{"id":"c1650e7d-3e51-4c68-ae76-e8ca76de64dc","arxiv_id":"2411.14691","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":8,"one_line_summary":"EV-PINN uses a PINN with a physics loss and a Runge-Kutta network to predict EV battery power and energy from speed and time, with fitted vehicle parameters, validated on two Tesla drives.","lead":"A physics-informed neural network is trained to predict Tesla battery power and energy use from only vehicle speed and time, while also outputting estimates of vehicle parameters like mass and drag. The method is tested on short, controlled drives from a Model 3 and Model S, but lacks benchmarks, code, and a physically consistent regenerative braking model.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Eq. (21) has a regenerative-braking sign error: higher regen efficiency μ raises battery power during deceleration, so the learned μ, mass, Cd, and Crr are not physically meaningful.","rationale":"The paper's strongest novel claim is parameter identification, not curve fitting. Eq. (21) is the only physics constraint; if its sign is wrong, the fitted η, μ, m, Crr, and Cd cannot be interpreted as real vehicle parameters. The proposed derivative test demonstrates the sign error independently of any data, since it uses only the functional form of Eq. (21). The reported validation losses near 2e-3 show interpolation ability but do not validate the physics, because a flexible network can compensate for a misspecified residual term. The absence of baselines and error bars is secondary. The same load-bearing concern was identified by the Reader, and the REJECT verdict remains appropriate; no adjustment is needed.","tokens_in":8509,"tokens_out":6641,"duration_ms":119973,"concrete_test":"Compute ∂P_physics/∂μ at any logged deceleration point with a<β using Eq. (21): ∂P/∂μ = −(1/η) m v a. This value is positive because a<0, so the physics term says that a larger regeneration efficiency increases battery power consumption. A correct regenerative model has the opposite monotonicity: more regen efficiency must lower, or at least not raise, battery draw. Then retrain the same network with the corrected split model P_batt = P_aux + max(P_w,0)/η_drive + η_regen min(P_w,0), where P_w = (0.5ρACd v² + Crr mg + m a)v, and compare the recovered parameters and validation losses on the same Tesla logs. If μ changes materially or the validation loss rises, the original parameter set is an artifact of the erroneous operator.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim has two parts: accurate power/energy prediction and simultaneous learning of real-world parameters. The second part is the novel contribution, and it depends entirely on the physics operator 𝒩[P; ...] in Eq. (21) being the correct battery-power balance. It is not. From Eqs. (18)-(19), the no-regen model is P_battery = P_aux + (1/η)(F_drag+F_roll)v + (1/η) m v a. During braking a is negative, so the inertial term lowers P_battery. Regenerative braking should recover kinetic energy and lower P_battery further. But in Eq. (21) the inertial term is multiplied by (1 − μ I_{a<β}); with 0<μ<1 and a<β, (1−μ)m v a is less negative than m v a, so the model raises P_battery compared with the no-regen case. Equivalently, ∂P_physics/∂μ = −(1/η) m v a > 0 during braking: the model asserts that more efficient regenerative braking increases battery discharge. That is the wrong sign. The recovered power −μ m v a from Eq. (20) has been folded inside a term that is then divided by η, rather than subtracted as a credit to the battery. Since μ, η, m, Cd, and Crr are jointly fitted against this incorrect operator, the Table V 'predictions' are not identifiable physical parameters, even though the network can still interpolate the power trace. The reader's identified weak point is therefore load-bearing.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper presents EV-PINN, a physics-informed neural network that predicts instantaneous battery power and cumulative energy consumption for two Tesla vehicles using only time and vehicle speed as inputs. The network is trained with a combined data and physics loss, where the physics term uses a longitudinal vehicle dynamics model with parameters for motor efficiency, regenerative braking efficiency, mass, rolling resistance, and drag coefficient. The authors report validation losses of 0.002195 and 0.002292 for the Model 3 and Model S, respectively, and provide parameter estimates in Table V. The central claims are that the model achieves high accuracy and generalization and that it can learn physically meaningful vehicle parameters.","tokens_in":8873,"tokens_out":4388,"duration_ms":44334,"significance":"If correct, the approach would be practically interesting: onboard battery-power prediction with minimal sensor inputs and simultaneous estimation of vehicle parameters would be useful for EV energy management and path planning. The paper uses real OBD2 data from two Tesla models, which is a strength, and the architecture and loss construction are described in sufficient detail to be reproducible. However, the central parameter-learning claim is undermined by a sign error in the regenerative-braking physics operator, and the evaluation lacks baselines and uncertainty quantification. The reported parameter values are therefore not established as physical estimates, and the claimed advantage over simpler regression models is not demonstrated.","major_comments":[{"comment":"The regenerative-braking term in Eq. (21) has a sign error. Eq. (20) defines P_regen = -μ (m v dv/dt) for dv/dt < β, which should be a negative contribution (i.e., energy credit) during deceleration. However, in Eq. (21) the inertial term is multiplied by (1 - μ I_{a<β}), so for a < β the term (1-μ) m v a is less negative than m v a. Equivalently, ∂P_physics/∂μ = -(1/η) m v a > 0 during braking, meaning the model predicts that higher regenerative-braking efficiency increases battery discharge. This is the opposite of the intended physics. Since this operator is used in the physics loss (Eq. 22) and the parameters are jointly optimized against it, the learned values of μ, η, m, C_rr, and C_d in Table V are not physically meaningful; the parameter-estimation claim is therefore unsupported.","section":"Section III-C, Eq. (21)"},{"comment":"The parameters listed as \"Predicted Parameters\" are not predictions in the usual sense; they are the optimized variables of the loss function (Eq. 22). The \"Absolute Error\" column compares them against the initial guesses from Eqs. (24) and (25), not against independent ground-truth measurements. The model mass for the Model 3 is rationalized post hoc as the curb weight plus the two researchers and cargo, but no independent weighing or uncertainty analysis is provided. Consequently, the statement that the network \"learns real-world parameters\" is circular and not validated.","section":"Section IV-B, Table V"},{"comment":"The validation losses are reported without any baseline comparison, error bars, or multiple independent runs. A plain neural network with the same architecture would likely achieve comparable data-loss values because the physics-loss weight is only λ = 0.1, so the claimed superiority of the physics-informed approach is not established. In addition, the paper does not state whether the validation data come from a contiguous segment of the same drive or from separate trips; this matters for the \"generalization to dynamics\" claim. The evaluation needs comparison against simpler models and a clear description of the train/validation split.","section":"Section IV-A, Tables II-III and Fig. 9-10"}],"minor_comments":[{"comment":"There are several typographical errors: \"Reated Work\" should be \"Related Work,\" and \"breaking\" in the regenerative-braking discussion should be \"braking.\"","section":"Section I and III-C"},{"comment":"Equation numbering is not unique: the initial parameter vectors are Eqs. (24) and (25), and the RK4 update is also Eq. (25). Renumber to avoid ambiguity.","section":"Section III-C and III-E"},{"comment":"The description of the RKNN loss is unclear: the loss compares E_RK4(t+Δt) with E_pred(t+Δt), but the target value used for E_pred during training is not specified. Clarify whether the target is the measured energy or the RK4 approximation of the logged power.","section":"Section III-E, Eq. (30)"},{"comment":"The auxiliary power values (≈1100 W and ≈390 W) are stated without uncertainty or a formal estimation procedure; a brief explanation of the \"idle state\" derivation would improve reproducibility.","section":"Section III-C"},{"comment":"The column headings \"Absolute Error\" and \"Relative Error\" are misleading because the comparison is against the initial guess, not against a ground-truth measurement; this should be stated explicitly in the caption.","section":"Table V"}],"recommendation":"reject","confidential_remarks":"The reference list contains two DOIs that appear to be placeholder values (Ref. [2]: 10.1016/j.jclepro.2020.123456; Ref. [3]: 10.1016/j.egyr.2020.123456). The editor may wish to verify citation integrity. The sign error in the physics operator is fundamental to the paper's central claim, and the lack of baselines and uncertainty quantification further weakens the case for publication in its current form."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"First, what you should know: this is a modest, honest application of PINNs to EV battery power prediction, with genuine OBD2 data from two Teslas and low held-out losses. But the physics term in Eq. (21) treats regenerative braking with the wrong sign, so the learned 'physical parameters' in Table V are not physically meaningful.\n\nWhat is new and done well: They collect real voltage/current logs, define battery power consistently with Ohm's law, and formulate a PINN that takes (speed, time) and outputs instantaneous power. The RKNN integration for cumulative energy is a clever touch and works better than a plain DNN in their comparison. The held-out validation losses (0.0023 and 0.0034) and the energy traces look credible. The mass estimate of 1975 kg for a Model 3 LR is plausible given the two researchers and cargo, and they acknowledge it.\n\nThe load-bearing problem: the regenerative-braking model is wrong. Eq. (20) defines P_regen = -mu m v dv/dt, which is positive during deceleration. In Eq. (21) they replace the inertial term m v a with m v a (1 - mu I). Since a is negative in braking, (1 - mu) m v a is less negative than m v a. So higher mu gives higher battery discharge during braking. That is the opposite of what regen does. The correct form should subtract the regen credit from P_battery, not attenuate the inertial term. Because mu, eta, m, Cd, and Crr are jointly fitted against this operator, the parameters are compensating for the misspecified physics. The power prediction itself is likely still a decent curve fit because the data loss dominates, but the paper's stated contribution—learning real-world parameters—is not supported.\n\nMinor but worth noting: there are no baselines against a physics-free NN or a simple least-squares fit of Eq. (19); no error bars or multiple runs; the generalization claim rests on two short, flat, windless drives; and the related-work discussion omits prior PINN-based EV/battery models. A few references (e.g., [2], [3]) have placeholder-looking DOIs, which suggests incomplete citation checking.\n\nMy take: the paper is competent engineering with real data, and the authors are not hiding anything—they even flag the post hoc mass explanation. But the sign error is fatal for the parameter-estimation claim. If the authors fix Eq. (21), add baselines, and validate parameters independently (e.g., weigh the car), this could become a useful contribution. As is, I would not publish it. For peer review, I'd still send it to a referee rather than desk-reject: the error is subtle and the data collection deserves expert eyes, but the expected outcome after review is rejection unless the physics is corrected.","headline":"EV-PINN does solid empirical power prediction on two real Teslas, but the regenerative-braking term in its physics loss has the wrong sign, which invalidates the advertised parameter estimates.","tokens_in":9371,"tokens_out":5842,"would_cite":false,"duration_ms":58649,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"EV-PINN: a physics-informed neural network predicts EV battery power and energy from only speed and time, while learning mass, drag, and efficiency from real driving logs.","keywords":["physics-informed neural networks","electric vehicle","battery power prediction","energy consumption","parameter estimation","longitudinal vehicle dynamics","regenerative braking","Runge-Kutta neural network"],"falsifier":"Run a vehicle with a known weighed mass through a cycle with sustained regenerative braking while logging voltage and current, then compare the model's predicted battery power during deceleration against the measured charging power. If the model systematically underpredicts or sign-reverses the charging during braking, the regenerative-braking term in Eq. (21) is incorrect and the learned parameters are not trustworthy; a simpler check is to compare the fitted mass against the weighed mass, since the paper already reports a 152 kg offset for the Model 3.","tokens_in":8331,"feed_emoji":"⚡","tokens_out":8570,"duration_ms":74307,"temperature":0.7,"pith_summary":"EV-PINN claims that a physics-informed neural network can predict an electric vehicle's instantaneous battery power and cumulative energy consumption during cruising using only vehicle speed and time, without voltage or current sensors. The network is constrained by a longitudinal-dynamics ODE for battery power, and training simultaneously fits the logged power while recovering physical parameters such as vehicle mass, aerodynamic drag coefficient, rolling resistance coefficient, motor efficiency, and regenerative braking efficiency. On 15 minutes of Tesla Model 3 Long Range data and 35 minutes of Tesla Model S data, the model reports validation losses on the order of $10^{-3}$ and predicts energy consumption that tracks the ground-truth integration. The authors argue this makes onboard energy prediction and path planning feasible with minimal sensing.","feed_headline":"Speed and time alone predict an EV's battery use","feed_subtitle":"A physics-informed net recovers mass, drag, and efficiency from 15-minute logs.","key_machinery":"The central object is the physics-informed loss $\\mathcal{L}_{power} = \\sum_i \\left(\\|P_{data}^{(i)} - P_{pred}^{(i)}\\|^2 + \\lambda \\|P_{physics}^{(i)} - P_{pred}^{(i)}\\|^2\\right)$, where $P_{physics}$ is the output of the governing ODE with learnable parameters. The ODE encodes longitudinal vehicle dynamics: aerodynamic drag, rolling resistance, inertia, and an indicator-based regenerative-braking term that reduces the inertia draw during deceleration below a threshold. Automatic differentiation computes $\\frac{dv}{dt}$ from the speed input, letting the network backpropagate through the physics residual. A separate Runge-Kutta neural network, with four subnetworks producing $k_1, k_2, k_3, k_4$, integrates predicted power into cumulative energy via the RK4 update. Parameter initialization from known vehicle specifications helps avoid the local minima introduced by the added physics term.","core_discovery":"The paper's central claim is that a single physics-informed neural network, fed only with vehicle speed $v(t)$ and time $t$, can reproduce the battery power trace $P_{battery}$ of a real EV and integrate it into cumulative energy, while learning the governing physical parameters from data. The network is trained against a loss that combines a data term with a physics residual built from the ODE $P_{battery}=\\frac{1}{\\eta}\\left(\\frac{1}{2}\\rho A C_d v^3 + C_{rr} m g v + m v \\frac{dv}{dt}(1-\\mu I_{a<\\beta})\\right)+P_{aux}$, where the indicator term models regenerative braking below a deceleration threshold. Automatic differentiation supplies $\\frac{dv}{dt}$, and the parameters $\\eta$, $\\mu$, $m$, $C_{rr}$, $C_d$ are learned simultaneously with the network weights. The authors report that the fitted parameters land near realistic values (the learned Model 3 mass exceeds curb weight by 152 kg, roughly the weight of two occupants), and that the power and energy predictions closely track the logged ground truth, with validation losses around $2\\times10^{-3}$ to $3\\times10^{-3}$. They conclude that onboard energy prediction and path planning need only speed and time as sensor inputs.","pith_inferences":["If the regenerative-braking term were replaced by one that actually charges the battery during deceleration (the current term only reduces the inertia draw), the same framework could predict net energy flow in one-pedal driving; this is an extension the paper does not make.","The framework could be extended to route planning by making road grade $\\theta$ and auxiliary load time-varying inputs, since both already appear in the governing ODE.","Learned parameters could double as fleet diagnostics: a drifting inferred mass or rolling-resistance coefficient might flag added payload, tire-pressure loss, or brake drag without extra sensors.","A controlled comparison against a purely data-driven network on identical logs would isolate how much of the accuracy comes from the physics constraint rather than the network capacity; the paper does not include such a baseline."],"forward_implications":["Battery power and cumulative energy can be predicted from speed and time alone, eliminating the need for dedicated voltage and current sensors in energy-management and range-estimation systems.","The simultaneously learned parameters (mass, drag, rolling resistance, motor and regen efficiencies) reflect the actual test conditions, enabling per-vehicle calibration without a dynamometer.","The same architecture and physics constraint achieve low validation loss on two different Tesla models, suggesting the approach generalizes across EV platforms.","Integrating the PINN power output with a Runge-Kutta neural network yields stable cumulative energy predictions from short (15-35 minute) driving logs.","The recovered drag and rolling-resistance coefficients can feed directly into route-level energy path planning."],"supporting_citations":[{"why":"Introduces the physics-informed neural network training formulation whose loss structure EV-PINN adapts.","marker":"[5]"},{"why":"Provides the Runge-Kutta neural network architecture used to integrate predicted power into cumulative energy.","marker":"[13]"},{"why":"Adam optimizer is the training algorithm for the PINN and RKNN.","marker":"[12]"},{"why":"Tesla Model 3 Owner's Manual supplies initial values for vehicle mass and dimensions used for parameter initialization.","marker":"[10]"},{"why":"Tesla Model S Owner's Manual supplies initial values for vehicle mass and dimensions used for parameter initialization.","marker":"[11]"}],"fun_headline_variants":["Physics-informed net predicts EV battery use from speed alone","Neural net learns EV drag and mass from just speed and time","Speed and time inputs predict EV power via physics-constrained net","PINN estimates EV battery draw from speed traces without sensors"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the power-dynamics equation (Eq. 21), especially its regenerative-braking term, correctly describes how battery power depends on speed, acceleration, and the physical parameters; if this ODE is wrong, the fitted parameters and the physics constraint lose physical meaning.","fun_headline_variants_meta":{"raw":{"variants":["Physics-informed net predicts EV battery use from speed alone","Neural net learns EV drag and mass from just speed and time","Speed and time inputs predict EV power via physics-constrained net","PINN estimates EV battery draw from speed traces without sensors"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000179,"raw_usage":{"total_tokens":1320,"prompt_tokens":986,"completion_tokens":334,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":602,"completion_tokens_details":{"reasoning_tokens":277}},"tokens_in":602,"tokens_out":334,"duration_ms":3828,"temperature":1.0,"reasoning_tokens":277,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T15:00:07.127449+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run a vehicle with a known weighed mass through a cycle with sustained regenerative braking while logging voltage and current, then compare the model's predicted battery power during deceleration against the measured charging power. If the model systematically underpredicts or sign-reverses the charging during braking, the regenerative-braking term in Eq. (21) is incorrect and the learned parameters are not trustworthy; a simpler check is to compare the fitted mass against the weighed mass, since the paper already reports a 152 kg offset for the Model 3.","supporting_citations":[{"cited_title":"Runge -Kutta neural network for identification of dynamical systems in high accuracy,","cited_arxiv_id":null,"evidence_quote":"Provides the Runge-Kutta neural network architecture used to integrate predicted power into cumulative energy."},{"cited_title":"Adam: A method for stochastic optimization,","cited_arxiv_id":null,"evidence_quote":"Adam optimizer is the training algorithm for the PINN and RKNN."},{"cited_title":"[Online]","cited_arxiv_id":null,"evidence_quote":"Tesla Model 3 Owner's Manual supplies initial values for vehicle mass and dimensions used for parameter initialization."},{"cited_title":"[Online]","cited_arxiv_id":null,"evidence_quote":"Tesla Model S Owner's Manual supplies initial values for vehicle mass and dimensions used for parameter initialization."}],"review_version":1}