I present an adaptive regenerative braking controller for an electric vehicle that is intended to overcome three practical weaknesses of fixed recovery levels: poor adaptability to complex traffic and road conditions, degraded deceleration consistency when battery charging power is limited, and unnecessary driver intervention caused by abrupt drag torque. The method I develop fuses navigation speed limits, preceding and multiple dynamic targets, road grade, road-surface state, and available battery recovery power into one perception-decision-execution architecture. In my design, an electric vehicle does not simply select a low, medium, or high regeneration level. Instead, the electric vehicle continuously estimates the recovery torque that is appropriate for the present scene, checks signal validity, computes a dynamic torque request, and invokes electro-hydraulic compensation only when the battery cannot absorb the requested recovery power. I keep the safety logic explicit so that the electric vehicle can degrade smoothly when information is missing, when the battery is full or cold, or when the integrated brake system is unavailable.
The central idea is that an electric vehicle should recover energy predictively rather than reactively. Fixed-strength recovery is easy to implement, but it cannot know whether a speed-limit change is approaching, whether a lead vehicle is closing, whether a downhill grade can support more recovery, or whether a wet surface requires a lower torque gradient. My controller therefore treats energy recovery as a constrained multi-source optimization problem. The electric vehicle prioritizes motor regeneration whenever the battery can accept the requested power. When the battery cannot accept that power, the electric vehicle maintains deceleration continuity by blending a controlled hydraulic torque. This distinction is important because energy economy, drivability, and safety redundancy must be coordinated rather than traded against one another in an electric vehicle.
1. Multi-Source Architecture for an Electric Vehicle
I organize the electric vehicle controller as a three-layer distributed system. The perception layer collects vehicle, road, traffic, and battery information. The decision layer fuses the information, identifies the scene, and calculates the target recovery torque. The execution layer commands motor recovery, hydraulic compensation, and driver feedback. The electric vehicle therefore closes the loop from sensing to torque execution rather than relying on a single fixed map. I use the vehicle control unit as the core decision node, while the intelligent-driving controller, navigation system, battery management system, integrated power brake, motor controller, and instrument cluster exchange the signals required by the electric vehicle.
| Layer | Source or actuator | Information handled | Role in the electric vehicle |
|---|---|---|---|
| Perception | Intelligent-driving controller | Lead-vehicle distance, relative speed, multiple dynamic targets | Supports following and target-intrusion risk assessment |
| Perception | Navigation system | Speed limit, road type, distance to speed-limit point | Supports predictive deceleration of the electric vehicle |
| Perception | Battery management system | State of charge, permitted charge power, permitted discharge power | Constrains recoverable power and torque |
| Perception | Forward camera | Speed bump, rough road, wet road, ice and snow | Modifies recovery intensity and torque gradient |
| Perception | Inertial measurement and grade estimation | Road grade angle, longitudinal acceleration | Corrects recovery torque on slopes |
| Decision | Vehicle control unit | Fusion, enable logic, torque request, compensation request | Core controller of the electric vehicle |
| Execution | Motor controller | Motor recovery torque | Converts kinetic energy into electric energy |
| Execution | Integrated power brake | Hydraulic compensation torque | Fills the deceleration gap when battery recovery is limited |
| Execution | Instrument cluster | State, intensity, fault prompt | Provides transparent feedback to the driver |
I define the controller interfaces so that the electric vehicle can react within the same torque cycle as the powertrain. The intelligent-driving channel is updated at no more than 20 ms, the battery channel at 10 ms, the navigation channel at 100 ms, and the hydraulic compensation response is required within 100 ms. The motor torque accuracy target is plus or minus 5 N·m, and the torque response time is less than 100 ms. These values matter because an electric vehicle with fast battery and motor response can appear jerky if the decision layer is slower than the execution layer. I therefore match the fusion and validity logic to the communication periods rather than assuming that every signal is equally fresh.
| Interface | Direction | Main signals | Period or response |
|---|---|---|---|
| Vehicle control unit to intelligent-driving controller | Bidirectional | Enable state, target deceleration, lead-vehicle distance and speed, multiple-target intrusion | 20 ms or less |
| Vehicle control unit to navigation | Bidirectional | Speed-limit value, road type, validity flag, instrument request | 100 ms |
| Vehicle control unit to integrated power brake | Bidirectional | Compensation request, target compensation torque, available state, actual hydraulic torque | 100 ms or less |
| Vehicle control unit to battery management system | Bidirectional | Recovery torque request, state of charge, permitted charge and discharge power, recovery capability | 10 ms |
| Vehicle control unit to instrument cluster | Bidirectional | Adaptive state, fault prompt, driver suggestion | 100 ms |
2. Validity and Fusion of Multi-Source Signals
I do not allow an electric vehicle to act on a signal merely because a message exists on the bus. Each source must pass a validity test, and each invalid source must have a defined fallback behavior. This is especially important for an electric vehicle because a false speed-limit value or a false lead-vehicle distance can create an unexpected deceleration request. I therefore apply timeout counters, plausibility checks, and cached-value transitions. The electric vehicle keeps the last valid value only for a bounded interval and then degrades to a safe default. The battery signal receives special treatment: if the battery message is invalid, I assume that recovery capability is restricted and prepare hydraulic redundancy.
| Signal | Validity condition | Fault persistence | Fallback in the electric vehicle |
|---|---|---|---|
| Lead vehicle | Intelligent-driving permission is true and the message is valid | More than 3 communication cycles, about 60 ms | Use cached data for a short transition and then mark as untrusted |
| Navigation | Navigation permission is true and the message is valid | More than 5 communication cycles, about 500 ms | Hold the last valid speed limit and then use a safe default |
| Road surface | Camera is unobstructed and feature match is at least 80 percent | Obstruction or low match | Use cached road state, update every 500 ms, and rescan after timeout |
| Battery | Battery message is valid and state-of-charge fluctuation is within plus or minus 1 percent | Any invalid battery message | Assume limited recovery capability and enable hydraulic redundancy |
| Grade | Estimated grade is plausible and consistent with measured acceleration | Implausible jump or sensor fault | Use zero grade and reduce recovery aggressiveness |
I normalize the navigation speed limit before it enters the torque calculation. If the navigation signal is invalid, the electric vehicle holds the last valid limit and compares it with a minimum default of 20 km/h. The initialization value is set high so that the electric vehicle does not issue a false deceleration request during startup. Road type is encoded as highway, arterial, county road, or other road, and the electric vehicle retains the previous code when the message is invalid. The road-type code determines the preview distance used for speed-limit deceleration.
I estimate road grade from longitudinal acceleration and the rate of change of vehicle speed, and I correct the estimate with a grade sensor when available. The grade angle is
$$\theta=\arcsin\left(\frac{a-\dot{v}}{g}\right)$$
where theta is the road grade angle, positive uphill and negative downhill, a is the measured longitudinal acceleration, v-dot is the time derivative of vehicle speed, and g is gravitational acceleration. The electric vehicle uses this angle to increase recovery on a downhill and reduce recovery on an uphill. The purpose is not only energy capture. It is also to keep the deceleration felt by the driver consistent with the expectation created by the road profile.
3. Enable, Exit, and Degradation Logic
I make the adaptive function conditional rather than always active. The electric vehicle enters adaptive recovery only when the lead-vehicle signal and navigation signal are both trusted, the vehicle is ready, the driver has selected the adaptive mode, and either the integrated power brake is available or the battery charge power can satisfy the current target recovery torque. This prevents the electric vehicle from entering an adaptive mode that it cannot safely execute. I also define a clear exit priority so that competing faults do not produce ambiguous behavior.
| Condition | Requirement for enabling adaptive recovery | Action when not satisfied |
|---|---|---|
| Lead-vehicle signal | Trusted and fresh | Do not enter adaptive mode |
| Navigation signal | Trusted and fresh | Do not enter adaptive mode |
| Vehicle state | Ready state and adaptive mode selected | Keep the fixed mode selected by the driver |
| Hydraulic system | Integrated power brake available, or battery charge power sufficient | Limit recovery or exit adaptive mode |
| Battery recovery capability | Sufficient for the current request, or hydraulic redundancy available | Reduce motor torque and prepare compensation |
| Exit trigger | Priority | Electric vehicle response |
|---|---|---|
| Lead-vehicle or navigation signal becomes untrusted on a rising edge | 0x2 | Exit adaptive recovery, blend to medium recovery, show prompt |
| Battery charge power insufficient and integrated power brake unavailable | 0x3 | Exit adaptive recovery, blend to medium recovery, show prompt |
| Driver selects low, medium, or high fixed level | Driver command | Follow the driver selection immediately |
| Adaptive mode cannot be entered | 0x1 | Remain in the previous fixed mode |
I use a smooth transition when the electric vehicle exits adaptive recovery. A step change in torque would be unacceptable because the driver would feel it as a sudden drag or release. The electric vehicle therefore ramps the motor torque toward the medium-recovery target and coordinates the ramp with any active hydraulic torque. The instrument cluster indicates the state change so that the driver understands why the electric vehicle has changed its behavior.
4. Dynamic Recovery Torque Calculation
I formulate the adaptive recovery request as a constrained multi-objective problem. The electric vehicle must maintain a safe gap to a lead vehicle, respect an approaching speed limit, exploit downhill grade, avoid excessive deceleration, and remain within battery and motor limits. I combine the following-scene torque, the speed-limit-scene torque, and the slope-compensation torque, and I apply a lower bound that prevents the electric vehicle from commanding an excessive deceleration. The request is
$$T_{q,\mathrm{Auto}}=\max\left\{T_{q3},\min\left[\left(T_{q1}r_{t1}+T_{q,\mathrm{slope}}\right),\left(T_{q2}+T_{q,\mathrm{slope}}\right),0\right]\right\}$$
where T-q-Auto is the adaptive recovery torque request, T-q1 is the following-scene recovery torque, r-t1 is the following-scene correction factor, T-q-slope is the slope-compensation torque, T-q2 is the speed-limit-scene recovery torque, and T-q3 is the lower recovery bound. I set T-q3 no lower than minus 2000 N·m, which corresponds to a maximum deceleration not greater than 0.2 g in the electric vehicle. At low speed, the electric vehicle sets the recovery request to zero to avoid an abrupt stop and to preserve creep behavior.
| Symbol | Meaning | Constraint or role |
|---|---|---|
| T-q-Auto | Adaptive recovery torque request | Final request sent by the vehicle control unit |
| T-q1 | Following-scene torque | Maintains a safe gap to the lead vehicle |
| T-q2 | Speed-limit-scene torque | Reduces speed before a limit point |
| T-q-slope | Slope-compensation torque | Increases downhill recovery and reduces uphill drag |
| T-q3 | Lower recovery bound | No less than minus 2000 N·m |
| r-t1 | Following correction factor | Adjusts following torque for comfort and stability |
4.1 Following-Scene Torque
I calculate the following-scene target deceleration from relative speed, relative distance, and safety distance. The electric vehicle should begin to recover early when the relative speed is closing and the distance is decreasing, but it should not overreact to a small distance error. The target deceleration is
$$a_1=-\frac{\min\left(V,0\right)}{\max\left(L_x-L_s,0.1\right)}\times 2$$
where a-1 is the following-scene target deceleration, V is the relative speed, L-x is the lead-vehicle relative distance, and L-s is the safety distance. The safety distance is
$$L_s=\max\left\{V_0t,\ L_0+0.5V_0\right\}$$
where V-0 is the current vehicle speed, t is the driver reaction time used by the electric vehicle, and L-0 is the minimum safety distance. The following-scene recovery torque is
$$T_{q1}=\frac{a_1mr}{i_g i_0\eta}$$
where m is the vehicle mass, r is the wheel rolling radius, i-g is the transmission ratio, i-0 is the final-drive ratio, and eta is the driveline efficiency. I use this torque only after the electric vehicle has confirmed that the lead-vehicle signal is valid. If the lead vehicle is accelerating away, the electric vehicle reduces the recovery request instead of holding a fixed drag.
4.2 Speed-Limit-Scene Torque
I use navigation preview to make the electric vehicle decelerate before a speed-limit point rather than after the driver passes it. The target deceleration for the speed-limit scene is
$$a_2=\frac{\min\left(V_2^2-V_0^2,0\right)}{2\max\left(L_2,L_1\right)}$$
where a-2 is the speed-limit-scene target deceleration, V-2 is the speed-limit value including an overspeed offset, V-0 is the current speed, L-2 is the distance to the speed-limit point, and L-1 is the minimum deceleration distance, which I set to 50 m. The speed-limit value including offset is
$$V_2=\frac{V_3}{3.6}+V_{\mathrm{offset}}$$
where V-3 is the navigation speed limit in km/h and V-offset is a small overspeed offset that prevents the electric vehicle from issuing frequent corrections for tiny speed errors. The offset can be calibrated as a function of V-3. The speed-limit-scene recovery torque is
$$T_{q2}=\frac{a_2mr}{i_g i_0\eta}$$
I estimate the preview distance L-2 by road type. For a highway I use 600 m, for an arterial road I use 300 m, and for a county road or other road I use 100 m. When a navigation electronic-enforcement point is available, the electric vehicle gives priority to that distance. This preview logic is one of the main reasons the electric vehicle can reduce braking events without increasing trip time.
| Road type | Preview distance L-2 | Electric vehicle behavior |
|---|---|---|
| Highway | 600 m | Early, gentle recovery before the limit point |
| Arterial road | 300 m | Moderate preview and smooth speed reduction |
| County road | 100 m | Short preview with conservative torque |
| Other road | 100 m | Short preview and safe fallback |
4.3 Slope Compensation
I correct the recovery request for road grade. On an uphill, the electric vehicle reduces recovery intensity to avoid an unnatural drag feeling. On a downhill, the electric vehicle increases recovery intensity to capture more energy and to reduce mechanical brake use. The slope-compensation torque is
$$T_{q,\mathrm{slope}}=\frac{k\theta mgr}{i_g i_0\eta}$$
where k is the slope-compensation coefficient. I use 0.8 for uphill and minus 1.2 for downhill. The sign convention is chosen so that the electric vehicle increases recovery on a negative grade and reduces it on a positive grade. I limit the compensated torque by the same battery, motor, and stability constraints as the base request.
4.4 Torque Limits and Conflict Resolution
I limit the recovery torque by battery permitted charge power, motor capability, and vehicle stability. A simple power-based limit is
$$T_{q,\mathrm{lim}}=\min\left(T_{q,\mathrm{motor}},\frac{1000P_{\mathrm{batt,chg}}}{\omega_m\eta_m}\right)$$
where T-q-lim is the allowed recovery torque, T-q-motor is the motor recovery limit, P-batt-chg is the permitted battery charge power, omega-m is the motor speed, and eta-m is the motor efficiency. The electric vehicle applies the lower bound and the low-speed cutoff after this limit. If a lead-vehicle braking demand conflicts with a navigation speed-limit demand, the electric vehicle prioritizes the following-scene torque because longitudinal safety is more urgent than predictive energy saving.
| Constraint | Effect on the electric vehicle | Implementation |
|---|---|---|
| Battery charge power | Caps recoverable torque | Power-based torque limit |
| Motor capability | Caps torque at high speed and low speed | Motor map limit |
| Stability limit | Prevents excessive wheel slip | Surface-dependent gradient and torque limit |
| Lower torque bound | Prevents excessive deceleration | No less than minus 2000 N·m |
| Low-speed cutoff | Avoids abrupt stop | Zero recovery below the low-speed threshold |
| Conflict priority | Protects longitudinal safety | Following torque over speed-limit torque |
5. Electro-Hydraulic Coordination for the Electric Vehicle
I treat hydraulic compensation as a safety redundancy, not as the primary recovery path. The electric vehicle first requests motor recovery. If the battery can absorb the requested torque, the motor controller executes the request and the integrated power brake remains in a standby state. If the battery cannot absorb the request, the electric vehicle commands the motor to its available recovery limit and requests hydraulic torque equal to the difference. This preserves deceleration consistency when the battery is full, cold, or power-limited. The compensation torque difference is
$$T_{q,\mathrm{diff}}=\left|T_{q,\mathrm{Auto}}-T_{q,\mathrm{min}}\right|$$
where T-q-diff is the torque difference, T-q-Auto is the target recovery torque, and T-q-min is the available recovery torque from the battery and motor. I distribute the compensation torque mainly to the front axle and the remainder to the rear axle:
$$T_{q,\mathrm{front}}=0.7T_{q,\mathrm{diff}}$$
$$T_{q,\mathrm{rear}}=0.3T_{q,\mathrm{diff}}$$
The distribution is then adjusted according to wheel grip. The electric vehicle triggers compensation only when the absolute target recovery torque is greater than the absolute available recovery torque, the integrated power brake is available without a fault, the vehicle is not in a special D-to-R or R-to-D transition, and adaptive recovery is enabled. These conditions prevent the electric vehicle from using hydraulic torque when motor recovery alone is sufficient.
| Condition | Requirement for hydraulic compensation | Reason |
|---|---|---|
| Torque gap | Absolute target recovery torque greater than absolute available recovery torque | There is a deceleration deficit to fill |
| Brake availability | Integrated power brake available and no fault | Ensures the electric vehicle can execute the request |
| Driving state | Not in a D-to-R or R-to-D transition | Avoids unintended torque during direction change |
| Function state | Adaptive recovery enabled | Keeps compensation consistent with the selected mode |
| Torque quantity | Expression | Purpose in the electric vehicle |
|---|---|---|
| Target recovery torque | T-q-Auto | Desired deceleration torque from the scene model |
| Available recovery torque | T-q-min | Battery and motor limited torque |
| Compensation difference | Absolute value of T-q-Auto minus T-q-min | Deficit to be filled hydraulically |
| Front compensation | 0.7 times the difference | Primary hydraulic compensation |
| Rear compensation | 0.3 times the difference | Secondary hydraulic compensation |
I require the electric vehicle to update the compensation continuously. If battery recovery capability recovers while hydraulic torque is active, the electric vehicle transfers torque back to the motor in a ramped manner. If the brake system becomes unavailable, the electric vehicle limits the motor recovery request to the available battery power and exits the adaptive mode according to the priority table. This bidirectional transfer avoids a step in total deceleration and keeps the electric vehicle predictable to the driver.
6. Scenario-Specific Control
I add scenario-specific rules because a single torque formula cannot cover abnormal roads, crossing targets, and low-temperature battery states. The electric vehicle uses the same core torque calculation but changes the limits, gradients, and compensation permissions according to the scene. The aim is to preserve energy recovery where it is safe and to suppress it where it could cause a stability or comfort problem.
6.1 Abnormal Road Surfaces
I use the forward camera to identify speed bumps, rough road, wet road, and ice or snow within approximately 200 m. When the feature match is at least 80 percent, the electric vehicle accepts the road-state label. On a rough road, the electric vehicle limits speed and recovery torque to avoid a sharp drag over a bump. On a wet road, the electric vehicle reduces the maximum recovery capability and limits the torque gradient. On ice or snow, the electric vehicle uses an even lower gradient and a snow-like recovery calibration.
| Road state | Electric vehicle limit | Torque-gradient limit | Additional action |
|---|---|---|---|
| Rough road or speed bump | Recovery torque not below minus 500 N·m and target speed below 20 km/h | Conservative ramp | Suppress hydraulic compensation for a non-decoupled pedal to avoid pedal sink |
| Wet road | Recovery capability limited to 60 percent of maximum | No more than 50 N·m per second | Reduce wheel-slip risk in the electric vehicle |
| Ice or snow | Snow-mode recovery calibration | No more than 30 N·m per second | Improve low-friction stability of the electric vehicle |
| Normal road | Full adaptive range | Comfort-based ramp | Maximize energy recovery within constraints |
6.2 Multiple Dynamic Targets
I use the intelligent-driving controller to screen targets in the ego lane and in adjacent regions that could intrude into the ego lane. The electric vehicle ranks targets by distance and by risk. For a crossing target, I compare the time required for the target to cross the ego lane with the time required for the electric vehicle to reach the crossing point. The two times are
$$t_1=\frac{D_{\mathrm{target}}}{V_{\mathrm{target}}}$$
$$t_2=\frac{D_{\mathrm{ego}}}{V_0}$$
where t-1 is the target crossing time, D-target is the target travel distance to the ego lane, V-target is the target speed, t-2 is the electric vehicle arrival time, D-ego is the distance from the electric vehicle to the crossing point, and V-0 is the electric vehicle speed. If
$$t_2\le t_1+t_0,\quad t_0=1\ \mathrm{s}$$
the electric vehicle classifies the target as dangerous and prioritizes motor recovery. If the battery cannot absorb the recovery power, the electric vehicle triggers hydraulic compensation. If the target travels along the ego lane, the electric vehicle treats it as a lead vehicle and applies the following-scene torque.
| Target class | Condition | Electric vehicle response |
|---|---|---|
| Lead vehicle | Target in ego lane and moving along the lane | Apply following-scene torque and maintain gap |
| Dangerous crossing target | Arrival time less than or equal to target crossing time plus 1 s | Prioritize motor recovery and add hydraulic compensation if needed |
| Safe crossing target | Arrival time greater than target crossing time plus 1 s | Continue predictive recovery without aggressive intervention |
| Non-intruding target | Target remains outside the predicted ego path | Monitor only and avoid unnecessary deceleration |
6.3 Full Battery, Low Temperature, and Direction Changes
I handle full-battery and low-temperature conditions by preparing hydraulic torque before the driver feels a deceleration gap. When the battery recovery capability is insufficient, the electric vehicle reserves hydraulic braking torque at 1.2 times the demand. This reserve keeps deceleration consistent when the electric vehicle cannot recover energy. For driver cooperation, I issue a gentle instrument prompt when the electric vehicle is in D range, speed is at least 10 km/h, accelerator opening is at least 5 percent, and the adaptive recovery request is at or below minus 50 N·m. The prompt lasts 2 s, and the interval between prompts is at least 10 s. The purpose is to encourage predictive release of the accelerator without distracting the driver.
| Special condition | Electric vehicle strategy | Expected effect |
|---|---|---|
| Full battery | Reserve hydraulic torque at 1.2 times demand | Maintain deceleration consistency |
| Low temperature | Limit recovery and prepare hydraulic compensation | Avoid abrupt reduction of deceleration |
| D-to-R or R-to-D change | Suspend adaptive recovery and compensation | Prevent unintended torque during direction change |
| Driver prompt | Display a short suggestion under defined conditions | Encourage predictive driving of the electric vehicle |
7. Road Validation
I validated the method with two production electric vehicles of the same configuration. One electric vehicle ran with the adaptive recovery function enabled, and the other ran with the function disabled. I did not rely on a single measured point. I repeated the route three times so that traffic variation would not dominate the conclusion. The route was 32 km long and included 8 km of highway, 10 km of urban elevated road, and 14 km of urban road. The route covered following, speed-limit transitions, slopes, and mild congestion. Both electric vehicles started at 80 percent state of charge, used pure-electric priority, turned off air conditioning and high-power accessories, and maintained tire pressure at 0.25 MPa. The ambient temperature was 13 to 15 degrees Celsius, relative humidity was 50 to 60 percent, and the road was dry except during the special-surface tests.
| Test item | Condition | Value |
|---|---|---|
| Vehicle configuration | Two electric vehicles of the same type | One enabled, one disabled |
| Mass | Test electric vehicle | 2540 kg |
| Battery capacity | Test electric vehicle | 25.5 kWh |
| Motor maximum power | Test electric vehicle | 160 kW |
| Maximum recovery torque | Test electric vehicle | 2000 N·m |
| Initial state of charge | Both electric vehicles | 80 percent |
| Route length | Mixed road | 32 km |
| Route composition | Highway, elevated road, urban road | 8 km, 10 km, 14 km |
| Test rounds | Repeated measurement | 3 rounds with 30 min interval |
| Ambient temperature | Dry road | 13 to 15 degrees Celsius |
| Tire pressure | Both electric vehicles | 0.25 MPa |
7.1 Energy Improvement
I use state-of-charge consumption as the energy metric and compute the improvement as
$$\eta_E=\frac{E_{\mathrm{off}}-E_{\mathrm{on}}}{E_{\mathrm{off}}}\times100\%$$
where E-off is the energy consumption of the electric vehicle with the function disabled and E-on is the energy consumption with the function enabled. In practice, I use the state-of-charge difference over the same route as the measurable proxy. The average state-of-charge consumption fell from 15.92 percent to 15.28 percent, which corresponds to an average energy improvement of 4.02 percent. The third round showed a 6.46 percent improvement because that round contained more urban congestion and following scenes. The first round showed a lower improvement of 1.27 percent because steady-speed driving occupied a larger share of the route and left less room for predictive recovery.
| Round | Enabled electric vehicle state-of-charge consumption | Disabled electric vehicle state-of-charge consumption | Energy improvement |
|---|---|---|---|
| 1 | 15.50 percent | 15.70 percent | 1.27 percent |
| 2 | 15.15 percent | 15.80 percent | 4.11 percent |
| 3 | 15.20 percent | 16.25 percent | 6.46 percent |
| Average | 15.28 percent | 15.92 percent | 4.02 percent |
I interpret the result carefully. The improvement is not caused by stronger recovery alone. If the electric vehicle simply increased fixed drag, the driver would intervene more often and the energy benefit would be offset by additional acceleration. The improvement comes from predicting a speed-limit reduction or a closing lead vehicle and using a smooth recovery ramp before the event. The electric vehicle therefore avoids some brake-to-accelerate cycles and captures energy that would otherwise be dissipated by friction brakes.
7.2 Driver Intervention and Drivability
I recorded brake-pedal events, accelerator-pedal events, total brake-pedal time, and accelerator-pedal opening variance. The enabled electric vehicle reduced brake-pedal events from 112 to 62, a reduction of 44.64 percent. Accelerator-pedal events fell from 436 to 136, a reduction of 68.81 percent. Total brake-pedal time fell from 500.5 s to 252.6 s, a reduction of 49.53 percent. The accelerator-pedal opening variance was 186.9 for the enabled electric vehicle and 230.9 for the disabled electric vehicle. These values indicate that the adaptive electric vehicle required fewer corrective pedal actions from the driver.
| Metric | Adaptive electric vehicle | Fixed-recovery electric vehicle | Change |
|---|---|---|---|
| Driving duration | 3249.8 s | 3293.7 s | Shorter by 43.9 s |
| Brake-pedal events | 62 | 112 | Reduced by 44.64 percent |
| Accelerator-pedal events | 136 | 436 | Reduced by 68.81 percent |
| Total brake-pedal time | 252.6 s | 500.5 s | Reduced by 49.53 percent |
| Accelerator-opening variance | 186.9 | 230.9 | Lower pedal workload |
I also examined the time-domain records of speed, longitudinal acceleration, and wheel-end torque. The two electric vehicles followed similar overall speed profiles, which shows that the adaptive strategy did not achieve energy savings by simply driving much slower. The difference appeared in the smoothness of the acceleration traces and in the continuity of the wheel-end torque. The adaptive electric vehicle reduced acceleration fluctuation by approximately 33.8 percent and kept the deceleration fluctuation within plus or minus 0.02 g. The wheel-end torque fluctuation was reduced by approximately 9.87 percent. These results support the claim that the electric vehicle can improve energy economy without sacrificing drivability.
| Stability or comfort metric | Observed result for the adaptive electric vehicle | Interpretation |
|---|---|---|
| Longitudinal acceleration fluctuation | Reduced by about 33.8 percent | Smoother deceleration and release |
| Deceleration variation | Within plus or minus 0.02 g | Consistent braking feel |
| Wheel-end torque fluctuation | Reduced by about 9.87 percent | More continuous torque delivery |
| Brake-pedal workload | Lower event count and lower total time | Less driver correction in the electric vehicle |
7.3 Safety Redundancy Under Limited Recovery
I tested low-friction road, D-to-R direction change, and battery-limited recovery to verify the redundancy logic. During the simulated no-recovery condition, the electric vehicle requested motor recovery up to the available limit and then filled the deficit with hydraulic compensation. The total wheel-end torque remained continuous, and the electric vehicle did not show an obvious drag step or low-speed rollback. On the low-friction curve, the yaw response remained stable. These observations are consistent with the design intent: the electric vehicle should preserve deceleration consistency even when the battery cannot accept energy.
| Special test | Observed behavior | Safety conclusion |
|---|---|---|
| Battery recovery limited | Motor recovery limited and hydraulic torque filled the gap | Deceleration continuity maintained |
| Low-friction curve | Yaw response remained stable | No obvious stability degradation |
| Direction change | Adaptive recovery suspended during the transition | No unintended torque |
| Low-speed operation | Recovery reduced to zero near the cutoff | No abrupt stop or rollback |
8. Engineering Interpretation
I see three engineering reasons why this method is suitable for a production electric vehicle. First, the architecture separates perception, decision, and execution, so each controller can be diagnosed independently. Second, the validity logic gives the electric vehicle a defined behavior for every missing signal instead of an implicit fallback. Third, the hydraulic path is used only when the battery cannot absorb recovery power, which avoids unnecessary brake wear and keeps the energy benefit of the electric vehicle focused on motor recovery.
The method also changes how I think about deceleration consistency in an electric vehicle. In a fixed-recovery electric vehicle, the driver learns a certain drag level and then compensates when the battery is full or cold. In my method, the electric vehicle treats deceleration as the controlled variable and treats motor recovery and hydraulic torque as two actuators. This actuator view is more robust because the electric vehicle can preserve the deceleration target even when the energy-recovery path saturates. The same view also helps during a speed-limit approach: the electric vehicle can begin with motor recovery, add hydraulic torque only if needed, and then return torque to the motor when battery capability recovers.
I keep the calibration transparent. The following-scene gain, speed-limit preview distance, slope coefficient, surface limits, and compensation distribution are explicit parameters. A calibration engineer can adjust them for a different electric vehicle mass, tire, brake system, or battery chemistry without changing the architecture. This matters because an electric vehicle platform may share software across variants with different mass and battery power. The parameter tables make the adaptation visible and testable.
| Design feature | Benefit for the electric vehicle | Engineering consideration |
|---|---|---|
| Multi-source fusion | Predictive recovery instead of fixed drag | Requires validity and timeout logic |
| Dynamic torque request | Adapts to following, speed limit, and grade | Requires calibrated gains |
| Electro-hydraulic compensation | Maintains deceleration when battery is limited | Must avoid unnecessary brake activation |
| Surface-specific limits | Reduces slip and jerk risk | Depends on camera confidence |
| Explicit degradation | Predictable behavior for the electric vehicle | Needs clear driver feedback |
9. Conclusion and Future Work
I have described an adaptive energy recovery method for an electric vehicle that fuses navigation, intelligent-driving, battery, road-surface, and grade information. The method uses a perception-decision-execution architecture, a signal-validity layer, a dynamic torque calculation, an electro-hydraulic compensation path, and scenario-specific limits. The electric vehicle prioritizes motor recovery when the battery can accept power and adds hydraulic compensation only when the battery cannot meet the deceleration request. This combination allows the electric vehicle to improve energy economy while preserving deceleration consistency and safety redundancy.
The road validation showed a 4.02 percent average energy improvement, a 44.64 percent reduction in brake-pedal events, a 68.81 percent reduction in accelerator-pedal events, and deceleration fluctuation within plus or minus 0.02 g under mixed speed-limit and mild-congestion conditions. The adaptive electric vehicle also reduced acceleration fluctuation by about 33.8 percent and wheel-end torque fluctuation by about 9.87 percent. The special-condition tests showed that the electric vehicle maintained stable behavior when battery recovery was limited and when the road surface had low friction.
| Performance item | Result | Meaning for the electric vehicle |
|---|---|---|
| Average energy improvement | 4.02 percent | Better energy economy |
| Brake-pedal event reduction | 44.64 percent | Fewer corrective braking actions |
| Accelerator-pedal event reduction | 68.81 percent | Lower driver workload |
| Brake-pedal time reduction | 49.53 percent | Less friction-brake use |
| Deceleration fluctuation | Within plus or minus 0.02 g | Consistent braking feel |
| Acceleration fluctuation reduction | About 33.8 percent | Improved drivability |
| Torque fluctuation reduction | About 9.87 percent | More continuous torque |
In future work, I will improve the prediction layer with learned behavior models for lead vehicles and road-surface recognition, so that the electric vehicle can anticipate events earlier and with greater robustness. I will also adapt the hydraulic compensation ratio and response speed to battery aging and motor efficiency, because an older electric vehicle may have a different recovery boundary than a new one. Finally, I will extend the same architecture to higher-level driver assistance, smart charging coordination, and personalized driving modes so that the electric vehicle can use one consistent energy-recovery framework across multiple operating contexts.