locations where a metal edge or a narrow slot would strongly distort the received waveform. The sensor can be attached, clamped, or embedded, depending on the electric vehicle assembly process. The placement strategy is not intended to localize the source with a single sensor. Instead, I use multiple sensors and data fusion to estimate the approximate source region and the discharge severity. For an electric vehicle, this is more practical than a dense laboratory array because space, cost, wiring, and maintenance access are all constrained.
The signal acquisition strategy I use is event-driven. Partial discharge is random and intermittent, so continuous high-speed storage would consume too much power and memory. I use a low-power standby mode that continuously estimates the signal energy. When the energy exceeds a threshold, the system triggers high-speed acquisition and stores the waveform. The short-time energy is
$$ E_k=\sum_{n=0}^{N-1}x^2[kN+n] $$
and the trigger condition is
$$ E_k\gt E_{th} $$
where \(E_{th}\) is an adaptive threshold. I adapt \(E_{th}\) according to the background noise level so that the electric vehicle monitoring system does not trigger excessively during inverter switching or acceleration. After acquisition, I analyze the signal in both the time domain and the time-frequency domain. For time-frequency analysis, I use the short-time Fourier transform
$$ X(t,f)=\int x(\tau)w(\tau-t)e^{-j2\pi f\tau}\,d\tau $$
where \(w(t)\) is a window function. I also use empirical mode decomposition to separate the signal into intrinsic mode functions:
$$ x(t)=\sum_{i=1}^{M}c_i(t)+r_M(t) $$
where \(c_i(t)\) are the intrinsic mode functions and \(r_M(t)\) is the residual. These methods allow me to extract pulse intervals, amplitude distributions, frequency centroids, and energy ratios. Table 6 lists the features I use for electric vehicle partial discharge detection.
| Domain | Feature | Definition or expression | Role in detection |
|---|---|---|---|
| Time domain | Pulse amplitude | \(A_p=\max |x(t)|\) | Indicates discharge intensity |
| Time domain | Pulse interval | \(\Delta t_i=t_{i+1}-t_i\) | Separates periodic switching noise from quasi-periodic discharge |
| Time domain | Energy | \(E=\int x^2(t)\,dt\) | Supports trigger and event classification |
| Frequency domain | Spectral centroid | \(f_c=\frac{\int f|X(f)|^2df}{\int |X(f)|^2df}\) | Characterizes the frequency distribution of the electric vehicle event |
| Time-frequency domain | Short-time energy distribution | \(|X(t,f)|^2\) | Reveals transient structure |
| Statistical domain | Skewness and kurtosis | \(\mathrm{Skew}(x)\), \(\mathrm{Kurt}(x)\) | Differentiates impulsive discharge from background noise |
| Periodic domain | Autocorrelation | \(R_{xx}(\tau)=\int x(t)x(t+\tau)\,dt\) | Detects repetition patterns |
Interference recognition is necessary because the electric vehicle contains several transient sources that can produce radio-frequency emissions similar to partial discharge. Relay operations, electrostatic discharge, inverter switching, and connector arcing all generate short pulses. I build a feature library from measured events. Partial discharge often appears as a quasi-periodic pulse train with a relatively consistent interval under a fixed voltage phase. Switching noise is usually synchronized with the switching frequency and has a fixed repetition rate. Electrostatic discharge tends to appear as an isolated pulse with a large amplitude. I use pulse waveform parameters, autocorrelation, and time-frequency signatures to separate these events. Table 7 shows the classification cues I use.
| Event | Time-domain behavior | Frequency-domain behavior | Periodicity | Classification cue |
|---|---|---|---|---|
| Partial discharge | Short pulses, variable amplitude, sometimes clustered | Broadband energy in the ultra-high-frequency band | Quasi-periodic or phase-correlated | Pulse interval stability and broad spectral spread |
| Inverter switching | Repetitive pulses synchronized with switching | Harmonics and high-frequency edges | Fixed switching frequency | Strong correlation with switching state |
| Relay operation | Single or few transients | Broadband but short duration | Non-periodic | Isolated event with mechanical context |
| Electrostatic discharge | Large isolated pulse | Very broad spectrum | Random | High amplitude and no repetition pattern |
| Wireless communication | Modulated burst | Narrowband or band-limited | Protocol-dependent | Band occupancy and modulation structure |
I validated the sensor and the monitoring strategy on a dedicated partial discharge test platform. The platform includes an adjustable high-voltage source from 0 to 20 kV, several defect models, a reference pulse-current detection system, and the radio-frequency sensor. The defect models include a point-to-plane electrode, an air-gap model, and a surface discharge model. The tests are performed in a shielded room with a background noise level below -85 dB so that external electromagnetic interference does not dominate the result. Table 8 summarizes the test setup.
| Item | Specification | Purpose |
|---|---|---|
| High-voltage source | 0 to 20 kV adjustable | Excites partial discharge in defect models |
| Point-to-plane model | Sharp electrode and grounded plane | Generates corona-like discharge |
| Air-gap model | Controlled void in solid insulation | Represents winding and connector defects |
| Surface discharge model | Electrode along an insulating surface | Represents interface contamination and tracking |
| Reference detection | Pulse-current method consistent with IEC 60270:2025 | Provides apparent charge reference |
| Radio-frequency sensor | Compact ultra-high-frequency design described above | Measures radiated partial discharge signal |
| Shielded room | Background noise below -85 dB | Ensures controlled electric vehicle-like low-noise validation |
For sensitivity, I compare the apparent charge from the reference system with the output voltage of my sensor. I define the signal-to-noise ratio as
$$ \mathrm{SNR}=20\log_{10}\left(\frac{V_{pd,rms}}{V_{n,rms}}\right) $$
where \(V_{pd,rms}\) is the root-mean-square partial discharge signal voltage and \(V_{n,rms}\) is the root-mean-square background noise voltage. I test four apparent charge levels: 10 pC, 20 pC, 50 pC, and 100 pC. Each level is repeated ten times, and I use the mean value. At 10 pC, the sensor output is still clearly above the background, with a signal-to-noise ratio above 6 dB. As the discharge level increases, the output voltage grows approximately linearly. I fit the response as
$$ V_{out}=aQ_{app}+b $$
where \(Q_{app}\) is the apparent charge, \(a\) is the sensitivity slope, and \(b\) is the offset. The linear correlation coefficient is above 0.96. Table 9 presents representative results.
| Apparent charge \(Q_{app}\) (pC) | Mean output \(V_{out}\) (mV) | Signal-to-noise ratio (dB) | Observation |
|---|---|---|---|
| 10 | 4.8 | 6.5 | Detectable above background |
| 20 | 9.5 | 8.1 | Clear pulse waveform |
| 50 | 23.8 | 12.4 | Stable amplitude and interval features |
| 100 | 47.6 | 17.1 | Strong linear trend |
The sensitivity result is important for an electric vehicle because early insulation defects may produce only weak discharge. A sensor that can respond at the 10 pC level provides a useful margin for online monitoring. I do not claim that every 10 pC event in an electric vehicle will be detected at every location, because the metal enclosure and the distance from the source will change the received amplitude. However, the result shows that the front-end noise, antenna bandwidth, and amplification chain are sufficient for early-stage monitoring when the sensor is placed near a likely source.
I also test the anti-interference capability in a simulated electric vehicle electromagnetic environment. The interference sources include inverter switching noise, Bluetooth and Wi-Fi communication signals, and common-mode radiation from a motor controller. The inverter switching frequency is 10 kHz, and the direct-current voltage is 500 V. After filtering and differential processing, the interference components are suppressed. The out-of-band suppression ratio exceeds 30 dB. The partial discharge pulses remain clearly identifiable. Compared with a sensor without the anti-interference measures, the signal-to-noise ratio improves by about 12 dB. Table 10 gives representative results.
| Interference source | Before suppression | After suppression | Key mechanism |
|---|---|---|---|
| Inverter switching noise | Strong repetitive pulses obscure weak discharge | Suppressed and distinguishable | Band-pass filtering, differential output, adaptive cancellation |
| Bluetooth and Wi-Fi signals | Band-limited bursts increase background | Reduced by out-of-band rejection | Band-pass filter and shielding |
| Motor-controller common-mode radiation | Common-mode coupling raises baseline | Reduced by differential stage | Common-mode rejection |
| Combined interference | Low signal-to-noise ratio | Improved by about 12 dB | Multi-layer coordination |
I further perform an initial validation on a traction-motor test bench to approximate an electric vehicle operating condition. I mount the sensor outside the motor housing near the end-winding region. The motor runs at 3000 r/min and 50 N m. First, I record the background signal for 30 minutes under normal operation. Then I introduce a small insulation defect at the end winding by creating a pinhole damage. I monitor the same duration after the defect is introduced. Under normal operation, the sensor output is dominated by background noise, and no obvious abnormal pulse train appears. After the defect is introduced, the sensor captures abnormal radio-frequency pulses. The pulse occurrence frequency and the discharge intensity show a positive correlation, and the result agrees with the reference detection system. Table 11 summarizes the bench validation.
| Condition | Duration | Sensor observation | Reference observation | Interpretation |
|---|---|---|---|---|
| Normal operation | 30 min | Mostly background noise, no clear pulse train | No significant discharge | Baseline is stable |
| Pinhole defect at end winding | 30 min | Abnormal radio-frequency pulses detected | Discharge events detected | Positive correlation with defect activity |
| Increased defect activity | Short repeated runs | Higher pulse frequency and amplitude | Higher apparent charge | Sensor follows discharge severity |
From these results, I conclude that a compact ultra-high-frequency sensor can be a practical part of an electric vehicle high-voltage insulation monitoring system. The 300 MHz to 1.5 GHz band provides a good compromise for the electric vehicle electromagnetic environment. The planar antenna footprint of 50 mm by 50 mm is compatible with embedded or clamped installation. The voltage standing wave ratio below 2.0 and return loss above 10 dB ensure acceptable matching. The low-noise amplifier and differential output support weak-signal detection. The multi-layer anti-interference design provides more than 30 dB out-of-band suppression and about 12 dB signal-to-noise ratio improvement in the simulated electric vehicle environment. The multi-position placement strategy and event-driven acquisition make the concept suitable for online monitoring rather than laboratory-only measurement.
I also compare my approach with common alternatives to clarify its role in an electric vehicle. A purely conducted method can be sensitive, but it may require direct electrical access and can be affected by the high-voltage circuit topology. An acoustic method can detect some discharge events, but it is sensitive to mechanical noise and mounting conditions in an electric vehicle. A thermal method responds slowly and cannot easily capture intermittent discharge. My radio-frequency method is non-contact or loosely coupled, has a broad detection band, and can be placed near multiple high-risk components. Table 12 gives a qualitative comparison.
| Method | Access requirement | Bandwidth or response | Interference sensitivity | Suitability for an electric vehicle |
|---|---|---|---|---|
| Pulse-current method | Direct electrical connection | Broadband electrical response | Conducted noise | Good reference, less convenient for online electric vehicle monitoring |
| Acoustic method | Surface or airborne coupling | Mechanical frequency range | Vibration and mechanical noise | Useful as a complementary method |
| Thermal method | Temperature access | Slow thermal response | Cooling and load changes | Not ideal for intermittent discharge |
| Ultra-high-frequency radio-frequency method | Non-contact or loosely coupled | 300 MHz to 1.5 GHz in my design | Can be managed by filtering and shielding | Strong candidate for online electric vehicle monitoring |
My design still has limitations. The metal structures of an electric vehicle can shield weak sources, so the received amplitude depends strongly on the sensor position. A single sensor cannot reliably localize a source in a complex electric vehicle harness. The inverter switching noise can overlap with the discharge band in some operating points. The adaptive noise cancellation requires a suitable reference channel, and the reference quality affects the suppression performance. The defect models in the laboratory cannot fully reproduce the combined thermal, mechanical, and electrical aging of an electric vehicle. These limitations define the next steps I would take.
I would extend the current single-sensor design toward a sensor array. The array can provide time-difference-of-arrival information and spatial diversity. For a pair of sensors, the time difference satisfies
$$ \Delta t_{ij}=\frac{d_i-d_j}{c} $$
where \(d_i\) and \(d_j\) are the propagation distances from the source to sensors \(i\) and \(j\). With multiple sensors, I can estimate the source region by minimizing the residual between measured and predicted time differences. This would be valuable in an electric vehicle because the source may be inside a motor housing, a connector, or an inverter assembly. I would also combine the radio-frequency features with voltage, current, temperature, and motor-speed signals from the electric vehicle. A machine-learning classifier could then distinguish partial discharge from switching noise, relay transients, and electrostatic discharge. The classifier should be trained across multiple electric vehicle operating points so that it does not confuse a change in switching frequency with a change in discharge activity. Finally, I would integrate the sensor with the existing electronic and electrical architecture of the electric vehicle so that the monitoring result can be used by the vehicle controller and the maintenance system.
In summary, I have designed and evaluated a compact ultra-high-frequency radio-frequency sensor for partial discharge monitoring in an electric vehicle. I selected the 300 MHz to 1.5 GHz band to balance source energy, antenna size, path loss, and electromagnetic interference. I used a planar microstrip patch with meander-line loading and slot coupling to keep the footprint within 50 mm by 50 mm. I optimized the feed and geometry to achieve a voltage standing wave ratio below 2.0 and return loss above 10 dB. I integrated a low-noise amplifier with a noise figure below 2 dB, a differential output, a band-pass filter, metal shielding, and adaptive noise cancellation. I proposed a multi-position placement strategy near the motor end winding, the high-voltage connector, and the inverter direct-current bus entry. I used event-driven acquisition, short-time Fourier transform, empirical mode decomposition, autocorrelation, and statistical features to detect and classify discharge events. The experimental results show that the sensor can detect a 10 pC apparent charge with a signal-to-noise ratio above 6 dB, has a linear correlation above 0.96, suppresses out-of-band interference by more than 30 dB, and improves the signal-to-noise ratio by about 12 dB in a simulated electric vehicle environment. These results support the use of ultra-high-frequency radio-frequency sensing as a practical direction for online insulation monitoring in an electric vehicle, and they provide a foundation for future array-based localization, intelligent discharge classification, and deep integration with the electric vehicle electrical architecture.